LC-MS/MS based metabolomics and proteomics reveal candidate biomarkers and molecular mechanism of early IgA nephropathy

Plasma-based proteomic and metabolomic characterization of lung and lymph node metastases in cervical cancer patients

Metastasis is the leading cause of mortality in cervical cancer (CC), with a particular prevalence of lymph node and lung metastases. Patients with CC who have developed distant metastases typically face a poor prognosis, and there is a scarcity of non-invasive strategies for predicting CC metastasis. In this study, we utilized label-free proteomics and untargeted metabolomics to analyze plasma samples from 25 non-metastatic, 14 with lung metastasis, and 15 with lymph node metastasis CC patients. Pathway enrichment analysis revealed a shared inflammatory process between the two metastatic groups, while the central carbon metabolism in cancer showed distinct features in the lung metastasis cohort. Additionally, cholesterol metabolism, hypoxia-inducible factor 1, and ferroptosis signaling pathways were specifically altered in the lymph node metastasis group. Utilizing the receiver operating characteristic curve analysis and Random Forest algorithm, we identified two distinct biomarker panels for the prediction of lung metastasis and lymph node metastasis, respectively. The lung metastasis panel includes properdin, neural cell adhesion molecule 1, and keratin 6 A, whereas the lymph node metastasis panel consists of quiescin sulfhydryl oxidase 1, paraoxonase 1, and keratin 6 A. Each panel exhibited significant diagnostic potential, with high area under the curve (AUC) values for lung metastasis (training set: 0.989, testing set: 0.789) and lymph node metastasis (training set: 0.973, testing set: 0.900). This study conducted an integrated proteomic and metabolomic analysis to clarify the factors contributing to lung and lymph node metastases in CC and has successfully established two biomarker panels for their prediction.

Combined proteomics and metabolomics analysis reveal the effect of a training course on the immune function of Chinese elite short-track speed skaters

INTRODUCTION

The aim of this study was to combine proteomics and metabolomics to evaluate the immune system of short-track speed skaters (STSS) before and after a training course. Our research focused on changes in urinary proteins and metabolites that have the potential to serve as indicators for training load.

METHODS

Urine samples were collected from 21 elite STSS (13 male and 8 female) of the China National Team before and immediately after one training course. First-beat sports sensor was used to monitor the training load. Proteomic detection was performed using a Thermo UltiMate 3000 ultra high performence chromatography nano liquid chromatograph and an Orbitrap Exploris 480 mass spectrometer. MSstats (R package) was used for the statistical evaluation of significant differences in proteins from the samples. Two filtration criteria (fold change [FC] > 2 and p < 0.05) were used to identify the differential expressed proteins. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis for differential proteins was performed to identify the pathways involved. Nontargeted metabolomic detection was performed using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS_) with an ACQUITY 2D UPLC plus Q Exactive (QE) hybrid Quadrupole-Orbitrap mass spectrometer. Differential metabolites were identified using non-parametric statistical methods (Wilcox's rank test). Two filtration criteria (FC > 1.2 and p < 0.05) were used to identify differential metabolites. Combined analysis of proteomic and metabolomics were performed on the "Wu Kong" platform. Correlation analysis was performed using Spearman's rank correlation coefficient.

RESULTS

(1) The most upregulated proteins were immune-related proteins, including complement proteins (C9, C4-B, and C9) and immunoglobulins (IgA, IgM, and IgG). The most downregulated proteins were osteopontin (OPN) and CD44 in urine. The correlation analysis showed that the content of OPN and CD44 (the receptor for OPN) in urine were significantly negatively correlated with the upregulated immune-related proteins. The content of OPN and CD44 is sex-dependent and negatively correlated with the training load. (2) The most upregulated metabolites included lactate, cortisol, inosine, glutamine, argininosuccinate (the precursor for arginine synthesis), 3-methyl-2-oxobutyrate (the catabolite of valine), 3-methyl-2-oxovalerate (the catabolite of isoleucine), and 4-methyl-2-oxopentanoate (the catabolite of leucine), which is sex-dependent and negatively correlated with OPN and CD44. (3) The joint analysis revealed five main related pathways, including the immune and innate immune systems. The enriched immune-related proteins included complements, immunoglobulins, and protein catabolism-related proteins. The enriched immune-related metabolites included cAMP, N-acetylgalactosamine, and glutamate. (4) There is a significant negative correlation between the content of OPN and CD44 in urine and the training load.

CONCLUSION

One training course can lead to the activation of the immune system and a sex-dependent decrease in the content of OPN and CD44. Training load has a significant and negative correlation with the content of OPN and CD44, suggesting that OPN and CD44 could be potential indicators for training load.

Effect of resveratrol on spermatogenesis in breeding boars and the proteomic analysis for testes

Effect of resveratrol (RSV) on spermatogenesis and the mechanism of resveratrol in promoting spermatogenesis of breeding boars was explored by feeding sexually mature Duroc boars with normal diet and 20 mg/kg resveratrol diet for 14 days to the control group and experimental group, respectively. Semen volume, sperm density, motility, viability and abnormality rate were analyzed on day 0, 7, and 14. Blood samples were collected, and levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and testosterone (T) in serum were analyzed. On day 14, the testis tissue was collected for antioxidant and proteomics analysis etc. The semen volume, sperm density, motility, and viability of the experimental group and the contents of serum FSH, LH, T and plasma SOD activity were significantly higher than those in the control group. However, the serum IL-6, TNF-α and plasma MDA were remarkably lower in experimental group. The above results showed that resveratrol can simulate spermatogenesis in breeding boars. Proteomic results demonstrated that three differentially expressed proteins (DEPs) were up-regulated and 12 DEPs were down-regulated; ODF1, calmodulin, Cabs1, and Hp were involved in spermatogenesis; and the main enriched metabolic pathway is steroid hormone synthesis pathway. Therefore, the improvement in sperm quality by resveratrol may be achieved by regulating the changes in outer dense fiber 1, calmodulin, spermatid specific 1, and haptoglobin expression and steroid synthesis pathway.

Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis

Purpose

Plenty of studies have explored the diagnosis and prognosis of IgA nephropathy (IgAN) based on machine learning (ML), but the accuracy lacks the support of evidence-based medical evidence. We aim at this problem to guide the precision treatment of IgAN.

Methods

Embase, Pubmed, Cochrane Library, and Web of Science were searched systematically until February 24th, 2024, for publications on ML-based diagnosis and prognosis of IgAN. Subgroup analysis or meta-regression was conducted according to modeling method, follow-up time, endpoint definition, and variable type. Further, the rank sum test was applied to compare the discrimination ability of prognosis.

Results

A total of 47 studies involving 51,935 patients were eligible. Among the 38 diagnostic models, the pooled C-index was 0.902 (95 % CI: 0.878-0.926) in 27 diagnostic models. Of the 162 prognostic models, the C-index for model discrimination of 144 prognostic models was 0.838 (95 % CI: 0.827-0.850) in training. The overall discrimination ability of prognosis was as follows: COX regression > new ML models (e.g. ANN, DT, RF, SVM, XGBoost) > traditional ML models (logistic regression) > Naïve Bayesian network (P < 0.05). External validation of IIgAN-RPT in 19 models showed a pooled C-index of 0.801 (95 % CI: 0.784-0.817).

Conclusions

New ML models have shown application values that are as good as traditional ML models, both in diagnosis and prognosis. In addition, future models are desired to use a more sensitive prognostic endpoint (albuminuria), improve predictive ability in moderate progression risk, and ultimately translate into clinically applicable intelligent tools.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Metabolomics study reveals blood biomarkers for early diagnosis of chronic kidney disease and IgA nephropathy: A retrospective cross-sectional study

BACKGROUND AND AIMS

Chronic kidney disease (CKD) causes low quality of life and alarming morbidity and mortality. The crucial to retard CKD progression is to diagnose early for timely treatment. IgA nephropathy (IgAN) is a typical CKD and the most common glomerulonephritis. Both CKD and IgAN lack accurate and sensitive blood biomarkers for early diagnosis. Here we report the potential of plasma biomarkers for early diagnosis of CKD and IgAN.

MATERIALS AND METHODS

Plasma levels of metabolites derived from tryptophan were quantified with an LC-MS/MS-based metabolomics for two cohorts. Based on the predictive probability of each metabolite, multivariate models including logistic regression and random forest were used to establish the early diagnostic biomarkers for CKD and IgAN.

RESULTS

The plasma melatonin diagnosed early CKD (stages Ⅰ-Ⅱ) with an accuracy exceeding 95%, and a panel of melatonin and tryptophan achieved a remarkable 100% accuracy in diagnosing early CKD. Furthermore, indole-3-lactic acid had an excellent ability to distinguish IgAN among CKD patients. Based on the CKD screening and IgAN diagnosis primarily contributed by melatonin and indole-3-lactic acid, early IgAN could be diagnosed with an accuracy of over 85%.

CONCLUSIONS

This study provides promising plasma biomarkers for early diagnosis of CKD and IgAN.

Kidney Disease and Proteomics: A Recent Overview of a Useful Tool for Improving Early Diagnosis

Kidney disease is a critical and potentially life-threatening degenerative condition that poses a significant global public health challenge due to its elevated rates of morbidity and mortality. It manifests primarily in two distinct clinical forms: acute kidney injury (AKI) and chronic kidney disease (CKD). The development of these conditions hinges on a multitude of factors, including the etiological agents and the presence of coexisting medical conditions. Despite disparities in their underlying pathogenic mechanisms, both AKI and CKD can progress to end-stage kidney disease (ESKD). This advanced stage is characterized by organ failure and its associated complications, greatly increasing the risk of mortality. There is an urgent need to delve into the pathogenic mechanisms underlying these diseases and to identify novel biomarkers that can facilitate earlier diagnosis. Such early detection is crucial for enhancing the efficacy of therapy and impeding disease progression. In this context, proteomic approaches have emerged as invaluable tools for uncovering potential new markers of different pathological conditions, including kidney diseases. In this chapter, we overview the recent discoveries achieved through diverse proteomic techniques aimed at identifying novel molecules that may play a pivotal role in kidney diseases such as diabetic kidney disease (DKD), IgA nephropathy (IgAN), CKD of unknown origin (CKDu), autosomal dominant polycystic kidney disease (ADPKD), lupus nephritis (LN), hypertensive nephropathy (HN), and COVID-19-associated acute kidney injury (COVID-AKI).

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

Altered serum metabolic profile in patients with IgA nephropathy

BACKGROUND

We investigated alterations in the serum metabolomic profile of IgA nephropathy (IgAN) patients and screen biomarkers of IgA nephropathy based on ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

METHODS

Serum samples from 65 IgAN patients and 31 healthy controls were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Univariate and multivariate analysis were performed to screen the differential metabolites. Differential metabolites should meet both the following two criteria: adjusted P < 0.05 in the univariate analysis and VIP value > 1 in the multivariate model. Pathway analysis was performed to reveal the metabolic pathways that were significantly influenced in IgAN. Spearman correlation analysis was applied to explore the correlation between metabolites and between the metabolites and clinicopathological features of IgAN. A random forest model and Logistics regression analysis were conducted to evaluate the predictive ability of the metabolites.

RESULTS

The metabolic profile was significantly altered in IgAN patients compared with healthy controls. Thirty-nine metabolites were identified, including glycerophospholipids, sphingolipids, vitamin K1, vitamin K2, bile acids and amino acids. Sphingolipid metabolism, ubiquinone and other terpenoid-quinone biosynthesis, and glycerophospholipid metabolism were found to be significantly disturbed in the pathway analysis. Differential metabolites were found to be associated with the clinical and pathological features of IgAN patients. Lanosterol, vitamin K1, vitamin K2, and β-elemonic acid were found to have promising predictive ability for IgAN.

CONCLUSIONS

We confirmed the differences in the metabolic profiles of IgAN patients and healthy controls and identified the differential metabolites of IgAN, which may help with the further exploration of the pathogenesis and treatment of IgAN.

Omics are Getting Us Closer to Understanding IgA Nephropathy

During the last decade, thanks to omics technologies, new light has been shed on the pathogenesis of many diseases. Genomics, epigenomics, transcriptomics, and proteomics have helped to provide a better understanding of the origin and heterogeneity of several diseases. However, the risk factors for most autoimmune diseases remain unknown. The successes and pitfalls of omics have also been observed in nephrology, including immunoglobulin A nephropathy (IgAN), the most common form of glomerulonephritis and a principal cause of end-stage renal disease worldwide. Unfortunately, the immense progress in basic research has not yet been followed by the satisfactory development of a targeted treatment. Although, most omics studies describe changes in the immune system, there is still insufficient data to apply their results in the constantly evolving multi-hit pathogenesis model and thus do to provide a complete picture of the disease. Here, we describe recent findings regarding the pathophysiology of IgAN and link omics studies with immune system dysregulation. This review provides insights into specific IgAN markers, which may lead to the identification of potential targets for personalised treatment in the future.