Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, accounting for over 90% of pancreatic cancers, and is characterized by limited treatment options and poor survival rates. Systems biology provides in-depth insights into the molecular mechanisms of PDAC. In this context, novel algorithms and comprehensive strategies are essential for advancing the identification of critical network nodes and therapeutic targets within disease-related protein-protein interaction networks. This study employed a comprehensive computational strategy using the metaheuristic algorithm Trader to enhance the identification of potential therapeutic targets. Analysis of the expression data from the PDAC dataset (GSE132956) involved co-expression analysis and clustering of differentially expressed genes to identify key disease-associated modules. The STRING database was used to construct a network of differentially expressed genes, and the Trader algorithm pinpointed the top 30 DEGs whose removal caused the most significant network disconnections. Enriched gene ontology terms included "Signaling by Rho GTPases," "Signaling by receptor tyrosine kinases," and "immune system." Additionally, nine hub genes-FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1-were identified as central to PDAC pathogenesis. This integrated approach, combining co-expression analysis with protein-protein interaction network analysis using a metaheuristic algorithm, provides valuable insights into PDAC mechanisms and highlights several hub genes as potential therapeutic targets.

Preclinical Detection of Early Glomerular Injury in Children with Kidney Diseases-Independently of Usual Markers of Kidney Impairment and Inflammation

Glomerular kidney diseases typically begin insidiously and can progress to end stage kidney failure. Early onset of therapy can slow down disease progression. Early diagnosis is required to ensure such timely therapy. The goal of our study was to evaluate protein biomarkers (BMs) for common nephropathies that have been described for children with Alport syndrome. Nineteen candidate BMs were determined by commercial ELISA in children with congenital anomalies of the kidneys and urogenital tract, inflammatory kidney injury, or diabetes mellitus. It is particularly essential to search for kidney disease BMs in children because they are a crucial target group that likely exhibits early disease stages and in which misleading diseases unrelated to the kidney are rare. Only minor differences in blood between affected individuals and controls were found. However, in urine, several biomarker candidates alone or in combination seemed to be promising indicators of renal injury in early disease stages. The BMs of highest sensitivity and specificity were collagen type XIII, hyaluronan-binding protein 2, and complement C4-binding protein. These proteins are unrelated to inflammation markers or to risk factors for and signs of renal failure. In conclusion, our study evaluated several strong candidates for screening for early stages of kidney diseases and can help to establish early nephroprotective regimens.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients

Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

Gut microbiota microbial metabolites in diabetic nephropathy patients: far to go

Diabetic nephropathy (DN) is one of the main complications of diabetes and a major cause of end-stage renal disease, which has a severe impact on the quality of life of patients. Strict control of blood sugar and blood pressure, including the use of renin-angiotensin-aldosterone system inhibitors, can delay the progression of diabetic nephropathy but cannot prevent it from eventually developing into end-stage renal disease. In recent years, many studies have shown a close relationship between gut microbiota imbalance and the occurrence and development of DN. This review discusses the latest research findings on the correlation between gut microbiota and microbial metabolites in DN, including the manifestations of the gut microbiota and microbial metabolites in DN patients, the application of the gut microbiota and microbial metabolites in the diagnosis of DN, their role in disease progression, and so on, to elucidate the role of the gut microbiota and microbial metabolites in the occurrence and prevention of DN and provide a theoretical basis and methods for clinical diagnosis and treatment.

Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease

BACKGROUND

The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction.

METHODS

Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance.

RESULTS

Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors.

CONCLUSION

The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.

Jian-Pi-Gu-Shen-Hua-Yu Decoction Alleviated Diabetic Nephropathy in Mice through Reducing Ferroptosis

Background

Diabetic nephropathy (DN), one of the most frequent complications of diabetes mellitus, is a leading cause of end-stage renal disease. However, the current treatment methods still cannot effectively halt the progression of DN. Jian-Pi-Gu-Shen-Hua-Yu (JPGS) decoction can be used for the treatment of chronic kidney diseases such as DN, but the specific mechanism of action has not been fully elucidated yet.

Purpose

The aim of this study is to clarify whether JPGS alleviates the progression of diabetic nephropathy by inhibiting ferroptosis.

Materials and Methods

We established a DN mouse model to investigate the therapeutic effect of JPGS in a DN mouse model. Subsequently, we examined the effects of JPGS on ferroptosis- and glutathione peroxidase 4 (GPX4) pathway-related indices. Finally, we validated whether JPGS inhibited ferroptosis in DN mice via the GPX4 pathway using GPX4 inhibitor and ferroptosis inhibitors.

Results

The results indicate that JPGS has a therapeutic effect on DN mice by improving kidney function and reducing inflammation. Additionally, JPGS treatment decreased iron overload and oxidative stress levels while upregulating the expression of GPX4 pathway-related proteins. Moreover, JPGS demonstrated a similar therapeutic effect as Fer-1 in the context of DN treatment, and RSL3 was able to counteract the therapeutic effect of JPGS and antiferroptotic effect.

Conclusion

JPGS has significant therapeutic and anti-inflammatory effects on DN mice, and its mechanism is mainly achieved by upregulating the expression of GPX4 pathway-related proteins, thereby alleviating iron overload and ultimately reducing ferroptosis.

Jianpi Gushen Huayu decoction ameliorated diabetic nephropathy through modulating metabolites in kidney, and inhibiting TLR4/NF-κB/NLRP3 and JNK/P38 pathways

BACKGROUND

Jianpi Gushen Huayu Decoction (JPGS) has been used to clinically treat diabetic nephropathy (DN) for many years. However, the protective mechanism of JPGS in treating DN remains unclear.

AIM

To evaluate the therapeutic effects and the possible mechanism of JPGS on DN.

METHODS

We first evaluated the therapeutic potential of JPGS on a DN mouse model. We then investigated the effect of JPGS on the renal metabolite levels of DN mice using non-targeted metabolomics. Furthermore, we examined the effects of JPGS on c-Jun N-terminal kinase (JNK)/P38-mediated apoptosis and the inflammatory responses mediated by toll-like receptor 4 (TLR4)/nuclear factor-kappa B (NF-κB)/NOD-like receptor family pyrin domain containing 3 (NLRP3).

RESULTS

The ameliorative effects of JPGS on DN mice included the alleviation of renal injury and the control of inflammation and oxidative stress. Untargeted metabolomic analysis revealed that JPGS altered the metabolites of the kidneys in DN mice. A total of 51 differential metabolites were screened. Pathway analysis results indicated that nine pathways significantly changed between the control and model groups, while six pathways significantly altered between the model and JPGS groups. Pathways related to cysteine and methionine metabolism; alanine, tryptophan metabolism; aspartate and glutamate metabolism; and riboflavin metabolism were identified as the key pathways through which JPGS affects DN. Further experimental validation showed that JPGS treatment reduced the expression of TLR4/NF-κB/NLRP3 pathways and JNK/P38 pathway-mediated apoptosis related factors.

CONCLUSION

JPGS could markedly treat mice with streptozotocin (STZ)-induced DN, which is possibly related to the regulation of several metabolic pathways found in kidneys. Furthermore, JPGS could improve kidney inflammatory responses and ameliorate kidney injuries in DN mice via the TLR4/NF-κB/NLRP3 pathway and inhibit JNK/P38 pathway-mediated apoptosis in DN mice.

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Purpose

To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

Patients and methods

A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA).

Results

T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively.

Conclusion

The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

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.

Longitudinal metabolomics integrated with machine learning identifies novel biomarkers of gestational diabetes mellitus

BACKGROUND

Evidence from longitudinal studies is crucial to enhance our understanding of the role of metabolites in the progression of gestational diabetes mellitus (GDM). Herein, a longitudinal untargeted metabolomic study was conducted to reveal the metabolomic profiles and biomarkers associated with the progression of GDM, and characterize the changing patterns of metabolites.

METHODS

We collected serum samples at three trimesters from 30 patients with GDM and 30 healthy Chinese pregnant women with pre-pregnancy BMI, age, and parity matched, and untargeted metabolomic analysis was performed, followed by machine learning approaches that integrated bootstrap and LASSO. Cluster analysis was conducted to elucidate the patterns of metabolite changes. Pathway analyses were conducted to gain insights into the underlying pathways involved.

RESULTS

A total of 32 metabolites, mainly belonging to amino acid and its derivatives, were significantly associated with GDM across three trimesters, and were clustered into three distinct patterns. Metabolites belonging to phosphatidylcholines, lysophosphatidylcholines, lysophosphatidic acids, and lysophosphatidylethanolamines were consistently upregulated, and 2,3-Dihydroxypropyl dihydrogen phosphate was downregulated in GDM group. Amino acid-related, glycerophospholipid, and vitamin B6 metabolism were enriched in multiple trimesters. The levels of allantoic acid, which was positively correlated with blood glucose, was consistently higher in GDM patients and exhibited good discriminatory ability for GDM in the early and mid-pregnancy.

CONCLUSION

We identified and characterized distinct patterns of metabolites associated with GDM throughout pregnancy, and found that allantoic acid was a potential biomarker for early diagnosis of GDM.

Active role of amino acid metabolism in early diagnosis and treatment of diabetic kidney disease

Diabetic Kidney Disease (DKD) is one of the significant microvascular consequences of type 2 diabetes mellitus with a complex etiology and protracted course. In the early stages of DKD, the majority of patients experience an insidious onset and few overt clinical symptoms and indicators, but they are prone to develop end-stage renal disease in the later stage, which is life-threatening. The abnormal amino acid metabolism is tightly associated with the development of DKD, which involves several pathological processes such as oxidative stress, inflammatory response, and immune response and is also closely related to autophagy, mitochondrial dysfunction, and iron death. With a focus on taurine, branched-chain amino acids (BCAAs) and glutamine, we explored the biological effects of various amino acid mechanisms linked to DKD, the impact of amino acid metabolism in the early diagnosis of DKD, and the role of amino acid metabolism in treating DKD, to offer fresh objectives and guidelines for later early detection and DKD therapy.

Metabolomic Investigation of Blood and Urinary Amino Acids and Derivatives in Patients with Type 2 Diabetes Mellitus and Early Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease; however, few biomarkers of its early identification are available. The aim of the study was to assess new biomarkers in the early stages of DKD in type 2 diabetes mellitus (DM) patients. This cross-sectional pilot study performed an integrated metabolomic profiling of blood and urine in 90 patients with type 2 DM, classified into three subgroups according to albuminuria stage from P1 to P3 (30 normo-, 30 micro-, and 30 macroalbuminuric) and 20 healthy controls using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI* MS). From a large cohort of separated and identified molecules, 33 and 39 amino acids and derivatives from serum and urine, respectively, were selected for statistical analysis using Metaboanalyst 5.0. online software. The multivariate and univariate algorithms confirmed the relevance of some amino acids and derivatives as biomarkers that are responsible for the discrimination between healthy controls and DKD patients. Serum molecules such as tiglylglycine, methoxytryptophan, serotonin sulfate, 5-hydroxy lysine, taurine, kynurenic acid, and tyrosine were found to be more significant in the discrimination between group C and subgroups P1-P2-P3. In urine, o-phosphothreonine, aspartic acid, 5-hydroxy lysine, uric acid, methoxytryptophan, were among the most relevant metabolites in the discrimination between group C and DKD group, as well between subgroups P1-P2-P3. The identification of these potential biomarkers may indicate their involvement in the early DKD and 2DM progression, reflecting kidney injury at specific sites along the nephron, even in the early stages of DKD.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Candidate MicroRNA Biomarkers in Lupus Nephritis: A Meta-analysis of Profiling Studies in Kidney, Blood and Urine Samples

CONTEXT

Lupus nephritis (LN) is a kidney disease caused by systemic lupus erythematosus in which kidneys are attacked by the immune system. So far, various investigations have reported altered miRNA expression profiles in LN patients and different miRNAs have been introduced as biomarkers and/or therapeutic targets in LN. The aim of this study was to introduce a consensus panel of potential miRNA biomarkers by performing a meta-analysis of miRNA profiles in the LN patients.

MATERIALS AND METHODS

A comprehensive literature review approach was performed to find LN-related miRNA expression profiles in renal tissues, blood, and urine samples. After selecting the eligible studies and performing the data extraction, meta-analysis was done based on the vote-counting rank strategy as well as meta-analysis of p-values. The meta-miRNAs and their related genes were subjected to functional enrichment analyses and network construction.

RESULTS

The results of the meta-analysis of 41 studies were three lists of consensus miRNAs with altered expression profiles in the various tissue samples of LN patients (meta-analysis of p-values < 0.05). Of the 13 studies on kidney tissue, the meta-miRNAs were let-7a, miR-198, let-7e, miR-145, and miR-26a. In addition, meta-miRNAs of miR-199a, miR-21, miR-423, miR-1260b, miR-589, miR-150, miR-155, miR-146a, and miR-183 from 21 studies on blood samples, and miR-146a, miR-204, miR-30c, miR-3201, and miR-1273e from 11 studies on urine samples can be considered as non-invasive biomarker panels for LN. Functional enrichment analysis on the meta-miRNA lists confirmed the involvement of their target genes in nephropathy-related signaling pathways.

CONCLUSION

Using a meta-analytical approach, our study proposes three meta-miRNA panels that could be the target of further research to assess their potential as therapeutic targets/biomarkers in LN disease.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

METHODS

ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

RESULTS

Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

CONCLUSION

By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Potential Plasma Metabolite Biomarkers of Diabetic Nephropathy: Untargeted Metabolomics Study

Diabetic nephropathy (DN) is one of the specific complications of diabetes mellitus and one of the leading kidney-related disorders, often requiring renal replacement therapy. Currently, the tests commonly used for the diagnosis of DN, albuminuria (AU) and glomerular filtration rate (GFR), have limited sensitivity and specificity and can usually be noted when typical morphological changes in the kidney have already been manifested. That is why the extreme urgency of the problem of early diagnosis of this disease exists. The untargeted metabolomics analysis of blood plasma samples from 80 patients with type 1 diabetes and early and late stages of DN according to GFR was performed using direct injection mass spectrometry and bioinformatics analysis for diagnosing signatures construction. Among the dysregulated metabolites, combinations of 15 compounds, including amino acids and derivatives, monosaccharides, organic acids, and uremic toxins were selected for signatures for DN diagnosis. The selected metabolite combinations have shown high performance for diagnosing of DN, especially for the late stage (up to 99%). Despite the metabolite signature determined for the early stage of DN being characterized by a diagnostic performance of 81%, these metabolites as potential biomarkers might be useful in the evaluation of treatment of the disease, especially at early stages that may reduce the risk of kidney failure development.

Early renal structural changes and potential biomarkers in diabetic nephropathy

Diabetic nephropathy is one of the most serious microvascular complications of diabetes mellitus, with increasing prevalence and mortality. Currently, renal function is assessed clinically using albumin excretion rate and glomerular filtration rate. But before the appearance of micro-albumin, the glomerular structure has been severely damaged. Glomerular filtration rate based on serum creatinine is a certain underestimate of renal status. Early diagnosis of diabetic nephropathy has an important role in improving kidney function and delaying disease progression with drugs. There is an urgent need for biomarkers that can characterize the structural changes associated with the kidney. In this review, we focus on the early glomerular and tubular structural alterations, with a detailed description of the glomerular injury markers SMAD1 and Podocalyxin, and the tubular injury markers NGAL, Netrin-1, and L-FABP in the context of diabetic nephropathy. We have summarized the currently studied protein markers and performed bioprocess analysis. Also, a brief review of proteomic and scRNA-seq method in the search of diabetic nephropathy.

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

Discovery of Synergistic Drug Combinations for Colorectal Cancer Driven by Tumor Barcode Derived from Metabolomics “Big Data”

The accumulation of cancer metabolomics data in the past decade provides exceptional opportunities for deeper investigations into cancer metabolism. However, integrating a large amount of heterogeneous metabolomics data to draw a full picture of the metabolic reprogramming and to discover oncometabolites of certain cancers remains challenging. In this study, a tumor barcode constructed based upon existing metabolomics “big data” using the Bayesian vote-counting method is proposed to identify oncometabolites in colorectal cancer (CRC). Specifically, a panel of oncometabolites of CRC was generated from 39 clinical studies with 3202 blood samples (1332 CRC vs. 1870 controls) and 990 tissue samples (495 CRC vs. 495 controls). Next, an oncometabolite-protein network was constructed by combining the tumor barcode and its involved proteins/enzymes. The effect of anti-cancer drugs or drug combinations was then mapped into this network by the random walk with restart process. Utilizing this network, potential Irinotecan (CPT-11)-sensitizing agents for CRC treatment were discovered by random forest and Xgboost. Finally, a compound named MK-2206 was highlighted and its synergy with CPT-11 was validated on two CRC cell lines. To summarize, we demonstrate in the present study that the metabolomics “big data”-based tumor barcodes and the subsequent network analyses are potentially useful for drug combination discovery or drug repositioning.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.