Biology subjects

Mesner, L. D.

Publications and source records attributed to Mesner, L. D..

5 recordsLinked to original sources

bioRxiv2026

Targeted proteomics informed by long-read RNA-sequencing reveals protein isoform expression associated with bone mineral density

The expanding catalog of sQTLs from projects like GTEx has established that genetic risk for complex traits is frequently mediated through RNA splicing. Yet it is generally unknown whether disease-associated splice events produce stable, distinct protein isoforms and whether they are functionally consequent. Even the deepest shotgun proteomics efforts only capture a minority of annotated splice events; isoform-specific peptides are systematically under-sampled by nature of being lower abundance and intrinsically harder to detect. Here, we implement a genetics-guided targeted mass spectrometry (MS) strategy to detect splice-derived protein isoforms prioritized by sQTL colocalization. Using internal standard parallel reaction monitoring (IS-PRM), we targeted 288 peptides corresponding to 166 putative BMD-associated isoforms in differentiating osteoprogenitor cells and patient-derived primary mesenchymal stem cells (MSCs). Targeted acquisition increased recovery of isoform-informative peptides relative to conventional DDA, rescued splice events that were undetected or sparsely sampled under global acquisition, and confirmed protein-level expression of genetically prioritized isoforms in clinical material. Together, this work establishes that targeted MS is a useful approach to support validation of protein expression in studies of the genetics of splicing.

systems biology↗

bioRxiv2024

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations

Genome-wide association studies (GWASs) have identified many sources of genetic variation associated with bone mineral density (BMD), a clinical predictor of fracture risk and osteoporosis. Aside from the identification of causal genes, other difficult challenges to informing GWAS include characterizing the roles of predicted causal genes in disease and providing additional functional context, such as the cell type predictions or biological pathways in which causal genes operate. Leveraging single-cell transcriptomics (scRNA-seq) can assist in informing BMD GWAS by linking disease-associated variants to genes and providing a cell type context for which these causal genes drive disease. Here, we use large-scale scRNA-seq data from bone marrow-derived stromal cells cultured under osteogenic conditions (BMSC-OBs) from Diversity Outbred (DO) mice to generate cell type-specific networks and contextualize BMD GWAS-implicated genes. Using trajectories inferred from the scRNA-seq data that map cell state transitions, we identify networks enriched with genes that exhibit the most dynamic changes in expression across trajectories. We discover 21 network driver genes, which are likely to be causal for human BMD GWAS associations that colocalize with expression/splicing quantitative trait loci (eQTL/sQTL). These driver genes, including Fgfrl1 and Tpx2, along with their associated networks, are predicted to be novel regulators of BMD via their roles in the differentiation of mesenchymal lineage cells. In this work, we showcase the use of single-cell transcriptomics from mouse bone-relevant cells to inform human BMD GWAS and prioritize genetic targets with potential causal roles in the development of osteoporosis.

systems biology↗

bioRxiv2023

Long read proteogenomics to connect disease-associated sQTLs to the protein isoform effectors of disease

A major fraction of loci identified by genome-wide association studies (GWASs) lead to alterations in alternative splicing, but interpretation of how such alterations impact proteins is hindered by the technical limitations of short-read RNA-seq, which cannot directly link splicing events to full-length transcript or protein isoforms. Long-read RNA-seq represents a powerful tool to define and quantify transcript isoforms, and recently, infer protein isoform existence. Here we present a novel approach that integrates information from GWAS, splicing QTL (sQTL), and PacBio long-read RNA-seq in a disease-relevant model to infer the effects of sQTLs on the ultimate protein isoform products they encode. We demonstrate the utility of our approach using bone mineral density (BMD) GWAS data. We identified 1,863 sQTLs from the Genotype-Tissue Expression (GTEx) project in 732 protein-coding genes which colocalized with BMD associations (H4PP [≥] 0.75). We generated deep coverage PacBio long-read RNA-seq data (N=[~]22 million full-length reads) on human osteoblasts, identifying 68,326 protein-coding isoforms, of which 17,375 (25%) were novel. By casting the colocalized sQTLs directly onto protein isoforms, we connected 809 sQTLs to 2,029 protein isoforms from 441 genes expressed in osteoblasts. Using these data, we created one of the first proteome-scale resources defining full-length isoforms impacted by colocalized sQTLs. Overall, we found that 74 sQTLs influenced isoforms likely impacted by nonsense mediated decay (NMD) and 190 that potentially resulted in the expression of new protein isoforms. Finally, we identified colocalizing sQTLs in TPM2 for splice junctions between two mutually exclusive exons, and two different transcript termination sites, making it impossible to interpret without long-read RNA-seq data. siRNA mediated knockdown in osteoblasts showed two TPM2 isoforms with opposing effects on mineralization. We expect our approach to be widely generalizable across diverse clinical traits and accelerate system-scale analyses of protein isoform activities modulated by GWAS loci.

bioinformatics↗

bioRxiv2022

Evaluation of a scalable approach to generate cell-type specific transcriptomic profiles of mesenchymal lineage cells

Genome-wide association studies (GWASs) have revolutionized our understanding of the genetics of complex diseases, such as osteoporosis; however, the challenge has been converting associations to causal genes. Studies have demonstrated the utility of transcriptomics data in linking disease-associated variants to genes; though for osteoporosis, few population transcriptomics datasets have been generated on bone or bone cells, and an even smaller number have profiled individual cell-types. To begin to evaluate approaches to address this challenge, we profiled the transcriptomes of bone marrow-derived stromal cells (BMSCs) cultured under osteogenic conditions, a popular model of osteoblast differentiation and activity, from five Diversity Outbred (DO) mice using single-cell RNA-seq (scRNA-seq). The goal of the study was to determine if BMSCs could serve as a model for the generation of cell-type specific transcriptomic profiles of mesenchymal lineage cells derived from large populations of mice to inform genetic studies. We demonstrate that dissociation of BMSCs from a heavily mineralized matrix had little effect on viability or their transcriptomic signatures. Furthermore, we show that BMSCs cultured under osteogenic conditions are diverse and consist of cells with characteristics of mesenchymal progenitors, marrow adipogenic lineage precursors (MALPs), osteoblasts, osteocyte-like cells, and immune cells. Importantly, all cells were nearly identical from a transcriptomic perspective to cells isolated directly from bone. We also demonstrated the ability to multiplex single cells and subsequently assign cells to their "mouse-of-origin" using demultiplexing approaches based on genotypes inferred from coding SNPs. We employed scRNA-seq analytical tools to confirm the biological identity of profiled cell-types. SCENIC was used to reconstruct gene regulatory networks (GRNs) and we showed that identified cell-types show GRNs expected of osteogenic and pre-adipogenic lineage cells. Further, CELLECT analysis showed that osteoblasts, osteocyte-like cells, and MALPs captured a significant component of BMD heritability. Together, these data suggest that BMSCs cultured under osteogenic conditions coupled with scRNA-seq can be used as a scalable and biologically informative model to generate cell-type specific transcriptomic profiles of mesenchymal lineage cells in large mouse, and potentially human, populations.

bioinformatics↗

bioRxiv2018

Mouse Genome-Wide Association and Systems Genetics Identifies Lhfp as a Regulator of Bone Mass

Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P=3.1 x 10-12) BMD locus on Chromosome 3@52.5 Mbp that replicated in two seperate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts and its expression was regulated by local expression QTL (eQTL) in multiple tissues. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp-/- displayed increased osteogenic differentiation. Lfhp-/- mice also had elevated BMD due to increased cortical bone mass. In conclusion, we used GWAS and systems genetics in mice to identify Lhfp as a regulator of osteoblast activity and bone mass.

genetics↗