Assessment of uremic toxins in advanced chronic kidney disease patients on maintenance hemodialysis by LC-ESI-MS/MS

INTRODUCTION

In the advanced stage of chronic kidney disease (CKD), electrolytes, fluids, and metabolic wastes including various uremic toxins, accumulate at high concentrations in the patients' blood. Hemodialysis (HD) is the conventional procedure used worldwide to remove metabolic wastes. The creatinine and urea levels have been routinely monitored to estimate kidney function and effectiveness of the HD process. This study, first from in Indian perspective, aimed at the identification and quantification of major uremic toxins in CKD patients on maintenance HD (PRE-HD), and compared with the healthy controls (HC) as well as after HD (POST-HD).

OBJECTIVES

The study mainly focused on the identification of major uremic toxins in Indian perspective and the quantitative analysis of indoxyl sulfate and p-cresol sulfate (routinely targeted uremic toxins), and phenyl sulfate, catechol sulfate, and guaiacol sulfate (targeted for the first time), apart from creatinine and urea in PRE-HD, POST-HD, and HC groups.

METHODS

Blood samples were collected from 90 HD patients (both PRE-HD and POST-HD), and 74 HCs. The plasma samples were subjected to direct ESI-HRMS and LC/HRMS for untargeted metabolomics and LC-MS/MS for quantitative analysis.

RESULTS

Various known uremic toxins, and a few new and unknown peaks were detected in PRE-HD patients. The p-cresol sulfate and indoxyl sulfate were dominant in PRE-HD, the concentrations of phenyl sulfate, catechol sulfate, and guaiacol sulfate were about 50% of that of indoxyl sulfate. Statistical evaluation on the levels of targeted uremic toxins in PRE-HD, POST-HD, and HC groups showed a significant difference among the three groups. The dialytic clearance of indoxyl sulfate and p-cresol sulfate was found to be < 35%, while that of the other three sulfates was 50-58%.

CONCLUSION

LC-MS/MS method was developed and validated to evaluate five major uremic toxins in CKD patients on HD. The levels of the targeted uremic toxins could be used to assess kidney function and the effectiveness of HD.

High-resolution HLA genotyping identifies alleles associated with severe COVID-19: A preliminary study from India

Introduction

Human leukocyte antigen (HLA) variability has been demonstrated to be associated with susceptibility/severity of COVID‐19. High‐resolution HLA genotyping to identify alleles associated with severe COVID‐19 in an Indian cohort was performed.

Methods

Quantitative reverse‐transcription polymerase chain reaction‐confirmed SARS‐CoV‐2‐positive patients with mild/moderate/severe disease (n = 54) and asymptomatic (n = 42) were recruited and genotyped for 11‐HLA loci on MiSeq using NGSgo®‐MX11‐3 and analyzed (NGSengine; GenDx).

Results

A significant difference in alleles between the groups was identified for HLA‐C*04:01:01:01, HLA‐DRB5*01:01:01:02, HLA‐DQA1*03:01:01:01, HLA‐DPB1*04:01:01:41, and HLA‐DPA1*01:03:01:02. Alleles namely, HLA‐C*04:01:01:01 (OR: 5.71; 95% CI: 1.2–27.14; p = .02), HLA‐DRB5*01:01:01:02 (OR: 2.94; 95% CI: 1.1–7.84; p = .03), DQA1*03:01:01:01 (OR: 22.47; 95% CI: 1.28–393.5; p = .03), HLA‐DPB1*04:01:01:41 (OR: 9.44; 95% CI: 0.5–175.81; p = .13), and HLA‐DPA1*01:03:01:02 (OR: 8.27; 95% CI: 2.26–30.21; p = .001) were associated with severe COVID‐19.

Conclusion

Genotyping for these alleles will enable identification of individuals at risk of severe disease and stratification for preferential vaccination.

Structure of Human TMPRSS2 in Complex with SARS-CoV-2 Spike Glycoprotein and Implications for Potential Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 520 million people around the globe resulting in more than 6.2 million as of May 2022. Understanding the cell entry mechanism of SARS-CoV-2 and its entire repertoire is a high priority for developing improved therapeutics. The SARS-CoV-2 spike glycoprotein (S-protein) engages with host receptor ACE2 for adhesion and serine proteases furin and TMPRSS2 for proteolytic activation and subsequent entry. Recent studies have highlighted the molecular details of furin and S-protein interaction. However, the structural and molecular interplay between TMPRSS2 and S-protein remains enigmatic. Here, using biochemical, structural, computational, and molecular dynamics approaches, we investigated how TMPRSS2 recognizes and activates the S-protein to facilitate viral entry. First, we identified three potential TMPRSS2 cleavage sites in the S2 domain of S-protein (S2', T1, and T2) and reported the structure of TMPRSS2 with its individual catalytic triad. By employing computational modeling and structural analyses, we modeled the macromolecular structure of TMPRSS2 in complex with S-protein, which incited the mechanism of S-protein processing or cleavage for a new path of viral entry. On the basis of structure-guided drug screening, we also report the potential TMPRSS2 inhibitors and their structural interaction in blocking TMPRSS2 activity, which could impede the interaction with the spike protein. These findings reveal the role of TMPRSS2 in the activation of SARS-CoV-2 for its entry and insight into possible intervention strategies.

Integration of metabolites from meta-analysis with transcriptome reveals enhanced SPHK1 in PDAC with a background of pancreatitis

BACKGROUND

Pathophysiology of transformation of inflammatory lesions in chronic pancreatitis (CP) to pancreatic ductal adenocarcinoma (PDAC) is not clear.

METHODS

We conducted a systematic review, meta-analysis of circulating metabolites, integrated this data with transcriptome analysis of human pancreatic tissues and validated using immunohistochemistry. Our aim was to establish biomarker signatures for early malignant transformation in patients with underlying CP and identify therapeutic targets.

RESULTS

Analysis of 19 studies revealed AUC of 0.86 (95% CI 0.81-0.91, P < 0.0001) for all the altered metabolites (n = 88). Among them, lipids showed higher differentiating efficacy between PDAC and CP; P-value (< 0.0001). Pathway enrichment analysis identified sphingomyelin metabolism (impact value-0.29, FDR of 0.45) and TCA cycle (impact value-0.18, FDR of 0.06) to be prominent pathways in differentiating PDAC from CP. Mapping circulating metabolites to corresponding genes revealed 517 altered genes. Integration of these genes with transcriptome data of CP and PDAC with a background of CP (PDAC-CP) identified three upregulated genes; PIGC, PPIB, PKM and three downregulated genes; AZGP1, EGLN1, GNMT. Comparison of CP to PDAC-CP and PDAC-CP to PDAC identified upregulation of SPHK1, a known oncogene.

CONCLUSIONS

Our analysis suggests plausible role for SPHK1 in development of pancreatic adenocarcinoma in long standing CP patients. SPHK1 could be further explored as diagnostic and potential therapeutic target.

Plasma Metabolite Profiling Identifies Nondiabetic Chronic Pancreatitis Patients With Metabolic Alterations Progressing to Prediabetes Before HbA1c

INTRODUCTION

Diabetes (T3cDM) secondary to chronic pancreatitis (CP) arises due to endocrine dysfunction and metabolic dysregulations. Currently, diagnostic tests are not available to identify patients who may progress from normoglycemia to hyperglycemia in CP. We conducted plasma metabolomic profiling to diagnose glycemic alterations early in the course of disease.

METHODS

Liquid chromatography-tandem mass spectrometry was used to generate untargeted, targeted plasma metabolomic profiles in patients with CP, controls (n = 445) following TRIPOD guidelines. Patients were stratified based on glucose tolerance tests following ADA guidelines. Multivariate analysis was performed using partial least squares discriminant analysis to assess discriminatory ability of metabolites among stratified groups. COMBIROC and logistic regression were used to derive biomarker signatures. AI-ML tool (Rapidminer) was used to verify these preliminary results.

RESULTS

Ceramide, lysophosphatidylethanolamine, phosphatidylcholine, lysophosphatidic acid (LPA), phosphatidylethanolamine, carnitine, and lysophosphatidylcholine discriminated T3cDM CP patients from healthy controls with AUC 93% (95% CI 0.81-0.98, P < 0.0001), and integration with pancreatic morphology improved AUC to 100% (95% CI 0.93-1.00, P < 0.0001). LPA, phosphatidylinositol, and ceramide discriminated nondiabetic CP with glycemic alterations (pre-diabetic CP); AUC 66% (95% CI 0.55-0.76, P = 0.1), and integration enhanced AUC to 74% (95% CI 0.55-0.88, P = 0.86). T3cDM was distinguished from prediabetic by LPA, phosphatidylinositol, and sphinganine (AUC 70%; 95% CI 0.54-0.83, P = 0.08), and integration improved AUC to 83% (95% CI 0.68-0.93, P = 0.05). CombiROC cutoff identified 75% and 78% prediabetes in validation 1 and 2 cohorts. Random forest algorithm assessed performance of integrated panel demonstrating AUC of 72% in predicting glycemic alterations.

DISCUSSION

We report for the first time that a panel of metabolites integrated with pancreatic morphology detects glycemia progression before HbA1c in patients with CP.