Biology subjects

Vecchi, G.

Publications and source records attributed to Vecchi, G..

4 recordsLinked to original sources

bioRxiv2025

Drought dynamics explain once in a century yellow fever virus outbreak in Brazil with implications for climate change

While excess rainfall is associated with mosquito-borne disease because it sup-ports mosquito breeding, drought may also counterintuitively increase disease transmission by altering mosquito and host behavior. This phenomenon is important to understand because climate change is projected to increase both extreme rainfall and drought. In this study, we investigated the extent to which seasonally-driven mosquito and primate behavior drove the first yellow fever virus (YFV) epidemic in an urban area in Brazil in nearly a century, coinciding with an equally rare drought, and to assess the role of interventions in ending the outbreak. We hypothesized that drought triggered the outbreak by driving the forest mosquitoes and non-human primates towards the city in search of water and that the mosquitoes were biting more frequently to avoid desiccation. A dynamical YFV model supports these hypotheses, showing that both behavioral changes were needed to explain the outbreak timing and incidence. Fur-ther, a combination of vector control, conservation measures, and vaccination contributed to ending the outbreak, with the strongest effects from vaccination. Together, these results suggest that drought, likely to become more frequent in this region in the coming decades, can significantly influence mosquito-borne disease transmission, and that sustained control will require multiple interventions.

ecology↗

bioRxiv2024

GLP-1 receptor agonism ameliorates Parkinsons disease through modulation of neuronal insulin signalling and glial suppression

Neuronal insulin resistance is linked to the pathogenesis of Parkinsons disease through unclear, but potentially targetable, mechanisms. We delineated neuronal and glial mechanisms of insulin resistance and glucagon-like 1 peptide (GLP-1) receptor agonism in human iPSC models of synucleinopathy, and corroborated our findings in patient samples from a Phase 2 trial of a GLP-1R agonist in Parkinsons (NCT01971242). Human iPSC models of synucleinopathy exhibit neuronal insulin resistance and dysfunctional insulin signalling, which is associated with inhibition of the neuroprotective Akt pathways, and increased expression of the MAPK-associated p38 and JNK stress pathways. Ultimately, this imbalance is associated with cellular stress, impaired proteostasis, accumulation of -synuclein, and neuronal loss. The GLP-1R agonist exenatide led to restoration of insulin signalling, associated with restoration of Akt signalling and suppression of the MAPK pathways in neurons. GLP-1R agonism reverses the neuronal toxicity associated with the synucleinopathy, through reduction of oxidative stress, improved mitochondrial and lysosomal function, reduced aggregation of -synuclein, and enhanced neuronal viability. GLP-1R agonism further suppresses synuclein induced inflammatory states in glia, leading to neuroprotection through non cell autonomous effects. In the exenatide-PD2 clinical trial, exenatide treatment was associated with clinical improvement in individuals with higher baseline MAPK expression (and thus insulin resistance). Exenatide treatment led to a reduction of -synuclein aggregates, and a reduction in inflammatory cytokine IL-6. Taken together, our patient platform defines the mechanisms of GLP-1R action in neurons and astrocytes, identifies the population likely to benefit from GLP-1R agonism, and highlights the utility of GLP-1R agonism as a disease modifying strategy in synucleinopathies.

neuroscience↗

bioRxiv2022

Deep learning in human neurons predicts mechanistic subtypes of Parkinson's

Parkinsons disease (PD) is a common, devastating, and incurable neurodegenerative disorder. Several molecular mechanisms have been proposed to drive PD, with genetic and pathological evidence pointing towards aberrant protein homeostasis and mitochondrial dysfunction. PD is clinically highly heterogeneous, it is likely that different mechanisms underlie the pathology in different individuals, each requiring a specific targeted treatment. Recent advances in stem cell technology and fluorescent live-cell imaging have enabled the generation of patient-derived neurons with different mechanistic subtypes of PD. Here, we performed multi-dimensional fluorescent labelling of organelles in iPSC-derived neurons, in healthy control cells, and in four different disease subclasses. We generated a machine learning-based model that can simultaneously predict the presence of disease, and its primary mechanistic subtype. We independently trained a series of classifiers using both quantitative single-cell fluorescence variables and images to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whilst image based deep neural networks predict control, and four distinct disease subtypes with an accuracy of 95%. The classifiers achieve their accuracy across all subtypes primarily utilizing the organellar features of the mitochondria, with additional contribution of the lysosomes, confirming their biological importance in PD. Taken together, we show that machine learning approaches applied to patient-derived cells are able to predict disease subtypes, demonstrating that this approach may be used to guide personalized treatment approaches in the future.

cell biology↗

bioRxiv2019

Widespread remodelling of proteome solubility in response to different protein homeostasis stresses

The accumulation of protein deposits in neurodegenerative diseases involves the presence of a metastable subproteome vulnerable to aggregation. To investigate this subproteome and the mechanisms that regulates it, we measured the proteome solubility of the Neuro2a cell line under protein homeostasis stresses induced by Huntington Disease proteotoxicity; Hsp70, Hsp90, proteasome and ER-mediated folding inhibition; and oxidative stress. We found one-quarter of the proteome extensively changed solubility. Remarkably, almost all the increases in insolubility were counteracted by increases in solubility of other proteins. Each stress directed a highly specific pattern of change, which reflected the remodelling of protein complexes involved in adaptation to perturbation, most notably stress granule proteins, which responded differently to different stresses. These results indicate that the robustness of protein homeostasis relies on the absence of proteins highly vulnerable to aggregation and on large changes in aggregation state of regulatory mechanisms that restore protein solubility upon specific perturbations.

biochemistry↗