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Sanz, C.

Publications and source records attributed to Sanz, C..

3 recordsLinked to original sources

Multimodal neuroimaging-microbiota integration identifies Akkermansia as a modulator of alcohol-induced gut-liver-brain pathology

Alcohol use disorder (AUD) disrupts the gut-liver-brain axis, yet mechanistically grounded and therapeutically actionable targets within this network remain poorly defined. To identify microbial modulators of alcohol-induced tissue pathology, longitudinal advanced diffusion MRI and fecal 16S rRNA profiling were integrated across Marchigian Sardinian alcohol-preferring rats evaluated at baseline, after four weeks of voluntary alcohol intake, and following six weeks of abstinence. Machine learning, specifically random forest models combining neuroimaging and microbiota data, improved phase classification and identified Akkermansia as the microbial feature most strongly associated with alcohol-related white matter microstructural abnormalities. Alcohol exposure induced widespread white matter alterations alongside gut dysbiosis characterized by reduced microbial diversity. To evaluate functional relevance, Akkermansia muciniphila was administered during the abstinence phase. Supplementation with A. muciniphila restored intestinal mucus, reduced liver injury markers, and elevated myelin basic protein levels within affected white matter regions. Collectively, these findings highlight Akkermansia as a critical modulator of alcohol-induced gut-liver-brain pathology and provide experimental support for a causal contribution of specific gut bacteria to persistent white matter damage in AUD. More broadly, this work establishes a robust multimodal framework for microbiome-based target discovery with clear translational relevance for disorders characterized by dysfunction along the gut-liver-brain axis. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSAlcohol use disorder (AUD) is associated with gut dysbiosis, impaired intestinal barrier function, liver injury, and persistent white matter abnormalities. Previous studies in patients and animal models have linked alcohol exposure to reduced microbial diversity, altered gut permeability, and white matter microstructural damage, particularly during abstinence. Other work has shown that microbiota-derived interventions can ameliorate peripheral consequences of alcohol exposure, especially in the gut and liver. However, the specific microbial features linked to alcohol-induced brain pathology remain poorly defined, and no prior study has integrated longitudinal microbiota and neuroimaging data to identify candidate microbial modulators of alcohol-related white matter damage and then functionally test them in vivo across the gut-liver-brain axis. Added value of this studyWe developed a multimodal framework that integrates longitudinal advanced diffusion MRI with fecal microbiota profiling and machine learning in alcohol-preferring rats. This approach identified Akkermansia as the microbial feature most strongly associated with alcohol-induced white matter abnormalities. Guided by this result, we administered Akkermansia muciniphila during abstinence and observed coordinated beneficial effects across multiple organs, including restoration of intestinal mucus, reduction of liver injury markers, and recovery of myelin basic protein in affected white matter regions. To our knowledge, this is the first study to combine longitudinal microbiota-MRI integration with experimental validation of a microbiota-based intervention that mitigates alcohol-induced pathology across the gut-liver-brain axis while restoring central white matter integrity. Implications of all the available evidenceOur findings support a mechanistic contribution of specific gut bacteria to persistent alcohol-induced tissue damage and identify Akkermansia as a candidate modulator of gut-liver-brain axis dysfunction in AUD. More broadly, this study establishes a generalizable strategy for integrating microbiota and neuroimaging data to discover biologically meaningful and therapeutically actionable targets in complex disorders involving coordinated peripheral and central pathology.

neuroscience↗

miR-203 drives breast cancer cell differentiation

A hallmark of many malignant tumors is dedifferentiated (immature) cells bearing slight or no resemblance to the normal cells from which the cancer originated. Tumor dedifferentiated cells exhibit a higher capacity to survive to chemo and radiotherapies and have the ability to incite tumor relapse. Inducing cancer cell differentiation would abolish their self-renewal and invasive capacity and could be combined with the current standard of care, especially in poorly differentiated and aggressive tumors (with worst prognosis). However, differentiation therapy is still in its early stages and the intrinsic complexity of solid tumor heterogeneity demands innovative approaches in order to be efficiently translated into the clinic. We demonstrate here that microRNA 203, a potent driver of differentiation in pluripotent stem cells (ESCs and iPSCs), promotes the differentiation of mammary gland tumor cells. Combining mouse in vivo approaches and both mouse and human-derived tridimensional organoid cultures, we report here that miR-203 influences the self-renewal capacity, plasticity and differentiation potential of breast cancer cells, and prevents tumor cell growth in vivo. Our work sheds light on differentiation-based antitumor therapies and offers miR-203 as a promising tool for directly confronting the tumor-maintaining and regeneration capability of cancer cells.

cancer biology↗

Natural language signatures of psilocybin microdosing

Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics ("microdosing") on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose. Following a double-blind and placebo-controlled experimental design, we explored natural language as a resource to identify speech produced under the acute effects of psilocybin microdoses, focusing on variables known to be affected by higher doses: verbosity, semantic variability and sentiment scores. Except for semantic variability, these metrics presented significant differences between a typical active microdose of 0.5 g of psilocybin mushrooms and an inactive placebo condition. Moreover, machine learning classifiers trained using these metrics were capable of distinguishing between conditions with high accuracy (AUC{approx}0.8). Our results constitute first proof that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules.

pharmacology and toxicology↗