Biomarker Commons

Proteomics and Systems Biology for Understanding Diabetic Nephropathy.

J Cardiovasc Transl Res. 2012 May 12;

Authors: Starkey JM, Tilton RG

Abstract
Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

PMID: 22581264 [PubMed - as supplied by publisher]

Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics.

Proteomics. 2012 Apr;12(8):1074-92

Authors: Shi T, Su D, Liu T, Tang K, Camp DG, Qian WJ, Smith RD

Abstract
Selected reaction monitoring (SRM) - also known as multiple reaction monitoring (MRM) - has emerged as a promising high-throughput targeted protein quantification technology for candidate biomarker verification and systems biology applications. A major bottleneck for current SRM technology, however, is insufficient sensitivity for, e.g. detecting low-abundance biomarkers likely present at the low ng/mL to pg/mL range in human blood plasma or serum, or extremely low-abundance signaling proteins in cells or tissues. Herein, we review recent advances in methods and technologies, including front-end immunoaffinity depletion, fractionation, selective enrichment of target proteins/peptides including posttranslational modifications, as well as advances in MS instrumentation which have significantly enhanced the overall sensitivity of SRM assays and enabled the detection of low-abundance proteins at low- to sub-ng/mL level in human blood plasma or serum. General perspectives on the potential of achieving sufficient sensitivity for detection of pg/mL level proteins in plasma are also discussed.

PMID: 22577010 [PubMed - in process]

Development of systems biology-oriented biomarkers by permuted stepwise regression for the monitoring of seasonal allergic rhinitis treatment effects.

J Immunol Methods. 2012 Feb 12;

Authors: Baars EW, Nierop AF, Savelkoul HF

Abstract
BACKGROUND: The immune system, a complex set of integrated responses, often cannot be explained, predicted, or monitored by examining its separate components as biomarkers. Combining different components may therefore be a suitable approach to develop relevant biomarkers reflecting immune system functioning in an appropriate way. METHODS: Here we compute and test pattern variables that should reflect immune system functioning on the systems level. Computation was based on a dataset (from a randomized controlled trial comparing two routes of administration) of allergen-specifically induced expression levels of cytokines (IL-1β, IL-5, IL-10, IL-12, IL-13, IL-17, IFN-γ and TNF-α) and symptom severity scores from 22 seasonal allergic rhinitis (SAR) patients measured before and after six weeks of treatment with medicinal products containing Citrus and Cydonia. By means of stepwise regression analyses we explored and tested pattern variables of the immunological data using permuted stepwise regression (PStR) to distinguish optimally between (immunological) baseline and post-baseline data for the whole treatment group (22 patients) and the two separate treatment groups (11 patients in each group). The validity of the stepwise selection method for the computed pattern variables was tested by means of random permutation tests and evaluated with the cross-validated correct rate of classification (CV correct). RESULTS: For the total group a pattern variable was computed with three variables: IL-10 (day 7), TNF-α (day 1) and IL-10 (day 1) (CV correct: 0.91; p<0.001; R(2)=0.66), demonstrating a small improvement from the model with IL-10 (day 7) only (CV correct: 0.84; p<0.001; R(2)=0.47). For the subcutaneous injection group a pattern variable was computed with four variables: IL-10 (day 7), IL-10 (day 1), IL-17 (day 7) and IFN-γ (day 7) (CV correct: 0.90; p<0.01; R(2)=0.78), demonstrating a very small improvement from the model with IL-10 (day 7) only (CV correct: 0.86; p<0.01; R(2)=0.58). For the nasal spray group a pattern variable was computed with three variables: IL-10 (day 7), TNF-α (day 1) and IL-10 (day 1) (CV correct: 0.95; p<0.01; R(2)=0.79), demonstrating a moderate improvement from the model with IL-10 (day 7) only (CV correct: 0.79; p<0.05; R(2)=0.37). CONCLUSION/DISCUSSION: In this study three robust systems biology-oriented biomarkers for the monitoring of SAR were computed that demonstrated small to moderate improvement compared to monitoring of a single cytokine (IL-10 (day 7)) (CV correct improvement: 0.07 (total group), 0.04 (subcutaneous injection group), 0.16 (nasal spray group)). Further computation and biomarker validation with larger datasets, including data from healthy persons and SAR patients, are indicated.

PMID: 22349124 [PubMed - as supplied by publisher]

Data integration and systems biology approaches for biomarker discovery: Challenges and opportunities for multiple sclerosis.

J Neuroimmunol. 2012 Jan 24;

Authors: Villoslada P, Baranzini S

Abstract
New "omic" technologies and their application to systems biology approaches offer new opportunities for biomarker discovery in complex disorders, including multiple sclerosis (MS). Recent studies using massive genotyping, DNA arrays, antibody arrays, proteomics, glycomics, and metabolomics from different tissues (blood, cerebrospinal fluid, brain) have identified many molecules associated with MS, defining both susceptibility and functional targets (e.g., biomarkers). Such discoveries involve many different levels in the complex organizational hierarchy of humans (DNA, RNA, protein, etc.), and integrating these datasets into a coherent model with regard to MS pathogenesis would be a significant step forward. Given the dynamic and heterogeneous nature of MS, validating biomarkers is mandatory. To develop accurate markers of disease prognosis or therapeutic response that are clinically useful, combining molecular, clinical, and imaging data is necessary. Such an integrative approach would pave the way towards better patient care and more effective clinical trials that test new therapies, thus bringing the paradigm of personalized medicine in MS one step closer.

PMID: 22281286 [PubMed - as supplied by publisher]