A quantitative method for proteome reallocation using minimal regulatory interventions

Lastiri-Pancardo, Gustavo; Mercado-Hernández, Jonathan S.; Kim, Ju-Hyun; Jiménez, José I.; Utrilla, José

doi:10.1038/s41589-020-0593-y

Cited by 32 publications

(42 citation statements)

References 50 publications

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“… 47 Quantitative proteomics could be used for the accurate classification of TNBC subtypes. 48 Furthermore, the sub-network identified through quantitative phosphoproteomics was highly correlated with clinically identified breast cancer subtypes. 49 , 50 , 51 SWATH/DIA-MS (state-of-the-art sequential windowed acquisition of all theoretical fragment ion/data-independent acquisition mass spectrometry) presented a promising complement for the stable classification of ovarian cancer subtypes.…”

Section: Quantitative Proteomics Classification Of Tumor Subtypementioning

confidence: 98%

Quantitative proteomics characterization of cancer biomarkers and treatment

Yang

Shi

Zhang

et al. 2021

Molecular Therapy - Oncolytics

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Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.

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Section: Quantitative Proteomics Classification Of Tumor Subtypementioning

confidence: 98%

Quantitative proteomics characterization of cancer biomarkers and treatment

Yang

Shi

Zhang

et al. 2021

Molecular Therapy - Oncolytics

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“…The sRNA network is the most comprehensive and therefore the best suited to study the biological regulatory mechanisms of C. glutamicum. Having reliable regulatory network models has proven being important even for synthetic biology, for example to engineer resource allocation by rationally modifying the transcriptional regulatory network [35]. , and (e) 196627_v2020_s21_dsRNA (sRNA) networks.…”

Section: The Regulatory Network Of C Glutamicum and Potential Applimentioning

confidence: 99%

Corynebacterium glutamicumregulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

Escorcia-Rodríguez

Tauch

Freyre-González

2021

Preprint

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Corynebacterium glutamicum is a Gram-positive bacterium found in soil where the condition changes demand plasticity of the regulatory machinery. The study of such machinery at the global scale has been challenged by the lack of data integration. Here, we report three regulatory network models for C. glutamicum: strong (3040 interactions) constructed solely with regulations previously supported by directed experiments; all evidence (4665 interactions) containing the strong network, regulations previously supported by non-directed experiments, and protein-protein interactions with a direct effect on gene transcription; and sRNA (5222 interactions) containing the all evidence network and sRNA-mediated regulations. Compared to the previous version (2018), the strong and all evidence networks increased by 75 and 1225 interactions, respectively. We analyzed the system-level components of the three networks to identify how they differ and compared their structures against those for the networks of more than 40 species. The inclusion of the sRNAs regulations changed the proportions of the system-level components and increased the number of modules but decreased their size. The C. glutamicum regulatory structure contrasted with other bacterial regulatory networks. Finally, we used the strong networks of three model organisms to provide insights and future directions of the C. glutamicum regulatory network characterization.

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“…coli K-12 derivatives BW25113 was used as wild type. The combinatorial mutations of the PFC strain (BW25113 ∆phoB, ∆flhC, ∆cueR) developed by Lastiri and coworkers [6] were generated by sequential P1 phage transduction from the individual knockout using the Keio collection strain as donors. The recA gene was inactivated in both strains using the methodology proposed by Datsenko and Wanner [10].…”

Section: Bacterial Strains and Plasmidmentioning

confidence: 99%

“…This would generate an optimal resource allocation that maximizes the designed cell function [5]. Lastiri and co-workers [6] developed a method to engineer the resource allocation of Escherichia coli. This method identifies the minimum combinatorial set of genetic interventions that maximizes resource savings, which they applied by removing transcription factors that activate the expression of unused functions with the greater proteomic load.…”

Section: Introductionmentioning

confidence: 99%

“…This method identifies the minimum combinatorial set of genetic interventions that maximizes resource savings, which they applied by removing transcription factors that activate the expression of unused functions with the greater proteomic load. Namely, the deletion of phoB, phosphate scavenging system; flhC, flagella master regulator, and cueR, copper efflux system, resulted in a 0.5% reduction of the proteome compared to the wild type strain [6]. The mutant strain, named PFC, displayed an increased proteomic budget and improved performance when expressing a synthetic pathway for violacein production.…”

Section: Introductionmentioning

confidence: 99%

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Plasmid DNA Production in Proteome-Reduced Escherichia coli

et al. 2020

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The design of optimal cell factories requires engineering resource allocation for maximizing product synthesis. A recently developed method to maximize the saving in cell resources released 0.5% of the proteome of Escherichia coli by deleting only three transcription factors. We assessed the capacity for plasmid DNA (pDNA) production in the proteome-reduced strain in a mineral medium, lysogeny, and terrific broths. In all three cases, the pDNA yield from biomass was between 33 and 53% higher in the proteome-reduced than in its wild type strain. When cultured in fed-batch mode in shake-flask, the proteome-reduced strain produced 74.8 mg L−1 pDNA, which was four times greater than its wild-type strain. Nevertheless, the pDNA supercoiled fraction was less than 60% in all cases. Deletion of recA increased the pDNA yields in the wild type, but not in the proteome-reduced strain. Furthermore, recA mutants produced a higher fraction of supercoiled pDNA, compared to their parents. These results show that the novel proteome reduction approach is a promising starting point for the design of improved pDNA production hosts.

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A quantitative method for proteome reallocation using minimal regulatory interventions

Cited by 32 publications

References 50 publications

Quantitative proteomics characterization of cancer biomarkers and treatment

Quantitative proteomics characterization of cancer biomarkers and treatment

Corynebacterium glutamicumregulation beyond transcription: Organizing principles and reconstruction of an extended regulatory network incorporating regulations mediated by small RNA and protein-protein interactions

Plasmid DNA Production in Proteome-Reduced Escherichia coli

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