Louie, R. | Search bioRxiv

bioRxiv2017

Disrupting myeloid-specific LXRα phosphorylation promotes FoxM1 expression and modulates atherosclerosis by inducing macrophage proliferation

Macrophages are key immune cells for the initiation and development of atherosclerotic lesions. However, the macrophage regulatory nodes that determine how lesions progress in response to dietary challenges are not fully understood. Liver X receptors (LXRs) are sterol-regulated transcription factors which play a central role in atherosclerosis by integrating cholesterol homeostasis and immunity. LXR pharmacological activation elicits a robust anti-atherosclerotic transcriptional program in macrophages that can be affected by LXR S196 phosphorylation in vitro. To investigate the impact of these transcriptional changes in atherosclerosis development, we have generated mice carrying a Ser-to-Ala mutation in myeloid cells in the LDLR-deficient atherosclerotic background (M-S196ALdlr-KO). M-S196ALdlr-KO mice fed a high fat diet exhibit increased atherosclerotic plaque burden and lesions with smaller necrotic cores and thinner fibrous caps. These diet-induced phenotypic changes are consistent with a reprogramed macrophage transcriptome promoted by LXR-S196A during atherosclerosis development. Remarkably, expression of several proliferation-promoting factors including the proto-oncogene FoxM1 and its targets are induced by LXR-S196A. This is consistent with increased proliferation of plaque-resident cells in M-S196ALdlr-KO mice. Moreover, disrupted LXR phosphorylation increases expression of phagocytic molecules resulting in increased apoptotic cell removal by macrophages, explaining the reduced necrotic cores. Finally, the macrophage transcriptome promoted by LXR-S196A under dietary perturbation is markedly distinct from that revealed by LXR ligand activation, highlighting the singularity of this post-translational modification. Overall, our findings demonstrate that LXR phosphorylation at S196 is an important determinant of atherosclerotic plaque development through selective changes in gene transcription that affect multiple pathways.

physiology↗

bioRxiv2017

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.\n\nMethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.\n\nResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.\n\nConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

genetics↗