The effects of environmental stressors on candidate aging associated genes

Andrawus, Mariana; Sharvit, Lital; Shekhidem, Huda Adwan; Roichman, Asael; Cohen, Haim Y.; Atzmon, Gil

doi:10.1016/j.exger.2020.110952

Cited by 5 publications

(4 citation statements)

References 67 publications

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“…The specific downregulation of mrpl2 in the retina was identified in elderly APP/PS1 mice aged 8 months [131]. The expression of mrpl4 decreased after a short period of fasting treatment and the methylation level of mrpl4 increased with age through the epigenetic map in mice [105]. A single nucleotide polymorphism rs3209 in MRPL10 associated with early age-related macular degeneration (AMD) was also reported in Chinese Americans [9].…”

Section: Mrps Associated With Age and Other Related Mitochondrial Diseasesmentioning

confidence: 98%

“…Scientists explored the new mutations, epigenetic disorders, abnormal genes expression and protein abundance patterns based on genomics and proteomics, which establish causal relationships between MRPs abnormalities and carcinogenesis or provide diagnostic and therapeutic markers in some cases [2,105]. Based on additional experimental results, the same MRPs may affect multiple cancers and multiple MRPs abnormalities can be detected in the same cancer, which forms a complex and changeable network system.…”

Section: Mrps Associated With Cancersmentioning

confidence: 99%

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Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

Huang

Zhang

2020

IJMS

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Mammalian mitochondrial ribosomes translate 13 proteins encoded by mitochondrial genes, all of which play roles in the mitochondrial respiratory chain. After a long period of reconstruction, mitochondrial ribosomes are the most protein-rich ribosomes. Mitochondrial ribosomal proteins (MRPs) are encoded by nuclear genes, synthesized in the cytoplasm and then, transported to the mitochondria to be assembled into mitochondrial ribosomes. MRPs not only play a role in mitochondrial oxidative phosphorylation (OXPHOS). Moreover, they participate in the regulation of cell state as apoptosis inducing factors. Abnormal expressions of MRPs will lead to mitochondrial metabolism disorder, cell dysfunction, etc. Many researches have demonstrated the abnormal expression of MRPs in various tumors. This paper reviews the basic structure of mitochondrial ribosome, focuses on the structure and function of MRPs, and their relationships with cell apoptosis and diseases. It provides a reference for the study of the function of MRPs and the disease diagnosis and treatment.

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Section: Mrps Associated With Age and Other Related Mitochondrial Diseasesmentioning

confidence: 98%

Section: Mrps Associated With Cancersmentioning

confidence: 99%

Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

Huang

Zhang

2020

IJMS

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“…The genes found by our scShapes method that are exclusively switching between a unimodal distribution and a zero-inflated distribution represent over 25% of the genes in adipose and over 45% of the genes in muscle. Some of the unique genes detected by our pipeline include the TFs FOXO3 and PKNOX1 , involved in apoptosis, regulation of oxidative stress, and DNA damage [ 42 ] in adipose. Similarly, the TFs IRF3 and TFE3 , involved in autophagy, are some of the uniquely detected genes by our pipeline in muscle.…”

Section: Analysesmentioning

confidence: 99%

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

et al. 2022

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Background Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell–cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. Results We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. Conclusions This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

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“…The genes found by our scShapes method that are exclusively switching between a unimodal distribution and a zero-inflated distribution represent over 25% of the genes in adipose and over 45% of the genes in muscle. Some of the unique genes detected by our pipeline include the TFs FOXO3, PKNOX1 involved in apoptosis, regulation of oxidative stress and DNA damage [39] in adipose. Similarly, the TFs IRF3, SRF, TFE3 involved in autophagy are some of the uniquely detected genes by our pipeline in muscle.…”

Section: Analysesmentioning

confidence: 99%

scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

Dharmaratne

Kulkarni

Fard

et al. 2022

Preprint

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Background: Single cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell-cell variability therefore are useful for going beyond changes based on average expression and instead, identifying genes with homogenous expression versus those that vary widely from cell to cell. Results: We present a novel statistical framework scShapes for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single cell data are driven by over-dispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically-relevant genes that were not discovered through standard approaches. Conclusions: This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes helps to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into Bioconductor R package (https://github.com/Malindrie/scShapes).

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The effects of environmental stressors on candidate aging associated genes

Cited by 5 publications

References 67 publications

Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

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