Identifying mitigating strategies for endothelial cell dysfunction and hypertension in response to VEGF receptor inhibitors.

Vascular endothelial growth factor receptor inhibitors (VEGFRis) improve cancer survival but are associated with treatment-limiting hypertension, often attributed to endothelial cell (EC) dysfunction. Using phosphoproteomic profiling of VEGFRi-treated ECs, drugs were screened for mitigators of VEGFRi-induced EC dysfunction and validated in primary aortic ECs, mice, and canine cancer patients. VEGFRi treatment significantly raised systolic blood pressure (SBP) and increased markers of endothelial and renal dysfunction in mice and canine cancer patients. α-Adrenergic-antagonists were identified as drugs that most oppose the VEGFRi proteomic signature. Doxazosin, one such α-antagonist, prevented EC dysfunction in murine, canine, and human aortic ECs. In mice with sorafenib-induced-hypertension, doxazosin mitigated EC dysfunction but not hypertension or glomerular endotheliosis, while lisinopril mitigated hypertension and glomerular endotheliosis without impacting EC function. Hence, reversing EC dysfunction was insufficient to mitigate VEGFRi-induced-hypertension in this mouse model. Canine cancer patients with VEGFRi-induced-hypertension were randomized to doxazosin or lisinopril and both agents significantly decreased SBP. The canine clinical trial supports safety and efficacy of doxazosin and lisinopril as antihypertensives for VEGFRi-induced-hypertension and the potential of trials in canines with spontaneous cancer to accelerate translation. The overall findings demonstrate the utility of phosphoproteomics to identify EC-protective agents to mitigate cardio-oncology side effects.

A Genome-Wide Association Study of 2304 Extreme Longevity Cases Identifies Novel Longevity Variants.

We performed a genome-wide association study (GWAS) of human extreme longevity (EL), defined as surviving past the 99th survival percentile, by aggregating data from four centenarian studies. The combined data included 2304 EL cases and 5879 controls. The analysis identified a locus in CDKN2B-AS1 (rs6475609, p = 7.13 × 10-8) that almost reached genome-wide significance and four additional loci that were suggestively significant. Among these, a novel rare variant (rs145265196) on chromosome 11 had much higher longevity allele frequencies in cases of Ashkenazi Jewish and Southern Italian ancestry compared to cases of other European ancestries. We also correlated EL-associated SNPs with serum proteins to link our findings to potential biological mechanisms that may be related to EL and are under genetic regulation. The findings from the proteomic analyses suggested that longevity-promoting alleles of significant genetic variants either provided EL cases with more youthful molecular profiles compared to controls or provided some form of protection from other illnesses, such as Alzheimer's disease, and disease progressions.

Metformin attenuates post-epidural fibrosis by inhibiting the TGF-ß1/Smad3 and HMGB1/TLR4 signaling pathways.

Excessive post-epidural fibrosis is a common cause of recurrent back pain after spinal surgery. Though various treatment methods have been conducted, the safe and effective drug for alleviating post-epidural fibrosis remains largely unknown. Metformin, a medicine used in the treatment of type 2 diabetes, has been noted to relieve fibrosis in various organs. In the present study, we aimed to explore the roles and mechanisms of metformin in scar formation in a mouse model of laminectomy. Post-epidural fibrosis developed in a mouse model of laminectomy by spinous process and the T12-L2 vertebral plate with a rongeur. With the administration of metformin, post-epidural fibrosis was reduced, accompanied with decreased collagen and fibronectin in the scar tissues. Mechanistically, metformin decreased fibronectin and collagen deposition in fibroblast cells, and this effect was dependent on the HMGB1/TLR4 and TGF-ß1/Smad3 signalling pathways. In addition, metformin influenced the metabolomics of the fibroblast cells. Taken together, our study suggests that metformin may be a potential option to mitigate epidural fibrosis after laminectomy.

Fabrication of uniform-aperture multi-focus microlens array by curving microfluid in the microholes with inclined walls.

A variety of techniques have been proposed for fabricating high-density, high-numerical-aperture microlens arrays. However, a microlens array with a uniform focal length has a narrow depth of field, limiting the ability of depth perception. In this paper, we report on a fabrication method of multi-focus microlens arrays. The method for the preparation of the mold of the microlens array is based on 3D printing and microfluidic manipulation techniques. In the preparation of the mold, curved surfaces of the photo-curable resin with different curvatures are formed in the 3D printed microholes whose walls are inclined with different angles. The replicated microlens array consists of hundreds of lenslets with a uniform diameter of 500 µm and different focal lengths ranging from 635 µm to 970 µm. The multi-focus microlens array is capable of extending the depth of field for capturing clear images of objects at different distances ranging from 14.3 mm to 45.5 mm. The multi-focus microlens array has the potential to be used in a diversity of large-depth-of-field imaging and large-range depth perception applications.

Loss of PP2A Disrupts the Retention of Radial Glial Progenitors in the Telencephalic Niche to Impair the Generation for Late-Born Neurons During Cortical Development†.

Telencephalic radial glial progenitors (RGPs) are retained in the ventricular zone (VZ), the niche for neural stem cells during cortical development. However, the underlying mechanism is not well understood. To study whether protein phosphatase 2A (PP2A) may regulate the above process, we generate Ppp2cα conditional knockout (cKO) mice, in which PP2A catalytic subunit α (PP2Acα) is inactivated in neural progenitor cells in the dorsal telencephalon. We show that RGPs are ectopically distributed in cortical areas outside of the VZ in Ppp2cα cKO embryos. Whereas deletion of PP2Acα does not affect the proliferation of RGPs, it significantly impairs the generation of late-born neurons. We find complete loss of apical adherens junctions (AJs) in the ventricular membrane in Ppp2cα cKO cortices. We observe abundant colocalization for N-cadherin and PP2Acα in control AJs. Moreover, in vitro analysis reveals direct interactions of N-cadherin to PP2Acα and to ß-catenin. Overall, this study not only uncovers a novel function of PP2Acα in retaining RGPs into the VZ but also demonstrates the impact of PP2A-dependent retention of RGPs on the generation for late-born neurons.

yQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

yQTL Pipeline: a structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. In order to facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to genome-wide association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. Upon uploading the result file, it can generate Manhattan plots of user-selected phenotype traits and trait-QTL connection networks based on user-specified p-value thresholds. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs cumulatively associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTL-Pipeline.

CD146-dependent macrophage infiltration promotes epidural fibrosis via the Erdr1/ERK/CCR2 pathway.

Low back pain due to epidural fibrosis is a major complication after spine surgery. Macrophages infiltrate the wound area post laminectomy, but the role of macrophages in epidural fibrosis remains largely elusive. In a mouse model of laminectomy, macrophage depletion decreased epidural fibrosis. CD146, an adhesion molecule involved in cell migration, is expressed by macrophages. CD146-defective macrophages exhibited impaired migration, which was mediated by reduced expression of CCR2 and suppression of the MAPK/ERK signaling pathway. CD146-defective macrophages suppress the MAPK/ERK signaling pathway by increasing Erdr1. In vivo, CD146 deficiency decreased macrophage infiltration and reduced extracellular matrix deposition in wound tissues. Moreover, the anti-CD146 antibody AA98 suppressed macrophage infiltration and epidural fibrosis. Taken together, these findings demonstrated that CD146 deficiency alleviates epidural fibrosis by decreasing the migration of macrophages via the Erdr1/ERK/CCR2 pathway. Blocking CD146 and macrophage infiltration may help alleviate epidural fibrosis.

Learning Gaussian Graphical Models from Correlated Data.

Gaussian Graphical Models (GGM) have been widely used in biomedical research to explore complex relationships between many variables. There are well established procedures to build GGMs from a sample of independent and identical distributed observations. However, many studies include clustered and longitudinal data that result in correlated observations and ignoring this correlation among observations can lead to inflated Type I error. In this paper, we propose a Bootstrap algorithm to infer GGM from correlated data. We use extensive simulations of correlated data from family-based studies to show that the Bootstrap method does not inflate the Type I error while retaining statistical power compared to alternative solutions. We apply our method to learn the GGM that represents complex relations between 47 Polygenic Risk Scores generated using genome-wide genotype data from a family-based study known as the Long Life Family Study. By comparing it to the conventional methods that ignore within-cluster correlation, we show that our method controls the Type I error well in this real example.

Unlocking osmotic energy harvesting potential in challenging real-world hypersaline environments through vermiculite-based hetero-nanochannels.

Nanochannel membranes have demonstrated remarkable potential for osmotic energy harvesting; however, their efficiency in practical high-salinity systems is hindered by reduced ion selectivity. Here, we propose a dual-separation transport strategy by constructing a two-dimensional (2D) vermiculite (VMT)-based heterogeneous nanofluidic system via an eco-friendly and scalable method. The cations are initially separated and enriched in micropores of substrates during the transmembrane diffusion, followed by secondary precise sieving in ultra-thin VMT laminates with high ion flux. Resultantly, our nanofluidic system demonstrates efficient osmotic energy harvesting performance, especially in hypersaline environment. Notably, we achieve a maximum power density of 33.76 W m-2, a 6.2-fold improvement with a ten-fold increase in salinity gradient, surpassing state-of-the-art nanochannel membranes under challenging conditions. Additionally, we confirm practical hypersaline osmotic power generation using various natural salt-lake brines, achieving a power density of 25.9 W m-2. This work triggers the hopes for practical blue energy conversion using advanced nanoarchitecture.