Biology subjects

Rodriguez-Navarro, S.

Publications and source records attributed to Rodriguez-Navarro, S..

3 recordsLinked to original sources

bioRxiv2024

Decoding Transcriptional Memory in Yeast Heat Shock and the Functional Implication of the RNA Binding Protein Mip6

Cells not only adapt to environmental changes, but they also "remember" specific signals, allowing them to respond more rapidly to future stressors. This phenomenon, known as transcriptional memory, is orchestrated by a complex interplay of epigenetics, transcription regulators and RNA metabolism factors. Although transcriptional memory has been well-studied in various contexts, its role in the heat shock (HS) response of yeast remains largely unexplored. In our study, we delve into the dynamics of HS memory in wild-type yeast and a mip6{Delta} mutant, which lacks the RNA-binding protein Mip6. Notably, Mip6 has been shown to influence the expression of key stress-related genes and maintain low Msn2/4-dependent transcript levels under standard conditions. Our transcriptomic analysis offers novel insights into how yeast cells remember HS exposure. We uncover striking differences in gene expression patterns depending on whether genes are induced or repressed during HS memory. Furthermore, we find that an initial 15-minute heat shock triggers a response that becomes attenuated with just 5 additional minutes of stress. While the response kinetics between memory and non-memory conditions are similar, we report a subtle but important role for Mip6 in fine-tuning transcriptional memory and adaptation to heat stress. Biochemical and genetic evidence also suggests that Mip6 cooperates with alternative survival pathways independent of MSN2/4, and functionally interacts with the Rpd3 histone deacetylase complex, a key player in transcriptional memory and the HS response. These findings open up new avenues for understanding the molecular mechanisms behind heat stress adaptation in eukaryotes.

molecular biology↗

bioRxiv2022

MAMBA: a model-driven, constraint-based multiomic integration method

The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).

bioinformatics↗

bioRxiv2017

SQANTI: extensive characterization of long read transcript sequences for quality control in full-length transcriptome identification and quantification

High-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in very well annotated organisms as mice and humans. Nonetheless, there is a need for studies and tools that characterize these novel isoforms. Here we present SQANTI, an automated pipeline for the classification of long-read transcripts that computes 47 descriptors that can be used to assess the quality of the data and of the preprocessing pipelines. We applied SQANTI to a neuronal mouse transcriptome using PacBio long reads and illustrate how the tool is effective in readily describing the composition of and characterizing the full-length transcriptome. We perform extensive evaluation of ToFU PacBio transcripts by PCR to reveal that an important number of the novel transcripts are technical artifacts of the sequencing approach, and that SQANTI quality descriptors can be used to engineer a filtering strategy to remove them. Most novel transcripts in this curated transcriptome are novel combinations of existing splice sites, result more frequently in novel ORFs than novel UTRs and are enriched in both general metabolic and neural specific functions. We show that these new transcripts have a major impact in the correct quantification of transcript levels by state-of-the-art short-read based quantification algorithms. By comparing our iso-transcriptome with public proteomics databases we find that alternative isoforms are elusive to proteogenomics detection and are variable in protein changes with respect to the principal isoform of their genes. SQANTI allows the user to maximize the analytical outcome of long read technologies by providing the tools to deliver quality-evaluated and curated full-length transcriptomes. SQANTI is available at https://bitbucket.org/ConesaLab/sqanti.

bioinformatics↗