Biology subjects

Gabriel, S.

Publications and source records attributed to Gabriel, S..

18 recordsLinked to original sources

bioRxiv2025

Electric field intensity modulates keratocyte migration without altering turning dynamics

Cell migration is a cornerstone of biological systems, enabling organisms to adapt to environmental stimuli and maintain homeostasis. Disruptions in this process can lead to functional impairment or system failure. In many cases, cells do not move randomly; instead, they migrate directionally in response to external cues, allowing them to perform essential biological functions. This directed movement is especially important in processes such as morphogenesis, cancer invasion, and wound healing. To unravel the complexities of directional cell migration, investigating natural guiding stimuli is crucial. Among these, electrical fields stand out as precise and physiologically relevant stimulus. Using a platform designed to apply programmable electric fields, the SCHEEPDOG device; we applied controlled electric field of varying intensities to keratocytes and quantitatively analyzed their migratory behavior. Our findings reveal that electric field stimulation not only induces robust directional migration but also enhances migration speed in an intensity-dependent manner. Additionally, cells initially moving in random directions gradually align with the field vector, with higher intensities accelerating the alignment. Intriguingly, while both speed and alignment time can be modulated through stimulation, the overall shape of migration trajectories remains unchanged. In other terms, for cells initially moving to the opposite direction of the field, the alignment is accompanied by making a turn and the size and shape of this turn is not affected by the magnitude of the electrical stimulation. Together, these results demonstrate that electrical stimulation can tune the speed and directional alignment of keratocyte migration without altering turning dynamics. These findings contribute to a deeper understanding of electrotaxis and offers new insights into how biophysical cues regulate cell migration in both physiological and pathological contexts.

biophysics↗

bioRxiv2023

Resolution of ring chromosomes, Robertsonian translocations, and complex structural variants from long-read sequencing and telomere-to-telomere assembly

The capacity to resolve structural variants (SVs) at sequence resolution in highly repetitive genomic regions has long been intractable. Consequently, the properties, origins, and functional effects of multiple classes of complex rearrangement are unknown. To resolve these challenges, we leveraged recent technical milestones: 1) Oxford-Nanopore (ONT) sequencing; 2) the gapless Telomere-to-Telomere (T2T) genome assembly; and 3) a novel tool to discover large-scale rearrangements from long-reads. We applied these technologies across 13 patients with ring chromosomes, Robertsonian translocations, and complex balanced SVs that were unresolved by short-read sequencing. We resolved 10 of 13 events, including ring chromosomes, the complex SVs, and a Robertsonian translocation. Multiple breakpoints were localized to highly repetitive regions inaccessible to short-read alignment, such as acrocentric p-arms, ribosomal DNA arrays, and telomeric repeats, and involved complex structures such as a deletion-inversion and interchromosomal dispersed duplications. We also leveraged ONT native methylation detection to discover phased differential methylation in a gene promoter proximal to a ring fusion site, suggesting a long-range positional effect with heterochromatin spreading. Breakpoint sequences were consistent with common mechanisms of SV formation, including microhomology-mediated mechanisms, non-homologous end-joining, and non-allelic homologous recombination. These methods provide some of the first glimpses into the sequence resolution of ring chromosomes and Robertsonian translocations and illuminate the structural diversity of chromosomal rearrangements with implications for molecular diagnosis and genome biology. HighlightsO_LICryptic rearrangements revealed in repetitive and previously inaccessible regions C_LIO_LIAligning long-reads to telomere-to-telomere assembly enabled breakpoint discovery C_LIO_LIFirst sequence resolution of a Robertsonian translocation breakpoint C_LIO_LIHaplotype-specific methylation changes associated with ring formation C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/555775v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@188cef8org.highwire.dtl.DTLVardef@cc2ed5org.highwire.dtl.DTLVardef@116b7e6org.highwire.dtl.DTLVardef@837f73_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

genomics↗

bioRxiv2023

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

genetics↗

bioRxiv2023

Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-seq data in GTEx v8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased WGS data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

genetics↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗

bioRxiv2022

Multiset correlation and factor analysis enables exploration of multi-omic data

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce Multi-set Correlation and Factor Analysis, an unsupervised integration method that enables fast inference of shared and private factors in multi-modal data. Applied to 614 ancestry-diverse participant samples across five omics types, MCFA infers a shared space that captures clinically relevant molecular processes.

genomics↗

bioRxiv2022

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗

bioRxiv2022

A genome-wide mutational constraint map quantified from variation in 76,156 human genomes

The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts to assess constraint for non-protein-coding regions have proven more difficult. Here we aggregate, process, and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD), the largest public open-access human genome reference dataset, and use this dataset to build a mutational constraint map for the whole genome. We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation across the genome. As expected, proteincoding sequences overall are under stronger constraint than non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association, and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, while non-coding constraint captures additional functional information underrecognized by gene constraint metrics. We demonstrate that this genome-wide constraint map provides an effective approach for characterizing the non-coding genome and improving the identification and interpretation of functional human genetic variation.

genetics↗

bioRxiv2021

Effect sizes of causal variants for gene expression and complex traits differ between populations

Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

genetics↗

bioRxiv2021

High-throughput RNA isoform sequencing using programmable cDNA concatenation

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

genetics↗

bioRxiv2021

Gene expression in African Americans and Latinos reveals ancestry-specific patterns of genetic architecture

We analyzed whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children to explore ancestry- and heterozygosity-related differences in the genetic architecture of whole blood gene expression. We found that heritability of gene expression significantly increases with greater proportion of African genetic ancestry and decreases with higher levels of Indigenous American ancestry, consistent with a relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of statistically significant ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most of the anc-eQTLs (89%) were driven by population differences in allele frequency, demonstrating the importance of measuring gene expression across multiple populations. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait pairs using models trained in our admixed population than models trained in GTEx. Our study highlights the importance of large and ancestrally diverse genomic studies for enabling new discoveries of complex trait architecture and reducing disparities.

genetics↗

bioRxiv2021

Ing4-deficiency enhances HSC quiescence and confers resistance to inflammatory stress

Hematopoiesis is tightly regulated by a network of transcription factors and complexes that are required for the development and maintenance of hematopoietic stem cells (HSCs). We recently identified the tumor suppressor, Ing4, as a critical regulator of HSC homeostasis. Though the Ing4 mechanism of action remains poorly characterized, it has been shown to promote stem-like cell characteristics in malignant cells. This activity is, in part, due to Ing4 mediated regulation of several major signaling pathways, including NF-{kappa}B and c-Myc. In murine hematopoiesis, Ing4 deficiency induces G0 arrest in HSCs, while simultaneously promoting gene expression signatures associated with differentiation. This results in a poised state for Ing4-deficient HSCs. Long term HSCs are unable to overcome this block, but short-term HSCs convert the poised state into regenerative capacity during hematopoietic challenges, including irradiation and transplantation. Overall, our findings suggest that Ing4 plays a crucial role in the regulation of hematopoiesis. Our model provides key tools for further identification and characterization of pathways that control quiescence and differentiation in HSCs.

cell biology↗

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2019

Heterozygous ATP-binding Cassette Transporter G5 Gene Deficiency and Risk of Coronary Artery Disease

BackgroundFamilial sitosterolemia is a rare, recessive Mendelian disorder characterized by hyperabsorption and decreased biliary excretion of dietary sterols. Affected individuals typically have complete genetic deficiency - homozygous loss-of-function (LoF) variants -- in the ATP-binding cassette transporter G5 (ABCG5) or G8 (ABCG8) genes, and have substantially elevated plasma sitosterol and low-density lipoprotein cholesterol (LDL-C) levels. The impact of partial genetic deficiency of ABCG5 or ABCG8, as occurs in heterozygous carriers of LoF variants, on LDL-C and risk of coronary artery disease (CAD) has remained uncertain.\n\nMethodsWe first recruited nine sitosterolemia families, identified causative LoF variants in ABCG5 or ABCG8, and evaluated the associations of these ABCG5 or ABCG8 LoF variants with plasma phytosterols and lipid levels. We next assessed for LoF variants in ABCG5 or ABCG8 in CAD cases (n=29,361) versus controls (n=357,326). We tested the association of rare LoF variants in ABCG5 or ABCG8 with blood lipids and risk for CAD. Rare LoF variants were defined as protein-truncating variants with minor allele frequency less than 0.1% in ABCG5 or ABCG8.\n\nResultsIn sitosterolemia families, seven pedigrees harbored causative LoF variants in ABCG5 and two pedigrees in ABCG8. Homozygous LoF variants in either ABCG5 or ABCG8 led to marked elevations in sitosterol and LDL-C. Of those sitosterolemia families, heterozygous carriers of ABCG5 LoF variants exhibited increased sitosterol and LDL-C levels compared to non-carriers. Within the large-scale CAD case-control cohorts, prevalence of rare LoF variants in ABCG5 and in ABCG8 were approximately 0.1% each. ABCG5 heterozygous LoF variant carriers had significantly elevated LDL-C levels (24.7 mg/dL; 95% confidence interval [CI] 14 to 35; P=1.1x10-6), and were at two-fold increased risk of CAD (odds ratio 2.06, 95% CI 1.27 to 3.35; P=0.004). By contrast, ABCG8 heterozygous LoF carrier status was not associated with increased LDL-C or risk of CAD.\n\nConclusionsAlthough familial sitosterolemia is traditionally considered as a recessive disorder, we observed that heterozygous carriers of a LoF variant in ABCG5 had significantly increased sitosterol and LDL-C levels and a two-fold increase in risk of CAD.

genetics↗

bioRxiv2019

A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease

Analyzing 5770 all-cause cirrhosis cases and 572,850 controls from seven cohorts, we identify a missense variant in the Mitochondrial Amidoxime Reducing Component 1 gene (MARC1 p.A165T) that associates with protection from all-cause cirrhosis (OR 0.88, p=2.1*10-8). This same variant also associates with lower levels of hepatic fat on computed tomographic imaging and lower odds of physician-diagnosed fatty liver as well as lower blood levels of alanine transaminase (-0.012 SD, 1.4*10-8), alkaline phosphatase (-0.019 SD, 6.6*10-9), total cholesterol (-0.037 SD, p=1*10-18) and LDL cholesterol (-0.035 SD, p=7.3*10-16). Carriers of rare protein-truncating variants in MARC1 had lower liver enzyme levels, cholesterol levels, and reduced odds of liver disease (OR 0.19, p= 0.04) suggesting that deficiency of the MARC1 enzyme protects against cirrhosis.

genomics↗

bioRxiv2017

Scaling accurate genetic variant discovery to tens of thousands of samples

Comprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel assembly-based approach to variant calling, the GATK HaplotypeCaller (HC) and Reference Confidence Model (RCM), that determines genotype likelihoods independently per-sample but performs joint calling across all samples within a project simultaneously. We show by calling over 90,000 samples from the Exome Aggregation Consortium (ExAC) that, in contrast to other algorithms, the HC-RCM scales efficiently to very large sample sizes without loss in accuracy; and that the accuracy of indel variant calling is superior in comparison to other algorithms. More importantly, the HC-RCM produces a fully squared-off matrix of genotypes across all samples at every genomic position being investigated. The HC-RCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.

genomics↗

bioRxiv2017

Characterization and remediation of sample index swaps by non-redundant dual indexing on massively parallel sequencing platforms

Here, we present an in-depth characterization of the index swapping mechanism on Illumina instruments that employ the ExAmp chemistry for cluster generation (HiSeqX, HiSeq4000, and NovaSeq). We discuss best practices for eliminating the effects of index swapping on data integrity by utilizing unique dual indexing for complete filtering of index swapped reads. We calculate mean swap rates across multiple sample preparation methods and sequencer models, demonstrating that different methods can have vastly different swap rates, and show that even non-ExAmp chemistry instruments display trace levels of index swapping. Finally, using computational methods we provide a greater insight into the mechanism of index swapping.

genomics↗