Copper (Cu) is a vitally important micronutrient, whose balance between essential and toxic levels requires a tightly regulated network of proteins. Dysfunction in key components of this network leads to the disruption of Cu homeostasis, resulting in fatal disorders such as Wilson disease, which is caused by mutations in the hepatic Cu efflux transporter ATP7B. Unfortunately, the molecular targets for normalizing Cu homeostasis in Wilson disease remain poorly understood. Here, using genome-wide screening, we identified the cellular prion protein (PrP) as an important mediator of Cu toxicity in WD. Loss of ATP7B stimulates hepatic expression of PrP, which promotes endocytic Cu uptake, leading to toxic Cu overload. Suppression of PrP significantly reduces Cu toxicity in cell and animal models of Wilson disease. These findings highlight the critical regulatory role of PrP in copper metabolism and open new avenues for exploring the therapeutic potential of PrP suppression in Wilson disease.

Non-coding RNA transcripts originating from Ultraconserved Regions (UCRs) have tissue-specific expression and play relevant roles in the pathophysiology of multiple cancer types. Among them, we recently identified and characterized the ultra-conserved-transcript-8+ (uc.8+), whose levels correlate with grading and staging of bladder cancer. Here, to validate uc.8+ as a potential biomarker in bladder cancer, we assessed its expression and subcellular localization by using tissue microarray on 73 human bladder cancer specimens. We quantified uc.8+ by in-situ hybridization and correlated its expression levels with clinical characteristics and patient survival. The analysis of subcellular localization indicated the simultaneous presence of uc.8+ in the cytoplasm and nucleus of cells from the Low-Grade group, whereas a prevalent cytoplasmic localization was observed in samples from the High-Grade group, supporting the hypothesis of uc.8+ nuclear-to-cytoplasmic translocation in most malignant tumor forms. Moreover, analysis of uc.8+ expression and subcellular localization in tumor-surrounding stroma revealed a marked down-regulation of uc.8+ levels compared to the paired (adjacent) tumor region. Finally, deep machine-learning approaches identified nucleotide sequences associated with uc.8+ localization in nucleus and/or cytoplasm, allowing to predict possible RNA binding proteins associated with uc.8+, recognizing also sequences involved in mRNA cytoplasm-translocation. Our model suggests uc.8+ subcellular localization as a potential prognostic biomarker for bladder cancer.

ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. It handles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data from the Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPS classification) and, together with information available on the samples, performs several exploratory and statistical analyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix, providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundant lipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomics has a user-friendly graphical user interface and supports an extensive series of interactive graphics. ADViSELipidomics is freely available at https://github.com/ShinyFabio/ADViSELipidomics Supplementary data are available at Bioinformatics online.

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes’ fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

Skeletal muscles, which constitute 40–50% of body mass, regulate whole-body energy expenditure and glucose and lipid metabolism. Peroxisomes are dynamic organelles that play a crucial role in lipid metabolism and clearance of reactive oxygen species, however their role in skeletal muscle remains poorly understood. To clarify this issue, we generated a muscle-specific transgenic mouse line with peroxisome import deficiency through the deletion of peroxisomal biogenesis factor 5 (Pex5). Here, we show that Pex5 inhibition results in impaired lipid metabolism, reduced muscle force and exercise performance. Moreover, mitochondrial structure, content, and function are also altered, accelerating the onset of age-related structural defects, neuromuscular junction degeneration, and muscle atrophy. Consistent with these observations, we observe a decline in peroxisomal content in the muscles of control mice undergoing natural aging. Altogether, our findings show the importance of preserving peroxisomal function and their interplay with mitochondria to maintain muscle health during aging.

Peripartum depression can have severe impact on the mother’s and the infant’s health. Yet, its biological underpinnings are largely unknown. The present study sought to identify transcriptomic signatures of depressive symptoms during pregnancy and postpartum. Blood samples were collected during late pregnancy or early postpartum for mRNA isolation and sequencing, while depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS). Based on the timepoint when the samples were collected, differentially expressed genes (DEGs) were identified by (1) comparing mRNA levels between the depression symptom trajectory groups, and (2) correlating with EPDS scores. DEGs for samples collected during late pregnancy, but not postpartum, were associated with depressive symptoms occurring only during pregnancy or persisting postpartum, compared with controls. There were 16 upregulated and 109 downregulated DEGs significantly associated with EPDS score at week 32 among samples collected during late pregnancy. Gene Set Enrichment Analysis identified immune response and cell motility as processes linked to these DEGs. Hypothesis-based analysis on previously identified postpartum depressive symptoms-related DEGs replicated a positive association between expression of immune-related genes ISG15 and RSAD2 with postpartum-onset depressive symptoms, both in samples taken during late pregnancy and postpartum. The present findings point to transcriptomic signatures associated with peripartum depressive symptoms, mostly related to immune system dysregulation.