Relationships between the abundance of 29 proteins and several meat or carcass quality traits in two bovine muscles revealed by a combination of univariate and multivariate analyses.

We aimed to evaluate the relationships between meat or carcass properties and the abundance of 29 proteins quantified in two muscles, Longissimus thoracis and Rectus abdominis, of Rouge des Prés cows. The relative abundance of the proteins was evaluated using a high throughput immunological method: the Reverse Phase Protein array. A combination of univariate and multivariate analyses has shown that small HSPs (CRYAB, HSPB6), fast glycolytic metabolic and structural proteins (MYH1, ENO3, ENO1, TPI1) when assayed both in RA and LT, were related to meat tenderness, marbling, ultimate pH, as well as carcass fat-to-lean ratio or conformation score. In addition to some small HSP, ALDH1A1 and TRIM72 contributed to the molecular signature of muscular and carcass adiposity. MYH1 and HSPA1A were among the top proteins related to carcass traits. We thus shortened the list to 10 putative biomarkers to be considered in future tools to manage both meat and carcass properties. SIGNIFICANCE: In three aspects this manuscript is notable. First, this is the first proteomics study that aims to evaluate putative biomarkers of both meat and carcass qualities that are of economic importance for the beef industry. Second, the relationship between the abundance of proteins and the carcass or meat traits were evaluated by a combination of univariate and multivariate analyses on 48 cows that are representative of the biological variability of the traits. Third, we provide a short list of ten proteins to be tested in a larger population to feed the pipeline of biomarker discovery.

Utility of 18F-Fluorodeoxyglucose Positron Emission Tomography in Inflammatory Rheumatism, Particularly Polymyalgia Rheumatica: A Retrospective Study of 222 PET/CT.

Purpose: The objective of this study was to evaluate periarticular FDG uptake scores from 18F-FDG-PET/CT to identify polymyalgia rheumatica (PMR) within a population presenting rheumatic diseases. Methods: A French retrospective study from 2011 to 2015 was conducted. Patients who underwent 18F-FDG-PET/CT for diagnosis or follow-up of a rheumatism or an unexplained biological inflammatory syndrome were included. Clinical data and final diagnosis were reviewed. Seventeen periarticular sites were sorted by a visual reading enabling us to calculate two scores: mean FDG visual uptake score, number of sites with significant uptake same as that or higher than liver uptake intensity and by a semi-quantitative analysis using mean maximum standardized uptake value (SUVmax). Optimal cutoffs of visual score and SUVmax to diagnose PMR were determined using receiver operating characteristics curves. Results: Among 222 18F-FDG PET/CT selected for 215 patients, 161 18F-FDG PET/CT were performed in patients who presented inflammatory rheumatism as a final diagnosis (of whom 57 PMR). The presence of at least three sites with significant uptake identified PMR with a sensitivity of 86% and a specificity of 85.5% (AUC 0.872, 95% CI [0.81-0.93]). The mean FDG visual score cutoff to diagnose a PMR was 0.765 with a sensitivity of 82.5% and a specificity of 75.8% (AUC 0.854; 95% CI [0.80-0.91]). The mean SUVmax cutoff to diagnose PMR was 2.168 with a sensitivity of 77.2% and a specificity of 77.6% (AUC 0.842; 95% CI [0.79-0.89]). Conclusions: This study suggests that 18F-FDG PET/CT had good performances to identify PMR within a population presenting rheumatic diseases.

Rarity of microbial species: In search of reliable associations.

The role of microbial interactions in defining the properties of microbiota is a topic of key interest in microbial ecology. Microbiota contain hundreds to thousands of operational taxonomic units (OTUs), most of them rare. This feature of community structure can lead to methodological difficulties: simulations have shown that methods for detecting pairwise associations between OTUs, which presumably reflect interactions, yield problematic results. The performance of association detection tools is impaired when there is a high proportion of zeros in OTU tables. Our goal was to understand the impact of OTU rarity on the detection of associations. We explored the utility of common statistics for testing associations; the sensitivity of alternative association measures; and the performance of network inference tools. We found that a large proportion of pairwise associations, especially negative associations, cannot be reliably tested. This constraint could hamper the identification of candidate biological agents that could be used to control rare pathogens. Identifying testable associations could serve as an objective method for filtering datasets in lieu of current empirical approaches. This trimming strategy could significantly reduce the computational time needed to infer networks and network inference quality. Different possibilities for improving the analysis of associations within microbiota are discussed.