Leveraging cell-type-specific regulatory networks to interpret genetic variants in abdominal aortic aneurysm

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

[Abdominal aortic malformation in 2 dogs]

Aneurysms of the abdominal aorta are only sporadically documented in the veterinary literature. This publication describes 2 canine cases in which abdominal aortic malformation was detected by sonography and confirmed by computed tomography. In one case a histological diagnosis of an aortic aneurysm was possible.One dog showed posterior weakness, in the second dog the aortic aneurysm had been noticed sonographically during a routine examination.In the patient with the proven aortic aneurysm, it may be presumed that a hemodynamically relevant component in consequence to the altered flow profile and occurring turbulence exists. In accordance with human medical standards, regular monitoring of these patients, both clinically and by ultrasound, would therefore appear to be useful in order to be able to detect the occurrence or progression of secondary hemodynamic changes and possible thrombus formation at an early stage. In contrast, the second case presented here has not shown any clinical signs with regard to the abdominal vascular malformation up to the present time.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Ganglioside GM3 Protects Against Abdominal Aortic Aneurysm by Suppressing Ferroptosis

BACKGROUND

Abdominal aortic aneurysm (AAA) is a potentially life-threatening vascular condition, but approved medical therapies to prevent AAA progression and rupture are currently lacking. Sphingolipid metabolism disorders are associated with the occurrence and development of AAA. It has been discovered that ganglioside GM3, a sialic acid-containing type of glycosphingolipid, plays a protective role in atherosclerosis, which is an important risk factor for AAA; however, the potential contribution of GM3 to AAA development has not been investigated.

METHODS

We performed a metabolomics study to evaluated GM3 level in plasma of human patients with AAA. We profiled GM3 synthase (ST3GAL5) expression in the mouse model of aneurysm and human AAA tissues through Western blotting and immunofluorescence staining. RNA sequencing, affinity purification and mass spectrometry, proteomic analysis, surface plasmon resonance analysis, and functional studies were used to dissect the molecular mechanism of GM3-regulating ferroptosis. We conditionally deleted and overexpressed St3gal5 in smooth muscle cells (SMCs) in vivo to investigate its role in AAA.

RESULTS

We found significantly reduced plasma levels of GM3 in human patients with AAA. GM3 content and ST3GAL5 expression were decreased in abdominal aortic vascular SMCs in patients with AAA and an AAA mouse model. RNA sequencing analysis showed that ST3GAL5 silencing in human aortic SMCs induced ferroptosis. We showed that GM3 interacted directly with the extracellular domain of TFR1 (transferrin receptor 1), a cell membrane protein critical for cellular iron uptake, and disrupted its interaction with holo-transferrin. SMC-specific St3gal5 knockout exacerbated iron accumulation at lesion sites and significantly promoted AAA development in mice, whereas GM3 supplementation suppressed lipid peroxidation, reduced iron deposition in aortic vascular SMCs, and markedly decreased AAA incidence.

CONCLUSIONS

Together, these results suggest that GM3 dysregulation promotes ferroptosis of vascular SMCs in AAA. Furthermore, GM3 may constitute a new therapeutic target for AAA.

Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease

Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility. In this review, we highlight the emerging potential of genomics for identifying causal genes and candidate drug targets for aortic disease. We describe how deep learning technologies have accelerated the pace of genetic discovery in this field. We then provide a blueprint for translating genetic associations to biological insights, reviewing techniques for locus and cell type prioritization, high-throughput functional screening, and disease modeling using cellular and animal models of aortic disease.

Genetic effects of sequence-conserved enhancer-like elements on human complex traits

BACKGROUND

The vast majority of findings from human genome-wide association studies (GWAS) map to non-coding sequences, complicating their mechanistic interpretations and clinical translations. Non-coding sequences that are evolutionarily conserved and biochemically active could offer clues to the mechanisms underpinning GWAS discoveries. However, genetic effects of such sequences have not been systematically examined across a wide range of human tissues and traits, hampering progress to fully understand regulatory causes of human complex traits.

RESULTS

Here we develop a simple yet effective strategy to identify functional elements exhibiting high levels of human-mouse sequence conservation and enhancer-like biochemical activity, which scales well to 313 epigenomic datasets across 106 human tissues and cell types. Combined with 468 GWAS of European (EUR) and East Asian (EAS) ancestries, these elements show tissue-specific enrichments of heritability and causal variants for many traits, which are significantly stronger than enrichments based on enhancers without sequence conservation. These elements also help prioritize candidate genes that are functionally relevant to body mass index (BMI) and schizophrenia but were not reported in previous GWAS with large sample sizes.

CONCLUSIONS

Our findings provide a comprehensive assessment of how sequence-conserved enhancer-like elements affect complex traits in diverse tissues and demonstrate a generalizable strategy of integrating evolutionary and biochemical data to elucidate human disease genetics.

CCN2 deficiency in smooth muscle cells triggers cell reprogramming and aggravates aneurysm development

Vascular smooth muscle cell (SMC) phenotypic switching is widely recognized as a key mechanism responsible for the pathogenesis of several aortic diseases, such as aortic aneurysm. Cellular communication network factor 2 (CCN2), often upregulated in human pathologies and animal disease models, exerts myriad context-dependent biological functions. However, current understanding of the role of SMC-CCN2 in SMC phenotypic switching and its function in the pathology of abdominal aortic aneurysm (AAA) is lacking. Here, we show that SMC-restricted CCN2 deficiency causes AAA in the infrarenal aorta of angiotensin II-infused (Ang II-infused) hypercholesterolemic mice at a similar anatomic location to human AAA. Notably, the resistance of naive C57BL/6 WT mice to Ang II-induced AAA formation is lost upon silencing of CCN2 in SMC. Furthermore, the pro-AAA phenotype of SMC-CCN2-KO mice is recapitulated in a different model that involves the application of elastase-β-aminopropionitrile. Mechanistically, our findings reveal that CCN2 intersects with TGF-β signaling and regulates SMC marker expression. Deficiency of CCN2 triggers SMC reprograming associated with alterations in Krüppel-like factor 4 and contractile marker expression, and this reprograming likely contributes to the development of AAA in mice. These results identify SMC-CCN2 as potentially a novel regulator of SMC phenotypic switching and AA biology.

Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues. Ablation studies, independent data validation, and comparison experiments with existing methods on GWAS of six phenotypes show that SpecVar can improve heritability enrichment, accurately detect relevant tissues, and reveal causal regulations. Furthermore, SpecVar correlates the relevance patterns for pairs of phenotypes and better reveals shared SNP-associated regulations of phenotypes than existing methods. Studying GWAS of 206 phenotypes in UK Biobank demonstrates that SpecVar leverages the context-specific regulatory network atlas to prioritize phenotypes' relevant tissues and shared heritability for biological and therapeutic insights. SpecVar provides a powerful way to interpret SNPs via context-specific regulatory networks and is available at https://github.com/AMSSwanglab/SpecVar, copy archived at swh:1:rev:cf27438d3f8245c34c357ec5f077528e6befe829.

Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development-New Insights From Technical Advances

Epigenetic regulation is integral in orchestrating the spatiotemporal regulation of gene expression which underlies tissue development. The emergence of new tools to assess genome-wide epigenetic modifications has enabled significant advances in the field of vascular biology in zebrafish. Zebrafish represents a powerful model to investigate the activity of cis-regulatory elements in vivo by combining technologies such as ATAC-seq, ChIP-seq and CUT&Tag with the generation of transgenic lines and live imaging to validate the activity of these regulatory elements. Recently, this approach led to the identification and characterization of key enhancers of important vascular genes, such as gata2a, notch1b and dll4. In this review we will discuss how the latest technologies in epigenetics are being used in the zebrafish to determine chromatin states and assess the function of the cis-regulatory sequences that shape the zebrafish vascular network.