Julián Ávila-Pacheco

The paper identifies N-acyl putrescines, particularly NOP, as immunomodulatory metabolites produced by gut microbiota that suppress inflammation in IBD models.

The paper identifies tissue-specific transcriptional regulatory programs in rats undergoing endurance exercise training by integrating chromatin accessibility, DNA methylation, and gene expression data.

This study identifies Blautia wexlerae's role in converting dietary fats into bioactive metabolites that enhance gut hormone secretion and improve metabolic health in mice and humans.

This study identifies two distinct molybdenum-dependent enzyme families that produce urolithin A from ellagic acid, linking gut bacterial metabolism to host inflammation in IBD.

The paper links lower plasma plasmalogen levels to increased risk of lymph node positive breast cancer in humans and mice, showing dietary PUFA depletion promotes cancer survival in lymph nodes.

The paper introduces a deep learning model, ICEBERG, for structural elucidation using tandem mass spectrometry, achieving high accuracy in identifying molecular structures from complex mixtures.

Eclipse aligns multiple nontargeted LC-MS metabolomics datasets using a graph-based subalignment approach.

The paper identifies a toggle-switch mechanism in the ERECTA receptor that prevents inappropriate signaling by autoinhibition and rapid degradation after activation.

This study examines how endurance exercise training affects rat subcutaneous white adipose tissue, revealing sex-specific molecular responses and differences in adiposity and metabolism.

This study profiles multi-omic changes in multiple tissues of rats during endurance exercise training, identifying molecular adaptations and sex-specific responses across time.

The paper introduces Compass, an algorithm that uses single-cell RNA-Seq and flux balance analysis to characterize metabolic states in Th17 cells, revealing a metabolic switch and novel anti-inflammatory regulators.

The paper analyzes de novo mutations in 1,465 genomes to show similar mutation rates across ancestries but reduced rates in the Amish population, with environmental factors possibly influencing mutation rates.

netome provides tools for analyzing LC-MS metabolomics data, including quality control, peak alignment, and network-based statistical testing.