Intracellular vesicle trafficking plays an essential role in mitochondrial quality control

The Matrix of Mitochondrial Imaging: Exploring Spatial Dimensions

Mitochondria play a crucial role in human biology, affecting cellular processes at the smallest spatial scale as well as those involved in the functionality of the whole system. Imaging is the most important research tool for studying the fundamental role of mitochondria across these diverse spatial scales. A wide array of available imaging techniques have enabled us to visualize mitochondrial structure and behavior, as well as their effect on cells and tissues in a range from micrometers to centimeters. Each of the various imaging techniques that are available offers unique advantages tailored to specific research needs. Selecting an appropriate technique suitable for the scale and application of interest is therefore crucial, but can be challenging due to the large range of possibilities. The aim of this review is two-fold. First, we provide an overview of the available imaging techniques and discuss their strengths and limitations for applications across the sub-mitochondrial, cellular, tissue and organ levels for the imaging of mitochondria. Second, we identify opportunities for novel applications and advancement in the field. We emphasize the importance of integration across scales in mitochondrial imaging studies, particularly to bridge the gap between microscopic and non-invasive techniques. While integrating these diverse scales is challenging, primarily because such multi-scale approaches require expertise that spans different imaging modalities, we argue that integration has the potential to provide groundbreaking insights into mitochondrial biology. By providing a comprehensive overview of imaging techniques, this review paves the way for multi-scale imaging initiatives in mitochondrial research.

Studying Cellular Senescence Using the Model Organism Drosophila melanogaster

Cellular senescence, a complex biological process characterized by irreversible cell cycle arrest, contributes significantly to the development and progression of aging and of age-related diseases. Studying cellular senescence in vivo can be challenging due to the high heterogeneity and dynamic nature of senescent cells. Recently, Drosophila melanogaster has emerged as a powerful model organism for studying aging and cellular senescence due to its tractability and short lifespan, as well as due to the conservation of age-related genes and of key age-related pathways with mammals. Consequently, several research studies have utilized Drosophila to investigate the cellular mechanisms and pathways implicated in cellular senescence. Herein, we provide an overview of the assays that can be applied to study the different features of senescent cells in D. melanogaster tissues, highlighting the benefits of this model in aging research. We also emphasize the importance of selecting appropriate biomarkers for the identification of senescent cells, and the need for further understanding of the aging process including a more accurate identification and detection of senescent cells at the organismal level; a far more complex process as compared to single cells.

Mitochondria: great balls of fire

Recent experimental studies indicate that mitochondria in mammalian cells are maintained at temperatures of at least 50 °C. While acknowledging the limitations of current experimental methods and their interpretation, we here consider the ramifications of this finding for cellular functions and for evolution. We consider whether mitochondria as heat-producing organelles had a role in the origin of eukaryotes and in the emergence of homeotherms. The homeostatic responses of mitochondrial temperature to externally applied heat imply the existence of a molecular heat-sensing system in mitochondria. While current findings indicate high temperatures for the innermost compartments of mitochondria, those of the mitochondrial surface and of the immediately surrounding cytosol remain to be determined. We ask whether some aspects of mitochondrial dynamics and motility could reflect changes in the supply and demand for mitochondrial heat, and whether mitochondrial heat production could be a factor in diseases and immunity.

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

Summary

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. 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.

Availability and implementation

Data and source code can be accessed at https://gitlab.bsc.es/sdoria/axes-of-biology.git.

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

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.

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

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.