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Background: The prevalence of atopic diseases has increased with atopic dermatitis (AD) as the earliest manifestation. We assessed if molecular risk factors in atopic mothers influence their infants' susceptibility to an atopic disease. Methods: Pregnant women and their infants with (n = 174, high‐risk) or without (n = 126, low‐risk) parental atopy were enrolled in a prospective birth cohort. Global differentially methylated regions (DMRs) were determined in atopic (n = 92) and non‐atopic (n = 82) mothers. Principal component analysis was used to predict atopy risk in children dependent on maternal atopy. Genome‐wide transcriptomic analyses were performed in paired atopic (n = 20) and non‐atopic (n = 15) mothers and cord blood. Integrative genomic analyses were conducted to define methylation–gene expression relationships. Results: Atopic dermatitis was more prevalent in high‐risk compared to low‐risk children by age 2. Differential methylation analyses identified 165 DMRs distinguishing atopic from non‐atopic mothers. Inclusion of DMRs in addition to maternal atopy significantly increased the odds ratio to develop AD in children from 2.56 to 4.26. In atopic compared to non‐atopic mothers, 139 differentially expressed genes (DEGs) were identified significantly enriched of genes within the interferon signaling pathway. Expression quantitative trait methylation analyses dependent on maternal atopy identified 29 DEGs controlled by 136 trans‐acting methylation marks, some located near transcription factors. Differential expression for the same nine genes, including MX1 and IFI6 within the interferon pathway, was identified in atopic and non‐atopic mothers and high‐risk and low‐risk children. Conclusion: These data suggest that in utero epigenetic and transcriptomic mechanisms predominantly involving the interferon pathway may impact and predict the development of infant atopy. [ABSTRACT FROM AUTHOR]

Background: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. Objective: The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Study design: Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. Results: The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Conclusions: Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power. [ABSTRACT FROM AUTHOR]

Aims Non-compaction cardiomyopathy is a devastating genetic disease caused by insufficient consolidation of ventricular wall muscle that can result in inadequate cardiac performance. Despite being the third most common cardiomyopathy, the mechanisms underlying the disease, including the cell types involved, are poorly understood. We have previously shown that endothelial cell-specific deletion of the chromatin remodeller gene Ino80 results in defective coronary vessel development that leads to ventricular non-compaction in embryonic mouse hearts. We aimed to identify candidate angiocrines expressed by endocardial and endothelial cells (ECs) in wildtype and LVNC conditions in Tie2Cre;Ino80 fl/fl transgenic embryonic mouse hearts, and test the effect of these candidates on cardiomyocyte proliferation and maturation. Methods and results We used single-cell RNA-sequencing to characterize endothelial and endocardial defects in Ino80 -deficient hearts. We observed a pathological endocardial cell population in the non-compacted hearts and identified multiple dysregulated angiocrine factors that dramatically affected cardiomyocyte behaviour. We identified Col15a1 as a coronary vessel-secreted angiocrine factor, downregulated by Ino80 -deficiency, that functioned to promote cardiomyocyte proliferation. Furthermore, mutant endocardial and endothelial cells up-regulated expression of secreted factors, such as Tgfbi, Igfbp3, Isg15 , and Adm , which decreased cardiomyocyte proliferation and increased maturation. Conclusions These findings support a model where coronary endothelial cells normally promote myocardial compaction through secreted factors, but that endocardial and endothelial cells can secrete factors that contribute to non-compaction under pathological conditions. [ABSTRACT FROM AUTHOR]

To efficiently distribute blood flow to cardiac muscle, the coronary artery tree must follow a specific branching pattern over the heart. How this pattern arises in humans is unknown due to the limitations of studying human heart development. Here, we leveraged a natural variation of coronary artery anatomy, known as coronary dominance, in genetic association studies to identify the first known driver of human coronary developmental patterning. Coronary dominance refers to whether the right, left, or both coronary arteries branch over the posterior left ventricle, but whether this variability is heritable and how it would be genetically regulated was completely unknown. By conducting the first large-scale, multi-ancestry genome-wide association study (GWAS) of coronary dominance in 61,043 participants of the VA Million Veteran Program, we observed moderate heritability (27.7%) with ten loci reaching genome wide significance. An exceptionally strong association mapped DNA variants to a non-coding region near the chemokine CXCL12 in both European and African ancestries, which overlapped with variants associated with coronary artery disease. Genomic analyses predicted these variants to impact CXCL12 levels, and imaging revealed dominance to develop during fetal life coincident with CXCL12 expression. Reducing Cxcl12 in mice to model the human genetics altered septal artery dominance patterns and caused coronary branches to develop away from Cxcl12 expression domains. Cxcl12 heterozygosity did not compromise overall artery coverage as seen with full deletion, but instead changed artery patterning, reminiscent of the human scenario. Together, our data support CXCL12 as a critical determinant of human coronary artery growth and patterning and lay a foundation for the utilization of developmental pathways to guide future precision 'medical revascularization' therapeutics., Competing Interests: Competing interests: A.K. is on the scientific advisory board of SerImmune, TensorBio and OpenTargets, and a consultant with Arcadia Science and Inari Agriculture. A.K. was a scientific co-founder of RavelBio, a paid consultant with Illumina, was on the SAB of PatchBio and owns shares in DeepGenomics, Immunai, Freenome, and Illumina. All other authors declare they have no competing interests.