Genome-wide association studies in kidney transplantation: Advantages and constraints

Identification of Important Modules and Hub Gene in Chronic Kidney Disease Based on WGCNA

Chronic kidney disease (CKD) is an ongoing deterioration of renal function that often progresses to end-stage renal disease. In this study, we aimed to screen and identify potential key genes for CKD using the weighted gene coexpression network (WGCNA) analysis tool. Gene expression data related to CKD were screened from GEO database, and expression datasets of GSE66494 and GSE62792 were obtained. After discrete analysis of samples, WGCNA analysis was performed to construct gene coexpression module, and the correlation between the module and disease was calculated. The modules with a significant correlation with the disease were selected for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the interaction network of related molecules was constructed, and the high score subnetwork was selected, and the candidate key molecules were identified. A total of 882 DEGs were identified in the screening datasets. A subnetwork containing 6 nodes was found with a high score of 12.08, including CEBPZ, IFI16, LYAR, BRIX1, BMS1, and DDX18. DEGs could significantly differentiate CKD and healthy individuals in principal component analysis. In addition, the MEturquiose, MEred, and MEblue in group were significantly correlated with disease in WGCNA. These 6 hub genes were found to significantly discriminate between CKD and healthy controls in the validation dataset, suggesting that they could use these molecules as candidate markers to distinguish CKD from healthy people. Overall, our study indicated that 6 hub genes may play key roles in the occurrence and development of CKD.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

A Single-Nucleotide Polymorphism (rs1131243) of the Transforming Growth Factor Beta Signaling Pathway Contributes to Risk of Acute Rejection in Chinese Renal Transplant Recipients

BACKGROUND Acute rejection (AR) is a common complication of kidney transplantation. The transforming growth factor beta (TGF-ß) signaling pathway has been observed to be involved in several cellular functions. Our study aimed to investigate the correlations between single-nucleotide polymorphisms (SNPs) in TGF-ß-related genes and the risk of AR in renal transplant recipients. MATERIAL AND METHODS This retrospective, single-center study included 200 Chinese renal transplant recipients. All exons, exon/intron boundaries, and flanking regions of the TGF-ß signaling pathway were detected by targeting sequencing (TS) based on next-generation sequencing technology. Tagger SNPs and haplotypes were identified after adjustment. A general linear model (GLM) was used to explore the confounding effect of clinical variables. Five adjusted inheritance models were utilized to investigate the influence of SNPs on AR, and Banff score was applied to evaluate the effect of related SNPs on pathological changes. RESULTS A total of 188 SNPs on TGF-ß genes were detected. Analysis of adjustment led to identification of 31 tagger SNPs and 10 haplotype blocks. After the analysis of a general linear model and 5 sirolimus-adjusted multiple inheritance models, 1 of the SNPs - rs1131243 on the TGF-ßR3 gene - was observed to be significantly associated with the occurrence of AR. Based on Banff score, no significant association was observed between SNPs and pathological changes. CONCLUSIONS In this study, we observed that the SNP rs1131243 on the TGF-ßR3 gene was significantly associated with the occurrence of AR in Chinese renal transplant recipients.

Genomic approaches in the search for molecular biomarkers in chronic kidney disease

BACKGROUND

Chronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD.

MAIN BODY

Conventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management.

CONCLUSION

CKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases.