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Garcia-Valiente, A.

Publications and source records attributed to Garcia-Valiente, A..

2 recordsLinked to original sources

Integrated metabolomic and metagenomic profiling reveals distinct signatures in treatment naive multiple sclerosis patients.

Imbalances in gut microbiota composition and microbiota-associated metabolites have been linked with several neurological diseases, including multiple sclerosis (MS). However, a comprehensive multi-biofluid approach remains lacking. Most studies on MS include patients already receiving treatment and often neglect to account for sex-related differences, which could serve as potential confounding factors. Moreover, they mostly investigate only stools or plasma metabolomics. In this study, we recruited 18 treatment-naive neuroinflammatory patients at diagnosis and compared them with 20 healthy controls, matched for sex and age. We conducted multi-biofluids metabolomic analysis of urine, stool, serum, and cerebrospinal fluid, complemented by taxonomical and functional profiling of the gut microbiome using shotgun metagenomic sequencing. Our results show that MS patients exhibit distinct microbiome composition and urinary metabolomic profiles compared to healthy controls. Furthermore, neuroinflammation is associated with dysregulation of microbially-produced short-chain fatty acids, their intestinal absorption and systemic bioavailability, as illustrated by the altered plasma levels.

neuroscience↗

CRISPR-Analytics (CRISPR-A): a platform for precise analytics and simulations for gene editing

Gene editing characterization with currently available tools does not always give precise relative proportions among the different types of gene edits present in an edited bulk of cells. We have developed CRISPR-Analytics, CRISPR-A, which is a comprehensive and versatile genome editing web application tool and a nextflow pipeline to give support to gene editing experimental design and analysis. CRISPR-A provides a robust gene editing analysis pipeline composed of data analysis tools and simulation. It achieves higher accuracy than current tools and expands the functionality. The analysis includes mock-based noise correction, spike-in calibrated amplification bias reduction, and advanced interactive graphics. This expanded robustness makes this tool ideal for analyzing highly sensitive cases such as clinical samples or experiments with low editing efficiencies. It also provides an assessment of experimental design through the simulation of gene editing results. Therefore, CRISPR-A is ideal to support multiple kinds of experiments such as double-stranded DNA break-based engineering, base editing (BE), primer editing (PE), and homology-directed repair (HDR), without the need of specifying the used experimental approach.

bioinformatics↗