Biology subjects

Albizuri, M.

Publications and source records attributed to Albizuri, M..

3 recordsLinked to original sources

bioRxiv2026

Chronic morphine treatment induces a conserved Smchd1-dependent epigenetic memory that disrupts X-chromosome inactivation and genomic imprinting

Epigenetic memory ensures stable inheritance of gene expression patterns critical for embryonic development. Environmental exposures can disrupt this memory, yet the mechanisms remain unclear. Here we demonstrate that chronic morphine exposure induces a persistent transcriptomic and epigenetic memory by repressing Smchd1, a key chromatin regulator, in mouse embryonic stem cells, preimplantation embryos, and human induced pluripotent stem cells. This repression compromises maintenance of X-chromosome inactivation and genomic imprinting, leading to sustained dysregulation of developmentally important gene clusters. Morphine-induced epigenetic alterations also involve changes in DNA methylation and histone modifications along the X chromosome and notably increased H3K27me3 at the Smchd1 locus. These findings reveal a conserved mechanism by which opioid exposure disrupts higher-order chromatin architecture and epigenetic memory during early development, potentially contributing to long-term developmental and clinical outcomes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/713629v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@16986cborg.highwire.dtl.DTLVardef@1108eaaorg.highwire.dtl.DTLVardef@66373org.highwire.dtl.DTLVardef@16b37f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

Lipidomic and metabolomic profiling on low count human spermatozoa: A robust and reproducible method for untargeted HPLC-ESI-MS/MS-based approach

1.BackgroundHuman infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. The identification of reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. In this study, we developed and optimized an untargeted high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS) workflow for comprehensive lipidomic and metabolomic profiling of human spermatozoa using only 1.25 million cells per sample. ResultsCompared to previous reports, our optimized method achieved unprecedented analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites, corresponding to nearly 7.600-fold improvements in detection efficiency per cell over published approaches. Lipidomic analysis revealed cholesterol, fatty acids, phosphatidylcholines, and phosphatidylethanolamine plasmalogens as the most abundant lipid classes, consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling showed strong enrichment of lipid-related and steroidogenic pathways, including phospholipid biosynthesis, glycerolipid metabolism and androgen and estrogen metabolism. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. ConclusionsOverall, this work establishes a robust, sensitive, and scalable analytical framework enabling high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. One Sentence SummaryDevelopment of a highly sensitive untargeted HPLC-ESI-MS/MS lipidomic and metabolomic workflow that achieves unprecedented molecular coverage from only 1.25 million human spermatozoa, revealing interconnected lipid and metabolic pathways and providing a robust foundation for biomarker discovery in male infertility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/703749v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@10b3132org.highwire.dtl.DTLVardef@1caf850org.highwire.dtl.DTLVardef@746adborg.highwire.dtl.DTLVardef@1135539_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

A Comparative Assessment of edgeR and methylKit Pipelines for DNA Methylation Detection

Despite the improvements in tool development for DNA methylation analysis, there is a lack of a consensus on computational and statistical models used for differentially methylated cytosine (DMC) identification. This variability complicates the interpretation of findings and raises concerns about the reproducibility and biological significance of the detected results. In this regard, the primary objective of this study was to compare the performance, concordance, and biological relevance of edgeR and methylKit tools in detecting DMCs (the first one based on fold change and the second one based on percentage), following morphine exposure model in mouse embryonic stem cells (mESCs). While a different number of total DMCs was identified by each tool, both pipelines detected a global hypomethylation as a result. Genomic analysis revealed a predominant distribution of DMCs in intergenic and intronic regions on one hand, and in open sea regions on the other hand. Despite the differences in sensitivity, both tools demonstrated moderate concordance in DMCs detection ([~]56%) and high concordance in gene level analysis ([~]90%), identifying similar differentially methylated genes (DMGs). Overall, the results underscore the complementary strengths of methylKit and edgeR and highlight the importance of tool selection for epigenetic studies. As a conclusion, integrating both pipelines is recommended for comprehensive analysis, particularly in studies with complex experimental designs.

bioinformatics↗