Genome scale metabolic model driven strategy to delineate host response to Mycobacterium tuberculosis infection

2021 ◽  
Author(s):  
Ankur Gupta ◽  
Ajay Kumar ◽  
Rajat Anand ◽  
Nandadulal Bairagi ◽  
Samrat Chatterjee
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Cell Line ◽  
High Throughput ◽  
Host Response ◽  
Metabolic Model ◽  
Thp1 Cell ◽  
Proteomics Data ◽  
Mycobacterium Tuberculosis Infection ◽  
Model Driven ◽  
Genome Scale

We analyzed high throughput proteomics data reflecting the response of the Mϕ-like THP1 cell line to Mycobacterium tuberculosis (M. tuberculosis) infection.

scholarly journals Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data

2015 ◽  
Vol 112 (34) ◽  
pp. 10810-10815 ◽  
Author(s):  
Laurence Yang ◽  
Justin Tan ◽  
Edward J. O’Brien ◽  
Jonathan M. Monk ◽  
Donghyuk Kim ◽  
...  
Keyword(s):  
Systems Biology ◽  
Comparative Genomics ◽  
High Throughput ◽  
Practical Importance ◽  
Scale Model ◽  
Proteomics Data ◽  
Cell Functions ◽  
Core Proteome ◽  
Definition Of ◽  
Genome Scale

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5′UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.

scholarly journals Reconstruction of a Genome-Scale Metabolic Model of Streptomyces albus J1074: Improved Engineering Strategies in Natural Product Synthesis

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 304
Author(s):  
Cheewin Kittikunapong ◽  
Suhui Ye ◽  
Patricia Magadán-Corpas ◽  
Álvaro Pérez-Valero ◽  
Claudio J. Villar ◽  
...  
Keyword(s):  
Natural Products ◽  
De Novo ◽  
Streptomyces Coelicolor ◽  
Metabolic Model ◽  
Streptomyces Species ◽  
Model Driven ◽  
A Genome ◽  
Streptomyces Albus ◽  
Genome Scale ◽  
Streptomyces Albus J1074

Streptomyces albus J1074 is recognized as an effective host for heterologous production of natural products. Its fast growth and efficient genetic toolbox due to a naturally minimized genome have contributed towards its advantage in expressing biosynthetic pathways for a diverse repertoire of products such as antibiotics and flavonoids. In order to develop precise model-driven engineering strategies for de novo production of natural products, a genome-scale metabolic model (GEM) was reconstructed for the microorganism based on protein homology to model species Streptomyces coelicolor while drawing annotated data from databases and literature for further curation. To demonstrate its capabilities, the Salb-GEM was used to predict overexpression targets for desirable compounds using flux scanning with enforced objective function (FSEOF). Salb-GEM was also utilized to investigate the effect of a minimized genome on metabolic gene essentialities in comparison to another Streptomyces species, S. coelicolor.

scholarly journals Reconciling high-throughput gene essentiality data with metabolic network reconstructions

2018 ◽  
Author(s):  
Anna S. Blazier ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
High Throughput ◽  
Large Scale ◽  
Essential Genes ◽  
Growth And Survival ◽  
Gene Essentiality ◽  
Model Driven ◽  
Functional Explanations ◽  
Genome Scale

AbstractThe identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.Author SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

scholarly journals Genome-Scale Metabolic Model Driven Design of a Defined Medium for Campylobacter jejuni M1cam

2020 ◽  
Vol 11 ◽  
Author(s):  
Noemi Tejera ◽  
Lisa Crossman ◽  
Bruce Pearson ◽  
Emily Stoakes ◽  
Fauzy Nasher ◽  
...  
Keyword(s):  
Campylobacter Jejuni ◽  
Metabolic Model ◽  
Defined Medium ◽  
Model Driven ◽  
Genome Scale

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

scholarly journals The Roles of Host Noncoding RNAs in Mycobacterium tuberculosis Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Li Wei ◽  
Kai Liu ◽  
Qingzhi Jia ◽  
Hui Zhang ◽  
Qingli Bie ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Host Response ◽  
Noncoding Rnas ◽  
Host Cells ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Mycobacterium Tuberculosis Infection ◽  
Major Health Problem ◽  
Major Health ◽  
Tuberculosis Diagnosis

Tuberculosis remains a major health problem. Mycobacterium tuberculosis, the causative agent of tuberculosis, can replicate and persist in host cells. Noncoding RNAs (ncRNAs) widely participate in various biological processes, including Mycobacterium tuberculosis infection, and play critical roles in gene regulation. In this review, we summarize the latest reports on ncRNAs (microRNAs, piRNAs, circRNAs and lncRNAs) that regulate the host response against Mycobacterium tuberculosis infection. In the context of host-Mycobacterium tuberculosis interactions, a broad and in-depth understanding of host ncRNA regulatory mechanisms may lead to potential clinical prospects for tuberculosis diagnosis and the development of new anti-tuberculosis therapies.

scholarly journals Model Driven Analysis of the Biosynthesis of 1,4-butanediol from Renewable Feedstocks in Escherichia coli

2019 ◽  
Vol 70 (11) ◽  
pp. 3808-3817
Author(s):  
Zsolt Bodor ◽  
Szabolcs Lanyi ◽  
Beata Albert ◽  
Katalin Bodor ◽  
Aurelia Cristina Nechifor ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Metabolic Model ◽  
E Coli ◽  
Model Driven ◽  
Metabolic Characteristics ◽  
Strain Design ◽  
Renewable Feedstocks ◽  
Coa Ligase ◽  
Coa Transferase ◽  
Genome Scale

Bio-based, environmentally benign production of commodity chemicals such as 1,4-butanediol (BDO) from renewable feedstocks is highly challenging due to the lack of natural synthesis pathways. Herein, we present a systematic model-driven evaluation of the production potential for Escherichia coli to produce BDO from renewable carbohydrates (glucose, glycerol). Computational analysis was carried out in order to decipher the metabolic characteristics under various genetic and environmental conditions. Optimal strain designs were achieved using only two (adhE2- alcohol dehydrogenase and cat/sucCD- 4-hydroxybutyrate-CoA transferase/4-hydroxybutyryl-CoA ligase) heterologous reactions; highest yields were attained for: glucose ~0.37 g g-1 (3 knockouts, anaerobically) and glycerol ~0.43 g g-1 (4 knockouts, microaerobically). The maximum achievable production yield was over 95% of the theoretical maximum potential for glucose and over 75% for glycerol. In regards to the genome-scale metabolic model predictions, a metabolically engineered E. coli was created to analyze the new biosynthetic pathway stability and functionality. Considering the preliminary outcomes the strain and pathway is stable under fermentative conditions and a limited quantity of BDO ~1 mg L-1 was obtained, therefore long-term adaptive evolution is mandatory. This study outlines a strain design and analysis pipeline -systems biology-based approach- for non-native compounds production strains.

scholarly journals Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

2020 ◽  
Author(s):  
Yu Chen ◽  
Eunice van Pelt-KleinJan ◽  
Berdien van Olst ◽  
Sieze Douwenga ◽  
Sjef Boeren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Proteomics Data ◽  
Available Nitrogen ◽  
Regulatory Strategies ◽  
Trade Offs ◽  
Evolutionary Changes ◽  
Genome Scale

Cells adapt to different conditions via gene expression that tunes metabolism and stress resistance for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs1; Resource allocation under proteome constraints has emerged as a powerful paradigm to explain regulatory strategies in bacteria2. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as the lactic acid bacterium Lactococcus lactis. Here we present an approach to identify preferred nutrients from integration of experimental data with a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis), which explicitly accounts for gene expression processes and associated constraints. Using glucose-limited chemostat data3, we identified the uptake of glucose and arginine as dominant constraints, whose pathway proteins were indeed upregulated in evolved mutants. However, above a growth rate of 0.5 h-1, pcLactis suggests that available enzymes function at their maximum capacity, which allows an increase in growth rate only by altering gene expression to change metabolic fluxes, as was mainly observed for arginine metabolism. Thus, our integrative analysis of flux and proteomics data with a proteome-constrained model is able to identify and explain the constraints that form targets of regulation and fitness improvement in nutrient-rich growth environments.

Sign in / Sign up

Export Citation Format

Share Document