Despite their emerging relevance to fully understand disease pathogenesis, we have as yet a poor understanding as to how biomechanical signals are integrated with specific biochemical pathways to determine cell behaviour. Mesothelial-to-mesenchymal transition (MMT) markers colocalized with TGF-β1-dependent signaling and yes-associated protein (YAP) activation across biopsies from different pathologies exhibiting peritoneal fibrosis, supporting mechanotransduction as a central driving component of these class of fibrotic lesions and its crosstalk with specific signaling pathways. Transcriptome and proteome profiling of the response of mesothelial cells (MCs) to linear cyclic stretch revealed molecular changes compatible with bona fide MMT, which (i) overlapped with established YAP target gene subsets, and were largely dependent on endogenous TGF-β1 signaling. Importantly, TGF-β1 blockade blunts the transcriptional upregulation of these gene signatures, but not the mechanical activation and nuclear translocation of YAP per se. We studied the role therein of caveolin-1 (CAV1), a plasma membrane mechanotransducer. Exposure of CAV1-deficient MCs to cyclic stretch led to a robust upregulation of MMT-related gene programs, which was blunted upon TGF-β1 inhibition. Conversely, CAV1 depletion enhanced both TGF-β1 and TGFBRI expression, whereas its re-expression blunted mechanical stretching-induced MMT. CAV1 genetic deficiency exacerbated MMT and adhesion formation in an experimental murine model of peritoneal ischaemic buttons. Taken together, these results support that CAV1-YAP/TAZ fine-tune the fibrotic response through the modulation of MMT, onto which TGF-β1-dependent signaling coordinately converges. Our findings reveal a cooperation between biomechanical and biochemical signals in the triggering of MMT, representing a novel potential opportunity to intervene mechanically induced disorders coursing with peritoneal fibrosis, such as post-surgical adhesions.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Adipocytes expand massively to accommodate excess energy stores and protect the organism from lipotoxicity. Adipose tissue expandability is at the center of disorders such as obesity and lipodystrophy; however, little is known about the relevance of adipocyte biomechanics on the etiology of these conditions. Here, we show in male mice in vivo that the adipocyte plasma membrane undergoes caveolar domain reorganization upon lipid droplet expansion. As the lipid droplet grows, caveolae disassemble to release their membrane reservoir and increase cell surface area, and transfer specific caveolar components to the LD surface. Adipose tissue null for caveolae is stiffer, shows compromised deformability, and is prone to rupture under mechanical compression. Mechanistically, phosphoacceptor Cav1 Tyr14 is required for caveolae disassembly: adipocytes bearing a Tyr14Phe mutation at this residue are stiffer and smaller, leading to decreased adiposity in vivo; exhibit deficient transfer of Cav1 and EHD2 to the LD surface, and show distinct Cav1 molecular dynamics and tension adaptation. These results indicate that Cav1 phosphoregulation modulates caveolar dynamics as a relevant component of the homeostatic mechanoadaptation of the differentiated adipocyte.

CAV1 is a protein-coding gene linked to several disorders, including cancer, lipodystrophy, and cardiovascular diseases. While its ability to respond to various mechanical and metabolic stimuli has been documented, a comprehensive understanding of its physiological regulation in humans is lacking. We leveraged the comprehensiveness of human post-mortem tissue data from the Genotype-Tissue Expression (GTEx) consortium, systematically exploring the sources of variability in CAV1 transcriptional levels using extensive bulk and single-nuclei RNA-seq datasets. This human-centric approach, avoiding inter-species comparisons, constitutes a unique resource to explore CAV1 regulation within the complexity of human tissues. Notably, cell type proportion was identified as a major determinant of CAV1 transcription levels across tissues. Donor physiological conditions, including disease states and end-of-life circumstances, also exhibited a tissue-specific influence. Among primary upstream regulators associated with CAV1, chromatin modifiers stood out, especially SMARCA2, which showed a positive correlation across tissues, and PRC2 complexes, which exhibited tissue-specific correlation. Upstream regulatory networks determining CAV1 levels are also enriched for annotations such as mechanobiology (e.g., TEAD4), immunity (e.g., RELA and STAT3), and metabolism (e.g., MYC and NRF1). A remarkable observation was a strong correlation between CAV1 and the relative infiltration of immune cells across tissues, supporting a potential role for CAV1 as a marker and driver of tissue immune infiltration.

Background and Aims: Nomophobia, the anxiety associated with smartphone separation, is well-studied in adolescents and adults, but less so in younger children. This study aimed to evaluate nomophobia severity among Spanish children aged 9– 13 in urban settings and establish a data-driven cutoff for identifying severe risk. Methods: A sample of 1153 children with weekly smartphone use from seven urban schools (populations > 50,000) completed the 32-item Nomophobia Questionnaire for Children (NQC) and the STAIC State Anxiety measure. Data were collected from February to April 2024. Hierarchical clustering grouped NQC scores into severity categories, followed by Receiver Operating Characteristic (ROC) analysis to determine a severe-risk threshold. Results: Hierarchical clustering identified two groups: a “Low-to-Moderate Nomophobia” cluster (96.88%; n=1117) and a “Severe Nomophobia” cluster (3.12%; n=36). ROC analysis established a cutoff of 101.5 (AUC=0.993, sensitivity=0.889, specificity=0.979), with 4.86% (n=56) exceeding this threshold. Children above the cutoff showed higher state anxiety (p=0.019), with no significant sex differences. Discussion: Most children exhibited mild to moderate nomophobia, but ~5% displayed severe levels associated with increased state anxiety and phone use duration. The NQC ≥ 101.5 cutoff effectively identifies this at-risk minority without overestimating prevalence, offering a practical screening tool. Conclusion: Combining hierarchical clustering and ROC analysis yielded a robust threshold (NQC ≥ 101.5) for detecting severe nomophobia in urban Spanish children, highlighting a small subgroup at elevated psychological risk. Future studies should validate this cutoff cross-culturally and longitudinally to guide early interventions.