Biology subjects

Hsu, W.

Publications and source records attributed to Hsu, W..

4 recordsLinked to original sources

bioRxiv2025

Prelamin A Does Not Promote Atherosclerosis or Vascular Smooth Muscle Loss

BACKGROUNDHutchinson-Gilford progeria syndrome (HGPS) is an accelerated aging disorder characterized by numerous symptoms, including early-onset atherosclerosis, with most patients suffering fatal myocardial infarctions or strokes by the second decade of life. HGPS is caused by mutations in LMNA that lead to expression of an internally truncated, farnesylated prelamin A variant called progerin, which induces loss of vascular smooth muscle cells (VSMCs). Some studies have also reported that accumulation of full-length farnesylated prelamin A, which is normally completely processed to mature non-farnesylated lamin A, can also drive vascular pathology during physiological aging. METHODSTo assess the effects of prelamin A expression on atherosclerosis and aortic VSMCs, we used LmnaL648R/L648R mice that express a prelamin A variant with a lysine to arginine point mutation that prevents its processing to mature lamin A. To determine if prelamin A expression has an impact on atherosclerotic plaques, we crossed LmnaL648R/L648R mice to LDL receptor-deficient Ldlr-/- mice that develop hyperlipidemia on a high-fat diet. RESULTSAtherosclerotic plaque lesion area and necrotic core area were not different in hyperlipidemic LmnaL648R/L648R mice that expressed only prelamin A, and no mature lamin A, compared to hyperlipidemic Lmna+/+ mice that expressed only fully-processed mature lamin A and no prelamin A. Additionally, exclusive prelamin A expression did not result in loss of aortic VSMCs or adventitial thickening in hyperlipidemic LmnaL648R/L648R mice with atherosclerosis at 28 weeks of age. Indeed, aortic vascular smooth muscle remained normal in older LmnaL648R/L648R mice at 52 weeks of age. CONCLUSIONSIn contrast to the prelamin A variant progerin expressed in HGPS, prelamin A does not appear to cause vascular smooth muscle loss, promote atherosclerosis or drive vascular aging.

molecular biology↗

bioRxiv2022

MicroRNA-27a is essential for bone remodeling by modulating p62-mediated osteoclast signaling

The ability to simultaneously modulate a set of genes for lineage-specific development has made microRNA an ideal master regulator for organogenesis. However, most microRNA deletions do not exhibit obvious phenotypic defects possibly due to functional redundancy. MicroRNAs are known to regulate skeletal lineages as the loss of their maturation enzyme Dicer impairs bone remodeling processes. Therefore, it is important to identify specific microRNA essential for bone homeostasis. We report the loss of miR-27a causing severe osteoporosis in mice. MiR-27a affects osteoclast-mediated bone resorption but not osteoblast-mediated bone formation during skeletal remodeling. Gene profiling and bioinformatics further identify the specific targets of miR-27a in osteoclast cells. MiR-27a exerts its effects on osteoclast differentiation through modulation of Squstm1/p62 whose mutations have been linked to Pagets disease of bone. Our findings reveal a new miR-27a-p62 axis necessary and sufficient to mediate osteoclast differentiation and highlight a therapeutic implication for osteoporosis.

developmental biology↗

bioRxiv2021

Abolishing the prelamin A ZMPSTE24 cleavage site leads to progeroid phenotypes with near-normal longevity in mice

Prelamin A is a farnesylated precursor of lamin A, a nuclear lamina protein. Accumulation of the farnesylated prelamin A variant progerin, with an internal deletion including its processing site, causes Hutchinson-Gilford progeria syndrome. Loss of function mutations in ZMPSTE24, which encodes the prelamin A processing enzyme, lead to accumulation of full-length farnesylated prelamin A and cause related progeroid disorders. Some data suggest that prelamin A also accumulates with physiological aging. Zmpste24-/- mice die young, at ~20 weeks. Because ZMPSTE24 has functions in addition to prelamin A processing, we generated a mouse model to examine effects solely due to the presence of permanently farnesylated prelamin A. These mice have an L648R amino acid substitution in prelamin A that blocks ZMPSTE24-catalyzed processing to lamin A. The LmnaL648R/L648R mice express only prelamin and no mature protein. Notably, nearly all survive to 65-70 weeks, with approximately 40% of male and 75% of female LmnaL648R/L648R mice having near-normal lifespans of 90 weeks (almost 2 years). Starting at ~10 weeks of age, LmnaL648R/L648R mice of both sexes have lower body masses and body fat than controls. By ~20-30 weeks of age, they exhibit detectable cranial, mandibular and dental defects similar to those observed in Zmpste24-/- mice, and have decreased vertebral bone density compared to age- and sex-matched controls. Cultured embryonic fibroblasts from LmnaL648R/L648R mice have aberrant nuclear morphology that is reversible by treatment with a protein farnesyltransferase inhibitor. These novel mice provide a robust model to study the effects of farnesylated prelamin A during physiological aging.

cell biology↗

bioRxiv2020

Identifying transcription patterns of histology and radiomics features in NSCLC with neural networks

PurposeTo investigate the use of deep neural networks to learn associations between gene expression and radiomics or histology in non-small cell lung cancer (NSCLC). Materials and MethodsDeep feedforward neural networks were used for radio-genomic mapping, where 21,766 gene expressions were inputs to individually predict histology and 101 CT radiomic features. Models were compared against logistic regression, support vector machines, random forests, and gradient boosted trees on 262 training and 89 testing patients. Neural networks were interpreted using gene masking to derive the learned associations between subsets of gene expressions to a radiomic feature or histology type. ResultsNeural networks outperformed other classifiers except in five radiomic features, where training differences were <0.026 AUC. In testing, neural networks classified histology with AUCs of 0.86 (adenocarcinoma), 0.91 (squamous), and 0.71 (other); and 14 radiomics features with >= 0.70 AUC. Gene masking of the models showed new and previously reported histology-gene or radiogenomic associations. For example, hypoxia genes could predict histology with >0.90 test AUC and published gene signatures for histology prediction were also predictive in our models (>0.80 test AUC). Gene sets related to the immune or cardiac systems and cell development processes were predictive (>0.70 test AUC) of several different radiomic features while AKT signaling, TNF, and Rho gene sets were each predictive of tumor textures. ConclusionWe demonstrate the ability of neural networks to map gene expressions to radiomic features and histology in NSCLC and interpret the models to identify predictive genes associated with each feature or type. Author SummaryNon-small-cell lung cancer (NSCLC) patients can have different presentations as seen in the CT scans, tumor gene expressions, or histology types. To improve the understanding of these complementary data types, this study attempts to map tumor gene expressions associated with a patients CT radiomic features or a histology type. We explore a deep neural network approach to learn gene-radiomic associations (i.e., the subsets of co-expressed genes that are predictive of a value of an individual radiomic feature) and gene-histology associations in two separate public cohorts. Our modeling approach is capable of learning relevant information by showing the model can predict histology and that the learned relationships are consistent with prior works. The study provides evidence for coherent patterns between gene expressions and radiomic features and suggests such integrated associations could improve patient stratification.

bioinformatics↗