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Iturbe, A.

Publications and source records attributed to Iturbe, A..

2 recordsLinked to original sources

Aβ Amyloid Scaffolds the Accumulation of Matrisome and Additional Proteins in Alzheimer's Disease

We report a highly significant correlation between human Alzheimers disease (AD) brain proteome changes and those in CRND8 APP695NL/F transgenic mice. Comparing protein changes observed in the CRND8 mice with co-expression networks derived from human Alzheimers disease (AD), reveals both conserved and divergent module changes. Many proteins in the most highly conserved module (M42, matrisome) accumulate in plaques, cerebrovascular amyloid (CAA), dystrophic neuronal processes, or a combination thereof. Overexpression of two M42 proteins, midkine (Mdk) and pleiotrophin (PTN), in CRND8 mice brains leads to increased accumulation of A{beta} in plaques and in blood vessels; further, recombinant MDK and PTN enhance A{beta} aggregation into amyloid structures. Multiple M42 proteins bind to fibrillar A{beta}42 and a non-human amyloid fibril in vitro. Supporting this binding data, MDK and PTN co-accumulate with transthyretin (TTR) amyloid in the heart. Notably, our findings establish that proteomic changes in modules observed in human AD brains define an A{beta} amyloid "responsome" that is well conserved from mouse models to humans. Further, distinct amyloid structures appear to serve as scaffolds, facilitating the co-accumulation of proteins with signaling functions, and this co-accumulation may contribute to downstream pathological sequalae. Overall, this contextualized understanding of proteomic changes and their interplay with amyloid deposition provides valuable insights into the complexity of AD pathogenesis and potential biomarkers and therapeutic targets.

neuroscience↗

CIARA: a cluster-independent algorithm for the identification of markers of rare cell types from single-cell RNA seq data

A powerful feature of single-cell RNA-sequencing data analysis is the possibility to identify novel rare cell types. However, rare cell types are often missed by standard clustering approaches. We have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a computational tool available in R and Python that outperforms existing methods for rare cell type detection. With CIARA, we found a small group of precursor cells among mouse embryonic stem cells and previously uncharacterized rare populations of cells in a human gastrula.

bioinformatics↗