Biology subjects

Clancy, E.

Publications and source records attributed to Clancy, E..

3 recordsLinked to original sources

bioRxiv2026

A Perturb-seq screen guided by species divergence uncovers pathways for collateral artery formation

Collateral arteries are natural bypasses that can reroute blood flow around arterial blockages, limiting tissue injury during stroke and coronary artery disease. Despite their clinical effectiveness, therapeutic strategies to stimulate collateral artery growth remain unavailable due to our limited understanding of their developmental mechanisms. Remarkably, guinea pigs display exceptionally dense collateral artery networks across various organs, resulting in complete resistance to ischemic damage in the brain and heart. In this study, we compared single-cell RNA sequencing (scRNA-seq) from guinea pig and mouse tissues to identify endothelial cell (EC) gene expression patterns associated with extensive collateral artery development. We then developed an in vivo Perturb-seq platform in mice to test whether genes differentially expressed in guinea pigs influence artery EC specification. This pipeline identified artery repressors that were downregulated in guinea pigs and increased pial collateral abundance when inhibited in mice. Downstream analysis suggests that artery repressors, including WNT and hypoxia response genes, function in two capillary EC subsets--Esm1+ pre-artery and Apln+ angiogenic tip cells. Reduced activity of these repressors allows more ECs to acquire arterial identity, potentiating collateral artery formation. Collectively, our study establishes a strategy for discovering the genes underlying species-specific traits, suggests that guinea pigs have collaterals due to decreased activity of artery inhibitor pathways and hypoxia responses, and identifies novel targets for stimulating collateral artery formation (Graphical abstract). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/721711v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@18b36cdorg.highwire.dtl.DTLVardef@bafbaforg.highwire.dtl.DTLVardef@1e1a6bdorg.highwire.dtl.DTLVardef@189f585_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

Specialized cingulo-opercular network activation in judging ambiguity of facial expressions in a slow reveal fMRI paradigm

Cortical task control networks, including the cingulo-opercular (CO) network play a key role in decision-making across a variety of functional domains. In particular, the CO network functions in a performance reporting capacity that supports successful task performance, especially in response to errors and ambiguity. In two studies testing the contribution of the CO network to ambiguity processing, we presented a valence bias task in which masked clearly and ambiguously valenced emotional expressions were slowly revealed over several seconds. This slow reveal task design provides a window into the decision-making mechanisms as they unfold over the course of a trial. In the main study, the slow reveal task was administered to 32 young adults in the fMRI environment and BOLD time courses were extracted from regions of interest in three control networks. In a follow-up study, the task was administered to a larger, online sample (n = 81) using a more extended slow reveal design with additional unmasking frames. Positive judgments of surprised faces were uniquely accompanied by slower response times and strong, late activation in the CO network. These results support the initial negativity hypothesis, which posits that the default response to ambiguity is negative and positive judgments are associated with a more effortful controlled process, and additionally suggests that this controlled process is mediated by the CO network. Moreover, ambiguous trials were characterized by a second CO response at the end of the trial, firmly placing CO function late in the decision-making process.

neuroscience↗

bioRxiv2021

Protein complex prediction with AlphaFold-Multimer

While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 13 targets and high accuracy (DockQ [≥] 0.8) on 7 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,446 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 70% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 26% of cases, an improvement of +27 and +14 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric inter-faces we successfully predict the interface in 72% of cases, and produce high accuracy predictions in 36% of cases, an improvement of +8 and +7 percentage points respectively.

bioinformatics↗