YAP and TAZ Mediate Osteocyte Perilacunar/Canalicular Remodeling

Bone fragility fractures are caused by low bone mass or impaired bone quality. Osteoblast/osteoclast coordination determines bone mass, but the factors that control bone quality are poorly understood. Osteocytes regulate osteoblast and osteoclast activity on bone surfaces but can also directly reorganize the bone matrix to improve bone quality through perilacunar/canalicular remodeling; however, the molecular mechanisms remain unclear. We previously found that deleting the transcriptional regulators Yes-associated protein (YAP) and Transcriptional co-activator with PDZ-motif (TAZ) from osteoblast-lineage cells caused lethality in mice due to skeletal fragility. Here, we tested the hypothesis that YAP and TAZ regulate osteocyte-mediated bone remodeling by conditional ablation of both YAP and TAZ from mouse osteocytes using 8kb-DMP1-Cre. Osteocyte-conditional YAP/TAZ deletion reduced bone mass and dysregulated matrix collagen content and organization, which together decreased bone mechanical properties. Further, YAP/TAZ deletion impaired osteocyte perilacunar/canalicular remodeling by reducing canalicular network density, length, and branching, as well as perilacunar flourochrome-labeled mineral deposition. Consistent with recent studies identifying TGF-β as a key inducer of osteocyte expression of matrix-remodeling enzymes, YAP/TAZ deletion in vivo decreased osteocyte expression of matrix proteases MMP13, MMP14, and CTSK. In vitro, pharmacologic inhibition of YAP/TAZ transcriptional activity in osteocyte-like cells abrogated TGF-β-induced matrix protease gene expression. Together, these data show that YAP and TAZ control bone matrix accrual, organization, and mechanical properties by regulating osteocyte-mediated bone remodeling. Elucidating the signaling pathways that control perilacunar/canalicular remodeling may enable future therapeutic targeting of bone quality to reverse skeletal fragility

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p

Multi-Platform Next-Generation Sequencing of the Domestic Turkey (Meleagris gallopavo): Genome Assembly and Analysis

The combined application of next-generation sequencing platforms has provided an economical approach to unlocking the potential of the turkey genome

Changes in Vertebral Bone Density and Paraspinal Muscle Morphology Following Spaceflight and 1 Year Readaptation on Earth

ABSTRACT Astronauts have an increased risk of back pain and disc herniation upon returning to Earth. Thus, it is imperative to understand the effects of spaceflight and readaptation to gravity on the musculoskeletal tissues of the spine. Here we investigated whether ~6 months of spaceflight led to regional differences in bone loss within the vertebral body. Additionally, we evaluated the relationships between vertebral bone density and paraspinal muscle morphology before flight, after flight, and after readaptation on Earth. We measured vertebral trabecular bone mineral density (Tb.BMD), paraspinal muscle cross‐sectional area (CSA), and muscle density in 17 astronauts using computed tomography (CT) images of the lumbar spine obtained before flight (before flight, n = 17), after flight (spaceflight, n = 17), and ~12 months of readaptation to gravitational loading on Earth (follow‐up, n = 15). Spaceflight‐induced declines in Tb.BMD were greater in the superior region of the vertebral body (−6.7%) than the inferior (−3.1%, p = 0.052 versus superior region) and transverse regions (−4.3%, p = 0.057 versus superior region). After a year of readaptation to Earth's gravity, Tb.BMD in the transverse region remained significantly below preflight levels (−4.66%, p = 0.0094). Paraspinal muscle CSA and muscle density declined −1.0% (p = 0.005) and −0.83% (p = 0.001) per month of spaceflight, respectively. Ultimately, bone loss in the superior vertebral body, along with fatty infiltration of paraspinal muscles and incomplete recovery even after a year of readaptation on Earth, may contribute to spinal pathology in long‐duration astronauts. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research

Integrated Omics Analysis of Pathogenic Host Responses during Pandemic H1N1 Influenza Virus Infection: The Crucial Role of Lipid Metabolism

Pandemic influenza viruses modulate pro-inflammatory responses that can lead to immunopathogenesis. We present an extensive and systematic profiling of lipids, metabolites and proteins in respiratory compartments of ferrets infected with either 1918 or 2009 human pandemic H1N1 influenza viruses. Integrative analysis of high-throughput omics data with virologic and histopathologic data uncovered relationships between host responses and phenotypic outcomes of viral infection. Pro-inflammatory lipid precursors in the trachea following 1918 infection correlated with severe tracheal lesions. Using an algorithm to infer cell quantity changes from gene expression data, we found enrichment of distinct T cell subpopulations in the trachea. There was also a predicted increase in inflammatory monocytes in the lung of 1918 virus-infected animals that was sustained throughout infection. This study presents a unique resource to the influenza research community and demonstrates the utility of an integrative systems approach for characterization of lipid metabolism alterations underlying respiratory responses to viruses

Predicting September Arctic sea ice: a multi-model seasonal skill comparison

This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance