Baratt, Arie | Search NASA

DOE OSTI2017

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics: HIP BMD LOSS & FRACTURE RISK MARKERS BY POPULATION-BASED SERUM PROTEOMICS

Accelerated bone loss significantly increases the risk of osteoporosis and fracture. The mechanisms underlying bone loss remain incompletely understood, and there are few available biomarkers. We utilized a novel proteomics approach to identify serum peptides and proteins associated with bone loss in 1967 older men who were randomly chosen from the Osteoporotic Fracture in Men Study (MrOS study) (age ≥ 65 yrs). Men had 2-3 measures of femoral neck BMD over an average follow-up of 4.6 years. Change in BMD was estimated and then categorized into three groups: maintained BMD (n=453), expected loss (n=1185) and accelerated loss (n=237). A liquid chromatography–ion mobility separation-mass spectrometry (LC-IMS-MS) proteomics platform was used to identify and quantify peptides from serum proteins. The whole cohort was randomly divided into discovery (N= 960) and validation (N= 915) sub-cohorts. Linear regression models and a random forest approach were used to discover differentially abundant individual peptides and a proteomic signature that distinguished individuals with accelerated bone loss from those who maintained BMD. Network analyses were performed using the MetaCore knowledgebase. We identified 12 peptides that were associated with BMD loss in both discovery (P< 0.1 FDR) and replication sub-cohorts (P<0.05). Those 12 peptides mapped to the following proteins: ALS, LYVE1, RNAS1, C2, ICOSL, C163A, C7, HEMO, CD14, CERU, CRAC1 and CD59. Meta-analysis of peptidesassociated with bone loss identified 6 additional proteins including GRP78, IGF-2, SHBG, ENPP2, IBP2 and IBP6. We also identified a proteomic signature that was predictive of BMD loss with a discriminative value similar to serum bone marker carboxy-terminal collagen crosslink peptide (CTX). Interestingly, combining the proteomic signature with CTX significantly improved the ability to discriminate men with accelerated loss. In summary, we have identified potential new biomarkers for bone loss that provide a more in depth understanding of its pathophysiology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Protein Co-Expression Network Analysis (ProCoNA)

Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Environmental Molecular Sciences Laboratory↗