A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1

Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care

LEMONS – A Tool for the Identification of Splice Junctions in Transcriptomes of Organisms Lacking Reference Genomes

<div><p>RNA-seq is becoming a preferred tool for genomics studies of model and non-model organisms. However, DNA-based analysis of organisms lacking sequenced genomes cannot rely on RNA-seq data alone to isolate most genes of interest, as DNA codes both exons and introns. With this in mind, we designed a novel tool, LEMONS, that exploits the evolutionary conservation of both exon/intron boundary positions and splice junction recognition signals to produce high throughput splice-junction predictions in the absence of a reference genome. When tested on multiple annotated vertebrate mRNA data, LEMONS accurately identified 87% (average) of the splice-junctions. LEMONS was then applied to our updated Mediterranean chameleon transcriptome, which lacks a reference genome, and predicted a total of 90,820 exon-exon junctions. We experimentally verified these splice-junction predictions by amplifying and sequencing twenty randomly selected genes from chameleon DNA templates. Exons and introns were detected in 19 of 20 of the positions predicted by LEMONS. To the best of our knowledge, LEMONS is currently the only experimentally verified tool that can accurately predict splice-junctions in organisms that lack a reference genome.</p></div

LEMONS sensitivity and precision assessment using a motif search and multiple reference databases.

<p>(A-B) Identification of splice-junctions by LEMONS. Our analysis accounted for the distance (in nucleotides) between splice-junctions predicted by LEMONS and the true splice junctions. (C) Comparison of LEMONS similarity, sensitivity and precision plotted for the five species tested. The analysis was performed using five databases, including the human and four of the model organisms (excluding the tested organism). The nomenclature used is as in the legend to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143329#pone.0143329.g002" target="_blank">Fig 2</a>.</p

LEMONS sensitivity and precision assessment.

<p>(A) Demonstration of the different splice-junction predictions made by LEMONS and their occurrence in the examined organism’s coding regions, according to genome annotation. P—"true" splice-junction; TP (true positive)–correct identification of splice-junction by LEMONS; FN -false negative; FP—false positive splice-junctions. TP+FN (true positive + false negative)–total number of true splice-junctions in the examined organism, according to genome annotation; TP+FP (true positive + false positive)–total number of splice-junctions predicted by LEMONS; (B-C) LEMONS-based identification of splice-junctions. Our analysis accounted for the distance (in nucleotides) between the splice-junction predicted by LEMONS and the true splice-junction. The analysis presented is of five species: <i>M</i>. <i>musculus</i>, <i>G</i>. <i>gallus</i>, <i>A</i>. <i>carolinensis</i>, <i>X</i>. <i>tropicalis and D</i>. <i>rerio</i>. (D) Comparison of LEMONS similarity, sensitivity and precision for the five species tested. For absolute numbers, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143329#pone.0143329.s007" target="_blank">S3 Table</a>.</p

Conformational states during vinculin unlocking differentially regulate focal adhesion properties

Abstract Focal adhesions (FAs) are multi-protein complexes that connect the actin cytoskeleton to the extracellular matrix, via integrin receptors. The growth, stability and adhesive functionality of these structures are tightly regulated by mechanical stress, yet, despite the extensive characterization of the integrin adhesome, the detailed molecular mechanisms underlying FA mechanosensitivity are still unclear. Besides talin, another key candidate for regulating FA-associated mechanosensing, is vinculin, a prominent FA component, which possesses either closed (“auto-inhibited”) or open (“active”) conformation. A direct experimental demonstration, however, of the conformational transition between the two states is still absent. In this study, we combined multiple structural and biological approaches to probe the transition from the auto-inhibited to the active conformation, and determine its effects on FA structure and dynamics. We further show that the transition from a closed to an open conformation requires two sequential steps that can differentially regulate FA growth and stability