**1. Introduction**

Feed represents about 60–70% of total pork production costs in modern pig production. Thus, to decrease costs and increase profitability, it is pivotal to identify feed efficient (FE) animals [1]. However, due to the polygenic architecture of FE, individual pigs in a herd exhibit considerable variation in FE despite belonging to similar genetic background and environment [2]. Considering this variation, different methods have been proposed and widely used to measure the FE, including feed conversion ratio (FCR) and residual feed intake (RFI) [1,3]. FCR is the ratio of feed intake (FI) per unit body weight gain and is affected by many factors such as breed, sex, diet, and environmental conditions [4,5]. Pigs with low FCR are considered high FE and vice-versa. RFI estimates the difference between actual and expected FI predicted on production traits as average daily gain (ADG) [6]. Since FCR considers both FI and weight gain, and FCR is also one of the critical predictors of FE, it suggests that feed efficient pigs may possess different physiological-biochemical profiles compared to the inefficient ones [2].

Based on the advances in omics technologies, several approaches have been adopted to shed light on the genetic mechanisms underlying FE in pigs. Among these omics technologies, transcriptomics

and metabolomics have provided tools to elucidate the molecular basis of FE. While transcriptomics allows us to have a transcriptional snapshot of genes underpinning the phenotype under investigation, metabolomics bridges the gap between genotype and phenotype. Recently, an increasing number of metabolomic studies have reported the role of metabolites in economically important traits [7], such as meat quality [8,9], pre-slaughter stress [10], and FE [3]. Likewise, several transcriptomic studies have pointed out candidate genes underpinning FE and other related-traits such as immune response, growth, and metabolism in pigs [11–13].

Recently, we have investigated RNA-seq data on the 41 Danish production pigs that underwent feed e fficiency and performance testing trials to identify di fferentially expressed genes and gene networks and reported 13 genes as potential biomarkers for feed e fficiency [14]. Despite the new insights into key genes and molecular mechanisms reported in these studies, these approaches rely solely on data from a single biological layer. It has been shown that the integration of transcriptomics datasets with genomic and other omics datasets (systems genomics) increases the power to detect causal and regulatory factors and molecular pathways underlying complex phenotypes or diseases in animals [15,16].

To gain further insights into biochemical aspects of complex traits, data integration analysis has emerged as a fruitful tool, unveiling potential biomarkers via integration of metabolomics and transcriptomics [17]. By the development of analytical technologies for data integration, the assessment of system-wide changes of transcript levels as surrogate measurements of metabolic rearrangements can be widely assessed. Metabolite-transcript co-responses using combined profiling can yield vital information on the complex biological regulation of the trait. Transcriptome-metabolome integration is a powerful combination as the metabolome is a ffected by the phenotypic measurements to which the global measures of transcriptome can be anchored [18]. Therefore, herein, we integrated data from metabolome-transcriptome approaches to unveil the unique gene-metabolite pairs. To this end, we adopted a two-step framework, as follows: (1) we first employed the numerical integration of gene-metabolite levels to identify gene-metabolite interaction pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) using IntLIM R-package; (2) next, a knowledge-based integration approach based on pathway over-representation analysis was used to reveal the underlying pathways in each group (breed-specific and FE-specific). To the best of our knowledge, this is the first study of its kind to ever combine high throughput metabolomics data with RNA sequence based gene expression data in pigs to unravel the missing links between genes and metabolites and to shed light on the molecular basis that characterizes the specific di fferences based on breed and feed e fficiency.
