Abstract
The muscle myopathy wooden breast (WB) has recently appeared in broiler production and has a negative impact on meat quality. WB is described as hard/firm consistency found within the pectoralis major (PM). In the present study, we use machine learning from our PM and liver transcriptome dataset to capture the complex relationships that are not typically revealed by traditional statistical methods. Gene expression data was evaluated between the PM and liver of birds with WB and those that were normal. Two separate machine learning algorithms were performed to analyze the data set including the sequential minimal optimization (SMO) of support vector machines (SVMs) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN). Machine learning algorithms were compared to identify genes within a gene expression data set of approximately 16,000 genes for both liver and PM, which can be correctly classified from birds with or without WB. The performance of both machine learning algorithms SMO and MLP was determined using percent correct classification during the cross-validations. By evaluating the WB transcriptome datasets by 5× cross-validation using ANNs, the expression of nine genes ranked based on Shannon Entropy (Information Gain) from PM were able to correctly classify if the individual bird was normal or exhibited WB 100% of the time. These top nine genes were all protein coding and potential biomarkers. When PM gene expression data were evaluated between normal birds and those with WB using SVMs they were correctly classified 95% of the time using 450 of the top genes sorted ranked based on Shannon Entropy (Information Gain) as a preprocessing step. When evaluating the 450 attributes that were 95% correctly classified using SVMs through Ingenuity Pathway Analysis (IPA) there was an overlap in top genes identified through MLP. This analysis allowed the identification of critical transcriptional responses for the first time in both liver and muscle during the onset of WB. The information provided has revealed many molecules and pathways making up a complex molecular mechanism involved with the progression of wooden breast and suggests that the etiology of the myopathy is not limited to activity in the muscle alone, but is an altered systemic pathology.
Highlights
The occurrence of wooden breast (WB) in commercial poultry production is rising, leaving producers with an inferior product and, unsatisfied consumers (Mudalal et al, 2014; Petracci et al, 2015)
By using the novel approach of evaluating the WB pectoralis major (PM) transcriptome dataset by 5-fold cross-validation using Multilayer Perceptron (MLP), the expression of nine genes (NUP43, KPNA7, DEAF1, NUD19, CCDC85A, SLC25A30, ENSGAL00000015075, PACSIN3, and RPL19) were able to correctly classify if the PM tissue from an individual bird was normal or exhibited WB in 100% of the individual genomes when using the top nine genes ranked based on Shannon Entropy (Information Gain) (Figure 1)
The individual expression of transcripts CARD19 and ITCH predicted WB or normal using MLP 5-fold cross validation 93.333% of the time whereas BUD13 and ENSGALG00000039590 individually predicted WB or normal 100% of the time using 66% split (Table 3)
Summary
The occurrence of wooden breast (WB) in commercial poultry production is rising, leaving producers with an inferior product and, unsatisfied consumers (Mudalal et al, 2014; Petracci et al, 2015). Much of the incidence is thought to be attributed to artificial selection that has led to the development of broilers with greater muscle yield, better feed conversion rates, and faster growth. Frequent detection of muscle myopathies has been thought to be associated with increased growth rates and breast muscle yields (Sihvo et al, 2014; Trocino et al, 2015; Kuttappan et al, 2016; Abasht et al, 2019). Alterations of the meat composition have been observed including increased moisture, collagen, sodium, calcium and fat content (Zambonelli et al, 2014). Extensive histological evaluation of WB has been characterized by necrosis, chronic fibrosis, infiltration of fat and connective tissue, and the presence of inflammatory cells and macrophages (Sihvo et al, 2014; Trocino et al, 2015; Kuttappan et al, 2016; Papah et al, 2017)
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