Analysing the meta-interaction between pathways by gene set topological impact analysis

Esm-1 mediates transcriptional polarization associated with diabetic kidney disease

Esm-1, endothelial cell-specific molecule-1, is a susceptibility gene for diabetic kidney disease (DKD) and is a secreted proteoglycan, with notable expression in kidney, which attenuates inflammation and albuminuria. However, little is known about Esm1 expression in mature tissues in the presence or absence of diabetes. We utilized publicly available single-cell RNA sequencing data to characterize Esm1 expression in 27,786 renal endothelial cells (RECs) obtained from three mouse and four human databases. We validated our findings using bulk transcriptome data from 20 healthy subjects and 41 patients with DKD and using RNAscope. In both mice and humans, Esm1 is expressed in a subset of all REC types and represents a minority of glomerular RECs. In patients, Esm1(+) cells exhibit conserved enrichment for blood vessel development genes. With diabetes, these cells are fewer in number and shift expression toward chemotaxis pathways. Esm1 correlates with a majority of genes within these pathways, delineating a glomerular transcriptional polarization reflected by the magnitude of Esm1 deficiency. Diabetes correlates with lower Esm1 expression and with changes in the functional characterization of Esm1(+) cells. Thus, Esm1 appears to be a marker for glomerular transcriptional polarization in DKD.NEW & NOTEWORTHY Esm-1 is primarily expressed in glomerular endothelium in humans. Cells expressing Esm1 exhibit a high degree of conservation in the enrichment of genes related to blood vessel development. In the context of diabetes, these cells are reduced in number and show a significant transcriptional shift toward the chemotaxis pathway. In diabetes, there is a transcriptional polarization in the glomerulus that is reflected by the degree of Esm1 deficiency.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Esm-1 mediates transcriptional polarization associated with diabetic kidney disease

Background

Esm-1, endothelial cell-specific molecule-1, is a susceptibility gene for diabetic kidney disease (DKD) and is a cytokine- and glucose-regulated, secreted proteoglycan, that is notably expressed in kidney and attenuates inflammation and albuminuria. Esm1 has restricted expression at the vascular tip during development but little is known about its expression pattern in mature tissues, and its precise effects in diabetes.

Methods

We utilized publicly available single-cell RNA sequencing data to explore the characteristics of Esm1 expression in 27,786 renal endothelial cells obtained from four adult human and three mouse databases. We validated our findings using bulk transcriptome data from an additional 20 healthy subjects and 41 patients with DKD and using RNAscope. Using correlation matrices, we relate Esm1 expression to the glomerular transcriptome and evaluated these matrices with systemic over-expression of Esm-1.

Results

In both mice and humans, Esm1 is expressed in a subset of all renal endothelial cell types and represents a minority of glomerular endothelial cells. In patients, Esm1 (+) cells exhibit a highly conserved enrichment for blood vessel development genes. With diabetes, these cells are fewer in number and profoundly shift expression to reflect chemotaxis pathways. Analysis of these gene sets highlight candidate genes such as Igfbp5 for cross talk between cell types. We also find that diabetes induces correlations in the expression of large clusters of genes, within cell type-enriched transcripts. Esm1 significantly correlates with a majority genes within these clusters, delineating a glomerular transcriptional polarization reflected by the magnitude of Esm1 deficiency. In diabetic mice, these gene clusters link Esm1 expression to albuminuria, and over-expression of Esm-1 reverses the expression pattern in many of these genes.

Conclusions

A comprehensive analysis of single cell and bulk transcriptomes demonstrates that diabetes correlates with lower Esm1 expression and with changes in the functional characterization of Esm1 (+) cells. Esm1 is both a marker for glomerular transcriptional polarization, and a mediator that re-orients the transcriptional program in DKD.

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction

Motivation

During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.

Results

To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery.

Availability and implementation

The code and data underlying this article are freely available at https://github.com/dubingxue/MLGL-MP.