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Oxygen Sensing in Osteocytes: From Physiology to Age-related Osteoporosis.
No full textPURPOSE OF THE REVIEW: The purpose of this review article is to discuss how oxygen sensing mechanisms regulate the expression of key osteocyte markers such as podoplanin (E11), sclerostin (SOST), receptor activator of nuclear factor-κB ligand (RANKL), and fibroblast growth factor 23 (FGF23); summarize the relevance of targeting oxygen sensing pathways in osteocytes to improve bone health; and highlight the importance of osteocyte oxygen sensing mechanisms in maintaining good bone health during aging.
RECENT FINDINGS: Oxygen sensing in osteocytes regulates osteocyte dendrites formation, bone mass and mineral metabolism through the regulation of E11, SOST, RANKL, and FGF23. Hypoxia Induced Factor (HIF) stabilization in osteocytes increases the activity of the histone deacetylase SIRT1 which represses SOST expression and increases the expression of FGF23. These recent findings suggest that targeting oxygen-associated pathways can be leveraged to control osteo-anabolic response and mineral metabolism. Aging is associated with the increase of circulating SOST; therefore, the mechanisms associated with SOST overproduction in bone may be linked to age-related changes in oxygen sensing in osteocytes. Understanding the changes of oxygen sensing mechanisms in osteocytes during aging may offer a therapeutic avenue to control SOST overproduction, a negative regulator of bone formation and therefore prevent age-related bone loss. We discuss how oxygen-sensing controls osteocyte physiology and how aging-mediated dysregulation of oxygen bioavailability promotes osteoporosis. We also explore how oxygen-modulating therapies can be used to improve bone healthspan
HNRNPC and m6A RNA methylation control oncogenic transcription and metabolism in T-cell leukemia.
No full textRNA homeostasis is dysregulated in cancer and affects disease progression and therapy resistance. N6-methyladenosine (m6A), the most abundant epitranscriptomic modification in eukaryotic messenger RNA, plays a pivotal role in RNA biology, affecting transcript stability, translation, and splicing. Our study uncovers the extensive m6A changes in patients with T-cell acute lymphoblastic leukemia (T-ALL), to our knowledge, for the first time. It reveals m6A\u27s regulatory role in the oncogenic MYC and cholesterol biosynthesis pathways. In addition, we discovered that T-ALL is highly dependent on the m6A reader heterogeneous nuclear ribonucleoprotein C (HNRNPC). HNRNPC is transcriptionally controlled by MYC and is an essential regulator of m6A-modified transcripts. Consequently, transcriptional silencing of HNRNPC profoundly impairs oncogenic pathways and critically diminishes leukemia cell growth. In addition, the levels of the m6A demethylase fat mass and obesity-associated protein (FTO) are significantly elevated in T-ALL cells compared with normal cells, and to other types of leukemia. Targeting FTO shows therapeutic potential in preclinical disease models and synergizes with clinically relevant therapeutics. Our findings underscore the integral role of RNA methylation in orchestrating cancer cell oncogene expression and metabolism and highlight promising novel therapeutic avenues for the treatment of T-cell leukemia
Clonal hematopoiesis in myeloid malignancies and solid tumors.
No full textClonal hematopoiesis (CH) results from clonal expansion of hematopoietic stem cells. In specific contexts, CH is linked with an increased risk of blood cancers and mortality in individuals with solid tumors. To understand the mechanisms and clinical relevance of this association, it is crucial to explore the reciprocal relationship between CH and cancer. Here, we provide an updated summary of the mechanisms known to drive CH in blood cancers and solid tumors. In addition, we review proposed strategies to intercept CH and examine their impact on solid tumor-directed therapies, including immunostimulatory therapies
Low Dietary Folate Increases Developmental Delays in the Litters of
Get PDFBackground/Objectives: Low folate intake before and during pregnancy increases the risk of neural tube defects and other adverse outcomes. Gene variants such as MTHFR 677C\u3eT (rs1801133) may increase risks associated with suboptimal folate intake. Our objective was to use BALB/cJ Mthfr677C\u3eT mice to evaluate the effects of the TT genotype and low folate diets on embryonic development and MTHFR protein expression in pregnant mice. Methods: Female 677CC (mCC) and 677TT (mTT) mice were fed control (2 mg folic acid/kg (2D)), 1 mg folic acid/kg (1D) and 0.3 mg folic acid/kg (0.3D) diets before and during pregnancy. Embryos and maternal tissues were collected at embryonic day 10.5. Embryos were examined for developmental delays and defects. Methyltetrahydrofolate (methylTHF) and total homocysteine (tHcy) were measured in maternal plasma, and MTHFR protein expression was evaluated in maternal liver. Results: MethylTHF decreased due to the experimental diets and mTT genotype. tHcy increased due to 0.3D and mTT genotype; mTT 0.3D mice had significantly higher tHcy than the other groups. MTHFR expression was lower in mTT liver than mCC. MTHFR protein expression increased due to low folate diets in mCC mice, whereas in mTT mice, MTHFR expression increased only due to 1D. Developmental delays were increased in the litters of mTT mice fed 1D and 0.3D. Conclusions: The Mthfr677C\u3eT mouse models the effects of the MTHFR 677TT genotype in humans and provides a folate-responsive model for examination of the effects of folate intake and the MTHFR 677C\u3eT variant during gestation
Performance of Algorithms Submitted in the 2023 RSNA Screening Mammography Breast Cancer Detection AI Challenge.
No full textBackground: The 2023 RSNA Screening Mammography Breast Cancer Detection AI Challenge invited participants to develop artificial intelligence (AI) models capable of independently interpreting mammograms. Purpose: To assess the performance of the submitted algorithms, explore the potential for improving performance by combining the best-performing AI algorithms, and investigate how performance was influenced by the demographic and clinical characteristics of the evaluation cohort. Materials and Methods: A total of 1687 AI algorithms were submitted from November 2022 to February 2023. Of these, 1537 algorithms were assessed using an evaluation dataset from two sites—one in the United States and one in Australia. Cancer cases were identified at screening and confirmed with pathologic examination; noncancer cases were followed up for at least 1 year. Results for ensemble models of top algorithms were computed by recalling a case when any of the included algorithms indicated recall. Odds ratios (ORs) were used to investigate differences in AI performance when the dataset was stratified by clinical or demographic characteristics. Results: The evaluation dataset consisted of 5415 women (median age, 59 years [IQR, 52–66 years]). Among the 1537 AI algorithms, the median recall rate, sensitivity, specificity, and positive predictive value (PPV) were 1.7%, 27.6%, 98.7%, and 36.9%, respectively. For the top-ranked algorithm, the recall rate, sensitivity, specificity, and PPV were 1.5%, 48.6%, 99.5%, and 64.6%, respectively. Ensemble models of the top 3 and top 10 algorithms had a sensitivity of 60.7% and 67.8%, respectively; the corresponding recall rates were 2.4% and 3.5%, and the corresponding specificities were 98.8% and 97.8%. Lower sensitivity was observed for the U.S. dataset than for the Australian dataset (top 3 ensemble model: 52.0% vs 68.1%; OR = 0.51; P = .02), and greater sensitivity was observed for invasive cancers than for noninvasive cancers (top 3 ensemble model: 68.0% vs 43.8%; OR = 2.73; P = .001). Conclusion: The different AI algorithms identified different cancers during screening mammography, and ensemble models had increased sensitivity while maintaining low recall rate
Analysis of Bimanual Motor Task Function in the Mouse Model After Ischemic Stroke Utilizing Deep-Learning Model DeepEthogram
No full textIschemic strokes are the most common stroke type and result from blockages in neural blood vessels, restricting oxygen flow and causing an ischemic event. These strokes often lead to disabilities that impair motor function of stroke survivors, especially their ability to use both hands at the same time through bimanual coordination. DeepEthogram is a program that utilizes a deep-learning model, allowing for analysis of a bimanual motor task. We use the string-pull task, in our mouse models of ischemic stroke, to study mechanisms of neurological function that could enable post-stroke motor function to return to pre-stroke patterns. The goal of this project was to create a DeepEthogram model to quantify motor behaviors over post-stroke time points and determine any correlations between motor deficit with stroke volume. A success-failure DeepEthogram model was trained to 92% accuracy to automate distinguishing right and left paw successes and failures during the string-pull task for four behaviors: RSuccess, RFail, LSuccess, and Lfail. This model was applied to videos of 10 animals of equal sexes, over 56 days post-stroke. Analysis revealed that the motor deficit and recovery of the right (paretic) paw occurred as expected with RSuccess occurrences to be highest pre-stroke, lowest at day 3 in the acute phase of stroke, and recovery to pre-stroke levels at day 56 in the chronic phase. In parallel, the lowest occurrence of RFails was lowest pre-stroke, highest at day 3, and recovered to pre-stroke levels at day 56. The left (non-paretic) paw is more variable although day 56 and pre-stroke values were not statistically different, indicating recovery to pre-stroke patterns. Only one statistically significant difference between sexes were determined at day 21 for LSuccess (p=0.007). A subsequent analysis for motor deficit differences, by measuring changes in RFail occurrences at day 7 from pre-stroke baselines, by sex demonstrated a lack of significant differences (p=0.133) when the linear mixed effects model was applied. An analysis of stroke volume with immunohistochemistry revealed little correlation between stroke volume and motor deficit with R2=0.293 indicating a lack of effect of the stroke volume on motor deficit. There were no statistically significant differences (p=0.255) in the stroke areas between males and females for slices containing the stroke and stroke volume, even when the outlier SPT1 with a z-score of 3.53 is included, when the linear mixed effects model was applied with stroke area as a function of sex. Bimanual coordination is a significant feature of many daily tasks of humans and observing patterns of behavior in the string-pull task pre- and post-stroke in the mouse model could allow the discovery of novel mechanisms of motor function recovery to inform current neurohabilitative strategies
Development of a Humanized Mouse Model of Atherosclerosis.
No full textAtherosclerosis remains a leading cause of mortality, driven by complex interactions between lipid metabolism and immune-mediated inflammation. While current mouse models such as Apoe⁻/⁻ mice have advanced our understanding of disease mechanisms, species-specific immune differences limit their translational relevance. In this study, we worked toward the development of a humanized model of atherosclerosis using NSG-Apoe⁻/⁻ mice engrafted with human CD34⁺ hematopoietic stem cells (HSCs) to develop both lipid-driven aortic plaque formation and human immune cell involvement. Mice were subjected to either a high-fat diet or normal chow, and disease progression was assessed through serum lipid profiling, histological analysis of aortic plaques, and immune cell characterization by flow cytometry and immunofluorescence microscopy. NSG-Apoe⁻/⁻ mice fed a high-fat diet exhibited elevated serum LDL and total cholesterol levels, plaque formation, and infiltration of human immune cells across tissues. These findings demonstrate steps toward the use of a humanized Apoe⁻/⁻ model for investigating human immune contributions to atherosclerosis, offering a valuable platform to understand human-specific mechanisms of disease progression and immune involvement
Mutational Load in Inbred-Outbred Wild Derived Mouse Trios
No full textInbred mice offer a genetically standardized and reproducible model for studying human disease, reproduction, and basic biology. However, due to their, often, completely homozygous genomes, many inbred models lack genomic variation present in genetically diverse natural populations, like humans. In contrast, wild-type models and their derivative inbred strains present a means of achieving high genetic diversity in laboratory settings that may have deep clinical relevance. In this study, I am assessing how mutational load changes throughout the inbreeding process, and what implications may arise from this allelic spectrum in laboratory mice models. My project shows that wild-derived inbred strains have a reduced mutational burden as compared to their wild-caught counterparts. This reduction indicates a disproportionate purging of deleterious alleles as compared to other alleles, shifting the allelic effects within the genome.
This could imply that the standard inbred strains used in biomedical research may have limitations when looking a disease relevant allele, due to their deleteriousness limiting their ability to make it into the model. Moreover, this suggests that wild-caught mice may offer a better means of studying the allelic effects found in human populations. Through mapping mutational burden on a genome wide scale, and identifying regions under selective pressures, we may gain insight into how the inbreeding process shaped genetic models in laboratory settings
Machine vision-based frailty assessment for genetically diverse mice.
No full textFrailty indexes (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. We previously introduced a machine vision-based visual frailty index (vFI) that uses mouse behavior in the open field to assess frailty using C57BL/6J (B6J) data. Aging trajectories are highly genetic and are frequently modeled in genetically diverse animals. In order to extend the vFI to genetically diverse mouse populations, we collect frailty and behavior data on a large cohort of aged Diversity Outbred (DO) mice. Combined with previous data, this represents one of the largest video-based aging behavior datasets to date. Using these data, we build accurate predictive models of frailty, chronological age, and even the proportion of life lived. The extension of automated and objective frailty assessment tools to genetically diverse mice will enable better modeling of aging mechanisms and enable high-throughput interventional aging studies
Direct-in-NOD genetic ablation of Bcl3 leads to complete type 1 diabetes protection.
No full textIt was previously reported that genetic ablation of the NF-κB atypical inhibitor Bcl3 through congenic introduction of a 129P2-embryo derived knockout allele (Bcl3tm1Ver) accelerated autoimmune diabetes in the NOD mouse model. Conversely, we found that direct CRISPR-mediated ablation of this gene in the NOD/ShiLtDvs substrain completely inhibited diabetes development. Our CRISPR approach excised exons 3-7 within the NOD Bcl3 gene. These new NOD-Bcl3-/- mice had very low levels of insulitis, indicating protective mechanisms elicited early in the disease process. Dissimilar to reports of Bcl3 ablation in nonautoimmune C57BL/6-background mice, we found that splenic and lymph node B cells were not reduced. However, splenic T2 and MZ cells were increased with a disruption of B-cell follicle formation. Diabetes protection was associated with elevated splenic and lymph node regulatory T cells, and increases in CD4 effector and CD8 central memory T cells in pancreatic lymph nodes. Diabetes protection was overridden by anti-PD-1 administration. Previous studies suggested that Bcl3 may influence diabetes development downstream of Nfkbid, another atypical NF-κB inhibitor. Indeed, co-introduction of this Bcl3 knockout allele also completely blocked diabetes in NOD-Nfkbid-/- mice normally characterized by accelerated disease. Collectively these findings support the possibility that prior findings may have been driven by congenic introduction of linked modifier genes from non-NOD background strains and initiate a critical reevaluation of the role of Bcl3 in type 1 diabetes pathogenesis