Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Identification of gene signatures related to hypoxia and angiogenesis in pancreatic cancer to aid immunotherapy and prognosis

Background

One of the most diverse tumors is pancreatic cancer (PC), which makes predicting the prognosis challenging. PC development is directly related to hypoxia, angiogenesis, and immunotherapy. It is still unclear how the three features are related.

Methods

The Genotype-Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) database were employed to obtain sequencing data for healthy pancreatic tissues and PC tissues, respectively. According to the constructed hypoxic prognostic model (HPM) and angiogenic prognostic model (APM), 4 subtypes of PC were identified. Hypoxia and angiogenesis prognostic model (HAPM) was established based on differentially expressed genes (DEGs) between high-angiogenesis/high-hypoxia (HH) and low-angiogenesis/low-hypoxia (LL) subgroups. Base on the median risk score, PC patients were separated into high-risk and low-risk groups, and clinical traits, prognosis, percentage of immune cell infiltration, PD-1 expression, and the fraction of T-cell depletion were compared between the groups. Finally, the predictive accuracy of the tumor immune dysfunction and rejection (TIDE) and tumor inflammatory signature (TIS) models, as well as HAPM, was compared.

Result

We analyzed the mRNA sequencing data from 178 PC tissues and 171 normal pancreatic tissues to obtain 9527 DEGs. We discovered 200 genes linked with hypoxia and 36 genes involved with angiogenesis through the literature. We found the core genes related with hypoxia and angiogenesis in PC by intersecting the DEGs of the HH and LL subgroups with those of PC via WGCNA. IL-17 signaling pathway, ECM-receptor interactions, cytokine receptor interactions, etc. were all enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) results of core genes. HAPM has good predictive efficiency, according to an evaluation of KM survival curves and ROC curves. The external dataset also validated the model’s ability to anticipate outcomes. Patients in the high- and low-risk groups were compared for PD1 expression and T-cell exclusion scores, which suggested that the model might be used to forecast which PC patients might benefit from immunotherapy.

Conclusions

The probable molecular processes connecting hypoxia and angiogenesis are described in this work, and a model is developed that may be utilized to forecast the prognosis for PC patients and the benefits of immunotherapy.

Role of kisspeptin in polycystic ovarian syndrome: A metabolomics study

OBJECTIVE

Polycystic ovary syndrome (PCOS) is a pathophysiological disease affecting reproductive and metabolic indicators. Research has shown that kisspeptin might be involved in the regulation of pituitary hormone secretion and energy metabolism. The aim of this study was to investigate the relationship between serum kisspeptin levels and abnormal metabolism in PCOS.

METHODS

Fifty patients with PCOS and 50 control patients were recruited for this study. Serum kisspeptin levels were measured via ELISA. High-performance liquid chromatography-tandem mass spectrometry metabolomics was used to study the changes in serum metabolism between the PCOS and control groups.

RESULTS

Serum kisspeptin levels were significantly elevated in individuals with PCOS compared with those in healthy controls (p = 0.011) and positively correlated with LH, T, FFA, BA, and LEP levels (p < 0.05). Significantly dysregulated expression of several metabolites was observed in the intergroup comparisons of the high-kisspeptin PCOS, low-kisspeptin PCOS, and healthy control groups. These primarily consisted of lipid, amino acid, and carbohydrate metabolites, among which palmitic acid and N-formylkynurenine levels were lower in the high-kisspeptin group than in controls. Metabolite set enrichment analysis was also performed based on metabolites in the KEGG database. The results showed that owing to the differences in kisspeptin concentrations in individuals with PCOS, there was a significant difference in amino acid and pyruvate metabolism.

CONCLUSIONS

Kisspeptin could be a potential biomarker for the diagnosis of PCOS and plays an important role in metabolic regulation in individuals with PCOS. In addition, metabolomics provides a promising method for the study of metabolic abnormalities in individuals with PCOS, which might contribute to our understanding of its mechanisms.

Blood volatile organic compounds as potential biomarkers for poly cystic ovarian syndrome (PCOS): An animal study in the PCOS rat model

Polycystic ovary syndrome (PCOS) as a common metabolic and endocrinological disorder can affect the metabolic profile in biological fluids. We studied the profile of blood volatile organic compounds (VOCs) in rats with PCOS and controls to identify potential specific biomarkers of blood VOCs in PCOS rats. For this purpose, 30 female adult Wistar rats were assigned to two groups: control and PCOS groups. PCOS model was induced using letrozole gavage (1 mg/kg) for 21 days. The rats in the control group received water of the same volume for 21 days. During treatment, a collection of vaginal smears was done every day for estrus cycle determination and weight was measured weekly. On the day after the last administration of letrozole, the rats were killed and their blood and ovaries were collected. Testosterone levels and histologic changes in ovaries were examined. Also, headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) analyzed the VOCs in the blood of PCOS and control rats. Multivariate and univariate statistical analyses were used to find the potential biomarkers for a rat model of PCOS. Weight gain, ovarian and vaginal pathological alteration, as well as hyperandrogenemia, confirmed the successful induction of the PCOS in rats. The results of blood VOCs analysis showed that nine VOCs were significantly elevated and one VOC decreased in the PCOS group than the control group (P < 0/05). The partial least-squares discriminant analysis (PLS-DA) and principal component analysis (PCA) showed good separation of VOCs between the PCOS rats and the control group. The 4-ethylphenol and capric (decanoic) acid were selected as the potential biomarkers for PCOS diagnosis in the blood of the PCOS rats. The blood of PCOS rats had a specific profile of VOCs, which could be detected by GC-MS analysis. These findings can pave the way for further studies towards developing a new screening method for PCOS detection and studying their pathology, based on VOCs analysis.

The gut microbe Bacteroides fragilis ameliorates renal fibrosis in mice

Renal fibrosis is an inevitable outcome of various manifestations of progressive chronic kidney diseases (CKD). The need for efficacious treatment regimen against renal fibrosis can therefore not be overemphasized. Here we show a novel protective role of Bacteroides fragilis (B. fragilis) in renal fibrosis in mice. We demonstrate decreased abundance of B. fragilis in the feces of CKD patients and unilateral ureteral obstruction (UUO) mice. Oral administration of live B. fragilis attenuates renal fibrosis in UUO and adenine mice models. Increased lipopolysaccharide (LPS) levels are decreased after B. fragilis administration. Results of metabolomics and proteomics studies show decreased level of 1,5-anhydroglucitol (1,5-AG), a substrate of SGLT2, which increases after B. fragilis administration via enhancement of renal SGLT2 expression. 1,5-AG is an agonist of TGR5 that attenuates renal fibrosis by inhibiting oxidative stress and inflammation. Madecassoside, a natural product found via in vitro screening promotes B. fragilis growth and remarkably ameliorates renal fibrosis. Our findings reveal the ameliorative role of B. fragilis in renal fibrosis via decreasing LPS and increasing 1,5-AG levels.

Urinary metabolomics identified metabolic disturbance associated with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder. Nevertheless, its accurate mechanisms remain unclear. Metabolomics is a powerful technique to identify small molecules that could be used to discover pathogenesis and therapeutical targets of disease. In the present study, a urinary untargeted metabolomics combined with targeted quantification analysis was performed to uncover metabolic disturbance associated with PCOS. A total of thirty-eight metabolites were obtained between PCOS patients and healthy controls, which were mainly involved in lipids (39.5%), organic acids and derivatives (23.7%), and organic oxygen compounds (18.4%). Based on enrichment analysis, fourteen metabolic pathways were found to be perturbed in PCOS, particularly glycerophospholipid metabolism and tryptophan metabolism. Targeted quantification profiling of tryptophan metabolism demonstrated that seven compounds (tryptophan, kynurenine, kynurenic acid, quinolinic acid, xanthurenic acid, 3-hydroxyanthranilic acid and 3-hydroxykynurenine) were up-regulated in PCOS. And these tryptophan-kynurenine metabolites showed significant correlations with PCOS clinical features, such as positively associated with testosterone, free androgen index, and the ratio of luteinizing hormone to follicle stimulating hormone. Thus, this study disclosed urinary metabolome changes associated with PCOS, and might provide new insights into PCOS pathogenesis elucidation and therapeutical target development.

Urinary Metabolites Reveal Hyperinsulinemia and Insulin Resistance in Polycystic Ovarian Syndrome (PCOS)

The identification of insulin resistance and hyperinsulinemia in polycystic ovary syndrome (PCOS) is not a minor issue. The homeostasis model assessment of insulin resistance index (HOMA) is the most used index of IR (Insulin Resistance), validated in overweight and obese patients but not in normal-weight PCOS subjects, who can still present with increased insulin secretion by an oral glucose tolerance test (OGTT). The evaluation of insulin secretion and resistance represents a still unresolved problem. The aim of this study is to identify a possible yet noninvasive method to properly evaluate the insulin metabolism in young non-diabetic subjects. Girls aged 14–22 years, afferent to the center of Gynecological Diseases in Childhood and Adolescence of Cagliari (Italy), were screened for PCOS. A total of 42 subjects comprised the study group. Hormonal assays, OGTT, transabdominal (TA) or transvaginal (TV) US, and urine collection for ¹H-NMR analysis were assayed in the early follicular phase. A ¹H-NMR coupled multivariate statistical analysis was performed. The OPLS model indicated that the NMR profile of urine had a good fit and prediction ability for the AUC OGTT with R² = 0.813. Metabolomics can be a promising tool to the potential identification of biomarkers of an exaggerated insulin response to OGTT and can encourage substantial progress for a more accurate and early diagnosis in PCOS.