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Agostino, M.

Publications and source records attributed to Agostino, M..

6 recordsLinked to original sources

Differential expression and modulation of presenilin-1 and presenilin-2 in neural cell lines

Presenilin proteins, the catalytic components of the {gamma}-secretase enzyme, play a key role in the pathogenesis of Alzheimers disease primarily by generating amyloid-{beta} peptides. Although often grouped together, Presenilin-1 (PS1) and Presenilin-2 (PS2) are distinct proteins that contribute differently to {gamma}-secretase activity. This study examines the differential expression of PS1 and PS2 in various neuroblastoma and microglial cell lines, along with compensatory responses following the ablation of one presenilin homologue. Using quantitative immunoblotting, we show that PS1 and PS2 expression levels vary significantly across cell types. Notably, the ablation of PS2 results in increased PS1 expression, particularly in microglial cells, highlighting the importance of PS2. Additionally, neuronal differentiation of two neuroblastoma lines, caused changes in protein expression levels resulting in similar expression profiles of PS1 and PS2, with PS2 levels being higher than PS1. Understanding PS expression profiles is crucial for distinguishing between PS1- and PS2-mediated {gamma}-secretase activity and for developing {gamma}-secretase-targeted therapeutics with improved selectivity.

cell biology↗

Single-molecule visualization of sequence-specific RNA binding by a designer PPR protein

Pentatricopeptide repeat (PPR) proteins are a large family of modular RNA-binding proteins that recognize specific ssRNA target sequences. There is significant interest in developing designer PPRs for use in diagnostics or as tools to detect and localize target RNA sequences. However, it is unclear how PPRs search for target sequences within complex transcriptomes and current models to predict PPR binding sites struggle to reconcile the effects that RNA mismatches and secondary structure have on PPR binding. To address this, we determined the structure of a designer PPR (dPPR10) bound to its target sequence and used two- and three-colour single-molecule FRET to interrogate the mechanism of ssRNA binding to individual PPR proteins in real time. We demonstrate that longer RNA sequences were significantly slower to bind (or could not bind at all) and that this is, in part, due to their propensity to form stable secondary structures that sequester the target sequence from dPPR10. Importantly, dPPR10 does not associate with non-target flanking sequences, binding specifically to its target sequence within longer ssRNA species. This data provides evidence that PPRs have limited to no capacity to scan RNA transcripts for target sequences and instead rely on diffusion for cognate searching. The kinetic constraints imposed by random three-dimensional diffusion may explain the long-standing conundrum of why PPR proteins are abundant in organelles, but almost unknown outside them (i.e. in the cytosol and nucleus). These findings will inform improved prediction of PPR binding sites for the development of designer PPRs. SummaryPentatricopeptide repeat proteins (PPR) are a large family of modular RNA-binding proteins, whereby each module can be designed to bind to a specific ssRNA nucleobase and thus any RNA sequence of interest. As such, there is substantial interest in developing designer PPRs for a range of biotechnology applications, including diagnostics or in vivo localisation of RNA species; however, the mechanistic details regarding how PPRs search for and bind to target sequences is unclear. As such, we combined structure-based and single- molecule approaches and determined that PPRs bind only to their target sequences (i.e., they do not associate with non-target RNA sequences) and do not scan longer RNA oligonucleotides for the target sequence. Instead, target searching appears kinetically-constrained by random three-dimensional diffusion, providing an explanation as to why PPRs are found almost exclusively in organelle compartments that typically have smaller transcriptomes. Collectively, this work identifies several key considerations for future designer PPR developments.

biochemistry↗

Presenilin homologues influence substrate binding and processing by γ-secretase: a molecular simulation study.

Presenilin homologues in the {gamma}-secretase complex play a pivotal role in substrate binding and processing, impacting {beta}-amyloid (A{beta}) peptide generation in Alzheimers disease. We conducted a molecular simulation study to determine substrate preferences between presenilin-1 (PS1) and presenilin-2 (PS2) {gamma}-secretase enzymes for amyloid precursor protein (APP) and Notch1 processing. Using homology modelling, we generated PS1- and PS2-{gamma}-secretase models bound to substrates in the A{beta}40 and A{beta}42 generation pathways and Notch1 S3 and S4 cleavage site substrates. Metadynamics simulations and binding free energy calculations were used to explore conformational ensembles and substrate preferences. PS2-{gamma}-secretase exhibited increased conformational flexibility and preferential binding energy for initiating the A{beta}42 pathway compared to PS1-{gamma}-secretase. Additionally, Notch1 exhibits a preference for binding to PS2-{gamma}-secretase over PS1-{gamma}-secretase. This study provides valuable insights into the conformational dynamics of {gamma}-secretase bound to different substrates within a cleavage pathway, improving our understanding of substrate processivity. The findings highlight the importance of considering both PS1- and PS2-{gamma}-secretase in structure-based drug design efforts, with implications for stabilizing or destabilizing specific states during APP processing.

neuroscience↗

Quantitative Comparison of Presenilin Protein Expression Reveals Greater Activity of PS2-gamma-Secretase.

{gamma}-Secretase processing of APP has long been of interest in the pathological progression of Alzheimers disease (AD) due to its role in the generation of amyloid-{beta}. The catalytic component of the enzyme are the presenilins of which there are two homologues, Presenilin-1 (PS1) and Presenilin-2 (PS2). The field has focussed on the PS1 form of this enzyme, as it is typically considered the more active at APP processing. However, much of this work has been completed without appropriate consideration of the specific levels of protein expression of PS1 and PS2. We propose that expression is an important factor in PS1- and PS2-{gamma}-secretase activity, and that when this is considered, PS1 does not have greater activity than PS2. We developed and validated tools for quantitative assessment of PS1 and PS2 protein expression levels to enable direct comparison of PS protein in exogenous and endogenous expression systems, in HEK-293 PS1 and/or PS2 knockout cells. We show that exogenous expression of Myc-PS1-NTF is 5.5-times higher than and Myc-PS2-NTF. Quantitating endogenous PS protein levels using a novel PS1/2 fusion standard we developed showed similar results. When the marked difference in PS1 and PS2 protein levels is considered, we show that compared to PS1-{gamma}-secretase, PS2-{gamma}-secretase has equal or more activity on APP and Notch1. This study has implications for understanding the PS1 and PS2 specific contributions to substrate processing, and their potential influence in AD pathogenesis.

cell biology↗

The RNA-binding protein EIF4A3 promotes axon development by direct control of the cytoskeleton

The exon junction complex (EJC), nucleated by EIF4A3, is indispensable for mRNA fate and function throughout eukaryotes. Unexpectedly, we discover that EIF4A3 directly controls microtubules independent of RNA, and this is critical for neural wiring. While neuronal survival in the developing mouse cerebral cortex depends upon an intact EJC, axonal tract formation requires only Eif4a3. Using human cortical organoids, we demonstrate that EIF4A3 disease mutations also impair neuronal maturation, highlighting conserved functions relevant for neurodevelopmental pathology. Employing biochemistry and molecular modeling we discover that EIF4A3 directly binds to microtubules, mutually exclusive of the RNA-binding complex. In growing neurons, EIF4A3 is essential for microtubule dynamics, and sufficient to promote microtubule polymerization and stability in vitro. Together, our data show that tubulin-bound EIF4A3 orchestrates microtubule dynamics, underlying key events of neuronal development. This reveals a new mechanism by which neurons re-utilize core gene expression machinery to rapidly and directly control the cytoskeleton. HighlightsO_LIThe Exon Junction Complex controls neuronal survival but only EIF4A3 directs axonal growth C_LIO_LIEIF4A3 controls axonal tract formation in vivo. C_LIO_LIHuman EIF4A3 deficient iPSC-derived cortical organoids recapitulate neuronal defects. C_LIO_LIEIF4A3 directly binds to microtubules to control their growth and stability in neurons. C_LI

neuroscience↗

Advancing pre-clinical species genetic utility through a high-quality assembly of the cynomolgus monkey (Macaca fascicularis) genome

The cynomolgus macaque is a non-human primate model, heavily used in biomedical research, but with outdated genomic resources. Here we have used the latest long-read sequencing technologies in order to assemble a fully phased, chromosome-level assembly for the cynomolgus macaque. We have built a hybrid assembly with PacBio, 10x Genomics, and HiC technologies, resulting in a diploid assembly that spans a length of 5.1 Gb with a total of 16,741 contigs (N50 of 0.86Mb) contained in 370 scaffolds (N50 of 138 Mb) positioned on 42 chromosomes (21 homologous pairs). This assembly is highly homologous to former assemblies and identifies novel inversions and provides higher confidence in the genetic architecture of the cynomolgus macaque genome. A demographic estimation is also able to capture the recent genetic bottleneck in the Mauritius population, from which the sequenced individual originates. We offer this resource as an enablement for genetic tools to be built around this important model for biomedical research.

genomics↗