Biology subjects

Wang, L. H.

Publications and source records attributed to Wang, L. H..

3 recordsLinked to original sources

bioRxiv2025

Multi-scale machine learning model predicts muscle and functional disease progression in FSHD

Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, current clinical metrics used tracking disease progression lack sensitivity for personalized assessment, which greatly limits the design and execution of clinical trials. This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. The goal this work is to create a digital twin of individual FSHD patients that can be leveraged in clinical trials. Using a combined dataset of over 100 patients from seven studies, MRI-derived metrics--including fat fraction, lean muscle volume, and fat spatial heterogeneity at baseline--were integrated with clinical and functional measures. A three-stage random forest model was developed to predict annualized changes in muscle composition and a functional outcome (timed up-and-go (TUG)). All model stages revealed strong predictive performance in separate holdout datasets. After training, the models predicted fat fraction change with a root mean square error (RMSE) of 2.16% and lean volume change with a RMSE of 8.1ml in a holdout testing dataset. Feature analysis revealed that metrics fat heterogeneity within muscle predicts muscle-level progression. The stage 3 model that combined functional muscle groups and predicted change in TUG with a RMSE of 0.6 seconds, in the holdout testing dataset. This study demonstrates the machine learning models incorporating individual muscle and performance data can effectively predict MRI disease progression and functional performance of complex tasks, addressing the heterogeneity and nonlinearity inherent in FSHD. Further studies incorporating larger longitudinal cohorts as well as comprehensive clinical and functional measures will allow for expanding and refining this model. As many neuromuscular diseases are characterized by varability and heterogeneity similar to FSHD, such approaches have broad applicability.

bioengineering↗

bioRxiv2023

Validation of the association between MRI and gene signatures in facioscapulohumeral dystrophy muscle: implications for clinical trial design

Identifying the aberrant expression of DUX4 in skeletal muscle as the cause of facioscapulohumeral dystrophy (FSHD) has led to rational therapeutic development and clinical trials. Several studies support the use of MRI characteristics and the expression of DUX4-regulated genes in muscle biopsies as biomarkers of FSHD disease activity and progression, but reproducibility across studies needs further validation. We performed lower-extremity MRI and muscle biopsies in the mid-portion of the tibialis anterior (TA) muscles bilaterally in FSHD subjects and validated our prior reports of the strong association between MRI characteristics and expression of genes regulated by DUX4 and other gene categories associated with FSHD disease activity. We further show that measurements of normalized fat content in the entire TA muscle strongly predict molecular signatures in the mid-portion of the TA. Together with moderate-to-strong correlations of gene signatures and MRI characteristics between the TA muscles bilaterally, these results suggest a whole muscle model of disease progression and provide a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design.

molecular biology↗

bioRxiv2019

Phylogenomics provides new insights into gains and losses of selenoproteins among Archaeplastida

Selenoproteins that contain selenocysteine (Sec) are found in all kingdoms of life. Although they constitute a small proportion of the proteome, selenoproteins play essential roles in many organisms. In photosynthetic eukaryotes, selenoproteins have been found in algae but are missing in land plants (embryophytes). In this study, we explored the evolutionary dynamics of Sec incorporation by conveying a genomic search for the Sec machinery and selenoproteins across Archaeplastida. We identified a complete Sec machinery and variable sizes of selenoproteomes in the main algal lineages. However, the entire Sec machinery was missing in the BV clade (Bangiophyceae-Florideophyceae) of Rhodoplantae (red algae) and only partial machinery was found in three species of Archaeplastida, indicating parallel loss of Sec incorporation in different groups of algae. Further analysis of genome and transcriptome data suggests that all major lineages of streptophyte algae display a complete Sec machinery, although the number of selenoproteins is low in this group, especially in subaerial taxa. We conclude that selenoproteins tend to be lost in Archaeplastida upon adaptation to a subaerial or acidic environment. The high number of redox-active selenoproteins found in some bloom-forming marine microalgae may be related to defense against viral infections. Some of the selenoproteins in these organisms may have been gained by horizontal gene transfer from bacteria.

molecular biology↗