Comparative Analysis on Proteomics Profiles of Intracellular and Extracellular M.tb and BCG From Infected Human Macrophages

Liu, Han; Su, Li; Zhu, Tingting; Zhu, Xiaojie; Zhu, Yifan; Peng, Yonchong; Zhang, Kailun; Wang, Longwei; Hu, Changmin; Chen, Huanchun; Chen, Yingyu

doi:10.3389/fgene.2022.847838

Cited by 3 publications

(3 citation statements)

References 45 publications

(46 reference statements)

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“…These mutants showed increased autophagy and reduced intracellular survival in macrophages [ 61 ], potentially contributing to their lower prevalence compared to L4.6.3 strains. The study also highlighted the role of Rv1928c , one of the L4.6.3-associated genes, in aiding MTB survival and reproduction in macrophage phagosomes under conditions of low pH, oxygen, and starvation [ 62 ]. Although the precise roles of Rv3093c and Rv0025 genes in MTB pathogenesis are not clearly defined, transcriptional analysis has indicated their involvement in the pathogenic processes of MTB [ 57 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Pangenome and genomic signatures linked to the dominance of the lineage-4 of Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

Chekesa,

Singh,

Gonzalez-Juarbe

et al. 2024

PLoS ONE

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Background The lineage 4 (L4) of Mycobacterium tuberculosis (MTB) is not only globally prevalent but also locally dominant, surpassing other lineages, with lineage 2 (L2) following in prevalence. Despite its widespread occurrence, factors influencing the expansion of L4 and its sub-lineages remain poorly understood both at local and global levels. Therefore, this study aimed to conduct a pan-genome and identify genomic signatures linked to the elevated prevalence of L4 sublineages among extrapulmonary TB (EPTB) patients in western Ethiopia. Methods A cross-sectional study was conducted at an institutional level involving confirmed cases of extrapulmonary tuberculosis (EPTB) patients from August 5, 2018, to December 30, 2019. A total of 75 MTB genomes, classified under lineage 4 (L4), were used for conducting pan-genome and genome-wide association study (GWAS) analyses. After a quality check, variants were identified using MTBseq, and genomes were de novo assembled using SPAdes. Gene prediction and annotation were performed using Prokka. The pan-genome was constructed using GET_HOMOLOGUES, and its functional analysis was carried out with the Bacterial Pan-Genome Analysis tool (BPGA). For GWAS analysis, Scoary was employed with Benjamini-Hochberg correction, with a significance threshold set at p-value ≤ 0.05. Results The analysis revealed a total of 3,270 core genes, predominantly associated with orthologous groups (COG) functions, notably in the categories of ‘[R] General function prediction only’ and ‘[I] Lipid transport and metabolism’. Conversely, functions related to ‘[N] Cell motility’ and ‘[Q] Secondary metabolites biosynthesis, transport, and catabolism’ were primarily linked to unique and accessory genes. The pan-genome of MTB L4 was found to be open. Furthermore, the GWAS study identified genomic signatures linked to the prevalence of sublineages L4.6.3 and L4.2.2.2. Conclusions Apart from host and environmental factors, the sublineage of L4 employs distinct virulence factors for successful dissemination in western Ethiopia. Given that the functions of these newly identified genes are not well understood, it is advisable to experimentally validate their roles, particularly in the successful transmission of specific L4 sublineages over others.

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Section: Discussionmentioning

confidence: 99%

Pangenome and genomic signatures linked to the dominance of the lineage-4 of Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

Chekesa,

Singh,

Gonzalez-Juarbe

et al. 2024

PLoS ONE

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“…RPSA has been found to be expressed in a variety of immune cells, including neutrophils, monocytes, and T cells (Sun et al, 2020), to participate in the immune process. In macrophages, RPSA expression levels were altered after infection with Mycoplasma pleuropneumoniae and porcine circovirus type 2 (Liu M. et al, 2021) or after BCG vaccination (Liu et al, 2022).…”

Section: Figure 10mentioning

confidence: 99%

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

Ren

et al. 2023

Front. Genet.

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Multiple types of COVID-19 vaccines have been shown to be highly effective in preventing SARS-CoV-2 infection and in reducing post-infection symptoms. Almost all of these vaccines induce systemic immune responses, but differences in immune responses induced by different vaccination regimens are evident. This study aimed to reveal the differences in immune gene expression levels of different target cells under different vaccine strategies after SARS-CoV-2 infection in hamsters. A machine learning based process was designed to analyze single-cell transcriptomic data of different cell types from the blood, lung, and nasal mucosa of hamsters infected with SARS-CoV-2, including B and T cells from the blood and nasal cavity, macrophages from the lung and nasal cavity, alveolar epithelial and lung endothelial cells. The cohort was divided into five groups: non-vaccinated (control), 2*adenovirus (two doses of adenovirus vaccine), 2*attenuated (two doses of attenuated virus vaccine), 2*mRNA (two doses of mRNA vaccine), and mRNA/attenuated (primed by mRNA vaccine, boosted by attenuated vaccine). All genes were ranked using five signature ranking methods (LASSO, LightGBM, Monte Carlo feature selection, mRMR, and permutation feature importance). Some key genes that contributed to the analysis of immune changes, such as RPS23, DDX5, PFN1 in immune cells, and IRF9 and MX1 in tissue cells, were screened. Afterward, the five feature sorting lists were fed into the feature incremental selection framework, which contained two classification algorithms (decision tree [DT] and random forest [RF]), to construct optimal classifiers and generate quantitative rules. Results showed that random forest classifiers could provide relative higher performance than decision tree classifiers, whereas the DT classifiers provided quantitative rules that indicated special gene expression levels under different vaccine strategies. These findings may help us to develop better protective vaccination programs and new vaccines.

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“…In this study, we used a straightforward, solution-based, label-free method for qualitative and quantitative proteomic analysis using LC-MS. Traditionally, quantitative proteomics has relied on stable isotope labeling and these labeling strategies provide elementary alternatives to former 2DE-MS; however, they require several sample preparation steps and are costly [13,14]. Moreover, the number of samples that can be analyzed in parallel is limited.…”

Section: Introductionmentioning

confidence: 99%

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

Zmyslia,

Fröhlich,

Dao

et al. 2023

Proteomes

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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.

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Comparative Analysis on Proteomics Profiles of Intracellular and Extracellular M.tb and BCG From Infected Human Macrophages

Cited by 3 publications

References 45 publications

Pangenome and genomic signatures linked to the dominance of the lineage-4 of Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

Pangenome and genomic signatures linked to the dominance of the lineage-4 of Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods

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

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