Biology subjects

Ascencio, D.

Publications and source records attributed to Ascencio, D..

6 recordsLinked to original sources

bioRxiv2025

Metformin-induced longevity is associated with retrotransposon dynamics in yest chronological aging

The widely used antidiabetic drug metformin extends lifespan across diverse model organisms, from yeast to primates. However, the cellular mechanisms underlying its anti-aging effects remain only partially understood. Here, we combined large-scale genetic screening and high-resolution lifespan phenotyping with transcriptomic and proteomic analyses to provide a systems view of metformins impact on the chronological lifespan of Saccharomyces cerevisiae. Unexpectedly, we uncovered pronounced gene-drug interactions between metformin and chromatin-modifying factors. Specifically, deletions of Set3C histone deacetylation complex subunits phenocopied the longevity effect of metformin, with no additive benefit when combined, suggesting convergence on shared pathways. Transcriptome profiling further revealed that metformin reprogrammed stationary-phase gene expression, with Ty1-copia retrotransposons emerging as a consistently induced signature, thereby suggesting a possible mechanism for the observed interactions with Set3C regulation. Paradoxically, TYA Gag-like protein levels and retrotransposition frequency were modestly reduced, indicating an uncoupling between transcriptional activation and retromobility. Proteome analysis revealed increased abundance of mitochondrial and stress-response proteins as primary outcomes of metformin exposure, both known modulators of Ty1 dynamics in yeast. Together, our findings position chromatin regulation and retrotransposon expression as integral components of metformins pro-longevity mechanisms, expanding its influence beyond signaling, metabolism, and stress response. HighlightsO_LILarge-scale genetic screening reveals that deletions of Set3C histone deacetylase phenocopy metformin-induced longevity. C_LIO_LIMetformin consistently induces Ty1 retrotransposon transcription but reduces Gag protein abundance and retromobility. C_LIO_LIProteomic changes highlight mitochondrial and stress-response proteins as primary outcomes of metformin exposure. C_LIO_LIRetrotransposon dynamics emerge as a key component associated with metformin-induced longevity. C_LI

cell biology↗

bioRxiv2021

BFG-PCA: tools and resources that expand the potential for binary protein interaction discovery

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein-protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, Dihydrofolate Reductase Protein-Fragment Complementation Assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11,000 protein pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

systems biology↗

bioRxiv2021

Automated classification of signal sources in mesoscale calcium imaging

Functional imaging of neural cell populations is critical for mapping intra- and inter-regional network dynamics across the neocortex. Recently we showed that an unsupervised machine learning decomposition of densely sampled recordings of cortical calcium dynamics results in a collection of components comprised of neuronal signal sources distinct from optical, movement, and vascular artifacts. Here we build a supervised learning classifier that automatically separates neural activity and artifact components, using a set of extracted spatial and temporal metrics that characterize the respective components. We demonstrate that the performance of the machine classifier matches human identification of signal components in novel data sets. Further, we analyze control data recorded in glial cell reporter and non-fluorescent mouse lines that validates human and machine identification of functional component class. This combined workflow of data-driven video decomposition and machine classification of signal sources will aid robust and scalable mapping of complex cerebral dynamics.

neuroscience↗

bioRxiv2021

Data-driven filtration and segmentation of mesoscale neural dynamics

Recording neuronal group activity across the cortical hemispheres from awake, behaving mice is essential for understanding information flow across cerebral networks. Video recordings of cerebral function comes with challenges, including optical and movement-associated vessel artifacts, and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface. Independent Component Analysis utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We also utilize isolated signal components to produce segmentations of the cortical surface, unique to each individuals functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis.

neuroscience↗

bioRxiv2019

The role of structural pleiotropy and regulatory evolution in the retention of heteromers of paralogs

Many paralogs derive from the duplication of genes encoding homomeric proteins. Such duplication events lead to the formation of homomers and heteromers, thus creating new structures from a single event. We exhaustively characterize this phenomenon using the budding yeast protein-protein interaction network. We observe that paralogs that heteromerize are very frequent and less functionally diverged than those that lost this property, raising the possibility that heteromerization prevents functional divergence. Using in silico evolution, we show that for homomers and heteromers that share binding interfaces, mutations on one complex have pleiotropic effects on the other complex, resulting in highly correlated responses to selection. As a result, heteromerization could be preserved indirectly due to selection for the maintenance of homomers. By integrating data on gene expression and protein localization, we find that regulatory evolution could play a role in overcoming these structural pleiotropic effects and in allowing paralog functional divergence.

evolutionary biology↗

bioRxiv2017

Protein Moonlighting Revealed by Non-Catalytic Phenotypes of Yeast Enzymes

A single gene can partake in several biological processes, and therefore gene deletions can lead to different--sometimes unexpected--phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene-deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, 30% could be readily complemented by a catalytically-inactive enzyme. Such non-catalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine-biosynthetic enzymes Ilv1 and Ilv2, suggesting novel \"moonlighting\" activities in these proteins. Furthermore, differential genetic-interaction profiles of gene-deletion and catalytic mutants indicated that ILV1 is functionally associated to regulatory processes, specifically to chromatin modification. Our systematic study shows that gene-loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.

genetics↗