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          Abstract

          Engineering faster cellulose deconstruction is difficult because it is a complex, cooperative, multi-enzyme process. Here we use experimental evolution to select for populations of Trichoderma citrinoviride that deconstruct up to five-fold more cellulose. Ten replicate populations of T. citrinoviride were selected for growth on filter paper by serial culture. After 125 periods of growth and transfer to fresh media, the filter paper deconstruction increased an average of 2.5 fold. Two populations were examined in more detail. The activity of the secreted cellulase mixtures increased more than two-fold relative to the ancestor and the largest increase was in the extracellular β-glucosidase activity. qPCR showed at least 16-fold more transcribed RNA for egl4 (endoglucanase IV gene), cbh1 (cellobiohydrolase I gene) and bgl1 (extracellular β-glucosidase I gene) in selected populations as compared to the ancestor, and earlier peak expressions of these genes. Deep sequencing shows that the regulatory strategies used to alter cellulase secretion differ in the two strains. The improvements in cellulose deconstruction come from earlier expression of all cellulases and increased relative amount of β-glucosidase, but with small increases in the total secreted protein and therefore little increase in metabolic cost.

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          Most cited references23

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          Genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei (syn. Hypocrea jecorina).

          Trichoderma reesei is the main industrial source of cellulases and hemicellulases used to depolymerize biomass to simple sugars that are converted to chemical intermediates and biofuels, such as ethanol. We assembled 89 scaffolds (sets of ordered and oriented contigs) to generate 34 Mbp of nearly contiguous T. reesei genome sequence comprising 9,129 predicted gene models. Unexpectedly, considering the industrial utility and effectiveness of the carbohydrate-active enzymes of T. reesei, its genome encodes fewer cellulases and hemicellulases than any other sequenced fungus able to hydrolyze plant cell wall polysaccharides. Many T. reesei genes encoding carbohydrate-active enzymes are distributed nonrandomly in clusters that lie between regions of synteny with other Sordariomycetes. Numerous genes encoding biosynthetic pathways for secondary metabolites may promote survival of T. reesei in its competitive soil habitat, but genome analysis provided little mechanistic insight into its extraordinary capacity for protein secretion. Our analysis, coupled with the genome sequence data, provides a roadmap for constructing enhanced T. reesei strains for industrial applications such as biofuel production.
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            Insights into the oxidative degradation of cellulose by a copper metalloenzyme that exploits biomass components.

            The enzymatic degradation of recalcitrant plant biomass is one of the key industrial challenges of the 21st century. Accordingly, there is a continuing drive to discover new routes to promote polysaccharide degradation. Perhaps the most promising approach involves the application of "cellulase-enhancing factors," such as those from the glycoside hydrolase (CAZy) GH61 family. Here we show that GH61 enzymes are a unique family of copper-dependent oxidases. We demonstrate that copper is needed for GH61 maximal activity and that the formation of cellodextrin and oxidized cellodextrin products by GH61 is enhanced in the presence of small molecule redox-active cofactors such as ascorbate and gallate. By using electron paramagnetic resonance spectroscopy and single-crystal X-ray diffraction, the active site of GH61 is revealed to contain a type II copper and, uniquely, a methylated histidine in the copper's coordination sphere, thus providing an innovative paradigm in bioinorganic enzymatic catalysis.
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              Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

              Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 January 2016
                2016
                : 11
                : 1
                : e0147024
                Affiliations
                [1 ]The Biotechnology Institute, University of Minnesota, Twin Cities, Minnesota, United States of America
                [2 ]Department of Ecology Evolution and Behavior, University of Minnesota, Twin Cities, Minnesota, United States of America
                [3 ]Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Twin Cities, Minnesota, United States of America
                The University of Wisconsin - Madison, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: HL MT RJK. Performed the experiments: HL. Analyzed the data: HL MT RJK. Wrote the paper: HL MT RJK.

                ‡ MT and RJK are joint senior authors on this work.

                Article
                PONE-D-15-31499
                10.1371/journal.pone.0147024
                4731210
                26820897
                efebe1f2-9ac2-4d18-8ed5-14c4ca3fec06
                © 2016 Lin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 17 July 2015
                : 28 December 2015
                Page count
                Figures: 5, Tables: 3, Pages: 17
                Funding
                This work was supported by the University of Minnesota Biotechnology Institute (HL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Equipment
                Laboratory Equipment
                Filter Paper
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Cellulases
                Biology and Life Sciences
                Biochemistry
                Proteins
                Enzymes
                Cellulases
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Cellulose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Cellulose
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and Life Sciences
                Mycology
                Fungal Evolution
                Biology and Life Sciences
                Mycology
                Fungal Structure
                Mycelium
                Biology and life sciences
                Genetics
                Gene expression
                DNA transcription
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Secretion
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Secretion
                Custom metadata
                All RNA sequencing files are available from the Sequence Read Archive database (SRP062059).

                Uncategorized
                Uncategorized

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