Biology subjects

Possenti, A.

Publications and source records attributed to Possenti, A..

3 recordsLinked to original sources

bioRxiv2022

Functional characterization of the thrombospondin-related paralogous proteins rhoptry discharge factor 1 and 2 unveils phenotypic plasticity in Toxoplasma gondii rhoptry exocytosis

To gain access to the intracellular cytoplasmic niche essential for their growth and replication, apicomplexan parasites such as Toxoplasma gondii rely on the timely secretion of two types of apical organelles named micronemes and rhoptries. Rhoptry proteins are key to host cell invasion and remodelling, however, the molecular mechanisms underlying the thight control of rhoptry discharge are poorly understood. Here, we report the identification and functional characterization of two novel T. gondii thrombospondin-related proteins implicated in rhoptry exocytosis. The two proteins, already annotated as MIC15 and MIC14, were renamed rhoptry discharge factor 1 (RDF1) and rhoptry discharge factor 2 (RDF2) and found to be exclusive of the Coccidia class of apicomplexan parasites. Furthermore, they were shown to have a paralogous relationship and share a C-terminal transmembrane domain followed by a short cytoplasmic tail. Immunofluorescence analysis of T. gondii tachyzoites revealed that RDF1 presents a diffuse cytoplasmic localization not reminiscent of any know subcellular compartment, whereas RDF2 was not detected. Using a conditional knockout approach, we demonstrated that RDF1 loss caused a marked growth defect. The lack of the protein did not affect parasite gliding motility, host cell attachment, replication and egress, whereas invasion was dramatically reduced. Notably, while RDF1 depletion did not result in altered microneme exocytosis, rhoptry discharge was found to be heavily impaired. Interestingly, rhoptry secretion was partially reversed by spontaneous upregulation of the RDF2 gene in knockdown parasites grown under constant RDF1 repression. Collectively, our results identify RDF1 and RDF2 as additional key players in the pathway controlling rhoptry discharge. Furthermore, this study unveils a new example of compensatory mechanism contributing to phenotypic plasticity in T. gondii.

microbiology↗

bioRxiv2021

Structure-based discovery of small molecule inhibitors of the autocatalytic proliferation of alpha-synuclein aggregates

The presence of amyloid fibrils of -synuclein is closely associated with Parkinsons disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of -synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a combination of kinetic and thermodynamic assays for their ability to bind -synuclein fibrils and prevent the further generation of toxic oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of -synuclein aggregates.

biophysics↗

bioRxiv2021

A Machine Learning Approach to Identify Specific Small Molecule Inhibitors of Secondary Nucleation in alpha-Synuclein Aggregation

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of -synuclein aggregation, a process implicated in Parkinsons disease and other synucleinopathies. Because the proliferation of -synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

biophysics↗