Biology subjects

Blackwell, T.

Publications and source records attributed to Blackwell, T..

9 recordsLinked to original sources

bioRxiv2024

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Circulating metabolite levels partly reflect the state of human health and diseases and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single-study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally diverse samples. We provided a set of reasonable strategies for outlier and imputation handling to process metabolite data. Following the practical analysis framework, we further performed a genome-wide association analysis on 1,135 selected metabolites using whole genome sequencing data from 16,359 individuals passing the quality control filters, and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus-metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2, D2HGDH, SUGP1, UTG2B17, strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

bioinformatics↗

bioRxiv2024

Harnessing molecular mechanism for precision medicine in dilated cardiomyopathy caused by a mutation in troponin T

Familial dilated cardiomyopathy (DCM) is frequently caused by autosomal dominant point mutations in genes involved in diverse cellular processes, including sarcomeric contraction. While patient studies have defined the genetic landscape of DCM, genetics are not currently used in patient care, and patients receive similar treatments regardless of the underlying mutation. It has been suggested that a precision medicine approach based on the molecular mechanism of the underlying mutation could improve outcomes; however, realizing this approach has been challenging due to difficulties linking genotype and phenotype and then leveraging this information to identify therapeutic approaches. Here, we used multiscale experimental and computational approaches to test whether knowledge of molecular mechanism could be harnessed to connect genotype, phenotype, and drug response for a DCM mutation in troponin T, deletion of K210. Previously, we showed that at the molecular scale, the mutation reduces thin filament activation. Here, we used computational modeling of this molecular defect to predict that the mutant will reduce cellular and tissue contractility, and we validated this prediction in human cardiomyocytes and engineered heart tissues. We then used our knowledge of molecular mechanism to computationally model the effects of a small molecule that can activate the thin filament. We demonstrate experimentally that the modeling correctly predicts that the small molecule can partially rescue systolic dysfunction at the expense of diastolic function. Taken together, our results demonstrate how molecular mechanism can be harnessed to connect genotype and phenotype and inspire strategies to optimize mechanism-based therapeutics for DCM. Significance statementDilated cardiomyopathy (DCM), a leading cause of heart failure, is characterized by the inability of the heart to perfuse the body at normal filling pressures. There are multiple causes of DCM, including point mutations in sarcomeric proteins, but most patients receive similar courses of treatment, regardless of the underlying cause of the DCM. Many patients remain unserved by current therapies, and there is a need for new approaches. Here, we use multiscale experimental and computational approaches to demonstrate how knowledge of molecular mechanism can be harnessed to accurately predict the effects of a patient-specific mutation and responses to presumptive therapeutics. Our approach lays the foundation for a precision medicine approach to DCM.

biophysics↗

bioRxiv2023

Myeloid Cell-Derived IL1β Contributes to Pulmonary Vascular Remodeling in Heart Failure with Preserved Ejection Fraction

BackgroundPulmonary hypertension (PH) in heart failure with preserved ejection fraction (HFpEF) is a common and highly morbid syndrome, but mechanisms driving PH-HFpEF are not well understood. We sought to determine whether a well-accepted murine model of HFpEF also displays features of PH in HFpEF, and we sought to identify pathways that might drive early remodeling of the pulmonary vasculature in HFpEF. MethodsEight week old male and female C57/BL6J mice were given either L-NAME and high fat diet (HFD) or control water/diet for 2,5, and 12 weeks. Bulk RNA sequencing and single cell RNA sequencing was performed to identify early and cell-specific pathways that might regulate pulmonary vascular remodeling in PH-HFpEF. Finally, clodronate liposome and IL1{beta} antibody treatments were utilized to deplete macrophages or IL1{beta}, respectively, to assess their impact on pulmonary vascular remodeling in HFpEF. ResultsMice given L-NAME/HFD developed PH, small vessel muscularization, and right heart dysfunction after 2 weeks of treatment. Inflammation-related gene ontologies were over-represented in bulk RNA sequencing analysis of whole lungs, with an increase in CD68+ cells in both murine and human PH-HFpEF lungs. Cytokine profiling of mouse lung and plasma showed an increase in IL1{beta}, which was confirmed in plasma from patients with HFpEF. Single cell sequencing of mouse lungs also showed an increase in M1-like, pro-inflammatory populations of Ccr2+ monocytes and macrophages, and transcript expression of IL1{beta} was primarily restricted to myeloid-type cells. Finally, clodronate liposome treatment prevented the development of PH in L-NAME/HFD treated mice, and IL1{beta} antibody treatment also attenuated PH in L-NAME/HFD treated mice. ConclusionsOur study demonstrated that a well-accepted model of HFpEF recapitulates features of pulmonary vascular remodeling commonly seen in patients with HFpEF, and we identified myeloid cell derived IL1{beta} as an important contributor to PH in HFpEF.

cell biology↗

bioRxiv2023

Structural variation across 138,134 samples in the TOPMed consortium

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

genomics↗

bioRxiv2021

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

genetics↗

bioRxiv2021

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328. Author summaryGene regulation is a critical mechanism underlying complex traits. Transcriptome-wide association studies (TWAS) have helped elucidate potential mechanisms because each association connects a gene rather than a variant to the complex trait. Like genome-wide association studies (GWAS), most TWAS are still conducted exclusively in populations of European ancestry, which misses the opportunity to test the full spectrum of human genetic variation for associations with complex traits. Here, move beyond the transcriptome and because protein measurement assays are growing to allow interrogation of the proteome, we use data from TOPMed MESA to develop genetic predictors of protein abundance in diverse ancestry populations. We compare model-building strategies with the goal of providing the best resource for protein association discovery with available data. We demonstrate how these prediction models can be used to perform proteome-wide association studies (PWAS) in diverse populations. We show the most protein-trait associations were discovered, colocalized, and replicated in independent cohorts using proteome prediction model training populations with similar ancestries to individuals in the GWAS. We shared our protein prediction models and performance statistics publicly to facilitate future proteome mapping studies in diverse populations.

genomics↗

bioRxiv2021

Tumor-secreted versican co-opts myeloid IKKβ during metastasis

The mechanisms tumor cells use to hijack the immune system are largely uncharted. Here we used bioluminescent nuclear factor (NF)-{kappa}B reporter mice and macrophages to discover that metastatic tumors trigger NF-{kappa}B activation in host macrophages, dependent on mutant KRAS signaling and delivered via secretory versican. Versican activates NF-{kappa}B in tumor-associated macrophages via inhibitor of NF-{kappa}B kinase (IKK) {beta}, resulting in release of interleukin (IL)-1{beta} into the tumor microenvironment. Versican silencing in cancer cells or conditional IKK{beta} deletion in macrophages prevents myeloid NF-{kappa}B activation and metastasis. Versican is overexpressed and/or mutated in human cancers and metastatic effusions with KRAS mutations, predicts poor survival, can aid in the development of diagnostic platforms for pleural metastasis, and is druggable via toll-like receptor (TLR) 1/2 inhibition. The data indicate a cardinal role for tumor-derived versican in establishing cross-talk with macrophage IKK{beta} during metastasis and may foster the development of new therapies and diagnostic tools.

cancer biology↗

bioRxiv2020

Presence and Transmission of Mitochondrial Heteroplasmic Mutations in Human Populations of European and African Ancestry

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in different types of maternal pairs (n=6,745 pairs) of European (EA, n=4,718 pairs) and African (AA, n=2,027 pairs) Americans with whole genome sequences (WGSs). The average concordance rate of heteroplasmies was highest between mother-offspring pairs, followed by sibling-sibling pairs and more distantly related maternal pairs in both EA and AA participants. The allele fractions of concordant heteroplasmies exhibited high correlation (R2=0.8) between paired individuals. Compared to concordant heteroplasmies, discordant ones were more likely to locate in coding regions, be nonsynonymous or nonsynonymous-deleterious (p<0.001). The average number of heteroplasmies per individual (i.e. heteroplasmic burden) was at a similar level until older age (70-80 years old) and increased significantly thereafter (p<0.01). The burden of deleterious heteroplasmies (combined annotation-dependent depletion score[≥]15), however, was significantly correlated with advancing age (20-44, 45-64, [≥]65 years, p-trend=0.01). A genome-wide association analysis of the heteroplasmic burden identified many significant (P<5e-8) common variants (minor allele frequency>0.05) at 11p11.12. Many of the top SNPs act as strong long-range cis regulators of protein tyrosine phosphatase receptor type J. This study provides further evidence that mtDNA heteroplasmies may be inherited or somatic. Somatic heteroplasmic variants increase with advancing age and are more likely to have an adverse impact on mitochondrial function. Further studies are warranted for functional characterization of the deleterious heteroplasmies occurring with advancing age and the association of the 11p11.12 region of the nuclear genome with mtDNA heteroplasmy.

genetics↗

bioRxiv2020

Newly discovered tilapia cichlid fish biodiversity threatened by hybridization with non-native species

Invasive freshwater fish systems are known to readily hybridize with indigenous congeneric species, driving loss of unique and irreplaceable genetic resources. Here we reveal that newly discovered (2013-2016) evolutionarily significant populations of Korogwe tilapia (Oreochromis korogwe) from southern Tanzania are threatened by hybridization with the larger invasive Nile tilapia (Oreochromis niloticus). We use a combination of morphology, microsatellite allele frequencies and whole genome sequences to show that O. korogwe from southern lakes (Nambawala, Rutamba and Mitupa) are distinct from geographically-disjunct populations in northern Tanzania (Zigi River and Mlingano Dam). We also provide genetic evidence of O. korogwe x niloticus hybrids in three lakes and demonstrate heterogeneity in the extent of admixture across the genome. Finally, using the least admixed genomic regions we estimate that the northern and southern O. korogwe populations most plausibly diverged approximately 140,000 years ago, suggesting that the geographical separation of the northern and southern groups is not a result of a recent translocation, and instead these populations represent independent evolutionarily significant units. We conclude that these newly-discovered and phenotypically unique cichlid populations are already threatened by hybridization with an invasive species, and propose that these irreplaceable genetic resources would benefit from conservation interventions.

zoology↗