Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference S. cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1∆ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1∆ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria towards cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1∆ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium

Flux-Enabled Exploration of the Role of Sip1 in galactose yeast metabolism

13C metabolic flux analysis (13C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant of 13C MFA known as 2-Scale 13C metabolic flux analysis (2S-13C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium

A Machine Learning Approach to Predict Metabolic Pathway Dynamics from Time Series Multiomics Data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones

Genome-Scale Metabolic Modeling of Chitin-Degrading Microbial Systems

As a major component of fungal cell walls and exoskeletons of invertebrates, chitin is widespread in soils, constituting the second most abundant biopolymer in nature. Composed of N-acetyl-D-glucosamine chains, it serves as a vital source of nutrients, including both carbon and nitrogen, for the growth of microorganisms. A solid understanding of the microbial degradation of chitin is critical for predicting their impacts on biogeochemical cycling in soil ecosystems. Organisms that degrade biopolymers (degraders) produce energetically expensive extracellular enzymes to break down complex organic carbons into simpler labile forms that are sharable with other species, including those that do not contribute directly to the degradation process (cheaters). Therefore, it impacts not only the metabolic and growth efficiencies of the degraders but also fosters diverse interspecies interactions within microbial communities. The level of complexity in this process necessitates the use of mechanistic metabolic models. However, reconstruction of phenotype-consistent genome-scale metabolic networks is still challenging due to the frequent occurrence of false positives (model prediction of biomass production in media where actual organism cannot grow) when gapfilled using typical sequential gapfilling approaches. In this work, I developed a new iterative gapfilling method to address this issue and applied it to build metabolic networks of chitin-degrading communities and their isolates—using a consortium of Cellvibrio japonicus (degrader) and Escherichia coli (non-degrader) as a model system. This new development revealed previously unknown and interesting findings on how bioenergetic cost on chitin degradation affects degrader’s metabolism and its interactions with non-degraders. The model also provided mechanistic interpretations of the predicted changes in metabolism and interactions based on carbon and nitrogen use efficiencies. Both the methods and findings are reproducible, and may be used in other biopolymer-degrading communities

A Method to Constrain Genome-Scale Models with 13C Labeling Data.

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems

A Method to Constrain Genome-Scale Models with 13C Labeling Data.

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13C labeling data. A comparison shows that the results of this new method are similar to those found through 13C Metabolic Flux Analysis (13C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems