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.

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.

Light-responsive and ultrapermeable two-dimensional metal-organic framework membrane for efficient ionic energy harvesting.

Nanofluidic membranes offer exceptional promise for osmotic energy conversion, but the challenge of balancing ionic selectivity and permeability persists. Here, we present a bionic nanofluidic system based on two-dimensional (2D) copper tetra-(4-carboxyphenyl) porphyrin framework (Cu-TCPP). The inherent nanoporous structure and horizontal interlayer channels endow the Cu-TCPP membrane with ultrahigh ion permeability and allow for a power density of 16.64 W m-2, surpassing state of-the-art nanochannel membranes. Moreover, leveraging the photo-thermal property of Cu-TCPP, light-controlled ion active transport is realized even under natural sunlight. By combining solar energy with salinity gradient, the driving force for ion transport is reinforced, leading to further improvements in energy conversion performance. Notably, light could even eliminate the need for salinity gradient, achieving a power density of 0.82 W m-2 in a symmetric solution system. Our work introduces a new perspective on developing advanced membranes for solar/ionic energy conversion and extends the concept of salinity energy to a notion of ionic energy.

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.

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.

Mosaic Chromosomal Alterations and Human Longevity.

Mosaic chromosomal alterations (mCAs) are structural alterations associated with aging, cancer, cardiovascular disease, infectious diseases, and mortality. The distribution of mCAs in centenarians and individuals with familial longevity is poorly understood. We used MOsaic CHromosomal Alteration (MoChA) to discover mCAs in 2050 centenarians, offspring, and 248 controls from the New England Centenarian Study (NECS) and in 3 642 subjects with familial longevity and 920 spousal controls from the Long-Life Family Study (LLFS). We analyzed study-specific associations of somatic mCAs with age, familial longevity, the incidence of age-related diseases, and mortality and aggregated the results by meta-analysis. We show that the accumulation of mCAs > 100 KB increased to 102 years and plateaued at older ages. Centenarians and offspring accumulated fewer autosomal mCAs compared with controls (relative risk 0.637, p = .0147). Subjects with the APOE E4 allele had a 35.3% higher risk of accumulating autosomal mCAs (p = .002). Males were at higher risk for mCAs compared to females (male relative risk 1.36, p = 5.15e-05). mCAs were associated with increased hazard for cancer (hazard ratio 1.2) and dementia (hazard ratio 1.259) at a 10% false discovery rate. We observed a borderline significant association between mCAs and risk for mortality (hazard ratio 1.07, p = .0605). Our results show that the prevalence of individuals with mCAs does not continue to increase at ages >102 years and factors promoting familial longevity appear to confer protections from mCAs. These results suggest that limited mCA accumulation could be an important mechanism for extreme human longevity that needs to be investigated.

Rare genetic variants correlate with better processing speed.

We conducted a genome-wide association study of Digit Symbol Substitution Test scores administered in 4207 family members of the Long Life Family Study (LLFS). Genotype data were imputed to the HRC panel of 64,940 haplotypes resulting in ∼15M genetic variants with a quality score > 0.7. The results were replicated using genetic data imputed to the 1000 Genomes phase 3 reference panel from 2 Danish twin cohorts: the study of Middle Aged Danish Twins and the Longitudinal Study of Aging Danish Twins. The genome-wide association study in LLFS discovered 18 rare genetic variants (minor allele frequency (MAF) < 1.0%) that reached genome-wide significance (p-value < 5 × 10-8). Among these, 17 rare variants in chromosome 3 had large protective effects on the processing speed, including rs7623455, rs9821776, rs9821587, rs78704059, which were replicated in the combined Danish twin cohort. These SNPs are located in/near 2 genes, THRB and RARB, that belonged to the thyroid hormone receptors family that may influence the speed of metabolism and cognitive aging. The gene-level tests in LLFS confirmed that these 2 genes are associated with processing speed.

A metabolomic signature of the APOE2 allele.

With the goal of identifying metabolites that significantly correlate with the protective e2 allele of the apolipoprotein E (APOE) gene, we established a consortium of five studies of healthy aging and extreme human longevity with 3545 participants. This consortium includes the New England Centenarian Study, the Baltimore Longitudinal Study of Aging, the Arivale study, the Longevity Genes Project/LonGenity studies, and the Long Life Family Study. We analyzed the association between APOE genotype groups E2 (e2e2 and e2e3 genotypes, N = 544), E3 (e3e3 genotypes, N = 2299), and E4 (e3e4 and e4e4 genotypes, N = 702) with metabolite profiles in the five studies and used fixed effect meta-analysis to aggregate the results. Our meta-analysis identified a signature of 19 metabolites that are significantly associated with the E2 genotype group at FDR < 10%. The group includes 10 glycerolipids and 4 glycerophospholipids that were all higher in E2 carriers compared to E3, with fold change ranging from 1.08 to 1.25. The organic acid 6-hydroxyindole sulfate, previously linked to changes in gut microbiome that were reflective of healthy aging and longevity, was also higher in E2 carriers compared to E3 carriers. Three sterol lipids and one sphingolipid species were significantly lower in carriers of the E2 genotype group. For some of these metabolites, the effect of the E2 genotype opposed the age effect. No metabolites reached a statistically significant association with the E4 group. This work confirms and expands previous results connecting the APOE gene to lipid regulation and suggests new links between the e2 allele, lipid metabolism, aging, and the gut-brain axis.

Evaluation of GENESIS, SAIGE, REGENIE and fastGWA-GLMM for genome-wide association studies of binary traits in correlated data.

Performing a genome-wide association study (GWAS) with a binary phenotype using family data is a challenging task. Using linear mixed effects models is typically unsuitable for binary traits, and numerical approximations of the likelihood function may not work well with rare genetic variants with small counts. Additionally, imbalance in the case-control ratios poses challenges as traditional statistical methods such as the Score test or Wald test perform poorly in this setting. In the last couple of years, several methods have been proposed to better approximate the likelihood function of a mixed effects logistic regression model that uses Saddle Point Approximation (SPA). SPA adjustment has recently been implemented in multiple software, including GENESIS, SAIGE, REGENIE and fastGWA-GLMM: four increasingly popular tools to perform GWAS of binary traits. We compare Score and SPA tests using real family data to evaluate computational efficiency and the agreement of the results. Additionally, we compare various ways to adjust for family relatedness, such as sparse and full genetic relationship matrices (GRM) and polygenic effect estimates. We use the New England Centenarian Study imputed genotype data and the Long Life Family Study whole-genome sequencing data and the binary phenotype of human extreme longevity to compare the agreement of the results and tools' computational performance. The evaluation suggests that REGENIE might not be a good choice when analyzing correlated data of a small size. fastGWA-GLMM is the most computationally efficient compared to the other three tools, but it appears to be overly conservative when applied to family-based data. GENESIS, SAIGE and fastGWA-GLMM produced similar, although not identical, results, with SPA adjustment performing better than Score tests. Our evaluation also demonstrates the importance of adjusting by full GRM in highly correlated datasets when using GENESIS or SAIGE.

Distribution of 54 polygenic risk scores for common diseases in long lived individuals and their offspring.

A surprising and well-replicated result in genetic studies of human longevity is that centenarians appear to carry disease-associated variants in numbers similar to the general population. With the proliferation of large genome-wide association studies (GWAS) in recent years, investigators have turned to polygenic scores to leverage GWAS results into a measure of genetic risk that can better predict the risk of disease than individual significant variants alone. We selected 54 polygenic risk scores (PRSs) developed for a variety of outcomes, and we calculated their values in individuals from the New England Centenarian Study (NECS, N = 4886) and the Long Life Family Study (LLFS, N = 4577). We compared the distribution of these PRSs among exceptionally long-lived individuals (ELLI), their offspring, and controls, and we also examined their predictive values, using t-tests and regression models adjusting for sex and principal components reflecting the ancestral background of the individuals (PCs). In our analyses, we controlled for multiple testing using a Bonferroni-adjusted threshold for 54 traits. We found that only 4 of the 54 PRSs differed between ELLIs and controls in both cohorts. ELLIs had significantly lower mean PRSs for Alzheimer's disease (AD) and coronary artery disease (CAD) than controls, suggesting a genetic predisposition to extreme longevity may be mediated by reduced susceptibility to these traits. ELLIs also had significantly higher mean PRSs for improved cognitive function and parental extreme longevity. In addition, the PRS for AD was associated with a higher risk of dementia among controls but not ELLIs (p = 0.003, 0.3 in NECS, p = 0.03, 0.9 in LLFS, respectively). ELLIs have a similar burden of genetic disease risk as the general population for most traits but have a significantly lower genetic risk of AD and CAD. The lack of association between AD PRS and dementia among ELLIs suggests that the genetic risk for AD that they do have is somehow counteracted by protective genetic or environmental factors.

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.

Effect of longevity genetic variants on the molecular aging rate.

We conducted a genome-wide association study of 1320 centenarians from the New England Centenarian Study (median age = 104 years) and 2899 unrelated controls using >9 M genetic variants imputed to the HRC panel of ~65,000 haplotypes. The genetic variants with the most significant associations were correlated to 4131 proteins that were profiled in the serum of a subset of 224 study participants using a SOMAscan array. The genetic associations were replicated in a genome-wide association study of 480 centenarians and ~800 controls of Ashkenazi Jewish descent. The proteomic associations were replicated in a proteomic scan of approximately 1000 Ashkenazi Jewish participants from a third cohort. The analysis replicated a protein signature associated with APOE genotypes and confirmed strong overexpression of BIRC2 (p < 5E-16) and under-expression of APOB in carriers of the APOE2 allele (p < 0.05). The analysis also discovered and replicated associations between longevity variants and slower changes of protein biomarkers of aging, including a novel protein signature of rs2184061 (CDKN2A/CDKN2B in chromosome 9) that suggests a genetic regulation of GDF15. The analyses showed that longevity variants correlate with proteome signatures that could be manipulated to discover healthy-aging targets.

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.

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.

nf-gwas-pipeline: A Nextflow Genome-Wide Association Study Pipeline.

A tool for conducting Genome-Wide Association Study (GWAS) in a systematic, automated and reproducible manner is overdue. We developed an automated GWAS pipeline by combining multiple analysis tools - including bcftools, vcftools, the R packages SNPRelate/GENESIS/GMMAT and ANNOVAR - through Nextflow, which is a portable, flexible, and reproducible reactive workflow framework for developing pipelines. The GWAS pipeline integrates the steps of data quality control and assessment and genetic association analyses, including analysis of cross-sectional and longitudinal studies with either single variants or gene-based tests, into a unified analysis workflow. The pipeline is implemented in Nextflow, dependencies are distributed through Docker, and the code is publicly available on Github.

Neutrophil extracellular traps promote scar formation in post-epidural fibrosis.

Low back pain following spine surgery is a major complication due to excessive epidural fibrosis, which compresses the lumbar nerve. The mechanisms of epidural fibrosis remain largely elusive. In the drainage samples from patients after spine operation, neutrophil extracellular traps (NETs) and NETs inducer high-mobility group box 1 were significantly increased. In a mouse model of laminectomy, NETs developed in the wound area post epidural operation, accompanied with macrophage infiltration. In vitro, macrophages ingested NETs and thereby increased the elastase from NETs via the receptor for advanced glycation end product. Moreover, NETs boosted the expression of fibronectin in macrophages, which was dependent on elastase and could be partially blocked by DNase. NF-κB p65 and Smad pathways contributed to the increased expression fibronectin in NETs-treated macrophages. In the mouse spine operation model, post-epidural fibrosis was significantly mitigated with the administration of DNase I, which degraded DNA and cleaved NETs. Our study shed light on the roles and mechanisms of NETs in the scar formation post spine operation.

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.