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Aguilar Salvador, D. I.

Publications and source records attributed to Aguilar Salvador, D. I..

2 recordsLinked to original sources

Vinculin targeting by Shigella IpaA promotes stable cell adhesion independent of mechanotransduction

The Shigella effector IpaA co-opts the focal adhesion protein vinculin to promote bacterial invasion. Here, we show that IpaA triggers an unreported mode of vinculin activation through the cooperative binding of its three vinculin-binding sites (VBSs) leading to vinculin oligomerization via its D1 and D2 head subdomains and highly stable adhesions resisting actin relaxing drugs. Using cross-linking mass spectrometry, we found that while IpaA VBSs1-2 bound to D1, IpaA VBS3 interacted with D2, a subdomain masked to other known VBSs. Structural modeling indicated that as opposed to canonical activation linked to interaction with D1, these combined VBSs interactions triggered major allosteric changes leading to D1D2 oligomerization. A cysteine-clamp preventing these changes and D1D2 oligomerization impaired growth of vinculin microclusters and cell adhesion. We propose that D1D2-mediated vinculin oligomerization occurs during the maturation of adhesion structures to enable the scaffolding of high-order vinculin complexes, and is triggered by Shigella IpaA to promote bacterial invasion in the absence of mechanotransduction. SummaryThe Shigella IpaA effector binds to cryptic vinculin sites leading to oligomerization via its head domain. This vinculin oligomerization mode appears required for the maturation and strengthening of cell adhesion but is co-opted by invasive bacteria independent of actomyosin contractility.

microbiology

Towards a Gamete Matching Platform: Using Immunogenetics and Artificial Intelligence to Predict Recurrent Miscarriage

The degree of Allele sharing of the Human Leukocyte Antigen (HLA) genes has been linked with recurrent miscarriage (RM). However, no clear genetic markers of RM have yet been identified, possibly because of the complexity of interactions between paternal and maternal genes. We propose a methodology to analyse HLA haplotypes from couples either with histories of successful pregnancies or RM. This article describes, for the first time, a method of RM genetic-risk calculation. Novel HLA representation techniques allowed us to create an algorithm (IMMATCH) to retrospectively predict RM with an AUC = 0.71 (p = 0.0035) thanks to high-resolution typing and the use of linear algebra on peptide binding affinity data. The algorithm features an adjustable threshold to increase either sensitivity or specificity. Combining immunogenetics with artificial intelligence could create personalized tools to better understand the genetic causes of unexplained infertility and a gamete matching platform that could increase pregnancy success rates.

bioinformatics