Biology subjects

Vecchio, D.

Publications and source records attributed to Vecchio, D..

8 recordsLinked to original sources

bioRxiv2026

Cardiomyocyte-derived Wnt5a drives doxorubicin-induced cardiomyopathy by amplifying cellular senescence

Doxorubicin (DOX) is an effective anthracycline chemotherapeutic agent, but its use is limited by cardiotoxicity that can progress to cardiomyopathy and heart failure. Cellular senescence contributes to DOX-induced cardiac injury, yet the upstream signals that initiate and propagate senescence in the injured heart remain unclear. Here, we identify Wnt5a, a non-canonical Wnt ligand, as a mediator of anthracycline cardiomyopathy. WNT5A was increased in serum from cancer patients receiving anthracycline therapy and in a pathologic human cardiomyocyte population in the context of DOX-induced cardiomyopathy. In mouse hearts, DOX induced early cardiomyocyte-enriched Wnt5a expression before overt cardiac dysfunction. Cardiomyocyte-specific Wnt5a deletion attenuated DOX-induced cardiac dysfunction, fibrosis and senescence marker induction, whereas recombinant Wnt5a and cardiomyocyte-targeted Wnt5a overexpression were sufficient to promote cardiomyocyte senescence and cardiac dysfunction. Mechanistically, DOX activated a Wnt5a-Fzd2 feed-forward axis that amplified Wnt5a expression in cardiomyocytes and propagated senescence to neighboring fibroblasts. Genetic disruption of this pathway in cardiomyocytes, fibroblasts or senescent cells reduced DOX-induced cardiomyopathy. Pharmacological inhibition of Wnt5a signaling with secreted frizzled-related protein 5 suppressed DOX-induced cardiac injury without compromising the anticancer efficacy of DOX. These findings identify Wnt5a-Fzd2 signaling as a senescence-amplifying mechanism in anthracycline cardiomyopathy and suggest a therapeutic strategy to mitigate DOX cardiotoxicity.

cell biology↗

bioRxiv2026

Environmental DNA reveals long-term persistence of a Midichloria-like bacterium in a rainbow trout aquaculture and links Ichthyopthirius multifiliis with the red mark syndrome

Red Mark Syndrome (RMS) is a widespread skin disease affecting rainbow trout (Onchorhynchus mykiss). It provokes substantial economic losses in aquaculture, and is putatively caused by a Rickettsiales bacterium named Midichloria-like organism (RMS-MLO), which is strongly associated with RMS lesions. However, RMS-MLO ecology and epidemiology in aquaculture systems remain poorly understood. In this study, we analysed environmental DNA to monitor the presence of RMS-MLO and its putative vector Ichthyophthirius multifiliis in a trout farm in Northern Italy over one year. Water and sediment samples were monthly collected from multiple water tanks. RMS-MLO was consistently detected by PCR throughout the study in all trout-containing tanks, both in water and sediment samples, but never in the trout-free inflow tank. We did not observe an increase in RMS-MLO abundance during the single RMS outbreak recorded nor in relation with the co-occurrence of I. multifiliis. Our findings indicate a long-term persistence of RMS-MLO in the aquaculture, possibly as a consequence of infections with low prevalence or abundance, rather than its entry from the external environment at the time of RMS outbreaks. Additionally, hints were recorded for a potential role of free-living aquatic microeukaryotes as additional occasional reservoirs. In contrast, I. multifiliis was negatively related with RMS-MLO, while it significantly increased in abundance during the RMS outbreak, particularly in the inflow tank. This supports that, rather than a stable reservoir, I. multifiliis may act as a facilitator of RMS outbreaks, which might indeed be triggered by the entry of this parasite in trout farms.

microbiology↗

bioRxiv2025

Neuroanatomical Correlates of Negative Symptoms in Schizophrenia

BackgroundSchizophrenia is characterized by widespread structural brain abnormalities, but associations between structural abnormalities and negative symptom severity are not well understood. Negative symptoms have been conceptualized in a hierarchical structure of two second-order dimensions--motivation and pleasure (MAP) and expression (EXP)--and five first-order domains: anhedonia, avolition, and asociality (MAP), and blunted affect and alogia (EXP). A better understanding of the neural circuitry underlying negative symptom dimensions and domains is important given their reported association with poor functional outcome and lack of available treatments. Study DesignThe meta-analysis included 1,591 individuals with schizophrenia across 16 samples with structural imaging and Scale for Assessment of Negative Symptoms data. The study generated correlations of cortical thickness and subcortical volumes with the negative symptom dimensions and domains. Study resultsNegative symptoms showed mainly negative associations with cortical thickness and subcortical volumes. The effect sizes were small but there was a pattern of associations in predominantly frontal lobe cortical thickness and limbic subcortical volumes. The regional correlation patterns of cortical thickness and subcortical volumes with symptom domains support the conceptualized hierarchical structure of negative symptoms: correlations of MAP domains were stronger with the MAP than EXP dimension, and vice versa. Exploratory analyses with receptor densities further supported the hierarchy. ConclusionOur findings reveal small but consistent associations between negative symptom dimensions and predominantly prefrontal region cortical thickness, and limbic region volumes. These findings advance our understanding of the network of anatomical regions that may contribute to the severity of negative symptoms in schizophrenia.

neuroscience↗

bioRxiv2023

Estimating multimodal brain variability in schizophreniaspectrum disorders: A worldwide ENIGMA study

ObjectiveSchizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. MethodsThis meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. ResultsWe found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. ConclusionsHigher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

neuroscience↗

bioRxiv2023

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9{+/-}18.0 years; time since stroke 12.2{+/-}0.2 months; normalised motor score 0.7{+/-}0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

neuroscience↗

bioRxiv2021

Automatic structural analysis of bioinspired percolating network materials using graph theory

Mimicking numerous biological membranes and nanofiber-based tissues, there are multiple materials that are structured as percolating nanoscale networks (PPNs). They reveal unique combination of properties and the family of PNN-based composites and nanoporous materials is rapidly expanding. Their technological significance and the necessity of their structural design require a unifying approach for their structural description. However, their complex aperiodic architectures are difficult to describe using traditional methods that are tailored for crystals. A related problem is the lack of computational tools that enable one to capture and enumerate the patterns of stochastically branching fibrils that are typical for these composites. Here, we describe a conceptual methodology and a computational package, StructuralGT, to automatically produce a graph theoretical (GT) description of PNNs from various micrographs. Using nanoscale networks formed by aramid nanofibers (ANFs) as examples, we demonstrate structural analysis of PNNs with 13 GT parameters. Unlike qualitative assessments of physical features employed previously, StructuralGT allows quantitative description of the complex structural attributes of PNNs enumerating the networks morphology, connectivity, and transfer patterns. Accurate conversion and analysis of micrographs is possible for various levels of noise, contrast, focus, and magnification while a dedicated graphical user interface provides accessibility and clarity. The GT parameters are expected to be correlated to material properties of PNNs (e.g. ion transport, conductivity, stiffness) and utilized by machine learning tools for effectual materials design. Table of Content O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/438877v1_ufig1.gif" ALT="Figure 1"> View larger version (144K): org.highwire.dtl.DTLVardef@1c929f0org.highwire.dtl.DTLVardef@1e07168org.highwire.dtl.DTLVardef@357e7aorg.highwire.dtl.DTLVardef@28d21_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Graph Theoretical Design of Biomimetic Aramid Nanofiber Nanocomposites as Insulation Coatings for Implantable Bioelectronics

An insulation material combining crack and delamination resistance, flexibility, strong adhesion, and biocompatibility is vital for implantable bioelectronic devices of all types. Creating a material with the combination of all these properties is a particularly distinct challenge for implantable electrodes. Here we describe a nanocomposite material addressing these technological challenges based on aramid nanofibers (ANFs) whose unique mechanical properties are complemented by the epoxy resins with strong adhesion to various surfaces. The nanoscale structure of the ANF/epoxy nanocomposite coating replicates the nanofibrous organization of human cartilage, which is known for its exceptional toughness and longevity. The structural analogy between percolating networks of cartilage and ANF was demonstrated using Graph Theory (GT) analysis. The match of multiple GT indexes indicated the near identical organization pattern of cartilage and ANF/epoxy nanocomposite. When compared with the standard insulating material for bioelectronics, Parylene C, the ANF/epoxy nanocomposite demonstrates excellent interfacial adhesion, biocompatibility, and low inflammatory response. This study opens the possibility for the development of insulation materials suitable for different types of electronics for neural engineering and other biomedical applications. Also important, GT analysis makes possible structural characterization of complex biological and biomimetic materials.

bioengineering↗

bioRxiv2019

MYC-associated factor MAX is an essential regulator of the clock core network

The circadian transcriptional network is based on a competition between transcriptional activator and repressor complexes regulating the rhythmic expression of clock-controlled genes. We show here that the MYC-Associated factor X, MAX, plays a repressive role in this network and operates through its MYC-independent binding to E-box-containing regulatory regions within the promoters of circadian BMAL1 targets. This clock function of MAX is essential for maintaining a proper circadian rhythm but separated by the role of MAX as a partner of MYC in controlling cell proliferation. We also identified MAX Network Transcriptional repressor, MNT, as a fundamental partner of MAX-mediated circadian regulation. Collectively, our data indicate that MAX is an integral part of the core molecular clock and keeps the balance between positive and negative elements of the molecular clock machinery. Accordingly, alteration of MAX transcriptional complexes may contribute to circadian dysfunction in pathological contexts.

molecular biology↗