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Gomez Varela, D.

Publications and source records attributed to Gomez Varela, D..

2 recordsLinked to original sources

Ultra-sensitive metaproteomics (uMetaP) redefines the dark field of metaproteome, enables single-bacterium resolution, and discovers hidden functions in the gut microbiome

The gut microbiome is a complex ecosystem with significant inter-individual variability determined by hundreds of low-abundant species as revealed by genomic methods. Functional redundancy demands direct quantification of microbial biological functions to understand their influence on host physiology. This functional landscape remains unexplored due to limited sensitivity in metaproteomics methods. We present uMetaP, an ultra-sensitive metaproteomic solution combining advanced LC-MS technologies with a novel FDR- controlled de novo strategy. uMetaP improves the taxonomic detection limit of the gut "dark metaproteome" by 5,000-fold with exceptional quantification precision and accuracy. In a mouse model of colonic injury, uMetaP extended metagenomics findings and identified host functions and microbial metabolic networks linked to disease. We obtained orthogonal validation using transcriptomic data from biopsies of 204 Crohns patients and presented the concept of a "druggable metaproteome". Among the drug-protein interactions discovered are treatments for intestinal inflammatory diseases, showcasing uMetaPs potential for disease diagnostics and data-driven drug repurposing strategies.

microbiology↗

Deep Proteome Profiling Reveals Signatures of Age and Sex Differences in Paw Skin and Sciatic Nerve of Naïve Mice

The age and sex of studied animals profoundly impact experimental outcomes in animal-based preclinical biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 14 weeks and do not assess study parameters in male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences. Furthermore, the molecular setup of tissues in dependence of age and sex is unknown in naive mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerve (SCN) - two tissues, which are crucially implicated in nociception and pain as well as diverse diseases induced by inflammation, trauma, and demyelination. Remarkably, we uncovered significant differences when comparing (i) male and female mice, and, in parallel, (ii) adolescent mice (4 weeks) with adult mice (14 weeks). Age was identified as a major discriminator of analyzed samples irrespective of tissue type. Moreover, our analysis enabled us to decipher protein subsets and networks that exhibit differential abundance in dependence on the age and/or sex of mice. Notably, among these were proteins and signaling pathways with known relevance for (patho)physiology, such as homeostasis and epidermal signaling in skin and, in SCN, multiple myelin proteins and regulators of neuronal development. In addition, extensive comparisons with available databases revealed that we quantified approx. 50% of gene products that were implicated in distinct skin diseases and pain, many of which exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study emphasizes the need for accurate age matching and uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique systems biology proteome resource, which facilitates mechanistic insights into somatosensory and skin biology in dependence on age and sex - a prerequisite for successful preclinical studies in mouse disease models. Graphic workflow O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/498721v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@fc5aaforg.highwire.dtl.DTLVardef@1a5b87dorg.highwire.dtl.DTLVardef@f353e9org.highwire.dtl.DTLVardef@109f6e5_HPS_FORMAT_FIGEXP M_FIG C_FIG The Figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.

systems biology↗