Biology subjects

Goulet, R. W.

Publications and source records attributed to Goulet, R. W..

3 recordsLinked to original sources

bioRxiv2023

Bone quality following peripubertal growth in a mouse model of transmasculine gender-affirming hormone therapy

During peri-puberty, bone growth and the attainment peak bone mass is driven predominantly by sex steroids. This is important when treating transgender and gender diverse youth, who have become increasingly present at pediatric clinics. Analogues of gonadotropin-releasing hormone (GnRH) are commonly prescribed to transgender and gender diverse youth prior to starting gender-affirming hormone therapy (GAHT). However, the impact of GnRH agonists on long bones with the addition of GAHT is relatively unknown. To explore this, we developed a trans-masculine model by introducing either GnRHa or vehicle treatment to female-born mice at a pre-pubertal age. This treatment was followed by male GAHT (testosterone, T) or control treatment three weeks later. Six weeks after T therapy, bone quality was compared between four treatment groups: Control (vehicle only), GnRHa-only, GnRHa + T, and T-only. Bone length/size, bone shape, mechanical properties, and trabecular morphology were modulated by GAHT. Independent of GnRHa administration, mice treated with T had shorter femurs, larger trabecular volume and increased trabecular number, higher trabecular bone mineral density, and wider superstructures on the surface of bone (e.g., third trochanters) when compared to control or GnRHa-only mice. In conclusion, prolonged treatment of GnRHa with subsequent GAHT treatment directly affect the composition, parameters, and morphology of the developing long bone. These findings provide insight to help guide clinical approaches to care for transgender and gender diverse youth.

physiology↗

bioRxiv2023

Optogenetic-Induced Muscle Loading Leads to Mechanical Adaptation of the Achilles Tendon Enthesis in Mice

The growth of the skeleton depends on the transmission of contractile muscle forces from tendon to bone across the extracellular matrix-rich enthesis. Loss of muscle loading leads to significant impairments in enthesis development. However, little is known about how the enthesis responds to increased loading during postnatal growth. To study the cellular and matrix adaptations of the enthesis in response to increased muscle loading, we used optogenetics to induce skeletal muscle contraction and unilaterally load the Achilles tendon and enthesis in young (i.e., during growth) and adult (i.e., mature) mice. In young mice, daily bouts of unilateral optogenetic loading led to expansion of the calcaneal apophysis and growth plate, as well as increased vascularization of the normally avascular enthesis. Daily loading bouts, delivered for 3 weeks, also led to a mechanically weaker enthesis with increased molecular-level accumulation of collagen damage in young mice. However, adult mice did not exhibit impaired mechanical properties or noticeable structural adaptations to the enthesis. We then focused on the transcriptional response of the young tendon and bone following optogenetic-induced loading. After 1 or 2 weeks of loading, we identified, in tendon, transcriptional activation of canonical pathways related to glucose metabolism (glycolysis) and inhibited pathways associated with cytoskeletal remodeling (e.g., RHOA and CREB signaling). In bone, we identified activation of inflammatory signaling (e.g., NFkB and STAT3 signaling) and inhibition of ERK/MAPK and PTEN signaling. Thus, we have demonstrated the utility of optogenetic-induced skeletal muscle contraction to elicit structural, functional, and molecular adaptation of the enthesis in vivo especially during growth.

bioengineering↗

bioRxiv2021

Deep learning-based segmentation of high-resolution computed tomography image data outperforms commonly used automatic bone segmentation methods

Segmenting bone from background is required to quantify bone architecture in computed tomography (CT) image data. A deep learning approach using convolutional neural networks (CNN) is a promising alternative method for automatic segmentation. The study objectives were to evaluate the performance of CNNs in automatic segmentation of human vertebral body (micro-CT) and femoral neck (nano-CT) data and to investigate the performance of CNNs to segment data across scanners. Scans of human L1 vertebral bodies (microCT [North Star Imaging], n=28, 53m3) and femoral necks (nano-CT [GE], n=28, 27m3) were used for evaluation. Six slices were selected for each scan and then manually segmented to create ground truth masks (Dragonfly 4.0, ORS). Two-dimensional U-Net CNNs were trained in Dragonfly 4.0 with images of the [FN] femoral necks only, [VB] vertebral bodies only, and [F+V] combined CT data. Global (i.e., Otsu and Yen) and local (i.e., Otsu r = 100) thresholding methods were applied to each dataset. Segmentation performance was evaluated using the Dice coefficient, a similarity metric of overlap. Kruskal-Wallis and Tukey-Kramer post-hoc tests were used to test for significant differences in the accuracy of segmentation methods. The FN U-Net had significantly higher Dice coefficients (i.e., better performance) than the global (Otsu: p=0.001; Yen: p=0.001) and local (Otsu [r=100]: p=0.001) thresholding methods and the VB U-Net (p=0.001) but there was no significant difference in model performance compared to the FN + VB U-net (p=0.783) on femoral neck image data. The VB U-net had significantly higher Dice coefficients than the global and local Otsu (p=0.001 for both) and FN U-Net (p=0.001) but not compared to the Yen (p=0.462) threshold or FN + VB U-net (p=0.783) on vertebral body image data. The results demonstrate that the U-net architecture outperforms common thresholding methods. Further, a network trained with bone data from a different system (i.e., different image acquisition parameters and voxel size) and a different anatomical site can perform well on unseen data. Finally, a network trained with combined datasets performed well on both datasets, indicating that a network can feasibly be trained with multiple datasets and perform well on varied image data.

bioengineering↗