Biology subjects

Rizzo, J.

Publications and source records attributed to Rizzo, J..

4 recordsLinked to original sources

bioRxiv2023

Extracellular vesicles production regulates fluconazole resistance in Cryptococcus neoformans

Resistance to fluconazole (FLC), the most widely used antifungal drug, is typically achieved by altering the azole drug target and/or drug efflux pumps. Recent reports have suggested a link between vesicular trafficking and antifungal resistance. Here, we identified novel Cryptococcus neoformans regulators of extracellular vesicle (EV) biogenesis that impact FLC resistance. In particular, the transcription factor Hap2 does not affect the expression of the drug target or efflux pumps, yet it impacts the cellular sterol profile. Subinhibitory FLC concentrations also downregulate EV production. Moreover, in vitro spontaneous FLC-resistant colonies showed altered EV production, and the acquisition of FLC resistance was associated with decreased EV production in clinical isolates. Finally, the reversion of FLC resistance was associated with increased EV production. These data suggest a model in which fungal cells can regulate EV production in place of regulating the drug target gene expression as a first line of defense against antifungal assault in this fungal pathogen. IMPORTANCEExtracellular vesicles (EVs) are membrane-enveloped particles that are released by cells into the extracellular space. Fungal EVs can mediate community interactions and biofilm formation but thier functions remain poorly understood. Here, we report the identification of the first regulators of EV production in the major fungal pathogen Cryptococcus neoformans. Surprisingly, we uncover a novel role of EVs in modulating antifungal drug resistance. Disruption of EV production was associated with altered lipid composition and changes in fluconazole susceptibility. Spontaneous azole-resistant mutants were deficient in EV production, while loss of resistance restored initial EV production levels. These findings were recapitulated in C. neoformans clinical isolates, indicating that azole resistance and EV production are coregulated in diverse strains. Our study reveals a new mechanism of drug resistance in which cells adapt to azole stress by modulating EV production.

microbiology↗

bioRxiv2022

Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal recorded from the motor cortex and its neighboring cortical areas. Since the MRCP encodes motor intention and execution, it may be utilized as an interface between patients and neurorehabilitation technologies. This study investigates the EEG signal recorded from the Cz electrode to discriminate between four levels of rate of force development (RFD) of the tibialis anterior muscle. For classification, three feature sets were evaluated to describe the EEG traces. These were (i) MRCP morphological characteristics in the{delta} -band such as amplitude and timing, (ii) MRCP statistical characteristics in the{delta} -band such as mean, standard deviation, and kurtosis, and (iii) wideband time-frequency features in the 0.5-90 Hz range. Using a support vector machine for classification, the four levels of RFD were classified with a mean (SD) accuracy of 82% (7%) accuracy when using the time-frequency feature space, and with an accuracy of 75% (12%) when using the MRCP statistical characteristics. It was also observed that some of the key features from the statistical and morphological sets responded monotonically to the intensity of the RFD. Examples are slope and standard deviation in the (0, 1)s window for the statistical, and min1 and minn for the morphological sets. This monotonical response of features explains the observed performance of the{delta} -band MRCP and corresponding high discriminative power. Results from temporal analysis considering the pre-movement phase ((-3, 0)s) and three windows of the post-movement phase ((0, 1)s, (1, 2)s, and (2, 3)s)) suggest that the complete MRCP waveform represents high information content regarding the planning, execution, duration, and ending of the isometric dorsiflexion task using the tibialis anterior muscle. Results shed light on the role of{delta} -band in translating to motor command, with potential applications in neural engineering systems.

bioengineering↗

bioRxiv2022

Deep Augmentation for Electrode Shift Compensation in Transient-phase High-density Electromyographical Neural Interfaces: Towards Application in Neurorobotics

Going beyond the traditional sparse multichannel peripheral human-machine interface that has been used widely in neurorobotics, high-density surface electromyography (HD-sEMG) has shown significant potential for decoding upper-limb motor control. We have recently proposed heterogeneous temporal dilation of LSTM in a deep neural network architecture for a large number of gestures (>60), securing spatial resolution and fast convergence. However, several fundamental questions remain unanswered. One problem targeted explicitly in this paper is the issue of "electrode shift," which can happen specifically for high-density systems and during doffing and donning the sensor grid. Another real-world problem is the question of transient versus plateau classification, which connects to the temporal resolution of neural interfaces and seamless control. In this paper, for the first time, we implement gesture prediction on the transient phase of HD-sEMG data while robustifying the human-machine interface decoder to electrode shift. For this, we propose the concept of deep data augmentation for transient HD-sEMG. We show that without using the proposed augmentation, a slight shift of 10mm may drop the decoders performance to as low as 20%. Combining the proposed data augmentation with a 3D Convolutional Neural Network (CNN), we recovered the performance to 84.6% while securing a high spatiotemporal resolution, robustifying to the electrode shift, and getting closer to large-scale adoption by the end-users, enhancing resiliency.

bioengineering↗

bioRxiv2020

Characterization of extracellular vesicles produced by Aspergillus fumigatus protoplasts

Extracellular vesicles (EVs) are outer membranous compartments produced by yeast and mycelial forms of several fungal species. One of the difficulties to perceive the role of EVs during the fungal life is the fact that an active secretion of these EVs has not been clearly demonstrated in situ due to the presence of a thick cell wall. One alternative to have a better access to these vesicles is to use protoplasts. This approach has been investigated here with Aspergillus fumigatus, one of the most common opportunistic fungal pathogens worldwide. Analysis of regenerating protoplasts by scanning electron microscopy and fluorescence microscopy indicated the occurrence of outer membrane projections in association with surface components and the release of particles with properties resembling those of fungal EVs. EVs in culture supernatants were characterized by transmission electron microscopy and nanoparticle tracking analysis. Proteomic and glycome analysis of EVs revealed the presence of a complex array of enzymes related to lipid / sugar metabolism, pathogenic processes, and cell wall biosynthesis. Our data indicate that i) EV production is a common feature of different morphological stages of this major fungal pathogen, and ii) protoplastic EVs are a promising tool to undertake studies of vesicle functions in fungal cells. IMPORTANCEFungal cells use extracellular vesicles (EVs) to export biologically active molecules to the outer space. Since fungal cells are encaged in a thick cell wall, it is reasonable to expect that this structure might impact the vesicle-mediated molecular export. In this study, we used protoplasts of Aspergillus fumigatus, a major fungal pathogen, as a model to evaluate EV production in the absence of a cell wall. Our results demonstrated that wall-less A. fumigatus exports plasma membrane-derived EVs containing a complex combination of proteins and glycans. Our study is the first to characterize fungal EVs in the absence of a cell wall. Our results suggest that protoplasts are a promising model for functional studies of fungal vesicles.

microbiology↗