The proteogenomic path towards biomarker discovery

Abstract

The desire for biomarkers for diagnosis and prognosis of diseases has never been greater. With the availability of genome data and an increased availability of proteome data, the discovery of biomarkers has become increasingly feasible. However, the task is daunting and requires collaborations among researchers working in the fields of transplantation, immunology, genetics, molecular biology, biostatistics and bioinformatics. With the advancement of high throughput omic techniques such as genomics and proteomics (collectively known as proteogenomics), efforts have been made to develop diagnostic tools from new and to-be discovered biomarkers. Yet biomarker validation, particularly in organ transplantation, remains challenging because of the lack of a true gold standard for diagnostic categories and analytical bottlenecks that face high-throughput data deconvolution. Even though microarray technique is relatively mature, proteomics is still growing with regards to data normalization and analysis methods. Study design, sample selection and rigorous data analysis are the critical issues for biomarker discovery using high-throughput proteogenomic technologies that combine the use and strengths of both genomics and proteomics. In this review, we look into the current status and latest developments in the field of biomarker discovery using genomics and proteomics related to organ transplantation, with an emphasis on the evolution of proteomic technologies.

Figure 1

An array of proteomic and genomic techniques that are being used for biomarker discovery. 2D gel/DIGE – 2 dimensional gel electrophoresis and difference gel electrophoresis; qRT-PCR – real-time quantitative PCR; LC-MS/MS – liquid chromatography mass spectrometry/mass spectrometry; SELDI MS - Surface-enhanced laser desorption/ionization mass spectrometry; LC MALDI – Liquid Chromatography Matrix Assisted Laser Desorption/Ionization.

Figure 2

Proteogenomics: Linking microarray and proteomic data. β2-microglobulin has been reported as a biomarker for acute renal allograft rejection. Panel A shows significant rise in the gene expression level of β-microglobulin in some rejection subtypes (AR I and AR II) from from previously published microarray data [3], Panel B shows significant rise in β2-microglobulin as measured by ELISA (Sigdel and Sarwal, unpublished data), and Panel C, a work published by Oetting et al using MALDI TOF [18]. This shows a similar and significant trend of β2-microglobulin in the graft and urine during the process of acute transplant rejection.

Figure 3. The use of 2D difference gel electrophoresis (DIGE) is an attractive tool to identify protein biomarker candidates that are differentially present in healthy and disease condition

A DIGE gel was run to see the treatment response on the urinary proteins of renal patients who had undergone through biopsy proven acute rejection (AR) episode. The urinary protein samples from AR and post-AR were labeled with Cy3-Dye (green) and Cy5-Dye (red) respectively. The green spots are proteins elevated in AR whereas the red spots are proteins elevated level in post-AR urine. Proteins in red and green (highlighted in the inset A and B) can be used as a marker of treatment response and AR respectively.

Figure 4

A schematic for possible pathways for the discovery pipeline for biomarkers for disease using proteogenomics. The ability to cross-annotate microarray and proteomic platforms, using publicly available tools (e.g. GEO [58], SymAtlas: (http://symatlas.gnf.org), AILUN, [59]) will allow for the ability to identify significant biomarkers by different technologies, within similar diseases.