Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann, David; Campeau, Anaamika; Lloyd, Colton J.; Phaneuf, Patrick V.; Hefner, Ying; Carrillo-Terrazas, Marvic; Feist, Adam M.; Gonzalez, David J.; Palsson, Bernhard Ø.

doi:10.1101/767996

Cited by 14 publications

(27 citation statements)

References 46 publications

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“…The bacterial growth rate is the result of a complex regulatory interplay that fine tunes the allocation of cellular resources such as the ribosomes. Several works have proven that the growth rate in a determined environment can be increased if the proteome is optimized, by regulatory and other mutations, to that specific environment (Cheng et al, 2014; Heckmann et al, 2020; LaCroix et al, 2015). Indeed, rewiring E. coli transcription networks with fusions of promoters and master regulators (e.g.…”

Section: Discussionmentioning

confidence: 99%

Regulatory perturbations of ribosome allocation reshape the growth proteome with a trade-off in adaptation capacity

Hidalgo

Martínez-Ortiz

Palsson

et al. 2021

Preprint

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Bacteria regulate their cellular resource allocation to enable their fast growth-adaptation to a variety of environmental niches. We studied the ribosomal allocation, growth and expression profile of two sets of fast-growing mutants of Escherichia coli K-12 MG1655. Mutants with 3 copies of the stronger ribosomal RNA operons grew faster than the wild-type strain in minimal media and show similar phenotype to previously studied rpoB mutants. All of them displayed increased ribosomal content, a longer diauxic shift and a reduced activity of the aceBAK operon, indicative of repressed gluconeogenic pathways. Transcriptomic profiles of fast-growing mutants showed common downregulation of hedging functions and upregulated growth functions. Proteome allocation estimations showed an increase in the growth-related proteome for fast-growing strains, but not an increased cellular budget for recombinant protein production. These results show that two different regulatory perturbations (rRNA promoters or rpoB mutations) increasing ribosomal allocation optimize the proteome for growth with a concomitant fitness cost.

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Section: Discussionmentioning

confidence: 99%

Regulatory perturbations of ribosome allocation reshape the growth proteome with a trade-off in adaptation capacity

Hidalgo

Martínez-Ortiz

Palsson

et al. 2021

Preprint

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“…Note 3. Ironically, the published ME models [49,[122][123][124]149] do not use any kinetic expressions (Table A1) containing the concentration terms s or M. Instead, they rely on a binary search and constraints, such as defining the top and low limits of s while applying objective functions. Three disadvantages are obvious: (i) the subjectivity of constraints, (ii) a longer computation time, and (iii) missing the opportunity to reproduce a fine metabolic control in growing cells, an instant adjustment of enzymatic activity to the available reactant concentration in the cytosol.…”

Section: Appendix B24 Substrate Saturation (Ss) Of Enzymes Catalyzing Polysubstrate Reactionsmentioning

confidence: 99%

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Panikov

2021

Microorganisms

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This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

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“…Processed data for the Proteomics-2 dataset was acquired from Heckmann et al 51 and augmented with additional samples for growth on different carbon sources that were acquired as described in Heckmann et al 51 (See Supplementary Dataset 1 for details). 460 Protein abundance estimation is described in Heckmann et al 51 ; in short, the top3 metric 63,64 was calibrated using the UPS2 standard to obtain protein amount loaded based on the average of three technical replicates per sample. For each strain, two biological replicates were profiled, and abundances were averaged before applying ICA.…”

Section: Proteomics Data Processing 455mentioning

confidence: 99%

Matrix factorization recovers consistent regulatory signals from disparate datasets

Sastry¹,

Hu²,

Heckmann³

et al. 2020

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15The availability of gene expression data has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent 20 datasets, including both RNA-seq and microarray datasets. We also show that echoes of this structure remain in the proteome, accelerating biological discovery through multi-omics analysis.We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. ICA thus enables deep analysis of disparate data 25 to uncover new insights that were not visible in the individual datasets.Publicly available datasets, such as the NCBI Gene Expression Omnibus (GEO) 1 and Array Express 2 , contain thousands of transcriptomics datasets that are often designed and 30 analyzed for a specific study. Historically, microarrays were the platform of choice for transcriptomic interrogation. Over the past decade, usage of RNA sequencing (RNA-seq) has surpassed microarrays due to its higher sensitivity and ability to detect new transcripts 3 . Public repositories for proteomics datasets have introduced additional reusable expression datasets 4,5 . 35Multiple consortia have performed extensive comparisons of expression levels across different microarray and RNA-seq platforms 6-8 . These studies showed that absolute gene expression levels cannot be accurately measured by either expression profiling technique, whereas relative abundances are consistent across a wide range of transcriptomics platforms, with appropriate quality controls. However, transcript levels alone cannot predict protein 40 expression levels 9 To further complicate matters, batch effects and technical heterogeneity continue to present significant challenges to successful integration of omics datasets 10 . Differential expression analysis is the most common analytical method applied to transcriptomics datasets. However, differential expression analysis is limited in dimensionality, 45 interpretability, and reproducibility; it can only be applied to pairs of experimental conditions, requires additional analysis to interpret large swaths of differentially expressed genes 11,12 , and is highly dependent on the quantification pipeline 13,14 . Alternatively, machine learning methods, especially matrix factorization 15,16 , have provided new tools for extracting low-dimensional biological information from large omics data. 50In particular, independent component analysis (ICA) has been shown to extract biologically significant gene sets from many transcriptomics datasets 17-22 and two proteomics datasets 23 . ICA outperformed 42 module detect...

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Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Cited by 14 publications

References 46 publications

Regulatory perturbations of ribosome allocation reshape the growth proteome with a trade-off in adaptation capacity

Regulatory perturbations of ribosome allocation reshape the growth proteome with a trade-off in adaptation capacity

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Matrix factorization recovers consistent regulatory signals from disparate datasets

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