TY - JOUR A1 - He, Hai A1 - Edlich-Muth, Christian A1 - Lindner, Steffen N. A1 - Bar-Even, Arren T1 - Ribulose Monophosphate Shunt Provides Nearly All Biomass and Energy Required for Growth of E. coli JF - ACS Synthetic Biology N2 - The ribulose monophosphate (RuMP) cycle is a highly efficient route for the assimilation of reduced one-carbon compounds. Despite considerable research, the RuMP cycle has not been fully implemented in model biotechnological organisms such as Escherichia coli, mainly since the heterologous establishment of the pathway requires addressing multiple challenges: sufficient formaldehyde production, efficient formaldehyde assimilation, and sufficient regeneration of the formaldehyde acceptor, ribulose 5-phosphate. Here, by efficiently producing formaldehyde from sarcosine oxidation and ribulose 5-phosphate from exogenous xylose, we set aside two of these concerns, allowing us to focus on the particular challenge of establishing efficient formaldehyde assimilation via the RuMP shunt, the linear variant of the RuMP cycle. We have generated deletion strains whose growth depends, to different extents, on the activity of the RuMP shunt, thus incrementally increasing the selection pressure for the activity of the synthetic pathway. Our final strain depends on the activity of the RuMP shunt for providing the cell with almost all biomass and energy needs, presenting an absolute coupling between growth and activity of key RuMP cycle components. This study shows the value of a stepwise problem solving approach when establishing a difficult but promising pathway, and is a strong basis for future engineering, selection, and evolution of model organisms for growth via the RuMP cycle. KW - ribulose monophosphate cycle KW - methylotrophy KW - metabolic engineering KW - growth selection KW - carbon labeling KW - flux modeling KW - formaldehyde assimilation Y1 - 2018 U6 - https://doi.org/10.1021/acssynbio.8b00093 SN - 2161-5063 VL - 7 IS - 6 SP - 1601 EP - 1611 PB - ACS CY - Washington, DC ER - TY - JOUR A1 - Edlich-Muth, Christian A1 - Muraya, Moses M. A1 - Altmann, Thomas A1 - Selbig, Joachim T1 - Phenomic prediction of maize hybrids JF - Biosystems : journal of biological and information processing sciences N2 - Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved. KW - Hybrid prediction KW - LASSO KW - Regression KW - Maize KW - Phenomics Y1 - 2016 U6 - https://doi.org/10.1016/j.biosystems.2016.05.008 SN - 0303-2647 SN - 1872-8324 VL - 146 SP - 102 EP - 109 PB - Elsevier CY - Oxford ER -