Metabolomics Study for Identification of Potential Biomarkers of Long-term Survival in Kidney Transplantation Recipients

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

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.

Combining the advantages of multilevel and orthogonal partial least squares data analysis for longitudinal metabolomics: Application to kidney transplantation

Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.

Metabolomics research on potential role for 9-cis-retinoic acid in breast cancer progression

Deciphering the molecular networks that discriminate organ-confined breast cancer from metastatic breast cancer may lead to the identification of critical biomarkers for breast cancer invasion and aggressiveness. Here metabolomics, a global study of metabolites, has been applied to explore the metabolic alterations that characterize breast cancer progression. We profiled a total of 693 metabolites across 87 serum samples related to breast cancer (46 clinically localized and 41 metastatic breast cancer) and 49 normal samples. These unbiased metabolomic profiles were able to distinguish normal individuals, clinically localized and metastatic breast cancer patients. 9-cis-Retinoic acid, an isomer of all-trans retinoic acid, was identified as a differential metabolite that significantly decreased during breast cancer progression to metastasis, and its levels were also reduced in urine samples from biopsy-positive breast cancer patients relative to biopsy-negative individuals and in invasive breast cancer cells relative to benign MCF-10A cells. The addition of exogenous 9-cis-retinoic acid to MDA-MB-231 cells and knockdown of aldehyde dehydrogenase 1 family member A1, a regulatory enzyme for 9-cis-retinoic acid, remarkably impaired cell invasion and migration, presumably through preventing the key regulator cofilin from activation and inhibiting MMP2 and MMP9 expression. Taken together, our study showed the potential inhibitory role for 9-cis-retinoic acid in breast cancer progression by attenuating cell invasion and migration.