mGWAS: next generation genetic prediction in kidney disease

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

BACKGROUND

Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression.

SUMMARY

This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes.

KEY MESSAGE

Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.

Mannose and glycine: Metabolites with potentially causal implications in chronic kidney disease pathogenesis

BACKGROUND

Chronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.

METHODS

Utilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.

RESULTS

Our analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.

CONCLUSION

This study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD's pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.

Serum metabolic profile and metabolome genome-wide association study in chicken

BACKGROUND

Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied.

RESULTS

Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids.

CONCLUSIONS

We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.