Biological and genomic analysis of a symbiotic nitrogen fixation defective mutant in Medicago truncatula.

Medicago truncatula has been selected as one of the model legume species for gene functional studies. To elucidate the functions of the very large number of genes present in plant genomes, genetic mutant resources are very useful and necessary tools. Fast Neutron (FN) mutagenesis is effective in inducing deletion mutations in genomes of diverse species. Through this method, we have generated a large mutant resource in M. truncatula. This mutant resources have been used to screen for different mutant using a forward genetics methods. We have isolated and identified a large amount of symbiotic nitrogen fixation (SNF) deficiency mutants. Here, we describe the detail procedures that are being used to characterize symbiotic mutants in M. truncatula. In recent years, whole genome sequencing has been used to speed up and scale up the deletion identification in the mutant. Using this method, we have successfully isolated a SNF defective mutant FN007 and identified that it has a large segment deletion on chromosome 3. The causal deletion in the mutant was confirmed by tail PCR amplication and sequencing. Our results illustrate the utility of whole genome sequencing analysis in the characterization of FN induced deletion mutants for gene discovery and functional studies in the M. truncatula. It is expected to improve our understanding of molecular mechanisms underlying symbiotic nitrogen fixation in legume plants to a great extent.

Vanilla pompona Leaves and Stems as New Sources of Bioactive Compounds: The Therapeutic Potential for Skin Senescence.

A large variety of natural plants are widely produced and utilised because of their remarkable pharmacological effects. In this study, two phenolic glycosides were isolated for the first time from Vanilla pompona Schiede (Orchidaceae) from Kyushu, Japan: bis [4-(ß-D - O-glucopyranosyloxy)-benzyl] (S)-2-isopropylmalate (1: ) and bis 4-[ß-D-O-glucopyranosyloxy)-benzyl]-(2R,3S)-2-isopropyl tartrate (2: ). We have discovered that the crude extract of V. pompona leaves and stems and its two phenolic glycosides (compounds 1:  - 2: ) are highly effective in reversing skin senescence. V. pompona and compounds 1:  - 2: were found to promote the synthesis of collagen, hyaluronic acid, and elastin in skin fibroblasts in a normal skin cell model; in a replicative senescence model, V. pompona and compounds 1:  - 2: significantly reduced the ageing phenotype in skin fibroblasts. These compounds also demonstrated a significant protective effect in a UV-induced photo-senescence model; the possible mechanisms of this effect were investigated in this study. To the best of our knowledge, this study is the first to develop V. pompona leaves and stems as new sources of bioactive compounds and to examine their therapeutic potential for skin senescence. The development potential of V. pompona leaves and stems for use in the cosmetics, cosmeceutical, and pharmaceutical industries remains to be investigated.

Multi-Proteomic Analysis Reveals the Effect of Protein Lactylation on Matrix and Cholesterol Metabolism in Tendinopathy.

Tendinopathy is a disease with surging prevalence. Lacking understanding of molecular mechanisms impedes the development of therapeutic approaches and agents. Lysine lactylation (Kla) is a newly discovered post-translational modification related to glycolysis. It has long been noted that manipulation of glycolysis metabolism could affect tendon cell function, tendon homeostasis, and healing process of tendon. However, protein lactylation sites in tendinopathy remain unexplored. Here, we conducted the first proteome-wide Kla analysis in tendon samples harvested from patients with rotator cuff tendinopathy (RCT), which identified 872 Kla sites across 284 proteins. Compared with normal counterparts, 136 Kla sites on 77 proteins were identified as upregulated in the pathological tendon, while 56 sites on 32 proteins were downregulated. Function enrichment analysis demonstrated that the majority of proteins with upregulated Kla levels functioned in organization of the tendon matrix and cholesterol metabolism, accompanied by lower expression levels which meant impaired cholesterol metabolism and degeneration of the tendon matrix, indicating potential cross-talk between protein lactylation and expression levels. At last, by western blotting and immunofluorescence, we verified the correlation between high lactylation and the downregulation of matrix and cholesterol-related proteins including BGN, MYL3, TPM3, and APOC3. ProteomeXchange: PXD033146.

Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that is strongly associated with aging. Telomeres are DNA sequences that protect chromosomes from damage and shorten with age. Telomere-related genes (TRGs) may play a role in AD's pathogenesis. OBJECTIVES: To identify TRGs related to aging clusters in AD patients, explore their immunological characteristics, and build a TRG-based prediction model for AD and AD subtypes. METHODS: We analyzed the gene expression profiles of 97 AD samples from the GSE132903 dataset, using aging-related genes (ARGs) as clustering variables. We also assessed immune-cell infiltration in each cluster. We performed a weighted gene co-expression network analysis to identify cluster-specific differentially expressed TRGs. We compared four machine-learning models (random forest, generalized linear model [GLM], gradient boosting model, and support vector machine) for predicting AD and AD subtypes based on TRGs and validated TRGs by conducting an artificial neural network (ANN) analysis and a nomogram model. RESULTS: We identified two aging clusters in AD patients with distinct immunological features: Cluster A had higher immune scores than Cluster B. Cluster A and the immune system are intimately associated, and this association could affect immunological function and result in AD via the digestive system. The GLM predicted AD and AD subtypes most accurately and was validated by the ANN analysis and nomogram model. CONCLUSION: Our analyses revealed novel TRGs associated with aging clusters in AD patients and their immunological characteristics. We also developed a promising prediction model based on TRGs for assessing AD risk.

Delayed denervation-induced muscle atrophy in Opg knockout mice.

Recent evidence has shown a crucial role for the osteoprotegerin/receptor activator of nuclear factor κ-B ligand/RANK (OPG/RANKL/RANK) signaling axis not only in bone but also in muscle tissue; however, there is still a lack of understanding of its effects on muscle atrophy. Here, we found that denervated Opg knockout mice displayed better functional recovery and delayed muscle atrophy, especially in a specific type IIB fiber. Moreover, OPG deficiency promoted milder activation of the ubiquitin-proteasome pathway, which further verified the protective role of Opg knockout in denervated muscle damage. Furthermore, transcriptome sequencing indicated that Opg knockout upregulated the expression of Inpp5k, Rbm3, and Tet2 and downregulated that of Deptor in denervated muscle. In vitro experiments revealed that satellite cells derived from Opg knockout mice displayed a better differentiation ability than those acquired from wild-type littermates. Higher expression levels of Tet2 were also observed in satellite cells derived from Opg knockout mice, which provided a possible mechanistic basis for the protective effects of Opg knockout on muscle atrophy. Taken together, our findings uncover the novel role of Opg in muscle atrophy process and extend the current understanding in the OPG/RANKL/RANK signaling axis.

Non-Negligible Role of Trace Elements in Influenza Virus Infection.

Influenza virus has continuously spread around the globe for more than 100 years since the first influenza epidemic in 1918. The rapid and unpredictable gene variation of the influenza virus could possibly bring about another pandemic in future, which might threaten to overwhelm us without adequate preparation. Consequently, it is extremely urgent to identify effective broad-spectrum antiviral treatments for a variety of influenza virus variants. As essential body components, trace elements are great potential candidates with an as yet poorly understood ability to protect the host from influenza infection. Herein, we have summarized the present state of knowledge concerning the function of trace elements in influenza virus replication along with an analysis of their potential molecular mechanisms. Modulation of host immune responses to the influenza virus is one of the most common modes to achieve the anti-influenza activity of trace elements, such as selenium and zinc. Simultaneously, some antioxidant and antiviral signal pathways can be altered with the participation of trace elements. More interestingly, some micro-elements including selenium, zinc, copper and manganese, directly target viral proteins and regulate their stability and activity to influence the life cycle of the influenza virus. Further verification of the antiviral effect and the mechanism will promote the application of trace elements as adjuvants in the clinic.

HEGANLDA: A Computational Model for Predicting Potential Lncrna-Disease Associations Based On Multiple Heterogeneous Networks.

Long non-coding RNAs (lncRNAs) play vital regulatory roles in many human complex diseases, however, the number of validated lncRNA-disease associations is notable rare so far. How to predict potential lncRNA-disease associations precisely through computational methods remains challenging. In this study, we proposed a novel method, LDVCHN (LncRNA-Disease Vector Calculation Heterogeneous Networks), and also developed the corresponding model, HEGANLDA (Heterogeneous Embedding Generative Adversarial Networks LncRNA-Disease Association), for predicting potential lncRNA-disease associations. In HEGANLDA, the graph embedding algorithm (HeGAN) was introduced for mapping all nodes in the lncRNA-miRNA-disease heterogeneous network into the low-dimensional vectors which severed as the inputs of LDVCHN. HEGANLDA effectively adopted the XGBoost (eXtreme Gradient Boosting) classifier, which was trained by the low-dimensional vectors, to predict potential lncRNA-disease associations. The 10-fold cross-validation method was utilized to evaluate the performance of our model, our model finally achieved an area under the ROC curve of 0.983. According to the experiment results, HEGANLDA outperformed any one of five current state-of-the-art methods. To further evaluate the effectiveness of HEGANLDA in predicting potential lncRNA-disease associations, both case studies and robustness tests were performed and the results confirmed its effectiveness and robustness. The source code and data of HEGANLDA are available at https://github.com/HEGANLDA/HEGANLDA.

Application of next-generation sequencing to identify different pathogens.

Early and precise detection and identification of various pathogens are essential for epidemiological monitoring, disease management, and reducing the prevalence of clinical infectious diseases. Traditional pathogen detection techniques, which include mass spectrometry, biochemical tests, molecular testing, and culture-based methods, are limited in application and are time-consuming. Next generation sequencing (NGS) has emerged as an essential technology for identifying pathogens. NGS is a cutting-edge sequencing method with high throughput that can create massive volumes of sequences with a broad application prospects in the field of pathogen identification and diagnosis. In this review, we introduce NGS technology in detail, summarizes the application of NGS in that identification of different pathogens, including bacteria, fungi, and viruses, and analyze the challenges and outlook for using NGS to identify clinical pathogens. Thus, this work provides a theoretical basis for NGS studies and provides evidence to support the application of NGS in distinguishing various clinical pathogens.

Mechanisms of estrogen deficiency-induced osteoporosis based on transcriptome and DNA methylation.

Osteoporosis is a disease that impacts the elderly. Low estrogen is related to changes in DNA methylation and consequent alterations in gene expression, leading to a new direction in research related to the pathophysiology of osteoporosis. We constructed an Ovariectomized (OVX) mouse model in our study, and the mouse models had osteoporosis based on the phenotype and methylation levels in the mouse's bone. Furthermore, the methylation level of the OVX mice was significantly changed compared to that of SHAM mice. Therefore, we performed genome-level analysis on the mouse model using transcriptome and Whole Genome Bisulfite Sequencing (WGBS) by combining the data of two omics and discovered that the changes in gene expression level caused by osteoporosis primarily focused on the decrease of bone and muscle development and the activation of the immune system. According to intersection analysis of methylation and transcriptome data, the differentially expressed genes and pathways are consistent with the differentially expressed methylation locations and regions. Further, the differentially expressed methylation sites were mainly concentrated in promoters, exons, and other critical functional regions of essential differentially expressed genes. This is also the primary cause of gene differential expression variations, indicating that estrogen deficiency might regulate gene expression by altering methylation modification, leading to osteoporosis. We demonstrated the clinical value of methylation modification research, and these findings would improve the current understanding of underlying molecular mechanisms of osteoporosis incidence and development and provide new ideas for early detection and treatment of osteoporosis.

Potential of Hibiscus sabdariffa L. and Hibiscus Acid to Reverse Skin Aging.

Hibiscus sabdariffa L. (HS) has a long history of edible and medicinal uses. In this study, the biological activities of the extracts, chromatographic fractions, and hibiscus acid obtained from HS were evaluated for their potential bioactivities. Their ability to promote extracellular matrix synthesis in skin fibroblasts was evaluated by enzyme-linked immunosorbent assays. Their anti-inflammatory activity was evaluated in a nitric oxide (NO)-Griess inflammatory experiment. Furthermore, hibiscus acid was found to have a strong anti-oxidative stress effect through the establishment of an oxidative stress model induced by hydrogen peroxide. Several assays indicated that hibiscus acid treatment can effectively reduce extracellular adenosine triphosphate (ATP) secretion and carbonyl protein production, as well as maintain a high level of reduced/oxidized glutathione (GSH/GSSG) in skin cells, thus providing a possible mechanism by which hibiscus acid can counter antioxidative stress. The present study is the first to explore the reversing skin aging potential and the contributory component of HS.

The effect of talus osteochondral defects of different area size on ankle joint stability: a finite element analysis.

BACKGROUND: Osteochondral lesion of the talus (OLT) is one of the most common ankle injuries, which will lead to biomechanical changes in the ankle joint and ultimately affect ankle function. Finite element analysis (FEA) is used to clarify the effect of talus osteochondral defects on the stability of the ankle joint at different depths. However, no research has been conducted on talus osteochondral defect areas that require prompt intervention. In this research, FEA was used to simulate the effect of the area size of talus osteochondral defect on the stress and stability of the ankle joint under a specific depth defect. METHODS: Different area sizes (normal, 2 mm* 2 mm, 4 mm* 4 mm, 6 mm* 6 mm, 8 mm* 8 mm, 10 mm* 10 mm, and 12 mm* 12 mm) of the three-dimensional finite element model of osteochondral defects were established. The model was used to simulate and calculate joint stress and displacement of the articular surface of the distal tibia and the proximal talus when the ankle joint was in the heel-strike, midstance, and push-off phases. RESULTS: When OLT occurred, the contact pressure of the articular surface, the equivalent stress of the proximal talus, the tibial cartilage, and the talus cartilage did not change significantly with an increase in the size of the osteochondral defect area when the heel-strike phase was below 6 mm * 6 mm. Gradual increases started at 6 mm * 6 mm in the midstance and push-off phases. Maximum changes were reached when the defect area size was 12 mm * 12 mm. The same patterns were observed in the talus displacement. CONCLUSIONS: The effect of the defect area of the ankle talus cartilage on the ankle biomechanics is evident in the midstance and push-off phases. When the size of the defect reaches 6 mm * 6 mm, the most apparent change in the stability of the ankle joint occurs, and the effect does not increase linearly with the increase in the size of the defect.

Frailty Factors and Outcomes in Patients Undergoing Orthopedic Surgery: Protocol for a Systematic Review and Meta-analysis.

BACKGROUND: Frailty is an aggregate expression of susceptibility to adverse health outcomes because of age- and disease-related deficits that accumulate across multiple domains. Previous studies have found the presence of preoperative frailty is associated with an increased risk of adverse outcomes. The number of older adults undergoing orthopedic surgery is rapidly increasing. However, there has been no evidence-based study on the relationship between frailty and outcomes in patients undergoing orthopedic surgery. OBJECTIVE: The aims of this study are to investigate the association between frailty and outcomes in patients who underwent orthopedic surgery as well as patient factors associated with frailty. METHODS: The methods to be used for this systematic review are reported according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) 2015 checklist. An extensive search will be conducted in PubMed, Embase, the Cochrane Library, and other mainstream databases. Any study where patients undergoing orthopedic surgery were assessed using a defined or validated measure of frailty and the association of frailty with patient factors and/or outcomes was reported will be included. A total of 2 researchers will independently screen articles for inclusion, with disagreements resolved by a third reviewer. We will perform a narrative synthesis of the factors associated with frailty, prevalence of frailty, effect of frailty on patient outcomes, and interventions for patients who are frail. A meta-analysis focusing on individual factors associated with frailty and the effect of frailty on patient outcomes will be performed, if applicable. The risk of bias will be evaluated. A subgroup analysis and sensitivity analysis will be performed. RESULTS: Literature searches were conducted in September 2021 and the review is anticipated to be completed by the end of July 2022. CONCLUSIONS: This systematic review and meta-analysis will provide an overview of frailty and investigate the relationship between frailty and patient outcomes as well as the relationship between patient factors and frailty in patients undergoing orthopedic surgery. This study could potentially increase patients' awareness of the outcomes associated with frailty, compel clinical specialties to further acknowledge the concept of frailty, and enhance the development of assessment instruments and tools for frailty. TRIAL REGISTRATION: PROSPERO CRD42020181846; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=181846. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28338.

SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec.

BACKGROUND: Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an increasing number of computational methods have been proposed for predicting potential lncRNA-disease associations. However, traditional predicting models lack the ability to precisely extract features of biomolecules, it is urgent to find a model which can identify potential lncRNA-disease associations with both efficiency and accuracy. RESULTS: In this study, we proposed a novel model, SVDNVLDA, which gained the linear and non-linear features of lncRNAs and diseases with Singular Value Decomposition (SVD) and node2vec methods respectively. The integrated features were constructed from connecting the linear and non-linear features of each entity, which could effectively enhance the semantics contained in ultimate representations. And an XGBoost classifier was employed for identifying potential lncRNA-disease associations eventually. CONCLUSIONS: We propose a novel model to predict lncRNA-disease associations. This model is expected to identify potential relationships between lncRNAs and diseases and further explore the disease mechanisms at the lncRNA molecular level.

The factors associated with nosocomial infection in elderly hip fracture patients: gender, age, and comorbidity.

PURPOSE: This is a retrospective case-control study to ascertain the factors influencing nosocomial infection (NI) in elderly patients with hip fractures. METHODS: A total of 80,174 patients (≥ 60 years) who suffered hip fractures between 2006 and 2017 were identified through a national inquiry of 94 hospitals. The patients were divided into an NI group and control group according to the occurrence or lack of occurrence of NI within 48 hours after surgery, respectively. Age, gender, hip fracture pattern, whether to operate, surgical treatments, and comorbidities were recorded as variables. RESULTS: A total of 9806 elderly hip fracture patients (60 years) were included, 1977 of whom were patients diagnosed with NI. The control group consisted of randomly drawn cases from the 9806 patients from different hospitals with a rate of one NI patient: four patients without NI. Patient gender, age, and in particular the number of comorbidities were associated with occurrence of NI. Using regression models to predict infection outcomes based on the number of comorbidities had an area under the curve (AUC) of 0.714, while using the Charlson comorbidity index (CCI) yielded a smaller value of 0.694. The most common comorbidities of this elderly cohort were chronic respiratory disease, hypertension, diabetes mellitus, cerebrovascular disease, and coronary heart disease. CONCLUSIONS: Older age, male gender, and greater number of comorbidities were found to be associated with the occurrence of NI. In particular, the number of comorbidities was the most accurate predictor of NI occurrence, and when used to build a regression model, it had greater predictive capability than CCI to predict NI in elderly hip fracture patients. Additionally, the common diseases of the elderly should be primarily considered when investigating the relationship between comorbidities and NI in older patients.

A Global Phosphorylation Atlas of Proteins Within Pathological Site of Rotator Cuff Tendinopathy.

Rotator cuff tendinopathy (RCT) is the most common cause of shoulder pain, therefore posing an important clinical problem. Understanding the mechanism and biochemical changes of RCT would be of crucial importance and pave the path to targeting novel and effective therapeutic strategies in translational perspectives and clinical practices. Phosphorylation, as one of the most important and well-studied post-translational modifications, is tightly associated with protein activity and protein functional regulation. Here in this study, we generated a global protein phosphorylation atlas within the pathological site of human RCT patients. By using Tandem Mass Tag (TMT) labeling combined with mass spectrometry, an average of 7,741 phosphorylation sites (p-sites) and 3,026 proteins were identified. Compared with their normal counterparts, 1,668 p-sites in 706 proteins were identified as upregulated, while 73 p-sites in 57 proteins were downregulated. GO enrichment analyses have shown that majority of proteins with upregulated p-sites functioned in neutrophil-mediated immunity whereas downregulated p-sites are mainly involved in muscle development. Furthermore, pathway analysis identified NF-κB-related TNF signaling pathway and protein kinase C alpha type (PKCα)-related Wnt signaling pathway were associated with RCT pathology. At last, a weighted kinase-site phosphorylation network was built to identify potentially core kinase, from which serine/threonine-protein kinase 39 (STLK3) and mammalian STE20-like protein kinase 1 (MST1) were proposed to be positively correlated with the activation of Wnt pathway.

WAFNRLTG: A Novel Model for Predicting LncRNA Target Genes Based on Weighted Average Fusion Network Representation Learning Method.

Long non-coding RNAs (lncRNAs) do not encode proteins, yet they have been well established to be involved in complex regulatory functions, and lncRNA regulatory dysfunction can lead to a variety of human complex diseases. LncRNAs mostly exert their functions by regulating the expressions of target genes, and accurate prediction of potential lncRNA target genes would be helpful to further understanding the functional annotations of lncRNAs. Considering the limitations in traditional computational methods for predicting lncRNA target genes, a novel model which was named Weighted Average Fusion Network Representation learning for predicting LncRNA Target Genes (WAFNRLTG) was proposed. First, a novel heterogeneous network was constructed by integrating lncRNA sequence similarity network, mRNA sequence similarity network, lncRNA-mRNA interaction network, lncRNA-miRNA interaction network and mRNA-miRNA interaction network. Next, four popular network representation learning methods were utilized to gain the representation vectors of lncRNA and mRNA nodes. Then, the representations of lncRNAs and target genes in the heterogeneous network were obtained with the weighted average fusion network representation learning method. Finally, we merged the representations of lncRNAs and related target genes to form lncRNA-gene pairs, trained the XGBoost classifier and predicted potential lncRNA target genes. In five-cross validations on the training and independent datasets, the experimental results demonstrated that WAFNRLTG obtained better AUC scores (0.9410, 0.9350) and AUPR scores (0.9391, 0.9350). Moreover, case studies of three common lncRNAs were performed for predicting their potential lncRNA target genes and the results confirmed the effectiveness of WAFNRLTG. The source codes and all data of WAFNRLTG can be freely downloaded at https://github.com/HGDYZW/WAFNRLTG.

Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network.

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA-disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA-disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA-disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA-disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA-disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA-disease associations could be regarded as a component recognition problem of lncRNA-disease characteristic graphs. The CNWGSN features of lncRNA-disease associations combined with known lncRNA-disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA-disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA-disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

Effects of unfractionated heparin and rivaroxaban on the expression of heparanase and fibroblast growth factor 2 in human osteoblasts.

Long-term treatment with anticoagulants may contribute to osteoporosis. Although unfractionated heparin and rivaroxaban have adverse effects on bone microstructure and function in adult rats, the underlying mechanism remains to be elucidated. Heparanase (HPSE) and fibroblast growth factor (FGF)2 are important signals in bone formation and fracture healing. Therefore, the present study was designed to investigate the effects of unfractionated heparin and rivaroxaban on the expression of HPSE and FGF2 in human osteoblasts. Human osteoblasts were treated with unfractionated heparin (0.5-50 IU/ml) or rivaroxaban (0.13­13 µg/ml) for different durations. Plasmids encoding HPSE and FGF2 were transfected into osteoblasts, and cell viability was assessed using MTT assays, with mRNA and protein expression levels determined using reverse transcription­quantitative polymerase chain reaction and western blot analyses, respectively. Osteoblast growth was significantly inhibited by treatment with unfractionated heparin (50 IU/ml) or rivaroxaban (13 µg/ml). Unfractionated heparin alone significantly inhibited the expression of HPSE and FGF2, whereas rivaroxaban inhibited the expression of FGF2 without affecting that of HPSE. Furthermore, the overexpression of HPSE or FGF2 significantly reversed the inhibitory effects of unfractionated heparin and rivaroxaban on osteoblasts. These findings suggested that HPSE and FGF2 signals were involved in the detrimental role of unfractionated heparin and rivaroxaban in human osteoblasts, providing novel information on the side effects of anticoagulants.

Comparison of the effects of heparin and the direct factor Xa inhibitor, rivaroxaban, on bone microstructure and metabolism in adult rats.

AIM: Deep venous thrombosis is a significant complication following surgery, and is associated with high morbidity and mortality in adults. The direct factor Xa inhibitor, rivaroxaban, is used to prevent venous thromboembolism in patients suffering from trauma and joint arthroplasty. The present study compared the effects of rivaroxaban and heparin on bone microstructure and metabolism in adult rats. MATERIALS AND METHODS: Twenty-four Wistar rats were divided into sham, rivaroxaban and heparin groups. Rivaroxaban (1.5 mg·kg(-1)·d(-1)) and heparin (2 IU·g(-1)·d(-1)) were administered for 4 weeks. To assess changes in bone metabolism, serum calcium and phosphorus levels, and bone formation and resorption markers were examined. Micro-CT analysis was used to examine the microstructure of both trabecular and cortical bone. Dual energy X-ray absorptiometry was employed to detect bone mineral density (BMD). RESULTS: Serum phosphorus levels were significantly lower in both rivaroxaban (1.33 ± 0.07 mmol/L) and heparin (1.33 ± 0.21 mmol/L) rats than in sham rats (1.71 ± 0.14 mmol/L). Activity and levels of bone formation markers, bone-specific alkaline phosphatase (BAP) and type I procollagen N-terminal pro-peptide (PINP), were 32.4 and 38.2% lower in heparin-treated rats than in sham rats. Bone resorption markers, pyridinoline (PYD) and deoxypyridinoline (DPD), were 20.1 and 34.3% higher in heparin-treated rats than in sham rats, respectively. By contrast, rivaroxaban only resulted in a decrease PINP levels. Bone volume fraction (BV/TV) decreased by 23.5 and 20.5% from those in sham rats, while trabecular separation (Tb.Sp) increased by 28.2 and 16.3% in trabecular bone of heparin- and rivaroxaban-treated rats, [corrected] respectively. Moreover, the microstructure of cortical bone and BMD were negatively affected by heparin but not by rivaroxaban. CONCLUSION: Rivaroxaban leads to fewer adverse effects on bone microstructure than heparin.