Cluster Analysis of Untargeted Metabolomic Experiments

Non-targeted Metabolomics Reveals the Potential Role of MFSD8 in Metabolism in Human Endothelial Cells

The major facilitator superfamily domain containing 8 (MFSD8) belongs to an orphan transporter protein expressed in a wide range of tissues. Nevertheless, the specific role of MFSD8 in human health and disease remains unknown. This study aimed to evaluate the role of MFSD8 protein on metabolic function using untargeted metabolomics analysis in human umbilical vein endothelial cells (HUVECs). HUVECs overexpressing MFSD8 were subjected to metabolomics analysis to evaluate changes in endogenous small molecules using LC-MS/MS analysis. In the positive scan mode, 634 metabolites from 1583 compounds were identified. In the negative scan mode, 169 metabolites from 405 compounds were identified. According to the established criteria for identifying differential metabolites, 96 metabolites exhibited significant differences between the MFSD8 and Vector groups. Among them, 62 metabolites were found to be up-regulated, whereas 34 metabolites were classified as down-regulated. Bioinformatics pipeline analysis revealed three common metabolic pathways, including arginine biosynthesis, beta-alanine metabolism, and pyrimidine metabolism, were found under the positive and negative scan modes. The semi-quantitative analysis was conducted on the differential metabolites, revealing that overexpression of MFSD8 resulted in increased levels of L-citrulline, L-aspartic acid, ornithine, N-acetyl-l-aspartic acid, L-histidine, beta-alanine metabolites and exhibited decreased levels of cytidine. The findings of our study indicated that MFSD8 had the most significant role in arginine biosynthesis, beta-alanine metabolism, and pyrimidine metabolism pathways within endothelial cells. The metabolomics data provide new insights into studying potential features of MFSD8 protein in the future.

Acute rapamycin treatment reveals novel mechanisms of behavioral, physiological, and functional dysfunction in a maternal inflammation mouse model of autism and sensory over-responsivity

Maternal inflammatory response (MIR) during early gestation in mice induces a cascade of physiological and behavioral changes that have been associated with autism spectrum disorder (ASD). In a prior study and the current one, we find that mild MIR results in chronic systemic and neuro-inflammation, mTOR pathway activation, mild brain overgrowth followed by regionally specific volumetric changes, sensory processing dysregulation, and social and repetitive behavior abnormalities. Prior studies of rapamycin treatment in autism models have focused on chronic treatments that might be expected to alter or prevent physical brain changes. Here, we have focused on the acute effects of rapamycin to uncover novel mechanisms of dysfunction and related to mTOR pathway signaling. We find that within 2 hours, rapamycin treatment could rapidly rescue neuronal hyper-excitability, seizure susceptibility, functional network connectivity and brain community structure, and repetitive behaviors and sensory over-responsivity in adult offspring with persistent brain overgrowth. These CNS-mediated effects are also associated with alteration of the expression of several ASD-,ion channel-, and epilepsy-associated genes, in the same time frame. Our findings suggest that mTOR dysregulation in MIR offspring is a key contributor to various levels of brain dysfunction, including neuronal excitability, altered gene expression in multiple cell types, sensory functional network connectivity, and modulation of information flow. However, we demonstrate that the adult MIR brain is also amenable to rapid normalization of these functional changes which results in the rescue of both core and comorbid ASD behaviors in adult animals without requiring long-term physical alterations to the brain. Thus, restoring excitatory/inhibitory imbalance and sensory functional network modularity may be important targets for therapeutically addressing both primary sensory and social behavior phenotypes, and compensatory repetitive behavior phenotypes.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

High expression of TARS is associated with poor prognosis of endometrial cancer

INTRODUCTION

Endometrial cancer is the second largest and most common cancer in the world. It is urgent to explore novel biomarkers.

METHODS

Data were obtained from The Cancer Genome Atlas (TCGA) database. The receiver operating characteristic (ROC) curves, Kaplan-Meier curves and Cox analysis, nomograms, gene set enrichment analysis (GSEA) were conducted. Cell proliferation experiments were performed in Ishikawa cell.

RESULTS

TARS was significantly highly expressed in serous type, G3 grade, and deceased status. Significant association was between high TARS expression with poor overall survival (P = 0.0012) and poor disease specific survival (P = 0.0034). Significant differences were observed in advanced stage, G3 and G4, and old. The stage, diabetes, histologic grade, and TARS expression showed independent prognostic value for overall survival of endometrial cancer. The stage, histologic grade, and TARS expression showed independent prognostic value for disease specific survival of endometrial cancer. Activated CD4⁺ T cell, effector memory CD4⁺ T cell, memory B cell and type 2 T helper cell may participate in the high TARS expression related immune response in endometrial cancer. The CCK-8 results showed significantly inhibited cell proliferation in si-TARS (P < 0.05) and promoted cell proliferation in O-TARS (P < 0.05), confirmed by the colony formation and live/dead staining.

CONCLUSION

High TARS expression was found in endometrial cancer with prognostic and predictive value. This study will provide new biomarker TARS for diagnosis and prognosis of endometrial cancer.

Genomic prediction of carcass traits using different haplotype block partitioning methods in beef cattle

Genomic prediction (GP) based on haplotype alleles can capture quantitative trait loci (QTL) effects and increase predictive ability because the haplotypes are expected to be in linkage disequilibrium (LD) with QTL. In this study, we constructed haploblocks using LD-based and the fixed number of single nucleotide polymorphisms (fixed-SNP) methods with Illumina BovineHD chip in beef cattle. To evaluate the performance of different haplotype block partitioning methods, we constructed haploblocks based on LD thresholds (from r 2 > 0.2 to r 2 > 0.8) and the number of fixed-SNPs (5, 10, 20). The performance of predictive methods for three carcass traits including liveweight (LW), dressing percentage (DP), and longissimus dorsi muscle weight (LDMW) was evaluated using three approaches (GBLUP and BayesB model based on the SNP, GHBLUP, and BayesBH models based on the haploblock, and GHBLUP+GBLUP and BayesBH+BayesB models based on the combined haploblock and the nonblocked SNPs, which were located between blocks). In this study, we found the accuracies of LD-based and fixed-SNP haplotype Bayesian methods outperformed the Bayesian models (up to 8.54 ± 7.44% and 5.74 ± 2.95%, respectively). GHBLUP showed a high improvement (up to 11.29 ± 9.87%) compared with GBLUP. The Bayesian models have higher accuracies than BLUP models in most scenarios. The average computing time of the BayesBH+BayesB model can reduce by 29.3% compared with the BayesB model. The prediction accuracies using the LD-based haplotype method showed higher improvements than the fixed-SNP haplotype method. In addition, to avoid the influence of rare haplotypes generated from haplotype construction, we compared the performance of GP by filtering four types of minor haplotype allele frequency (MHAF) (0.01, 0.025, 0.05, and 0.1) under different conditions (LD levels were set at r 2 > 0.3, and the fixed number of SNPs was 5). We found the optimal MHAF threshold for LW was 0.01, and the optimal MHAF threshold for DP and LDMW was 0.025.

High SURF4 expression is associated with poor prognosis of breast cancer

SURF4 has been suggested as an oncogene in cancer. However, the role of SURF4 in breast cancer has not been demonstrated yet. The data were obtained from TCGA database and 1104 patients were analyzed using bioinformatics analysis. SURF4 is significantly (P < 0.001) highly expressed in tumor. High expression of SURF4 was observed in T4, infiltrating ductal carcinoma, ER negative, PR negative, and HER2 positive, female, patients without lymph node metastasis, HER2 overexpression type, and deceased patients. As for characteristics correlated with high expression of SURF4, gender, histological type, molecular subtype, ER, PR, HER2, and vital status exhibited significant differences. The age (HR: 2.317, P < 0.001), stage (HR: 2.090, P < 0.001), and SURF4 expression (HR: 1.958, P = 0.005) exhibited independent prognostic value for overall survival (OS). Patients with high SURF4 expression, higher age, equivocal HER2, higher stages, or positive margin status had shorter OS. The stage (HR: 1.579, P < 0.001), and margin status (HR: 1.463, P = 0.006) exhibited independent prognostic value for relapse-free survival of breast cancer. High expression of SURF4 was first found in breast cancer. High SURF4 expression was observed in breast cancer tissue and cell. SURF4 promoted the proliferation and migration of 4T1 cells. SURF4 may be a biomarker in diagnosis and prognosis of breast cancer.

The serum metabolomic profile of a distinct, inflammatory subtype of acute psychosis

A range of studies suggest that a proportion of psychosis may have an autoimmune basis, but this has not translated through into clinical practice-there is no biochemical test able to accurately identify psychosis resulting from an underlying inflammatory cause. Such a test would be an important step towards identifying who might require different treatments and have the potential to improve outcomes for patients. To identify novel subgroups within patients with acute psychosis we measured the serum nuclear magnetic resonance (NMR) metabolite profiles of 75 patients who had identified antibodies (anti-glycine receptor [GlyR], voltage-gated potassium channel [VGKC], Contactin-associated protein-like 2 [CASPR2], leucine-rich glioma inactivated 1 [LGI1], N-methyl-D-aspartate receptor [NMDAR] antibody) and 70 antibody negative patients matched for age, gender, and ethnicity. Clinical symptoms were assessed using the positive and negative syndrome scale (PANSS). Unsupervised principal component analysis identified two distinct biochemical signatures within the cohort. Orthogonal partial least squared discriminatory analysis revealed that the serum metabolomes of NMDAR, LGI1, and CASPR2 antibody psychosis patients were indistinct from the antibody negative control group while VGKC and GlyR antibody patients had significantly decreased lipoprotein fatty acids and increased amino acid concentrations. Furthermore, these patients had more severe presentation with higher PANSS scores than either the antibody negative controls or the NMDAR, LGI1, and CASPR2 antibody groups. These results suggest that a proportion of patients with acute psychosis have a distinct clinical and biochemical phenotype that may indicate an inflammatory subtype.

Tertiary lymphoid structures: Associated multiple immune cells and analysis their formation in hepatocellular carcinoma

The prognostic value of immune cells in tertiary lymphoid structures (TLSs) remains unclear in hepatocellular carcinoma (HCC). Here, 59 of 145 patients had TLSs in training set, 48 of 120 patients had TLSs in testing set. Immunohistochemistry (IHC) were used to label CD3+ T cells, CD20+ B cells, CD8+ T cells, CD208+ dendritic cells, and CD21+ follicular dendritic cells in TLSs. High CD20+, CD208+, and CD8+ cell densities were favorable prognostic factors for overall survival (OS). High CD3+, CD20+, CD208+, and CD8+ cell densities were significantly associated with reduced early recurrence. TLSs were divided into three grades (A, B, and C) based on immune cell density. Patients with grade C or B had significantly improved OS. Patients with grade C had the lowest recurrence rate, followed by those with grade B, while patients with grade A had the highest recurrence rate. The stromal, immune, and ESTIMATE scores derived from the ESTIMATE package were significantly higher and tumor purity was significantly lower in patients with TLSs. Patients with TLSs had significantly higher relative numbers of memory B cells, plasma cells, CD8+ T cells, NK cells, and dendritic cells and lower relative numbers of Treg cells, macrophages, and M2 macrophages according to the CIBERSORT assessment. Bioinformatics analysis and experiments confirmed that KLRK1 and GZMA expression are associated TLSs formation and can predict TLSs existence. Grade B and grade C were favorable prognostic factors for OS and recurrence and could represent immune-active tumors.

Metabolomics and integrated network pharmacology analysis reveal attenuates cardiac hypertrophic mechanisms of HuoXin pill

ETHNOPHARMACOLOGICAL RELEVANCE

Cardiac hypertrophy (CH) is maladaptive and contributes to the pathogenesis of heart failure. Huoxin pill (HXP), a Chinese herbal prescription, is widely applied in the treatment of cardiovascular disease (CAD). Its mechanism, however, is unclear.

AIM OF THE STUDY

This study investigated the mechanism of action for Huoxin pill in the treatment of CH, an important stage of CAD.

MATERIALS AND METHODS

A total of 60 rats were injected with isoprenaline (ISO) to establish a model of CH. Echocardiography and histopathologic evaluation were performed to evaluate the disease severity, whereas ELISAs were conducted to determine the expression of oxidative stress. Network pharmacology and metabolomic analyses were conducted to identify the key compounds, core targets and pathways that mediate the effects of HXP against CH. Western blotting and immunohistochemistry were used to test apoptosis protein levels.

RESULTS

HXP administration in ISO-treated rats decreased hypertrophy indices, alleviated cardiac pathological damage, and downregulated oxidative stress levels when compared to those of rats subjected to ISO treatment only. Moreover, network pharmacology results suggested that the PI3K-Akt pathway is a main mechanism by which HXP inhibits cardiac hypertrophy, and experimental verification showed that HXP inhibited cardiomyocyte apoptosis via activation of the PI3K-Akt pathway. The results of metabolomic analysis identified 21 differential metabolites between the HXPH group and ISO group, which were considered to be metabolic biomarkers of HXP in the treatment of CH. Among them, 6 differential metabolites were significantly upregulated, and 15 were significantly downregulated.

CONCLUSIONS

The present study presents an integrated strategy for investigating the mechanisms of HXP in the treatment of CH and sheds new light on the application of HXP as a traditional Chinese medicine.

Gestational Dating by Urine Metabolic Profile at High Resolution Weekly Sampling Timepoints: Discovery and Validation

Background: Pregnancy triggers longitudinal metabolic alterations in women to allow precisely-programmed fetal growth. Comprehensive characterization of such a "metabolic clock" of pregnancy may provide a molecular reference in relation to studies of adverse pregnancy outcomes. However, a high-resolution temporal profile of metabolites along a healthy pregnancy remains to be defined. Methods: Two independent, normal pregnancy cohorts with high-density weekly urine sampling (discovery: 478 samples from 19 subjects at California; validation: 171 samples from 10 subjects at Alabama) were studied. Urine samples were profiled by liquid chromatography-mass spectrometry (LC-MS) for untargeted metabolomics, which was applied for gestational age dating and prediction of time to delivery. Results: 5,473 urinary metabolic features were identified. Partial least-squares discriminant analysis on features with robust signals (n = 1,716) revealed that the samples were distributed on the basis of the first two principal components according to their gestational age. Pathways of bile secretion, steroid hormone biosynthesis, pantohenate, and CoA biosynthesis, benzoate degradation, and phenylpropanoid biosynthesis were significantly regulated, which was collectively applied to discover and validate a predictive model that accurately captures the chronology of pregnancy. With six urine metabolites (acetylcholine, estriol-3-glucuronide, dehydroepiandrosterone sulfate, α-lactose, hydroxyexanoy-carnitine, and l-carnitine), models were constructed based on gradient-boosting decision trees to date gestational age in high accordance with ultrasound results, and to accurately predict time to delivery. Conclusion: Our study characterizes the weekly baseline profile of the human pregnancy metabolome, which provides a high-resolution molecular reference for future studies of adverse pregnancy outcomes.

Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques

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Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.

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Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.

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A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

The microbial metabolome in metabolic-associated fatty liver disease

Metabolism-associated fatty liver disease (MAFLD) is defined as the presence of excess fat in the liver in the absence of excess alcohol consumption and metabolic dysfunction. It has also been described as the hepatic manifestation of metabolic syndrome. The incidence of MAFLD has been reported to be 43-60% in diabetics, ~90% in patients with hyperlipidemia, and 91% in morbidly obese patients. Risk factors that have been associated with the development of MAFLD include male gender, increasing age, obesity, insulin resistance, diabetes, and hyperlipidemia. All of these risk factors have been linked to alterations of the gut microbiota, that is, gut dysbiosis. MAFLD can progress to non-alcoholic steatohepatitis with the presence of inflammation and ballooning, which can deteriorate into cirrhosis, MAFLD-related hepatocellular carcinoma, and liver failure. In this review, we will be focused on the role of the gut microbial metabolome in the development, progression, and potential treatment of MAFLD.

CXCL5/CXCL8 is a promising potential prognostic and tumor microenvironment-related cluster in hepatocellular carcinoma

Background

Immune checkpoint blockers (ICBs) are increasingly applied to treat patients with advanced HCC. However, the overall survival (OS) of HCC patients is still unsatisfactory, and there is no confirmed immune-related and prognostic gene to identify patients who could clinically benefit from this treatment. The tumor microenvironment (TME) is known to be closely related to immunotherapy and plays a pivotal role in the recurrence and progression of HCC. Our aim is to explore TME-related genes and identify the prognostic value in HCC patients.

Methods

ESTIMATE, immune, and stromal scores were calculated for HCC patients based on RNA expression data from The Cancer Genome Atlas database. Differential expression analysis was performed to screen the differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was constructed to identify the key DEGs. Univariate and multivariate Cox analyses were adopted to validate hub DEGs associated with clinical prognosis, and a single-sample gene set enrichment analysis (ssGSEA) algorithm was used to dissect the landscape of tumor-infiltrating cells (TIC) in HCC. Finally, the relationship between hub immune-related genes and TIC was explored through difference and correlation analyses.

Results

ESTIMATE, immune and stromal scores were all found to be associated with the OS of patients (P<0.05). A total of 1,112 DEGs were identified by comparing low and high score groups of immune and stromal scores. Most of DEGs were enriched in immune-related gene sets by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, the top 34 genes were included in the protein-protein interaction (PPI) network, and univariate Cox analysis focus on a novel prognosis-related gene cluster CXCL5/CXCL8 (P<0.001). Regarding the immune landscape of HCC, univariable Cox regression analysis showed six immune cells to be associated with OS. Finally, 21 immune cells were commonly determined between high and low expression of CXCL5/CXCL8, suggesting there is a close relationship between expression of CXCL5 and CXCL8 .

Conclusions

Our study has revealed that the immune-related gene cluster of CXCL5 /CXCL8 could be a promising prognostic indicator for HCC and a potential novel biomarker to guide the selection of HCC patients for ICB immunotherapy.

Metabolomic analysis in ophthalmology

Modern science takes into account phenotype complexity and establishes approaches to track changes on every possible level. Many "omics" studies have been developed over the last decade. Metabolomic analysis enables dynamic measurement of the metabolic response of a living system to a variety of stimuli or genetic modifications. Important targets of metabolomics is biomarker development and translation to the clinic for personalized diagnosis and a greater understanding of disease pathogenesis. The current review highlights the major aspects of metabolomic analysis and its applications for the identification of relevant predictive, diagnostic and prognostic biomarkers for some ocular diseases including dry eye, keratoconus, retinal diseases, macular degeneration, and glaucoma. To date, possible biomarker candidates for dry eye disease are lipid metabolites and androgens, for keratoconus cytokeratins, urea, citrate cycle, and oxidative stress metabolites. Palmitoylcarnitine, sphingolipids, vitamin D related metabolites, and steroid precursors may be used for distinguishing glaucoma patients from healthy controls. Dysregulation of amino acid and carnitine metabolism is critical in the development and progression of diabetic retinopathy. Further work is needed to discover and validate metabolic biomarkers as a powerful tool for understanding the molecular mechanisms of ocular diseases, to provide knowledge on their etiology and pathophysiology and opportunities for personalized clinical intervention at an early stage.