From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

Exploring the Interconnection between Metabolic Dysfunction and Gut Microbiome Dysbiosis in Osteoarthritis: A Narrative Review.

Osteoarthritis (OA) is a prevalent joint disorder and the most common form of arthritis, affecting approximately 500 million people worldwide, or about 7% of the global population. Its pathogenesis involves a complex interplay between metabolic dysfunction and gut microbiome (GM) alterations. This review explores the relationship between metabolic disorders-such as obesity, diabetes, and dyslipidemia-and OA, highlighting their shared risk factors, including aging, sedentary lifestyle, and dietary habits. We further explore the role of GM dysbiosis in OA, elucidating how systemic inflammation, oxidative stress, and immune dysregulation driven by metabolic dysfunction and altered microbial metabolites contribute to OA progression. Additionally, the concept of "leaky gut syndrome" is discussed, illustrating how compromised gut barrier function exacerbates systemic and local joint inflammation. Therapeutic strategies targeting metabolic dysfunction and GM composition, including lifestyle interventions, pharmacological and non-pharmacological factors, and microbiota-targeted therapies, are reviewed for their potential to mitigate OA progression. Future research directions emphasize the importance of identifying novel biomarkers for OA risk and treatment response, adopting personalized treatment approaches, and integrating multiomics data to enhance our understanding of the metabolic-GM-OA connection and advance precision medicine in OA management.

Intratumoral Microbiota: Metabolic Influences and Biomarker Potential in Gastrointestinal Cancer.

Gastrointestinal (GI) cancers impose a substantial global health burden, highlighting the necessity for deeper understanding of their intricate pathogenesis and treatment strategies. This review explores the interplay between intratumoral microbiota, tumor metabolism, and major types of GI cancers (including esophageal, gastric, liver, pancreatic, and colorectal cancers), summarizing recent studies and elucidating their clinical implications and future directions. Recent research revealed altered microbial signatures within GI tumors, impacting tumor progression, immune responses, and treatment outcomes. Dysbiosis-induced alterations in tumor metabolism, including glycolysis, fatty acid metabolism, and amino acid metabolism, play critical roles in cancer progression and therapeutic resistance. The integration of molecular mechanisms and potential biomarkers into this understanding further enhances the prognostic significance of intratumoral microbiota composition and therapeutic opportunities targeting microbiota-mediated tumor metabolism. Despite advancements, challenges remain in understanding the dynamic interactions within the tumor microenvironment (TME). Future research directions, including advanced omics technologies and prospective clinical studies, offer promising avenues for precision oncology and personalized treatment interventions in GI cancer. Overall, integrating microbiota-based approaches and molecular biomarkers into GI cancer management holds promise for improving patient outcomes and survival.

Identification and characterization of a novel type II toxin-antitoxin system in Aeromonas veronii.

The bacterial type II toxin-antitoxin (TA) system is a rich genetic element that participates in various physiological processes. Aeromonas veronii is the main bacterial pathogen threatening the freshwater aquaculture industry. However, the distribution of type II TA system in A. veronii was seldom documented and its roles in the life activities of A. veronii were still unexplored. In this study, a novel type II TA system AvtA-AvtT was predicted in a fish pathogen Aeromonas veronii biovar sobria with multi-drug resistance using TADB 2.0. Through an Escherichia coli host killing and rescue assay, we demonstrated that AvtA and AvtT worked as a genuine TA system, and the predicted toxin AvtT actually functioned as an antitoxin while the predicted antitoxin AvtA actually functioned as a toxin. The binding ability of AvtA with AvtT proteins were confirmed by dot blotting analysis and co-immunoprecipitation assay. Furthermore, we found that the toxin and antitoxin labelled with fluorescent proteins were co-localized. In addition, it was found that the transcription of AvtAT bicistronic operon was repressed by the AvtAT protein complex. Deletion of avtA gene and avtT gene had no obvious effect on the drug susceptibility. This study provides first characterization of type II TA system AvtA-AvtT in aquatic pathogen A. veronii.

Latest insights into the global epidemiological features, screening, early diagnosis and prognosis prediction of esophageal squamous cell carcinoma.

As a highly invasive carcinoma, esophageal cancer (EC) was the eighth most prevalent malignancy and the sixth leading cause of cancer-related death worldwide in 2020. Esophageal squamous cell carcinoma (ESCC) is the major histological subtype of EC, and its incidence and mortality rates are decreasing globally. Due to the lack of specific early symptoms, ESCC patients are usually diagnosed with advanced-stage disease with a poor prognosis, and the incidence and mortality rates are still high in many countries, especially in China. Therefore, enormous challenges still exist in the management of ESCC, and novel strategies are urgently needed to further decrease the incidence and mortality rates of ESCC. Although the key molecular mechanisms underlying ESCC pathogenesis have not been fully elucidated, certain promising biomarkers are being investigated to facilitate clinical decision-making. With the advent and advancement of high-throughput technologies, such as genomics, proteomics and metabolomics, valuable biomarkers with high sensitivity, specificity and stability could be identified for ESCC. Herein, we aimed to determine the epidemiological features of ESCC in different regions of the world, especially in China, and focused on novel molecular biomarkers associated with ESCC screening, early diagnosis and prognosis prediction.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

Automatic Detection of Scalp High-Frequency Oscillations Based on Deep Learning.

Scalp high-frequency oscillations (sHFOs) are a promising non-invasive biomarker of epilepsy. However, the visual marking of sHFOs is a time-consuming and subjective process, existing automatic detectors based on single-dimensional analysis have difficulty with accurately eliminating artifacts and thus do not provide sufficient reliability to meet clinical needs. Therefore, we propose a high-performance sHFOs detector based on a deep learning algorithm. An initial detection module was designed to extract candidate high-frequency oscillations. Then, one-dimensional (1D) and two-dimensional (2D) deep learning models were designed, respectively. Finally, the weighted voting method is used to combine the outputs of the two model. In experiments, the precision, recall, specificity and F1-score were 83.44%, 83.60%, 96.61% and 83.42%, respectively, on average and the kappa coefficient was 80.02%. In addition, the proposed detector showed a stable performance on multi-centre datasets. Our sHFOs detector demonstrated high robustness and generalisation ability, which indicates its potential applicability as a clinical assistance tool. The proposed sHFOs detector achieves an accurate and robust method via deep learning algorithm.

Identification of protein signatures for lung cancer subtypes based on BPSO method.

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Alterations of gut microbiota are associated with blood pressure: a cross-sectional clinical trial in Northwestern China.

BACKGROUND: The human gut microbiota (GM) is involved in the pathogenesis of hypertension (HTN), and could be affected by various factors, including sex and geography. However, available data directly linking GM to HTN based on sex differences are limited. METHODS: This study investigated the GM characteristics in HTN subjects in Northwestern China, and evaluate the associations of GM with blood pressure levels based on sex differences. A total of 87 HTN subjects and 45 controls were recruited with demographic and clinical characteristics documented. Fecal samples were collected for 16S rRNA gene sequencing and metagenomic sequencing. RESULTS: GM diversity was observed higher in females compared to males, and principal coordinate analysis showed an obvious segregation of females and males. Four predominant phyla of fecal GM included Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. LEfSe analysis indicated that phylum unidentified_Bacteria was enriched in HTN females, while Leuconostocaceae, Weissella and Weissella_cibaria were enriched in control females (P < 0.05). Functionally, ROC analysis revealed that Cellular Processes (0.796, 95% CI 0.620 ~ 0.916), Human Diseases (0.773, 95% CI 0.595 ~ 0.900), Signal transduction (0.806, 95% CI 0.631 ~ 0.922) and Two-component system (0.806, 95% CI 0.631 ~ 0.922) could differentiate HTN females as effective functional classifiers, which were also positively correlated with systolic blood pressure levels. CONCLUSIONS: This work provides evidence of fecal GM characteristics in HTN females and males in a northwestern Chinese population, further supporting the notion that GM dysbiosis may participate in the pathogenesis of HTN, and the role of sex differences should be considered. Trial registration Chinese Clinical Trial Registry, ChiCTR1800019191. Registered 30 October 2018 - Retrospectively registered, http://www.chictr.org.cn/ .

Updates on global epidemiology, risk and prognostic factors of gastric cancer.

Gastric cancer (GC) is defined as the primary epithelial malignancy derived from the stomach, and it is a complicated and heterogeneous disease with multiple risk factors. Despite its overall declining trend of incidence and mortality in various countries over the past few decades, GC remains the fifth most common malignancy and the fourth leading cause of cancer-related death globally. Although the global burden of GC has shown a significant downward trend, it remains severe in certain areas, such as Asia. GC ranks third in incidence and mortality among all cancer types in China, and it accounts for nearly 44.0% and 48.6% of new GC cases and GC-related deaths in the world, respectively. The regional differences in GC incidence and mortality are obvious, and annual new cases and deaths are increasing rapidly in some developing regions. Therefore, early preventive and screening strategies for GC are urgently needed. The clinical efficacies of conventional treatments for GC are limited, and the developing understanding of GC pathogenesis has increased the demand for new therapeutic regimens, including immune checkpoint inhibitors, cell immunotherapy and cancer vaccines. The present review describes the epidemiology of GC worldwide, especially in China, summarizes its risk and prognostic factors, and focuses on novel immunotherapies to develop therapeutic strategies for the management of GC patients.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data.

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SIGNIFICANCE: Functional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.

Identifying microbial signatures for patients with postmenopausal osteoporosis using gut microbiota analyses and feature selection approaches.

Osteoporosis (OP) is a metabolic bone disorder characterized by low bone mass and deterioration of micro-architectural bone tissue. The most common type of OP is postmenopausal osteoporosis (PMOP), with fragility fractures becoming a global burden for women. Recently, the gut microbiota has been connected to bone metabolism. The aim of this study was to characterize the gut microbiota signatures in PMOP patients and controls. Fecal samples from 21 PMOP patients and 37 controls were collected and analyzed using amplicon sequencing of the V3-V4 regions of the 16S rRNA gene. The bone mineral density (BMD) measurement and laboratory biochemical test were performed on all participants. Two feature selection algorithms, maximal information coefficient (MIC) and XGBoost, were employed to identify the PMOP-related microbial features. Results showed that the composition of gut microbiota changed in PMOP patients, and microbial abundances were more correlated with total hip BMD/T-score than lumbar spine BMD/T-score. Using the MIC and XGBoost methods, we identified a set of PMOP-related microbes; a logistic regression model revealed that two microbial markers (Fusobacteria and Lactobacillaceae) had significant abilities in disease classification between the PMOP and control groups. Taken together, the findings of this study provide new insights into the etiology of OP/PMOP, as well as modulating gut microbiota as a therapeutic target in the diseases. We also highlight the application of feature selection approaches in biological data mining and data analysis, which may improve the research in medical and life sciences.

RNA splicing factors in normal hematopoiesis and hematologic malignancies: novel therapeutic targets and strategies.

RNA splicing, a crucial transesterification-based process by which noncoding regions are removed from premature RNA to create mature mRNA, regulates various cellular functions, such as proliferation, survival, and differentiation. Clinical and functional studies over the past 10 y have confirmed that mutations in RNA splicing factors are among the most recurrent genetic abnormalities in hematologic neoplasms, including myeloid malignancies, chronic lymphocytic leukemia, mantle cell lymphoma, and clonal hematopoiesis. These findings indicate an important role for splicing factor mutations in the development of clonal hematopoietic disorders. Mutations in core or accessory components of the RNA spliceosome complex alter splicing sites in a manner of change of function. These changes can result in the dysregulation of cancer-associated gene expression and the generation of novel mRNA transcripts, some of which are not only critical to disease development but may be also serving as potential therapeutic targets. Furthermore, multiple studies have revealed that hematopoietic cells bearing mutations in splicing factors depend on the expression of the residual wild-type allele for survival, and these cells are more sensitive to reduced expression of wild-type splicing factors or chemical perturbations of the splicing machinery. These findings suggest a promising possibility for developing novel therapeutic opportunities in tumor cells based on mutations in splicing factors. Here, we combine current knowledge of the mechanistic and functional effects of frequently mutated splicing factors in normal hematopoiesis and the effects of their mutations in hematologic malignancies. Moreover, we discuss the development of potential therapeutic opportunities based on these mutations.

Identification of key biomarkers for STAD using filter feature selection approaches.

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.

Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients.

INTRODUCTION: Postmenopausal women with osteoporosis (PMOP) are prone to fragility fractures. Osteoporosis is associated with alterations in the levels of specific circulating metabolites. OBJECTIVES: To analyze the metabolic profile of individuals with PMOP and identify novel metabolites associated with bone mineral density (BMD). METHODS: We performed an unsupervised metabolomics analysis of plasma samples from participants with PMOP and of normal controls (NC) with normal bone mass. BMD values for the lumber spine and the proximal femur were determined using dual-energy X-ray absorptiometry. Principal component analysis (PCA) and supervised partial least squares discriminant analysis (PLS-DA) were performed for metabolomic profile analyses. Metabolites with P < 0.05 in the t-test, VIP > 1 in the PLS-DA model, and SNR > 0.3 between the PMOP and NC groups were defined as differential abundant metabolites (DAMs). The SHapley additive explanations (SHAP) method was utilized to determine the importance of permutation of each DAM in the predictive model between the two groups. ROC analysis and correlation analysis of metabolite relative abundance and BMD/T-scores were conducted. KEGG pathway analysis was used for functional annotation of the candidate metabolites. RESULTS: Overall, 527 annotated molecular markers were extracted in the positive and negative total ion chromatogram (TIC) of each sample. The PMOP and NC groups could be differentiated using the PLS-DA model. Sixty-eight DAMs were identified, with most relative abundances decreasing in the PMOP samples. SHAP was used to identify 9 DAM metabolites as factors distinguishing PMOP from NC. The logistic regression model including Triethanolamine, Linoleic acid, and PC(18:1(9Z)/18:1(9Z)) metabolites demonstrated excellent discrimination performance (sensitivity = 97.0, specificity = 96.6, AUC = 0.993). The correlation analysis revealed that the abundances of Triethanolamine, PC(18:1(9Z)/18:1(9Z)), 16-Hydroxypalmitic acid, and Palmitic acid were significantly positively correlated with the BMD/T score (Pearson correlation coefficients > 0.5, P < 0.05). Most candidate metabolites were involved in lipid metabolism based on KEGG functional annotations. CONCLUSION: The plasma metabolomic signature of PMOP patients differed from that of healthy controls. Marker metabolites may help provide information for the diagnosis, therapy, and prevention of PMOP. We highlight the application of feature selection approaches in the analysis of high-dimensional biological data.

Tanshinone IIA loaded chitosan nanoparticles decrease toxicity of ß-amyloid peptide in a Caenorhabditis elegans model of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that characterized by the accumulation of ß-amyloid peptide (Aß). Overexpressions of Aß could induce oxidative stress that might be a key insult to initiate the cascades of Aß accumulation. As a result, anti-oxidative stress and attenuating Aß accumulation might be one promising intervention for AD treatment. Tanshinone IIA (Tan IIA), a major component of lipophilic tanshinones in Danshen, is proven to be effective in several diseases, including AD. Due to the poor solubility in water, the clinical application of Tan IIA was limited. Therefore, a great number of nanoparticles were designed to overcome this issue. In the current study, we choose chitson as delivery carrier to load Tanshinone IIA (CS@Tan IIA) and explore the protective effects of CS@Tan IIA on the CL2006 strain, a transgenic C. elegans of AD model organism. Compared with Tan IIA monomer, CS@Tan IIA could significantly prolong the lifespan and attenuate the AD-like symptoms, including reducing paralysis and the Aß deposition by inhibiting the oxidative stress. The mechanism study showed that the protection of CS@Tan IIA was attenuated by knockdown of daf-16 gene, but not skn-1. The results indicated that DAF-16/SOD-3 pathway was required in the protective effects of CS@Tan IIA. Besides DAF-16/SOD-3 pathway, the Tan IIA-loaded CS nanoparticles might protect the C. elegans against the AD insults via promoting autophagy. All the results consistently suggested that coating by chitosan could improve the solubility of Tan IIA and effectively enhance the protective effects of Tan IIA on AD, which might provide a potential drug loading approach for the hydrophobic drugs as Tan IIA.

Associations of gut microbiota with dyslipidemia based on sex differences in subjects from Northwestern China.

BACKGROUND: The gut microbiota (GM) has been proven to play a role in the regulation of host lipid metabolism, which provides a new theory about the pathogenesis of dyslipidemia. However, the associations of GM with dyslipidemia based on sex differences remain unclear and warrant elucidation. AIM: To investigate the associations of GM features with serum lipid profiles based on sex differences in a Chinese population. METHODS: This study ultimately recruited 142 participants (73 females and 69 males) at Honghui Hospital, Xi'an Jiaotong University. The anthropometric and blood metabolic parameters of all participants were measured. According to their serum lipid levels, female and male participants were classified into a high triglyceride (H_TG) group, a high total cholesterol (H_CHO) group, a low high-density lipoprotein cholesterol (L_HDL-C) group, and a control (CON) group with normal serum lipid levels. Fresh fecal samples were collected for 16S rRNA gene sequencing. UPARSE software, QIIME software, the RDP classifier and the FAPROTAX database were used for sequencing analyses. RESULTS: The GM composition at the phylum level included Firmicutes and Bacteroidetes as the core GM. Different GM features were identified between females and males, and the associations between GM and serum lipid profiles were different in females and males. The GM features in different dyslipidemia subgroups changed in both female patients and male patients. Proteobacteria, Lactobacillaceae, Lactobacillus and Lactobacillus_salivarius were enriched in H_CHO females compared with CON females, while Coriobacteriia were enriched in L_HDL-C females. In the comparison among the three dyslipidemia subgroups in females, Lactobacillus_salivarius were enriched in H_CHO females, and Prevotellaceae were enriched in L_HDL-C females. Compared with CON or H_TG males, Prevotellaceae, unidentified_Ruminococcaceae, Roseburia and Roseburia_inulinivorans were decreased in L_HDL-C males (P value < 0.05), and linear discriminant analysis effect size analysis indicated an enrichment of the above GM taxa in H_TG males compared with other male subgroups. Additionally, Roseburia_inulinivorans abundance was positively correlated with serum TG and total cholesterol levels, and Roseburia were positively correlated with serum TG level. Furthermore, Proteobacteria (0.724, 95%CI: 0.567-0.849), Lactobacillaceae (0.703, 95%CI: 0.544-0.832), Lactobacillus (0.705, 95%CI: 0.547-0.834) and Lactobacillus_salivarius (0.706, 95%CI: 0.548-0.835) could distinguish H_CHO females from CON females, while Coriobacteriia (0.710, 95%CI: 0.547-0.841), Coriobacteriales (0.710, 95%CI: 0.547-0.841), Prevotellaceae (0.697, 95%CI: 0.534-0.830), Roseburia (0.697, 95%CI: 0.534-0.830) and Roseburia_inulinivorans (0.684, 95%CI: 0.520-0.820) could discriminate H_TG males from CON males. Based on the predictions of GM metabolic capabilities with the FAPROTAX database, a total of 51 functional assignments were obtained in females, while 38 were obtained in males. This functional prediction suggested that cellulolysis increased in L_HDL-C females compared with CON females, but decreased in L_HDL-C males compared with CON males. CONCLUSION: This study indicates associations of GM with serum lipid profiles, supporting the notion that GM dysbiosis may participate in the pathogenesis of dyslipidemia, and sex differences should be considered.

Lipid metabolism analysis for peripheral blood in patients with alcohol-induced and steroid-induced osteonecrosis of the femoral head. / é ’ç²¾æ€§å’Œæ¿€ç´ æ€§è‚¡éª¨å¤´åæ­»çš„å¤–å‘¨è¡€è„‚è´¨ç»„å­¦åˆ†æž.

OBJECTIVES: Osteonecrosis of the femoral head (ONFH), also known as vascular necrosis of the femoral head, is combined with lipid metabolism disorders in most patients. This study aims to explore the lipid metabolism profiles in different subtypes of ONFH. METHODS: The subjects were divided into an alcohol-induced osteonecrosis of the femoral head (AONFH) group, a steroid-induced osteonecrosis of the femoral head (SONFH) group, and a normal control (NC) group (n=16, 29, and 32, respectively). Ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS) was used to detect the lipidomics analysis in the peripheral blood samples of subjects and identify the underlying biomarkers. The samples were preprocessed, the partial least squares discriminant analysis (PLS-DA) was adopted, and the variable importance for the projection (VIP) values were calculated to measure the expression pattern of each lipid metabolite and observe the influence and explanatory power of the expression pattern of each lipid metabolite on the classification and discrimination between the different groups. The lipid metabolites with fold change (FC)>2, P<0.05 and VIP>1 in the different groups were screened as differential lipids. Among them, the differential lipids co-existing in the AONFH group and the SONFH group were regarded as common differential lipids for ONFH, and the differential lipids that exist separately were regarded as specific differential lipids in the AONFH group or the SONFH group. Binary logistic regression was used to evaluate the diagnostic value of differential lipid metabolites on the basis of the receiver operator characteristic (ROC) curve analysis. Based on the disease stage information, the correlation between the differential lipids and the disease stage was analyzed in the AONFH group and the SONFH group. RESULTS: In this study, 1 358 lipid metabolites were detected in each plasma sample. Compared with the NC group, there were significant difference in the expression patterns of lipid metabolism profiles in the AONFH group and the SONFH group. A total of 62 and 64 differential lipid metabolites were screened in the AONFH and SONFH patients (FC>2, P<0.05, VIP>1) respectively, and these differential lipids were mainly up-regulated in the disease samples. Nine differential lipid metabolites were further identified, which were shared by the AONFH group and the SONFH group; the area under the curve (AUC) in 6 kinds of lipid components was greater than 0.7, including 1-myristoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine, hypoxanthin, serotonin, PE (19:0/22:5), PE (19:0/22:5), and cholest-5-en-3-yl beta-D-glucopyranosiduronic acid. Fifty-three specific differential lipid metabolites were identified in the AONFH group, and 55 specific differential lipid metabolites were identified in the SONFH group. The AUC in 6 kinds of lipid components was greater than 0.9, including 1D-myo-Inositol 1,2-cyclic phosphate, L-pyroglutamic acid, DL-carnitine, 8-amino-7-oxononanoic acid, Clobetasol, and presqualene diphosphate. In the AONFH group, there were 9 differential lipid metabolites related to the disease stages, including LPG 18:1, serotonin, PC (22:4e/23:0), PC (19:2/18:5), hypoxanthin, PE (18:1/20:3), LPE 18:1, 1-stearoyl-2-arachidonoyl-sn-glycerol, and PE (16:0/18:1); with AONFH disease progresses from I/II stages to III/IV stages, the relative content of these 9 differential lipid metabolites was increased. In the SONFH group, 8 differential lipid metabolites were found to be related to the stage of the disease, including TM6076000, 4-(1,1-dimethylpropyl)phenol, D-617, asarone, phenylac-gln-OH, creatine, leu-pro, and 8-amino-7-oxononanoic acid; and with the SONFH progressed from stage I/II to stage III/IV, the content of these 8 differential lipid metabolites were gradually increased. CONCLUSIONS: This study analyzes the characteristics of the plasma lipid metabolism profile in the AONFH and SONFH patients, and which identifies the differential lipid metabolites related to disease diagnosis and evaluation. These results provide evidence for exploring lipid metabolism alterations and the mining of novel lipid biomarkers for the ONFH.