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Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.

ABSTRACT: Multi-omics research is a key ingredient of data-intensive life sciences research, permitting measurement of biological molecules at different functional levels in the same individual. For a complete picture at the biological systems level, appropriate statistical techniques must however be developed to integrate different 'omics' data sets (e.g., genomics and proteomics). We report here multivariate projection-based analyses approaches to genomics and proteomics data sets, using the case study of and applications to observations in kidney transplant patients who experienced an acute rejection event (n=20) versus non-rejecting controls (n=20). In this data sets, we show how these novel methodologies might serve as promising tools for dimension reduction and selection of relevant features for different analytical frameworks. Unsupervised analyses highlighted the importance of post transplant time-of-rejection, while supervised analyses identified gene and protein signatures that together predicted rejection status with little time effect. The selected genes are part of biological pathways that are representative of immune responses. Gene enrichment profiles revealed increases in innate immune responses and neutrophil activities and a depletion of T lymphocyte related processes in rejection samples as compared to controls. In all, this article offers candidate biomarkers for future detection and monitoring of acute kidney transplant rejection, as well as ways forward for methodological advances to better harness multi-omics data sets.

SUBMITTER: Gunther OP

PROVIDER: S-EPMC4229708 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.

Günther Oliver P OP Shin Heesun H Ng Raymond T RT McMaster W Robert WR McManus Bruce M BM Keown Paul A PA Tebbutt Scott J SJ Lê Cao Kim-Anh KA

Omics : a journal of integrative biology 20141101 11

Multi-omics research is a key ingredient of data-intensive life sciences research, permitting measurement of biological molecules at different functional levels in the same individual. For a complete picture at the biological systems level, appropriate statistical techniques must however be developed to integrate different 'omics' data sets (e.g., genomics and proteomics). We report here multivariate projection-based analyses approaches to genomics and proteomics data sets, using the case study ...[more]

PMID: 25387159

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Project description:Background and objectivesThe current allocation algorithm for deceased donor kidney transplantation takes into consideration HLA mismatches at the ABDR loci but not HLA mismatches at other loci, including HLA-DQ. However, the independent effects of incompatibilities for the closely linked HLA-DQ antigens in the context of HLA-DR antigen matched and mismatched allografts are uncertain. We aimed to determine the effect of HLA-DQ mismatches on renal allograft outcomes.Design, setting, participants, & measurementsUsing data from the Australia and New Zealand Dialysis and Transplant Registry, we examined the association between HLA-DQ mismatches and acute rejections in primary live and deceased donor kidney transplant recipients between 2004 and 2012 using adjusted Cox regression models.ResultsOf the 788 recipients followed for a median of 2.8 years (resulting in 2891 person-years), 321 (40.7%) and 467 (59.3%) received zero and one or two HLA-DQ mismatched kidneys, respectively. Compared with recipients who have received zero HLA-DQ mismatched kidneys, those who have received one or two HLA-DQ mismatched kidneys experienced greater numbers of any rejection (50 of 321 versus 117 of 467; P<0.01), late rejections (occurring >6 months post-transplant; 8 of 321 versus 27 of 467; P=0.03), and antibody-mediated rejections (AMRs; 12 of 321 versus 38 of 467; P=0.01). Compared with recipients of zero HLA-DQ mismatched kidneys, the adjusted hazard ratios for any and late rejections in recipients who had received one or two HLA-DQ mismatched kidneys were 1.54 (95% confidence interval [95% CI], 1.08 to 2.19) and 2.85 (95% CI, 1.05 to 7.75), respectively. HLA-DR was an effect modifier between HLA-DQ mismatches and AMR (P value for interaction =0.02), such that the association between HLA-DQ mismatches and AMR was statistically significant in those who have received one or two HLA-DR mismatched kidneys, with adjusted hazard ratio of 2.50 (95% CI, 1.05 to 5.94).ConclusionsHLA-DQ mismatches are associated with acute rejection, independent of HLA-ABDR mismatches and initial immunosuppression. Clinicians should be aware of the potential importance of HLA-DQ matching in the assessment of immunologic risk in kidney transplant recipients.

Dataset Information

Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.

Publications

Novel multivariate methods for integration of genomics and proteomics data: applications in a kidney transplant rejection study.

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