Intracellular vesicle trafficking plays an essential role in mitochondrial quality control

Gerards, Mike; Cannino, Giuseppe; Cózar, Jose M. González de; Jacobs, Howard T.

doi:10.1091/mbc.e17-10-0619

Cited by 5 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BET1 is a membrane protein involved in vesicle transport between the endoplasmic reticulum (ER) and the Golgi apparatus that is upregulated on mitochondrial genome loss. The orthologous Bet1 protein in Drosophila was determined to be necessary for the maintenance of the mitochondrial genome, with prolonged knockdown of Bet1 associated with an increase in mitochondrial membrane potential [49]. In this study, expression of BET1 is increased upon mitochondrial genome loss, presumably in an attempt to maintain a normal membrane potential.…”

Section: Resultsmentioning

confidence: 81%

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Roy

Sheikh

Furey

2023

Preprint

View full text Add to dashboard Cite

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an inference framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. We leverage ML-based network inference to find networks that capture the strength of regulatory interactions. Our model first pinpoints a subset of genes, termed variational, whose expression variabilities typify the differences in network connectivity between the control and perturbed data. Variational genes, by being differentially expressed themselves or possessing differentially expressed neighbor genes, capture gene expression variability. CoVar then creates subnetworks comprising variational genes and their strongly connected neighbor genes and identifies core genes central to these subnetworks that influence the bulk of the variational activity. Through the analysis of yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar identifies key genes not found through independent differential expression analysis.

show abstract

Section: Resultsmentioning

confidence: 81%

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Roy

Sheikh

Furey

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…BET1 is a membrane protein involved in vesicle transport between the endoplasmic reticulum (ER) and the Golgi apparatus that is upregulated on mitochondrial genome loss. The orthologous Bet1 protein in Drosophila was determined to be necessary for the maintenance of the mitochondrial genome, with prolonged knockdown of Bet1 associated with an increase in mitochondrial membrane potential [ 52 ]. In this study, expression of BET1 is increased upon mitochondrial genome loss, presumably in an attempt to maintain a normal membrane potential.…”

Section: 3 Comparison Against Baseline Approaches Using Simulated Datamentioning

confidence: 99%

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Roy,

Sheikh,

Furey

2024

PLoS Comput Biol

View full text Add to dashboard Cite

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

show abstract

“…However, Gerards et. al [31] demonstrated that vesicle transport at the Golgi apparatus is fundamental for the mitochondrial quality control of fruit flies. This quality control includes several processes, such as the mitochondrial unfolded protein response, which are highly influenced by mitochondrial mRNA stability [32].…”

Section: The Axes Of the Embedding Space Uncover New Functional Inter...mentioning

confidence: 99%

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell

Doria-Belenguer

Xenos

Ceddia

et al. 2023

Preprint

View full text Add to dashboard Cite

Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes’ embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes’ clusters and the limitations of the functional annotations’ resources hinder the identification of the currently unknown cell’s functioning mechanisms. Thus, we propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species.

show abstract

Intracellular vesicle trafficking plays an essential role in mitochondrial quality control

Cited by 5 publications

References 49 publications

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell

Contact Info

Product

Resources

About