Clinical evidence links arterial calcification and cardiovascular risk. Finite-element modelling of the stress distribution within atherosclerotic plaques has suggested that subcellular microcalcifications in the fibrous cap may promote material failure of the plaque, but that large calcifications can stabilize it. Yet the physicochemical mechanisms underlying such mineral formation and growth in atheromata remain unknown. Here, by using three-dimensional collagen hydrogels that mimic structural features of the atherosclerotic fibrous cap, and high-resolution microscopic and spectroscopic analyses of both the hydrogels and of calcified human plaques, we demonstrate that calcific mineral formation and maturation results from a series of events involving the aggregation of calcifying extracellular vesicles, and the formation of microcalcifications and ultimately large calcification areas. We also show that calcification morphology and the plaque’s collagen content—two determinants of atherosclerotic plaque stability—are interlinked.

Despite the global impact of macrophage activation in vascular disease, the underlying mechanisms remain obscure. Here we show, with global proteomic analysis of macrophage cell lines treated with either IFNγ or IL-4, that PARP9 and PARP14 regulate macrophage activation. In primary macrophages, PARP9 and PARP14 have opposing roles in macrophage activation. PARP14 silencing induces pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells, whereas it suppresses anti-inflammatory gene expression and STAT6 phosphorylation in M(IL-4) cells. PARP9 silencing suppresses pro-inflammatory genes and STAT1 phosphorylation in M(IFNγ) cells. PARP14 induces ADP-ribosylation of STAT1, which is suppressed by PARP9. Mutations at these ADP-ribosylation sites lead to increased phosphorylation. Network analysis links PARP9–PARP14 with human coronary artery disease. PARP14 deficiency in haematopoietic cells accelerates the development and inflammatory burden of acute and chronic arterial lesions in mice. These findings suggest that PARP9 and PARP14 cross-regulate macrophage activation.

We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems.

Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split (“Class-specific”). Via simulations with both real and simulated batch effects, we demonstrate that the “Class-specific” strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the “Class-specific” strategy.

Membrane integrity at the endoplasmic reticulum (ER) is tightly regulated, and its disturbance is implicated in metabolic diseases. Using an engineered sensor that activates the unfolded protein response (UPR) exclusively when normal ER membrane lipid composition is compromised, we identified pathways beyond lipid metabolism that are necessary to maintain ER integrity in yeast and in C. elegans. To systematically validate yeast mutants that disrupt ER membrane homeostasis, we identified a lipid bilayer stress (LBS) sensor in the UPR transducer protein Ire1, located at the interface of the amphipathic and transmembrane helices. Furthermore, transcriptome and chromatin immunoprecipitation analyses pinpoint the UPR as a broad-spectrum compensatory response wherein LBS and proteotoxic stress deploy divergent transcriptional UPR programs. Together, these findings reveal the UPR program as the sum of two independent stress responses, an insight that could be exploited for future therapeutic intervention.