Seminal Plasma Lipidomics Profiling to Identify Signatures of Kallmann Syndrome

Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia

BACKGROUND

Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia.

METHODS

Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model.

RESULTS

Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0).

CONCLUSIONS

LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.

A metabolomic perspective on the mechanisms by which environmental pollutants and lifestyle lead to male infertility

The incidence of male infertility (MI) is rising annually. According to epidemiological studies, environmental pollution (e.g., organic, inorganic, and air pollutants), occupational exposure (e.g., high temperature, organic solvents, and pesticides), and poor lifestyle (e.g., diet, sleep, smoking, alcohol consumption, and exercise) are important non-genetic causative factors of MI. Due to multiple and complex causative factors, the dose-effect relationship, and the uncertainty of pathogenicity, the pathogenesis of MI is far from fully clarified. Recent data show that the pathogenesis of MI can be monitored by the metabolites in serum, seminal plasma, urine, testicular tissue, sperm, and other biological samples. It is considered that these metabolites are closely related to MI phenotypes and can directly reflect the individual pathological and physiological conditions. Therefore, qualitative and quantitative analysis of the metabolome, the related metabolic pathways, and the identification of biomarkers will help to explore the MI-related metabolic problems and provide valuable insights into its pathogenic mechanisms. Here, we summarized new findings in MI metabolomics biomarkers research and their abnormal metabolic pathways triggered by the presented non-genetic risk factors, providing a metabolic landscape of semen and seminal plasma in general MI patients. Then, we compared the similarities and differences in semen and seminal plasma biomarkers between MI patients exposed to environmental and poor lifestyle factors and MI patients in general, and summarized some common biomarkers. We provide a better understanding of the biological underpinnings of MI pathogenesis, which might offer novel diagnostic, prognostic, and precise treatment approaches to MI.

Lipidomics-Paving the Road towards Better Insight and Precision Medicine in Rare Metabolic Diseases

Even though the application of Next-Generation Sequencing (NGS) has significantly facilitated the identification of disease-associated mutations, the diagnostic rate of rare diseases is still below 50%. This causes a diagnostic odyssey and prevents specific treatment, as well as genetic counseling for further family planning. Increasing the diagnostic rate and reducing the time to diagnosis in children with unclear disease are crucial for a better patient outcome and improvement of quality of life. In many cases, NGS reveals variants of unknown significance (VUS) that need further investigations. The delineation of novel (lipid) biomarkers is not only crucial to prove the pathogenicity of VUS, but provides surrogate parameters for the monitoring of disease progression and therapeutic interventions. Lipids are essential organic compounds in living organisms, serving as building blocks for cellular membranes, energy storage and signaling molecules. Among other disorders, an imbalance in lipid homeostasis can lead to chronic inflammation, vascular dysfunction and neurodegenerative diseases. Therefore, analyzing lipids in biological samples provides great insight into the underlying functional role of lipids in healthy and disease statuses. The method of choice for lipid analysis and/or huge assemblies of lipids (=lipidome) is mass spectrometry due to its high sensitivity and specificity. Due to the inherent chemical complexity of the lipidome and the consequent challenges associated with analyzing it, progress in the field of lipidomics has lagged behind other omics disciplines. However, compared to the previous decade, the output of publications on lipidomics has increased more than 17-fold within the last decade and has, therefore, become one of the fastest-growing research fields. Combining multiple omics approaches will provide a unique and efficient tool for determining pathogenicity of VUS at the functional level, and thereby identifying rare, as well as novel, genetic disorders by molecular techniques and biochemical analyses.

Protective Mechanism of Polygonum perfoliatum L. Extract on Chronic Alcoholic Liver Injury Based on UHPLC-QExactive Plus Mass Spectrometry Lipidomics and MALDI-TOF/TOF Mass Spectrometry Imaging

Polygonum perfoliatum L. has a long history of medicinal and edible applications. Studies have shown that it can significantly protect liver injury, but the mechanism is unclear. The purpose of this study was to explore the protective mechanism of P. perfoliatum on chronic alcoholic liver injury from the perspective of lipid metabolism. After 8 weeks of alcohol exposure in male Wister mice, the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) in serum were significantly increased, and the activities of alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) in liver were significantly decreased. Meanwhile, pathological changes of liver tissue in mice were observed by histopathology. Then, Ultra-High Performance Liquid Chromatography (UHPLC) QExactive Plus Mass Spectrometer lipidomics and matrix-assisted laser desorption/ionization time-of-flight/time -of-flight (MALDI-TOF/TOF) mass spectrometry imaging methods were established to analyze lipid metabolism in mice. Ten different lipids were identified by statistical analysis, including Fatty Acyls, Glycerophospholipids, Prenol lipids and Sphingomyelins. After intervention with P. perfoliatum extracts at different doses (25 to 100 mg/kg), levels of AST, ALT, ALP in serum, and activities of ADH and ALDH in liver were significantly corrected. The hepatic cord structure was clear, and the liver cells were closely arranged without other obvious abnormalities. Non-target lipidomics analysis showed that P. perfoliatum extract could regulate the metabolic disorders of the 10 different lipids caused by continuous alcohol exposure. Pathway analysis suggested that the mechanism of P. perfoliatum extract on chronic alcoholic liver injury may be related to the regulation of linoleic acid and α-linolenic acid.