Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs

Abstract

Background Improving feed efficiency (FE) is an important goal due to its economic and environmental significance for farm animal production. The FE phenotype is complex and based on the measurements of the individual feed consumption and average daily gain during a test period, which is costly and time-consuming. The identification of reliable predictors of FE is a strategy to reduce phenotyping efforts. Results Gene expression data of the whole blood from three independent experiments were combined and analyzed by machine learning algorithms to propose molecular biomarkers of FE traits in growing pigs. These datasets included Large White pigs from two lines divergently selected for residual feed intake (RFI), a measure of net FE, and in which individual feed conversion ratio (FCR) and blood microarray data were available. Merging the three datasets allowed considering FCR values (Mean = 2.85; Min = 1.92; Max = 5.00) for a total of n = 148 pigs, with a large range of body weight (15 to 115 kg) and different test period duration (2 to 9 weeks). Random forest (RF) and gradient tree boosting (GTB) were applied on the whole blood transcripts (26,687 annotated molecular probes) to identify the most important variables for binary classification on RFI groups and a quantitative prediction of FCR, respectively. The dataset was split into learning (n = 74) and validation sets (n = 74). With iterative steps for variable selection, about three hundred’s (328 to 391) molecular probes participating in various biological pathways, were identified as important predictors of RFI or FCR. With the GTB algorithm, simpler models were proposed combining 34 expressed unique genes to classify pigs into RFI groups (100% of success), and 25 expressed unique genes to predict FCR values (R2 = 0.80, RMSE = 8%). The accuracy performance of RF models was slightly lower in classification and markedly lower in regression. Conclusion From small subsets of genes expressed in the whole blood, it is possible to predict the binary class and the individual value of feed efficiency. These predictive models offer good perspectives to identify animals with higher feed efficiency in precision farming applications.

Analysis of merged transcriptomic and genomic datasets to identify genes and pathways underlying residual feed intake in growing pigs

“…Fifteen DE-genes were upregulated in the high RFI group of pigs. Rank-based enrichment test for log fold change values showed that expression upregulation in the high RFI group was enriched for immune system associated annotations in agreement with previous studies 14 , 28 . The identified immune system response in colon epithelium might indicate a link between gut immune function, the gut microbiome and FE.…”

“…For instance, metagenomics research has shown an association of dozens of bacterial taxonomic units in gut microbiota with FE 8 , 11 , and metabolomics studies in combination with transcriptomic 9 and GWA studies 10 have revealed significant gene-metabolite pairs. Transcriptomic studies have found FE associated differentially expressed genes (DE-genes) in various tissue, including blood 14 , liver 12 , brain 15 and intestine 16 .…”

Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake

“…Besides the modest differences in morphology, piglets from the two lines differed in plasma metabolome. In a previous study on the same lines, the use of an NMR metabolomics approach discriminated LRFI and HRFI growing pigs, with the most influential metabolites being AA (phenylalanine, glutamate, and tryptophan) and AA derivates 21 . The present study showed major differences for AA in newborn piglets as well.…”

Transcription Analysis of Liver and Muscle Tissues from Landrace Finishing Pigs with Different Feed Conversion Ratios

“…However, putative FE genes participated in a wide range of biological processes [ 5 , 6 ] and in many functional pathways in different tissues through gene expression profiles [ 9 ], suggesting that the biological strategies used to improve FE are diverse. Tissue- and organ-specific transcriptomes and miRNA profiles have been widely used to elucidate the molecular basis of inter-individual differences in FE [ 10 , 11 , 12 , 13 ]. In pigs, FE-related transcriptome research has focused on energy homeostasis and energy demand [ 9 , 10 , 14 , 15 , 16 ], mainly in the liver and adipose tissue [ 17 ] and in intestinal, blood, and muscle tissue [ 13 , 18 , 19 ].…”

“…Tissue- and organ-specific transcriptomes and miRNA profiles have been widely used to elucidate the molecular basis of inter-individual differences in FE [ 10 , 11 , 12 , 13 ]. In pigs, FE-related transcriptome research has focused on energy homeostasis and energy demand [ 9 , 10 , 14 , 15 , 16 ], mainly in the liver and adipose tissue [ 17 ] and in intestinal, blood, and muscle tissue [ 13 , 18 , 19 ]. The liver is the most important organ involved in metabolism and, as such, plays an important role in converting newly acquired energy into the muscle or adipose tissue, thereby affecting FE [ 20 , 21 ].…”