Identification of bone mineral density associated genes with shared genetic architectures across multiple tissues: Functional insights for EPDR1, PKDCC, and SPTBN1.

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, which are highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscles, adrenal glands, blood vessels, and the thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis.

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Revealing the Organ-Specific Expression of SPTBN1 using Single-Cell RNA Sequencing Analysis.

Despite the recent technological advances in single-cell RNA sequencing, it is still unknown how three marker genes (SPTBN1, EPDR1, and PKDCC), which are associated with bone fractures and highly expressed in the muscle tissue, are contributing to the development of other tissues and organs at the cellular level. This study aims to analyze three marker genes at the single-cell level using 15 organ tissue types of adult human cell atlas (AHCA). The single-cell RNA sequencing analysis used three marker genes and a publicly available AHCA data set. AHCA data set contains more than 84,000 cells from 15 organ tissue types. Quality control filtering, dimensionality reduction, clustering for cells, and data visualization were performed using the Seurat package. A total of 15 organ types are included in the downloaded data sets: Bladder, Blood, Common Bile Duct, Esophagus, Heart, Liver, Lymph Node, Marrow, Muscle, Rectum, Skin, Small Intestine, Spleen, Stomach, and Trachea. In total, 84,363 cells and 228,508 genes were included in the integrated analysis. A marker gene of SPTBN1 is highly expressed across all 15 organ types, particularly in the Fibroblasts, Smooth muscle cells, and Tissue stem cells of the Bladder, Esophagus, Heart, Muscle, Rectum, Skin, and Trachea. In contrast, EPDR1 is highly expressed in the Muscle, Heart, and Trachea, and PKDCC is only expressed in Heart. In conclusion, SPTBN1 is an essential protein gene in physiological development and plays a critical role in the high expression of fibroblasts in multiple organ types. Targeting SPTBN1 may prove beneficial for fracture healing and drug discovery.

Shared Genetic Architecture between Muscle and Bone: Identification and Functional Implications of EPDR1, PKDCC, and SPTBN1.

Recent studies suggest a shared genetic architecture between muscle and bone, yet the underlying molecular mechanisms remain elusive. This study aims to identify the functionally annotated genes with shared genetic architecture between muscle and bone using the most up-to-date genome-wide association study (GWAS) summary statistics from bone mineral density (BMD) and fracture-related genetic variants. We employed an advanced statistical functional mapping method to investigate shared genetic architecture between muscle and bone, focusing on genes highly expressed in muscle tissue. Our analysis identified three genes, EPDR1, PKDCC, and SPTBN1, highly expressed in muscle tissue and previously unlinked to bone metabolism. About 90% and 85% of filtered Single-Nucleotide Polymorphisms were located in the intronic and intergenic regions for the threshold at P≤5×10-8 and P≤5×10-100, respectively. EPDR1 was highly expressed in multiple tissues, including muscle, adrenal gland, blood vessels, and thyroid. SPTBN1 was highly expressed in all 30 tissue types except blood, while PKDCC was highly expressed in all 30 tissue types except the brain, pancreas, and skin. Our study provides a framework for using GWAS findings to highlight functional evidence of crosstalk between multiple tissues based on shared genetic architecture between muscle and bone. Further research should focus on functional validation, multi-omics data integration, gene-environment interactions, and clinical relevance in musculoskeletal disorders.

Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms.

BACKGROUND: Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture. METHODS: We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776). RESULTS: Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups. CONCLUSIONS: This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.

Evidence for a mixed-age group in a pterosaur footprint assemblage from the early Upper Cretaceous of Korea.

Here we describe a new pterosaur footprint assemblage from the Hwasun Seoyuri tracksite in the Upper Cretaceous Jangdong Formation of the Neungju Basin in Korea. The assemblage consists of many randomly oriented prints in remarkably high densities but represents a single ichnotaxon, Pteraichnus. Individuals exhibit a large but continuous size range, some of which, with a wingspan estimated at 0.5 m, are among the smallest pterosaurs yet reported from the Upper Cretaceous, adding to other recent finds which contradict the idea that large and giant forms entirely dominated this interval. Unusual features of the tracks, including relatively long, slender pedal digit impressions, do not match the pes of any known Cretaceous pterosaur, suggesting that the trackmakers are as yet unknown from the body fossil record. The Hwasun pterosaur footprints appear to record gregarious behavior at the exact location by individuals of different ages, hinting at the possibility that pterosaurs gathered in mixed-age groups.

Machine learning approaches for the prediction of bone mineral density by using genomic and phenotypic data of 5130 older men.

The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models' performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data.

Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density, and other risk factors as predictors. In model training, the synthetic minority oversampling technique was used to account for low fracture rate, and tenfold cross-validation was employed for hyperparameters optimization. In the testing, the area under curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also significantly better than that of logistic regression. This study suggested that improving fracture prediction in older men can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach. This result should not be extrapolated to women or young individuals.

Gold Nanoparticle-Coated Starch Magnetic Beads for the Separation, Concentration, and SERS-Based Detection of E. coli O157:H7.

Here, we report gold nanoparticle-coated starch magnetic beads (AuNP@SMBs) that were prepared by in situ synthesis of AuNPs on the surface of SMBs. Upon functionalization of the surface with a specific antibody, the immuno-AuNP@SMBs were found to be effective in separating and concentrating the target pathogenic bacteria, Escherichia coli O157:H7, from an aqueous sample as well as providing a hotspot for surface-enhanced Raman scattering (SERS)-based detection. We employed a bifunctional linker protein, 4× gold-binding peptide-tagged Streptococcal protein G (4GS), to immobilize antibodies on AuNP@SMBs and AuNPs in an oriented form. The linker protein also served as a Raman reporter, exhibiting a strong and unique fingerprint signal during the SERS measurement. The amplitude of the SERS signal was shown to have a good correlation with the concentration of target bacteria ranging from 100 to 105 CFU/mL. The detection limit was determined to be as low as a single cell, and the background signals derived from nontarget bacteria were negligible due to the excellent specificity and colloidal stability of the immuno-AuNP@SMBs and SERS tags. The highly sensitive nature of the SERS-based detection system will provide a promising means to detect the pathogenic microorganisms in food or clinical specimen.

Biological preparation of highly effective immunomagnetic beads for the separation, concentration, and detection of pathogenic bacteria in milk.

We introduce a system for the efficient separation and concentration of pathogenic bacteria using biologically prepared immunomagnetic beads. Amylose magnetic beads (AMBs) were synthesized by an enzymatic reaction of amylosucrase from Deinococcus geothermalis (DGAS). The simple and rapid conjugation of AMBs and antibodies was achieved by the MBP-SPG fusion protein. MBP (maltose binding protein) binds to the surface of an AMB owing to its intrinsic affinity to the di-glucose in the AMB. SPG (streptococcal protein G) fused to the MBP has specific affinity to the Fc region of the antibody. Anti-Escherichia coli O157 antibodies were conjugated to the AMBs through a MBP-SPG linker without any physical and chemical treatments. The efficiency of separation and concentration of the target E. coli O157:H7 by the functionalized AMBs was revealed by plating counting, conventional polymerase chain reaction (PCR), and real-time RCR analysis. The immuno-AMBs effectively separated and concentrated the target bacteria from a commercial milk sample spiked with known number of bacteria, which was then analyzed by PCR to a detection limit of 10CFU/mL. On the other hand, no PCR product was produced when milk was introduced directly to a PCR reaction. These results show that MBP-SPG is an effective linker and the resulting immuno-AMBs are capable of separating and concentrating the target bacteria from a food matrix.