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A classification modeling approach for determining metabolite signatures in osteoarthritis.

ABSTRACT: Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

SUBMITTER: Rockel JS

PROVIDER: S-EPMC6025859 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A classification modeling approach for determining metabolite signatures in osteoarthritis.

Rockel Jason S JS Zhang Weidong W Shestopaloff Konstantin K Likhodii Sergei S Sun Guang G Furey Andrew A Randell Edward E Sundararajan Kala K Gandhi Rajiv R Zhai Guangju G Kapoor Mohit M Kapoor Mohit M

PloS one 20180629 6

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator cur ...[more]

PMID: 29958292

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Project description:Introduction: Knee osteoarthritis (KOA) is characterized by articular cartilage degeneration. It has been widely accepted that the mechanical joint environment plays a significant role in the onset and progression of this disease. In silico models have been used to study the interplay between mechanical loading and cartilage degeneration, hereby relying mainly on two key mechanoregulatory factors indicative of collagen degradation and proteoglycans depletion. These factors are the strain in collagen fibril direction (SFD) and maximum shear strain (MSS) respectively. Methods: In this study, a multi-scale in silico modeling approach was used based on a synergy between musculoskeletal and finite element modeling to evaluate the SFD and MSS. These strains were evaluated during gait based on subject-specific gait analysis data collected at baseline (before a 2-year follow-up) for a healthy and progressive early-stage KOA subject with similar demographics. Results: The results show that both SFD and MSS factors allowed distinguishing between a healthy subject and a KOA subject, showing progression at 2 years follow-up, at the instance of peak contact force as well as during the stance phase of the gait cycle. At the peak of the stance phase, the SFD were found to be more elevated in the KOA patient with the median being 0.82% higher in the lateral and 0.4% higher in the medial compartment of the tibial cartilage compared to the healthy subject. Similarly, for the MSS, the median strains were found to be 3.6% higher in the lateral and 0.7% higher in the medial tibial compartment of the KOA patient compared to the healthy subject. Based on these intersubject SFD and MSS differences, we were additionally able to identify that the tibial compartment of the KOA subject at risk of progression. Conclusion/discussion: We confirmed the mechanoregulatory factors as potential biomarkers to discriminate patients at risk of disease progression. Future studies should evaluate the sensitivity of the mechanoregulatory factors calculated based on this multi-scale modeling workflow in larger patient and control cohorts.

Project description:BackgroundThe development of quantitative models of signal transduction, as well as parameter estimation to improve existing models, depends on the ability to obtain quantitative information about various proteins that are part of the signaling pathway. However, commonly-used measurement techniques such as Western blots and mobility shift assays provide only qualitative or semi-quantitative data which cannot be used for estimating parameters. Thus there is a clear need for techniques that enable quantitative determination of signal transduction intermediates.ResultsThis paper presents an integrated modeling and experimental approach for quantitatively determining transcription factor profiles from green fluorescent protein (GFP) reporter data. The technique consists of three steps: (1) creating data sets for green fluorescent reporter systems upon stimulation, (2) analyzing the fluorescence images to determine fluorescence intensity profiles using principal component analysis (PCA) and K-means clustering, and (3) computing the transcription factor concentration from the fluorescence intensity profiles by inverting a model describing transcription, translation, and activation of green fluorescent proteins. We have used this technique to quantitatively characterize activation of the transcription factor NF-kappaB by the cytokine TNF-alpha. In addition, we have applied the quantitative NF-kappaB profiles obtained from our technique to develop a model for TNF-alpha signal transduction where the parameters were estimated from the obtained data.ConclusionThe technique presented here for computing transcription factor profiles from fluorescence microscopy images of reporter cells generated quantitative data on the magnitude and dynamics of NF-kappaB activation by TNF-alpha. The obtained results are in good agreement with qualitative descriptions of NF-kappaB activation as well as semi-quantitative experimental data from the literature. The profiles computed from the experimental data have been used to re-estimate parameters for a NF-kappaB model and the results of additional experiments are predicted very well by the model with the new parameter values. While the presented approach has been applied to NF-kappaB and TNF-alpha signaling, it can be used to determine the profile of any transcription factor as long as GFP reporter fluorescent profiles are available.

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A classification modeling approach for determining metabolite signatures in osteoarthritis.

Publications

A classification modeling approach for determining metabolite signatures in osteoarthritis.

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