Dataset Information

Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions.

ABSTRACT: Understanding the complex mechanisms of mycobacterial pathophysiology and adaptive responses presents challenges that can hinder drug development. However, employing physiologically relevant conditions, such as those found in human macrophages or simulating physiological growth conditions, holds promise for more effective drug screening. A valuable tool in this pursuit is proteomics, which allows for a comprehensive analysis of adaptive responses. In our study, we focused on Mycobacterium smegmatis, a model organism closely related to the pathogenic Mycobacterium tuberculosis, to investigate the impact of various carbon sources on mycobacterial growth. To facilitate this research, we developed a cost-effective, straightforward, and high-quality pipeline for proteome analysis and compared six different carbon source conditions. Additionally, we have created an online tool to present and analyze our data, making it easily accessible to the community. This user-friendly platform allows researchers and interested parties to explore and interpret the results effectively. Our findings shed light on mycobacterial adaptive physiology and present potential targets for drug development, contributing to the fight against tuberculosis.

SUBMITTER: Zmyslia M

PROVIDER: S-EPMC10747050 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions.

Zmyslia Mariia M Fröhlich Klemens K Dao Trinh T Schmidt Alexander A Jessen-Trefzer Claudia C

Proteomes 20231207 4

Understanding the complex mechanisms of mycobacterial pathophysiology and adaptive responses presents challenges that can hinder drug development. However, employing physiologically relevant conditions, such as those found in human macrophages or simulating physiological growth conditions, holds promise for more effective drug screening. A valuable tool in this pursuit is proteomics, which allows for a comprehensive analysis of adaptive responses. In our study, we focused on <i>Mycobacterium sme ...[more]

PMID: 38133153

Similar Datasets

Project description:BackgroundMicroarray analysis is a powerful technique for investigating changes in gene expression. Currently, results (r-values) are interpreted empirically as either unchanged or up- or down-regulated. We now present a mathematical framework, which relates r-values to the macromolecular properties of population-average cells. The theory is illustrated by the analysis of published data for two species; namely, Mycobacterium bovis BCG Pasteur and Mycobacterium smegmatis mc(2) 155. Each species was grown in a chemostat at two different growth rates. Application of the theory reveals the growth rate dependent changes in the mycobacterial proteomes.Principal findingsThe r-value r (i) of any ORF (ORF(i)) encoding protein p (i) was shown to be equal to the ratio of the concentrations of p (i) and so directly proportional to the ratio of the numbers of copies of p (i) per population-average cells of the two cultures. The proportionality constant can be obtained from the ratios DNA: RNA: protein. Several subgroups of ORFs were identified because they shared a particular r-value. Histograms of the number of ORFs versus the expression ratio were simulated by combining the particular r-values of several subgroups of ORFs. The largest subgroup was ORF(j) (r (j) = 1.00± SD) which was estimated to comprise respectively 59% and 49% of ORFs of M. bovis BCG Pasteur and M. smegmatis mc(2) 155. The standard deviations reflect the properties of the cDNA preparations investigated.SignificanceThe analysis provided a quantitative view of growth rate dependent changes in the proteomes of the mycobacteria studied. The majority of the ORFs were found to be constitutively expressed. In contrast, the protein compositions of the outer permeability barriers and cytoplasmic membranes were found to be dependent on growth rate; thus illustrating the response of bacteria to their environment. The theoretical approach applies to any cultivatable bacterium under a wide range of growth conditions.

Project description:Industrial methanol production converts methane from natural gas into methanol through a multistep chemical process. Biological methane-to-methanol conversion under moderate conditions and using biogas would be more environmentally friendly. Methanotrophs, bacteria that use methane as an energy source, convert methane into methanol in a single step catalyzed by the enzyme methane monooxygenase, but inhibition of methanol dehydrogenase, which catalyzes the subsequent conversion of methanol into formaldehyde, is a major challenge. In this study, we used the thermoacidophilic methanotroph "Methylacidiphilum fumariolicum" SolV for biological methanol production. This bacterium possesses a XoxF-type methanol dehydrogenase that is dependent on rare earth elements for activity. By using a cultivation medium nearly devoid of lanthanides, we reduced methanol dehydrogenase activity and obtained a continuous methanol-producing microbial culture. The methanol production rate and conversion efficiency were growth-rate dependent. A maximal conversion efficiency of 63% mol methanol produced per mol methane consumed was obtained at a relatively high growth rate, with a methanol production rate of 0.88 mmol/g (dry weight)/h. This study demonstrates that methanotrophs can be used for continuous methanol production. Full-scale application will require additional increases in the titer, production rate, and efficiency, which can be achieved by further decreasing the lanthanide concentration through the use of increased biomass concentrations and novel reactor designs to supply sufficient gases, including methane, oxygen, and hydrogen.IMPORTANCE The production of methanol, an important chemical, is completely dependent on natural gas. The current multistep chemical process uses high temperature and pressure to convert methane in natural gas to methanol. In this study, we used the methanotroph "Methylacidiphilum fumariolicum" SolV to achieve continuous methanol production from methane as the substrate. The production rate was highly dependent on the growth rate of this microorganism, and high conversion efficiencies were obtained. Using microorganisms for the production of methanol might enable the use of more sustainable sources of methane, such as biogas, rather than natural gas.

Dataset Information

Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions.

Publications

Deep Proteomic Investigation of Metabolic Adaptation in Mycobacteria under Different Growth Conditions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets