Project description: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.

Project description:A study was conducted to understand the differentially expressed genes in Pectoralis (P) major under woody breast (WB) myopathy condition in a high yielding broiler strain using RNA-sequencing at the growing (d 21) and finishing (d 42 and d 56) grow-out ages. Follow-up study was conducted to understand the in vivo triglyceride (TG) synthesis (d 49) occurring in adipogenic tissues using deuterium oxide (2H2O) as a metabolic tracer. Results indicated the top physiological systems affected in myopathy broiler were related to the musculo-skeletal system (d 21, 42, and 56) and cardiovascular system (d 42 and 56). Ubiquitin-specific proteases are expressed higher in myopathy broiler at d 21 (OTUD1) and d 42 (SACS) that potentially indicated higher degradation of muscle protein occurring at those ages. While genes related to transcription factors and muscle cell differentiation (ZNF234, BTG2) and muscle growth (IGF1) were upregulated with myopathy broiler suggesting concurrent muscle fiber regeneration. The downregulation of PYGB and MGAM genes related to carbohydrate transport and metabolism at d 42 potentially indicated nutrient-deficient state of myopathy affected fibers; whereas the nutrient-deficient physiological state of cells seemed to be counteracted by up-regulation of genes related to carbohydrate (ALDOB, GPD1L2) at d 56. There was a reduced (P < 0.05) in vivo TG synthesis in liver of the myopathy broiler (0.123 %/hr) compared to non-myopathy broiler (0.197 %/hr). The majority of TG synthesized in liver with myopathy broiler could conceivably be delivered to P. major (rather than to abdominal fat pad storage) to fulfil the increased energy need of muscle cells (via TG lipolysis and fatty acid [FA] oxidation). The increased utilization of FAs in the WB affected muscle could result in reduced secretion of FAs into blood circulation leading to sub-optimal availability of FAs for re-esterification for TG synthesis in liver. Results indicated that myopathy broiler at later age (d 56) of grow-out period were synchronously going through adaptive physiological processes of feedback responses to adverse cellular states.

Project description:Collagen type IV (COL4) is one of the major components of animals' and humans' basement membranes of several tissues, such as skeletal muscles and vascular endothelia. Alterations in COL4 assembly and secretion are associated to muscular disorders in humans and animals among which growth-related abnormalities such as white striping and wooden breast affecting Pectoralis major muscles (PMs) in modern fast-growing (FG) chickens. Considering the high prevalence of these myopathies in FG broilers and that a worsening is observed as the bird slaughter age is increased, the present study was intended to evaluate the distribution and the expression level of COL4 protein and its coding genes in PMs of FG broilers at different stages of muscle development (i.e., 7, 14, 21, 28, 35, and 42 d of age). Medium-growing (MG) chickens have been considered as the control group in consideration of the lower selection pressure on breast muscle growth rate and hypertrophy. Briefly, 5 PM/sampling time/genotype were selected for western blot, immunohistochemistry (IHC), and gene expression analyses. The normalized expression levels of COL4 coding genes showed an overexpression of COL4A2 in FG than MG at d 28, as well as a significant decrease in its expression over their rearing period. Overall, results obtained through the gene expression analysis suggested that selection for the hypertrophic growth of FG broilers may have led to an altered regulation of fibroblast proliferation and COL4 synthesis. Moreover, western blot and IHC analyses suggested an altered secretion and/or degradation of COL4 protein in FG broilers, as evidenced by the fluctuating trend of 2 bands observed in FG over time. In view of the above, the present research supports the evidence about a potential aberrant synthesis and/or degradation of COL4 and corroborates the hypothesis regarding a likely involvement of COL4 in the series of events underlying the growth-related abnormalities in modern FG broilers.

Project description:The "Woody" or "Wooden" breast disease is a severe myopathy of pectoralis major muscle recently identified within rapidly growing broiler lines all around the world with a prevalence rate around 20%, or even higher. Although of significant ethical and economic impact, little is known regarding the structural and functional aspects of the contractile apparatus in the woody breast muscle. The aim of the present study was to determine physiological properties of the contractile system in the morphologically intact muscle fibers of focally damaged woody breast in comparison with normal muscle fibers to gain insight into the muscle function of the animal and possibly mechanisms involved in the disease development. Muscle samples were taken from woody breast (non-lesioned areas) and normal breast muscles from broilers. Length-tension curves, maximal active stress, maximal shortening velocity, calcium sensitivity, rate of tension development, lattice spacing and muscle biochemical composition were investigated on single skinned fibers. Sarcomeres of woody breast fibers were more compliant, which is very likely related to the wider spacing (18% wider compared to controls) between thick and thin filament. No differences were found in optimal sarcomere length (2.68 ± 0.04 vs. 2.65 ± 0.05 ?m) nor in maximal active stress (116 ± 17 vs. 125 ± 19 mN mm-2). However, woody breast fibers had less steep descending arm as shown in length-tension curve. Woody breast muscle fibers had 40% bigger sarcomeric volume compared to controls. Content of contractile proteins (myosin and actin), and maximal shortening velocity were unchanged indicating that the growth in woody breast muscle fiber was associated with synthesis of new contractile units with unaltered kinetics. Calcium sensitivity was decreased in woody breast muscle fibers significantly. In conclusion, the results show that the rapid growth of muscle in woody breast disease is associated with significant structural and functional changes in the pectoralis major musculature, associated with alterations in the mechanical anchoring of contractile filaments.

Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

Project description:BackgroundThe advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction.ResultsComparison between 16S rRNA amplicon and shotgun sequencing approaches as well as metagenomic analytical tools showed differences in normalized microbial abundance, especially for organisms present at low abundance. Shotgun sequence data analyzed using Kraken2 and Bracken, for taxonomic annotation, had higher detection sensitivity. As classification models are limited to labeling pre-trained origins, we took an alternative approach using Lasso-regularized multivariate regression to predict geographic coordinates for comparison. In both models, the prediction errors were much higher in Leave-1-city-out than in 10-fold cross validation, of which the former realistically forecasted the increased difficulty in accurately predicting samples from new origins. This challenge was further confirmed when applying the model to a set of samples obtained from new origins. Overall, the prediction performance of the regression and classification models, as measured by mean squared error, were comparable on mystery samples. Due to higher prediction error rates for samples from new origins, we provided an additional strategy based on prediction ambiguity to infer whether a sample is from a new origin. Lastly, we report increased prediction error when data from different sequencing protocols were included as training data.ConclusionsHerein, we highlight the capacity of predicting sample origin accurately with pre-trained origins and the challenge of predicting new origins through both regression and classification models. Overall, this work provides a summary of the impact of sequencing technique, protocol, taxonomic analytical approaches, and machine learning approaches on the use of metagenomics for prediction of sample origin.

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Evaluation via Supervised Machine Learning of the Broiler Pectoralis Major and Liver Transcriptome in Association with the Muscle Myopathy Wooden Breast

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