Biology subjects

Nordio, G.

Publications and source records attributed to Nordio, G..

4 recordsLinked to original sources

bioRxiv2025

Transcriptome-informed brain cartography of polygenic risk and association with brain structure in major psychiatric disorders

Psychiatric disorders are complex, polygenic conditions characterized by patterned structural brain alterations. Whether these changes reflect transcriptional dysregulation driven by genetic risk remains unclear. We introduce a novel imaging-transcriptomics framework that integrates transcriptome-wide association studies (TWAS) with brain transcriptomic atlases to predict macroscale structural brain abnormalities across seven disorders: ADHD, ASD, AN, BD, MDD, OCD, and schizophrenia (SCZ). We generated disorder-specific Gene Expression-based Disorder Associated Risk (GEDAR) maps and assessed their spatial correlation with observed brain alterations thereby establishing a structured approach to map polygenic transcriptional risk onto macroscale brain phenotypes. We found significant transcriptomic-anatomical correlations in MDD (cortical and subcortical), SCZ (subcortical), and ADHD (subcortical), indicating that regional transcriptional vulnerability might contribute to varying extents to the anatomical expression of genetic risk in these disorders. Pathway enrichment analysis on genetically predicted differentially expressed genes for those disorders where we found spatial correlations between GEDAR maps and observed structural changes revealed immune-related processes as dominant in MDD and SCZ, and neurodevelopmental pathways in ADHD. ASD, AN, OCD, and cortical SCZ lacked significant associations. Importantly, spatial transcriptomic-anatomical alignment did not scale with between-disorder differences in heritability, pointing instead toward additional influences like developmental timing or environmental interactions. These findings underscore the potential and limitations of imaging transcriptomics as a framework for bridging the gap between genetic architecture and systems-level brain changes in psychiatric disorders.

neuroscience↗

bioRxiv2024

The Main protease (Mpro) from SARS-CoV-2 triggers plasma clotting in vitro by activating coagulation factors VII and FXII

Although the connection between COVID-19 and coagulopathy has been clear since the early days of SARS-CoV-2 pandemic, the underlying molecular mechanisms remain unclear. Available data support that the burst of cytokines and bradykinin, observed in some COVID-19 patients, sustains systemic inflammation and the hypercoagulant state, thus increasing thrombotic risk. Here we show that the SARS-CoV-2 main protease (Mpro) can play a direct role in the activation of the coagulation cascade. Adding Mpro to human plasma from healthy donors increased clotting probability by 2.5-fold. The results of enzymatic assays and degradomics analysis indicate that Mpro triggers plasma clotting by proteolytically activating coagulation factors zymogens VII and XII at their physiological activation sites, i.e. Arg152-Ile153 bond for FVII and Arg353-Val354 bond for FXII, where FVII and FXII are strategically positioned at the very beginning of the extrinsic or intrinsic pathways of blood coagulation. These findings are not compatible with the substrate specificity of the protease known so far, displaying a prevalence for a Gln-residue in P1 and a hydrophobic amino acid in P2 position. This apparent discrepancy was resolved by High Throughput Protease Screen assay, unveiling an extended, time-dependent, secondary specificity of Mpro for Arg-X bonds, which was further confirmed by Hydrogen-Deuterium Exchange Mass spectrometry analysis of Arg-containing inhibitors binding to Mpro and by enzymatic assays showing that the protease can cleave peptide substrates containing Arg in P1. Overall, integrating biochemical, proteomics and structural biology experiments, we unveil a novel mechanism linking SARS-CoV-2 infection to thrombotic complications in COVID-19.

biochemistry↗

bioRxiv2022

Digital data repository and automatic analysis framework for FDOPA PET neuroimaging

IntroductionFDOPA PET has been used extensively to image the human brain in many clinical disorders and has the potential to be used for patient stratification and individualized treatment. However, to reach its full and effective clinical translation, FDOPA PET requires both a robust data infrastructure and analytical protocol that are capable of ensuring high quality data and metadata, accurate biological quantification, and replicable results. In this study we evaluate a digital data repository and automated analytical framework for FDOPA PET neuroimaging that can produce an individualised quantification of dopamine synthesis capacity in the living human brain. MethodsThe imaging platform XNAT was used to store the Kings College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. A fully automated analysis pipeline for imaging processing and data quantification was developed in Python and integrated in XNAT using the Docker technology. Reproducibility was assessed in test-retest datasets both in controls and patients with psychosis. The agreement between the automated analysis estimates and the results derived by the manual analysis were compared. Finally, using a sample of healthy controls (N=115), a sensitivity analysis was performed to explore the impact of experimental and demographic variables on the FDOPA PET measures. ResultsThe final data repository includes 892 FDOPA PET scans organized from 23 different studies, collected at five different imaging sites. After removing commercials studies, the infrastructure consisted of 792 FDOPA PET scans from 666 individuals (female 33.9%, healthy controls 29.1%) collected from four different imaging sites between 2004-2021. The automated analysis pipeline provided results that were in agreement with the results from the manual analysis, with a Pearsons correlation that ranged from 0.64 to 0.99 for Kicer, and from 0.79 to 1.00 for SUVR. The mean absolute difference between the two pipelines ranges from 3.4% to 9.4% for Kicer, and from 2.5% to 12.4% for SUVR. Moreover, we found good reproducibility of the data analysis by the automated pipeline (in the whole striatum for the Kicer: ICC for the controls = 0.71, ICC for the psychotic patients = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p <0.001), with women showing greater dopamine synthesis capacity than men, while the effects of weight, age, injected radioactivity, and scanner, varied by brain region and parameter of interest. ConclusionsCombining information from different neuroimaging studies has allowed us to test comprehensively the automated pipeline for quantification of dopamine synthesis capacity using FDOPA PET data and to validate its replicability and reproducibility performances on a large sample size. This validation process is a necessary methodological step for the development of the clinical application of FDOPA PET as precision medicine biomarker. The proposed infrastructure is generalisable behind the FDOPA radiotracer.

bioengineering↗

bioRxiv2019

Faster 3D saturation-recovery based myocardial T1 mapping using a reduced number of saturation points and denoising

PurposeTo accelerate the acquisition of free-breathing 3D saturation-recovery-based (SASHA) myocardial T1 mapping by acquiring fewer saturation points in combination with a novel post-processing 3D denoising technique to maintain high accuracy and precision.\n\nMethods3D SASHA T1 mapping acquires nine T1-weighted images along the saturation recovery curve, resulting in long acquisition times. In this work, we propose to accelerate conventional cardiac T1 mapping by reducing the number of saturation points. High T1 accuracy and precision is maintained by applying a 3D denoising technique to the T1-weighted images prior to pixel-wise T1 fitting. The proposed approach was evaluated on a T1 phantom and 20 healthy subjects, by varying the number of T1-weighted images acquired between three and nine, both prospectively and retrospectively. Three patients with suspected cardiovascular disease were acquired using five T1-weighted images. T1 accuracy and precision was determined for all the acquisitions before and after denoising.\n\nResultsIn the T1 phantom, no statistical difference was found in terms of accuracy and precision for the different number of T1-weighted images before or after denoising (P=0.99 and P=0.99 for accuracy, P=0.64 and P=0.42 for precision, respectively). In vivo, both prospectively and retrospectively, the precision improved considerably with the number of T1-weighted images employed before denoising (P<0.05) but was independent on the number of T1-weighted images after denoising.\n\nConclusionWe demonstrate the feasibility of accelerating 3D SASHA T1 mapping by reducing the number of acquired T1-weighted images in combination with an efficient 3D denoising, without affecting accuracy and precision of T1 values.

bioengineering↗