Multiple Mechanisms Explain Genetic Effects at the CPED1-WNT16 Bone Mineral Density Locus

Loss of cped1 does not affect bone and lean tissue in zebrafish

Human genetic studies have nominated Cadherin-like and PC-esterase Domain-containing 1 (CPED1) as a candidate target gene mediating bone mineral density (BMD) and fracture risk heritability. Recent efforts to define the role of CPED1 in bone in mouse and human models have revealed complex alternative splicing and inconsistent results arising from gene targeting, making its function in bone difficult to interpret. To better understand the role of CPED1 in adult bone mass and morphology, we conducted a comprehensive genetic and phenotypic analysis of cped1 in zebrafish, an emerging model for bone and mineral research. We analyzed two different cped1 mutant lines and performed deep phenotyping to characterize more than 200 measures of adult vertebral, craniofacial, and lean tissue morphology. We also examined alternative splicing of zebrafish cped1 and gene expression in various cell/tissue types. Our studies fail to support an essential role of cped1 in adult zebrafish bone. Specifically, homozygous mutants for both cped1 mutant alleles, which are expected to result in loss-of-function and impact all cped1 isoforms, exhibited no significant differences in the measures examined when compared to their respective wildtype controls, suggesting that cped1 does not significantly contribute to these traits. We identified sequence differences in critical residues of the catalytic triad between the zebrafish and mouse orthologs of CPED1, suggesting that differences in key residues, as well as distinct alternative splicing, could underlie different functions of CPED1 orthologs in the two species. Our studies fail to support a requirement of cped1 in zebrafish bone and lean tissue, adding to evidence that variants at 7q31.31 can act independently of CPED1 to influence BMD and fracture risk.

Revealing chronic disease progression patterns using Gaussian process for stage inference

OBJECTIVE

The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.

MATERIALS AND METHODS

We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA).

RESULTS

First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets.

CONCLUSION

Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.