Genes associated with body weight gain and feed intake identified by meta-analysis of the mesenteric fat from crossbred beef steers.

Mesenteric fat is a visceral fat depot that increases with cattle maturity and can be influenced by diet. There may be a relationship between the accumulation of mesenteric fat and feed efficiency in beef cattle. The purpose of this study was to identify genes that may be differentially expressed in steers with high and low BW gain and feed intake. RNA-Seq was used to evaluate the transcript abundance of genes in the mesenteric fat from a total of 78 steers collected over 5 different cohorts. A meta-analysis was used to identify genes involved with gain, feed intake or the interaction of both phenotypes. The interaction analysis identified 11 genes as differentially expressed. For the main effect of gain, a total of 87 differentially expressed genes (DEG) were identified (PADJ<0.05), and 24 were identified in the analysis for feed intake. Genes identified for gain were involved in functions and pathways including lipid metabolism, stress response/protein folding, cell proliferation/growth, axon guidance and inflammation. The genes for feed intake did not cluster into pathways, but some of the DEG for intake had functions related to inflammation, immunity, and/or signal transduction (JCHAIN, RIPK1, LY86, SPP1, LYZ, CD5, CD53, SRPX, and NF2). At PADJ<0.1, only 4 genes (OLFML3, LOC100300716, MRPL15, and PUS10) were identified as differentially expressed in two or more cohorts, highlighting the importance of evaluating the transcriptome of more than one group of animals and incorporating a meta-analysis. This meta-analysis has produced many mesenteric fat DEG that may be contributing to gain and feed intake in cattle.

Introduction

The most significant cost in beef cattle production is feed, which accounts for 60–80% of the total cost of production [1]. Improvements to the feed efficiency of beef cattle is a means by which costs can be reduced. In addition, it is also important to ensure that the feed is efficiently converted into meat products for consumption. Mesenteric fat is a visceral adipose depot located around the intestines that can be affected by diet. Cattle diets with high fat and starch leads to an accumulation of mesenteric fat [2]. In chickens, visceral fat has no economic value and is considered a source of feed inefficiency with more feed efficient chickens having lower abdominal fat [3]. This is also presumably the case for other livestock species, like cattle and swine. Because there may be a relationship between abdominal and visceral fat depots and feed efficiency in cattle; thus, we hypothesized that there may be differences in the mesenteric fat gene expression of feedlot steers with high and low gain and high and low feed intake.

A previous study using microarray analysis on the mesenteric fat of 16 of the steers in this study identified genes involved in proteolysis, transcription, transport, immune function, glycerol degradation and oxidative stress [4]. The current study was performed on five cohorts of steers, including the 16 from Lindholm-Perry et al. [4], and a meta-analysis was used to identify genes involved in gain and feed intake across cattle of various breeds and seasonal conditions. The genes presented here may be more robust across populations of beef cattle than those identified in only one cohort of animals.

A previous study performed on the muscle tissue of these same animals illustrated that there is significant variation in differentially expressed genes identified by cohort [5]. This may be due to various environmental and genetic differences represented by each cohort. However, the overarching goal of these types of transcriptome experiments is to identify genes that are contributing to the phenotype of the species being studied. One way to isolate the genes that contribute to phenotype, regardless of environment, is to evaluate several cohorts of animals and analyze the data using a meta-analysis [5]. These types of analyses are more likely to produce valid results in independent datasets [6-8].

The objective of this study was to determine whether differentially expressed genes in the mesenteric fat were associated with variation in gain and feed intake in five cohorts of beef steers. We hypothesized that the expression of genes in pathways such as lipid metabolism, protein turnover and oxidative stress would vary by phenotype. However, we designed this as a discovery study using RNA sequencing to allow us to identify all of the genes that were differentially expressed, rather than targeting specific candidate genes.

Materials and methods

Animal care and use

The U.S. Meat Animal Research Center (USMARC) Animal Care and Use Committee reviewed and approved all animal procedures. The procedures for handling cattle complied with the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching [9].

Cattle population

Steers (n = 80) from the USMARC Germplasm Evaluation project [10], were selected for this study over 5 cohorts. This population of cattle includes the common U.S. beef breeds: Angus, Beefmaster, Brahman, Brangus, Brown Swiss, Charolais, Chiangus, Gelbvieh, Hereford, Limousin, Maine Anjou, Red Angus, Romosinuano, Bonsmara, South Devon, Salers, Santa Gertrudis, Shorthorn, and Simmental. At age 350 ± 54 days, steers were placed on a corn-based finishing diet ad libitum feeding trial for 64–92 days. Steers from the Spring 2012 study were fed for 64 days: 57.57% high moisture corn, 8% ground alfalfa hay, 30% wet distiller’s grains with solubles (WDGS), and 4.25% Steakmaker (protein supplement, Land O’Lakes, Arden Hills, MN, USA) as dry matter. Steers from the Fall 2012 and Spring 2013 studies were fed for 92 and 64 days, respectively: 57.35% dry rolled corn, 8% ground alfalfa hay, 30% WDGS, 4.25% Steakmaker and 0.4% urea as dry matter. Steers from the Fall 2013 and Fall 2014 studies were fed for 84 and 78 days, respectively: 57.35% dry rolled corn, 8% ground alfalfa hay, 30% WDGS, 4.25% Steakmaker with Tylan (Tylan manufactured by Elanco Animal Health, Greenfield, IN, USA) and 0.4% urea as dry matter. All diets were formulated to meet or exceed nutrient requirements to express maximum growth potential [11]. Steers were housed in pens with 8 feed bunks with Insentec Roughage Intake Control Feeding Systems (Insentec B.V., Marknesse, The Netherlands) to measure feed intake. The selection of steers, 16 animals from each of 5 cohorts, was previously described [5]. Animals selected for this study were given ad libitum access to the same diet and water until slaughter. On each day of slaughter, one steer from each of the 4 phenotypic groups (n = 4) was stunned with captive bolt and all animals were processed within 3 hours. Length of time between the feed trial and slaughter varied slightly by cohort: cohort 1 was 12–18 days; cohort 2 was 19–22 days; cohort 3 was 5–8 days; cohort 4 was 20–28 days; and cohort 5 was 11–14 days. Steers were selected for the study as follows: body weight gain and feed intake were measured on 5 cohorts of steers from 2012 to 2014. Steer weights were taken on the first and last two days of the study and once every three weeks throughout the study. Total body weight gain was calculated by regressing body weight on days on study using a quadratic polynomial and average daily gain (ADG) was calculated by dividing total body weight gained by days on study. Average daily dry matter intake (ADDMI) was calculated by dividing the total feed intake by days on study. Sixteen steers were selected from each cohort by ranking them based on their bivariate mean (of ADG and ADDMI) and 4 animals with the greatest deviation from the mean in each Cartesian quadrant were sampled. Summary statistics for gain and feed intake by cohort is presented in Keel et al. [5]. Breed composition by phenotypic group and medical and health records were also evaluated. Phenotypes were not confounded [5].

Tissue collection and RNA isolation

On each day of slaughter, the four animals selected were processed serially within a three-hour time frame. The tissue sample was obtained from each steer within approximately 30–40 minutes after exsanguination. Mesenteric fat was removed from approximately 1-m distal to the duodenum. Mesenteric fat samples were diced into 50–100 mg pieces and flash frozen in liquid nitrogen then stored at -80°C until they could be processed further. Tissue was homogenized in TriPure reagent (Cat # 11667165001; Roche, Indianapolis, IN, USA). RNA was isolated according to the manufacturer’s protocol with an extra 20-minute centrifugation following the addition of chloroform. The resulting RNA pellets were further processed using RNeasy Plus Mini Kit columns (Cat # 74136; Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions to remove genomic DNA.

The concentration of the RNA was determined with a Nanodrop 8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The 260/280 ratios were ≥ 1.8 and a subset of 20 samples (at least 3 samples from each cohort) were analyzed for quality on either the Agilent Bioanalyzer 2100 or the Agilent Tapestation 2200 (Cat # 5067-1511 and 5067–5576; Agilent Technologies, Santa Clara, CA, USA) and produced an average RIN of 8.2 with a range of 7.1 to 9.2.

RNA sequencing

Samples were prepared for RNA sequencing with the Illumina TruSeq Stranded mRNA High Throughput Sample kit and protocol (Cat # RS-122-2103; Illumina Inc., San Diego, CA, USA). The libraries were diluted to 4nM (Teknova, Hollister, CA. USA). One sample failed to produce a library. The remaining 79 samples were paired-end sequenced with 150 cycle high output sequencing kits for the Illumina NextSeq. Sequence data is available in NCBI Sequence Read Archive under accession number PRJNA528040.

Processing RNA-seq data

The quality of the raw paired-end sequence reads in individual fastq files was assessed using FastQC (Version 0.11.5; www.bioinformatics.babraham.ac.uk/projects/fastqc), then reads were trimmed to remove adapter sequences and low-quality bases using the Trimmomatic software (Version 0.35) [12]. The remaining reads were mapped to the newly released ARS-UCD1.2 genome assembly (Genbank Accession GCA_002263795.2) using Hisat2 (Version 2.1.0) [13], and the NCBI annotation for ARS-UCD1.2 (Release 106) was used to guide the alignment. Stringtie [14] was used to determine read counts for each of the 34,624 annotated genes in the ARS-UCD1.2 genome assembly. Genes with low read counts were filtered out of the dataset when there were < 15 reads in at least 16 samples. This produced a set of 16,701 genes for downstream analyses. One library was removed from the analysis due to low read counts and lower mapping percentage (n = 78).

Meta-analysis of differential gene expression

The meta-analysis procedure developed in Keel et al. [5] was used to identify differentially expressed genes associated with gain and feed intake. Briefly, cohorts were analyzed separately using the DESeq2 package [15] with the following generalized linear model (GLM):

Raw P-values from each experiment were combined into one test statistic via Fisher’s method [16], and the Benjamini-Hochberg method [17] was applied to the raw meta P-values to correct for multiple testing. Genes with adjusted meta P-value ≤ 0.05 were considered statistically significant. DEGs associated with a main effect and also the interaction term were excluded from the main effect list of DEGs since this indicates that the main effect is dependent on the interaction term.

Jackknife reproducibility analysis

Jackknife sensitivity analysis was employed to evaluate the robustness of the results. The meta-analysis procedure was repeated multiple times, each time with removal of a single cohort from the baseline group of cohorts [18].

Functional and pathway analysis of DEGs

Over-represented gene functions and pathways of over-represented DEG (PADJ<0.1) were determined using the PANTHER classification system (Version 13.1) [19] and the Database for Annotation, Visualization and Integrated Discovery (DAVID; Version 6.8) [20,21]. Enrichment analysis of gene function was performed using PANTHER’s implementation of the binomial test of overrepresentation. Significance of gene ontology (GO) terms was assessed using the binomial test of overrepresentation and the default Ensembl Bos taurus GO annotation as background for the enrichment analysis. The default parameters for the KEGG pathway and gene function analyses in DAVID were used with the official gene symbols and Bos taurus annotation and to evaluate genes that were over-represented in the list of DEG (PADJ<0.1).

QIAGEN Ingenuity® Pathway Analysis

Ingenuity® Pathway Analysis (IPA®, QIAGEN Redwood City, CA; www.qiagen.com/ingenuity) was used to deduce direct and indirect molecular relationships among differentially expressed genes for main effects of gain and intake, and gain x intake interaction. Each of the data sets was imported with a Flexible Format using Gene symbol as the identifier. A core analysis was performed on genes in each set, where a P-value for each network is calculated according to the fit of the user’s set of significant genes and the size of the network.

Results

Sequencing statistics

RNA-Seq libraries from the mesenteric fat tissue of 78 steers with high and low ADG and ADDMI were sequenced. Over 4.5 billion 75-bp paired-end reads were generated, with an average of 58.3 million reads per sample. After trimming adapter sequences and low quality bases, the resulting high quality reads were mapped to the Bos taurus ARS-UCD1.2 genome assembly with an average 97.1% read mapping rate.

Meta-analysis of DEGs associated with gain and feed intake across cohorts

Average daily gain and ADDMI for each cohort is presented in Keel et al. [5]. After multiple testing correction, we identified 87 DEG for the main effect of gain, 24 genes for the main effect of feed intake, and 11 DEG for the gain x intake interaction (S1–S3 Tables). Significant genes were inspected for consistency, defined as having the same log-fold change direction across all 5 cohorts. We found that two significant genes (RAB7B and CHAC1) were consistent for direction of expression in all cohorts for the gain main effect, while no DEGs were consistent for the intake main effect or the gain x intake interaction.

Jackknife analysis

Robustness of the results were assessed using a jackknife sensitivity analysis, where for each term in the model, five separate meta-analyses were performed, each omitting a single cohort. The results are shown in S4–S6 Tables. For the gain main effect, the jackknife analyses produced similar numbers of DEGs to that of the original meta-analysis (87 DEGs): 109, 89, 52, 49, and 67 DEGs for the jackknife analysis that removed Cohort 1, 2, 3, 4, or 5; respectively (Jacknife P < 0.05 in S4 Table). The number of DEGs identified in the jackknife analyses for the intake main effect varied more than for gain, with 46, 42, 17, 5, and 44 DEGs for the jackknife analysis that removed Cohort 1, 2, 3, 4, or 5; respectively (Jacknife P < 0.05 in S5 Table). The jackknife analyses for the interaction effect identified 20, 15, 8, 4, and 7 DEGs for the jackknife analysis that removed Cohort 1, 2, 3, 4, or 5; respectively (Jacknife P < 0.05 in S6 Table), which was similar to the number of DEGs identified in the full meta-analysis (11 DEGs).

For the gain main effect, there was one DEG, OLFML3, that was robust enough to pass all five jackknife analyses. Thirty-one DEGs failed only 1 jackknife analysis, while 32, 14, 9, and 0 DEGs failed to pass 2, 3, 4, and 5 jackknife analyses, respectively. For the intake main effect, there were no DEGs that passed or failed all five jackknife analyses. Eleven DEGs failed only 1 jackknife analysis, while 9, 2, and 1 DEGs failed to pass 2, 3, and 4 jackknife analyses, respectively. For the interaction effect, there was one DEG, LOC100300716, that passed all five tests and none failed all five. A total of 1, 8, 0, and 1 DEGs failed to pass 1, 2, 3, and 4 tests, respectively.

Functions of DEG

PANTHER gene ontology analysis of the DEGs indicated that genes significant for the gain main effect were involved in catalytic activity (38.9%), binding (29.2%), metabolic process (18.1%), localization (15.3%), and response to stimulus (12.5%). No GO terms were significantly over- or under-represented in this gene set.

Pathway analysis in PANTHER produced four pathways over-represented in the list of DEG (P < 0.05; Table 1). These included: axon guidance mediated by Slit/Robo, axon guidance by netrin, cytoskeletal regulation by Rho GTPase, and inflammation mediated by chemokine and cytokine signaling pathway. The DAVID pathway analysis also identified axon guidance as over-represented in the list of DEG (P < 0.05; Table 2). In addition, two other pathways (MAPK signaling and protein processing in the endoplasmic reticulum) approached significance (P < 0.1).

Table 1

Panther pathways for Gain DEG.

Pathway	#	expected	Fold Enrichment	+/-	P value	FDR
Axon guidance mediated by Slit/Robo	4	0.12	32.60	+	1.13E-05	9.26E-04
Axon guidance mediated by netrin	4	0.17	23.17	+	3.87E-05	2.11E-03
Cytoskeletal regulation by Rho GTPase	5	0.42	11.83	+	8.33E-05	3.42E-03
Inflammation mediated by chemokine and cytokine signaling pathway	9	1.26	7.15	+	6.09E-06	9.98E-04

Table 2

DAVID pathways for main effect of gain.

Term	Count	P value	Genes
Axon guidance	4	0.040	UNC5A, PAK5, NTNG1, SLIT1
MAPK signaling	5	0.069	MAPT, RAP1A, HSPA1A, MAP2K6
Protein processing in endoplasmic reticulum	4	0.083	STT3B, HSPH1, HSPA1A

Similar to the gain main effect genes, genes significant for the intake main effect were involved in catalytic activity (50%), binding (30%), metabolic process (33.3%), and response to stimulus (6.7%). Again, no GO terms were identified by PANTHER to be significantly over- or under-represented in this set. Pathways identified by DAVID as over-represented from the list of DEG genes for intake were; hematopoietic cell lineage and Toll-like receptor signaling pathway (P < 0.1; Table 3).

Table 3

DAVID pathways for main effect of intake.

Term	Count	P value	Genes
Hematopoietic cell lineage	2	0.08	LOC782367, CD5
Toll-like receptor signaling pathway	2	0.09	RIPK1, SPP1

Ingenuity Pathway Analysis (IPA)

The top molecular and cellular functions represented in the list of DEG for gain included cellular development, cellular growth and proliferation, cellular assembly and organization, cellular function and maintenance, and lipid metabolism (Table 4). The genes involved in lipid metabolism, molecular transport and small molecule biochemistry included ACLY, AKR1B1, BAG3, GALR3, KCNMA3, LIPG, LPIN1, MAPT, NEIL1, PTGS2, TARDBP. Genes involved in cellular development, growth/proliferation, and function/maintenance included ACLY, AKR1B1, ARHGEF26, CDKN2B, DLX3, HAS3, HSP1A1, HSPA1B, LDB3, MAP2K6, MAPT, MYLK, NINL, PTGS2, SFRP1, STTG3B, TARDBP, UNC5A, VEGFC. Canonical pathways identified in the list of DEG for gain included ErbB signaling, cholecystokinin/gastrin-mediated signaling, agrin interactions at neuromuscular junctions, role of MAPK signaling in the pathogenesis of influenza, and IL-17 signaling. The genes that were responsible for these pathways were a combination of MAP2K6, NRG2, PAK5, PTGS2, RAP1A and RHOH.

Table 4

Significant molecular and cellular functions for DEGs associated with the gain main effect identified using IPA.

Canonical Pathway	P value	# Genes	Genes
Cellular development	0.012	24	ACLY, AKR1B1, ARHGEF26, CDKN2B, DLX3, HAS3, HSPA1A, HSPA1B, KCNMA1, LDB3, LPIN1, MAP2K6, MAPT, MYLK, NINL, NRG2, PTGS2, RHOH, SFRP1, SH2B2, STT3B, TARDBP, UNC5A, VEGFC
Cellular growth and proliferation	0.012	19	ACLY, AKR1B1, ARHGEF26, CDKN2B, DLX3, HAS3, HSP1A1, HSPA1B, LDB3, MAP2K6, MAPT, MYLK, NINL, PTGS2, SFRP1, STTG3B, TARDBP, UNC5A, VEGFC
Cellular assembly and organization	0.012	24	BAG3, CHMP1B, CNTN4, HAS3, HSPA1A, HSPA1B, LDB3, LPIN1, MAP2K6, MAPT, MYLK, NINL, NTNG1, PAK5, PTGS2, RAP1A, SFRP1, SGK2, SH2B2, SHROOM3, SLIT1, TARDBP, UNC5A, VEGFC
Cellular function and maintenance	0.012	35	ACLY, AKR1B1, ARHGEF26, BAG3, CCL1, CCL8, CD5, CHMP1B, CNTN4, HAS3, KCNMA1, LDB3, LIPG, LPIN1, MAP2K6, MAPT, MYLK, NINL, NRG2, NTNG1, PAK5, PDE4B, PTGS2, RAP1A, RHOH, SFRP1, SGK2, SH2B2, SHROOM3, SLIT1, SRPX, STT3B, TARDBP, UNC5A, VEGFC
Lipid metabolism	0.013	11	ACLY, AKR1B1, BAG3, GALR3, KCNMA1, LIPG, LPIN1, MAPT, NEIL1, PTGS2, TARDBP

The molecular and cellular functions over-represented in the list of DEG for intake included small molecule biochemistry, cellular compromise, cell morphology; cell signaling and post-translational modification. Genes that were included in more than one of these functions were: NF2, RIPK1, SPP1, CD5, LYZ, CPM, PRMT7, and B3GAT1.

Discussion

To date, there have been many transcriptome studies to identify differentially expressed genes related to feed efficiency from various tissues in livestock; however, mesenteric fat is largely under-represented in these studies. The goal of our study was to identify differentially expressed genes in the mesenteric fat that are associated with beef steer body weight gain and feed intake across several contemporary groups of animals. To do this, we used a meta-analysis method to detect differentially expressed genes from 5 cohorts of animals. This allowed us to identify several genes among the 80 animals tested that may be involved in gain and feed intake. Although we corrected the meta-analysis P-values and only reported the genes as differentially expressed with PADJ<0.05, this still implies that 5% of the significant tests are false positives.

This study evaluated the mesenteric fat transcriptome of 5 cohorts of steers, which produced differentially expressed genes for gain, feed intake and their interaction; however, the level of transcription is only one of many components that may be affecting phenotype. In addition, the lack of agreement of DEG from one cohort to another illustrates the importance of evaluating the transcriptome in more than one group or population of animals, especially those like cattle that are exposed to environmental variables such as weather, management, and subtle changes in feed ingredients. We hypothesized that we would identify genes involved in protein turnover, lipid metabolism and oxidative stress in the mesenteric fat of these steers. While we did identify some genes functioning within some of these pathways, they were not as numerous as we expected. This may be due, in part, to other factors that affect gene expression, such as epi-genetic modification, chromatin structure, nucleosomes and nucleic acid post transcriptional remodeling, miRNA abundance, long non-coding RNA. In addition, there may be post-transcriptional, translational modifications, and enzyme modifications that may be affecting protein turnover in these steers. Thus, while this study focuses on the transcriptome, it is important to consider that there are likely many other factors involved in these phenotypes.

Genes/pathways identified for gain

Differentially expressed genes with the same direction of expression over all of the cohorts may be the most robust and straightforward to validate among other cattle populations. While 87 genes were identified as differentially expressed for the main effect of gain, only 2 (CHAC1 and RAB7B) displayed the same direction of expression over all 5 cohorts of animals. One of the genes (CHAC1) has functions related to oxidative stress. The CHAC1 gene specifically cleaves glutathione and was down-regulated in animals with lower gain in the current study. The overexpression of CHAC1 in cell culture was shown to degrade cellular glutathione suggesting a feedback mechanism that sensitizes cells to oxidative stress [22]. The animals with higher gains may be mitigating the effects of oxidative stress by reducing cellular levels of glutathione. Genes involved in oxidative stress have been previously identified in other tissues from these same animals [4, 5, 23, 24]. A specific example of this includes the UQCRQ gene, which was identified as differentially expressed in the muscle tissue from these animals and displayed the same direction of expression in all cohorts [5]. Oxidative stress has been proposed in many other studies as a factor that affects feed efficiency in swine, poultry, and beef cattle [25-30].

The current study also produced several DEG for gain that failed 0 or 1 jackknife test. These genes included: OlFML3, GCAT, EBPL, HSPH1, MOSPD1, ADGRL4, LOC782367, SFRP1, GALR3, RHOH, HPS6, CDKN2B, MGC152281, SMUG1, SRPX, RAB7B, GPT, CCDC117, and MAPT. A gene that may be of particular interest is GALR3. GALR3 is one of 3 receptors that bind the galanin neuropeptide. Galanin has been shown to modulate physiologic processes including hormone secretion and feeding behavior. Mice without the GALR3 gene had reduced psoriasis symptoms as a result of delayed angiogenesis, reduced neutrophil infiltration, and lower levels of cytokines [31]. In the steers evaluated in the current study, expression of GALR3 was lower in the animals with higher gain. A reduction in neovascularization and inflammation in the mesenteric fat stores may allow for improvements in weight gain. Inflammation is an energetically expensive process and is negatively associated with growth in humans [32], and pigs [33]; thus, cattle with a reduction in the production of cytokines may be able to devote more energy towards gain.

For the main effect of gain, several genes involved in lipid metabolism (ACLY, AKR1B1, BAG3, GALR3, KCNMA1, LIPG, LPIN1, MAPT, NEIL1, PTGS2, TARDBP) were identified as differentially expressed, as we would expect for an adipose depot. Another notable cluster of genes differentially expressed included three stress response heat shock protein genes; HSPB3, HSPA1A, and HSPH1; which all mirrored direction of expression by cohort, except in cohort 4 where HSPA1A was up-regulated in animals with higher gain and the other two were down-regulated. Heat shock proteins can rescue unfolded proteins, and increased expression of HSPH1 (Hsp110) and HSPA1A (Hsp72) can be a result of physiological or cellular stress [34, 35]. Heat shock proteins have also been previously identified as differentially expressed in the small intestine and spleen of one cohort of the animals from the current study [24,36], and also in the liver and duodenum of pigs divergent for residual feed intake [37]. In the current study, HSPH1, was expressed in lower abundance in animals with high weight gain in 4 of the 5 cohorts. Further evidence of potential cellular stress was the differential expression of two genes identified with functions in glutathione metabolism, CHAC1 and GSTT2. GSTT2, a gene associated with oxidative stress, has been shown to respond in vitro to paraoxon, an agent that increases superoxide accumulation in cells [38]. In dogs, the expression of GSTT2 increased in the adipose tissue as feed intake increased [39]. Thus, Grant et al. [39] proposed that its expression may be related to metabolic stress during adipose tissue expansion. CHAC1 was found in lower transcript abundance in animals with low gain in all cohorts and GSTT2 displayed lower transcript abundance in 3 of the 5 cohorts. Assuming that cattle with high gains on a feedlot diet, are gaining lean tissue rather than accumulating fat, these data might suggest that less inflammation and a more effective clearance of glutathione and elevated reactive oxygen species reduces the need for heat shock proteins in the mesenteric fat of the higher gaining animals.

Axon guidance pathways were identified by DAVID as those that were enriched by differentially expressed genes for gain (UNC5A, PAK5, NTNG1, SLIT1). Three of these genes (UNC5A, NTNG1 and SLIT1) interact with each other for axon or neurite growth. White adipose tissue, of which mesenteric fat is comprised, is innervated by both the sympathetic and parasympathetic nervous system [40], which indicates a balance between the sympathetic “fight, flight or freeze” nervous system and the “feed and breed” and “rest and digest” parasympathetic nervous system [40]. This balance may be affected by the expression of these axon guidance genes. Visceral adipose tissue has been shown to have more efferent sympathetic axons and capillaries than subcutaneous fat [41]. Moreover, neurons from the spinal cord projecting into the visceral fat are different than those projecting into the subcutaneous fat, suggesting that these two fat depots have unique actions on, or in response to, the central nervous system.

Genes/pathways identified for intake

The genes identified as differentially expressed for the main effect of intake did not cluster into functional gene categories or pathways well. In fact, only trends towards significance for two pathways, hematopoietic cell lineage and Toll-like receptor signaling pathway, were detected. Based on literature searches of the functions of each of the DEG for intake, several appeared to have roles in inflammation or immune function (JCHAIN, RIPK1, SPP1, CD5, CD53, LYZ, LOC782367, and LY86). Four of these genes (JCHAIN, RIPK1, LOC782367, and LY86) were among the most robust genes failing none or only one of the jackknife tests. The number of immune and inflammatory response genes identified for both gain and intake in steers in the mesenteric fat depot may not be surprising considering that this tissue is often referred to as an endocrine organ [42,43]. Visceral fat is a unique type of adipose tissue that sequesters polyunsaturated fatty acids necessary for immune functions, like lymph node activity, and that mesenteric fat responds to immune and inflammatory signals [44, 45].

Genes identified for the gain x intake interaction

Two genes identified as differentially expressed for the interaction term passed at least 4 jackknife tests. These were LOC100300716 and PTAFR. The LOC100300716 gene is the immunoglobulin heavy variable 4-38-2 that is involved in antigen recognition and is conserved in human and other primates. The PTAFR receptor binds platelet activating factor (PAF), which has been implicated in a number of diseases and disorders like asthma, allergies and is also a lipid mediator of inflammatory responses [46]. These genes underscore the potential involvement that immune responses and inflammatory pathways seem to have with feed efficiency in the mesenteric adipose tissue. Other genes that were identified in the gain x intake interaction analysis of interest were AACS, H19 and IL18BP. The AACS gene was identified as a member of the INSIG1 hub of genes involved in residual feed intake in cattle [47]. The genes in this network are involved in energy, lipid and steroid metabolism, as all of which have been linked to feed efficiency [48, 49]. Interestingly, INSIG1 was identified as differentially expressed in the current study, and also in a previous study of the muscle tissue of these same animals [5]. Acetyl-CoA synthetase (AACS) enzyme is required for the conversion of ketone bodies into energy within adipose tissue. AACS transcripts are highly expressed in rodent visceral fat [50] and become up-regulated during adipocyte differentiation, utilizing ketone bodies for lipogenesis. H19 is located in an imprinted chromosomal region and encodes a long non-coding RNA. It is thought to regulate gene expression and may also have a role in negatively regulating body weight and cell proliferation [51]. Lastly, the protein encoded by IL18BP functions as an inhibitor of the proinflammatory cytokine, IL18. It binds IL18, prevents the binding of IL18 to its receptor, and thus inhibits IL18-induced IFN-gamma production, resulting in reduced T-helper type 1 immune responses. The IL18BP gene was down-regulated in the low gain animals in 2 cohorts with significant p-values (S4 Table).

Comparison of adipose and muscle DEG

The muscle tissue from these same animals was also analyzed in a meta-analysis [5]. There were four genes that were identified as differentially expressed in the both of these meta-analyses (Pcorrected<0.1). They were CIDEA, DEDD2, H19 and INSIG1. Interestingly, CIDEA knockout mice are resistant to diet-induced obesity [52]. Indeed, CIDEA expression was lower in the mesenteric fat of the low gain cattle in 4 of the 5 cohorts in the current study. The death effector domain containing 2 (DEDD2) gene was one of 12 candidate genes identified in an intra-module in the liver and duodenum of cattle with variation in residual feed intake [37]. In addition, knockout mice for DEDD maintained normal concentrations of blood glucose, hormones, and growth factors in comparison to wild-type mice; however, their body size was reduced indicating a possible alteration to the cellular or physiological mechanisms of metabolism [53]. The INSIG1 gene has been identified as a candidate gene in many GWAS and transcriptome studies designed to evaluate feed efficiency in cattle [47, 54, 55] and chickens [3, 56]. Expression of INSIG1 increased in fat tissue of normal mice constitutively with diet-induced obesity [57]. Li and others concluded that INSIG1 functions as a feedback mechanism to control lipogenesis and adipogenesis. In contrast, Graugnard and others [55] determined the activity of INSIG1 in cattle muscle was to initiate intramuscular fat deposition. These contradicting results may be species affiliated or rather tissue specific, murine epididymal fat pads, a visceral fat depot, versus longissimus lumborum of cattle.

Comparison with previous microarray study of one cohort

The transcriptome of one of the 5 cohorts of animals was previously assessed using microarray technology [4]. The DEG identified in that study were primarily associated with animals with the low gain/high intake phenotype. While some of the genes identified in that study were also identified within this cohort in the current study, they did not maintain significance in the meta-analysis. Conversely, a total of eight genes (CDKN2B, CHMP1B, CHAC1, DLX3, SLIT1, MOSPD1, ANTXRL, and B3GAT1) were identified as differentially expressed for gain or intake in the current meta-analysis and were also nominally significant in the previously performed microarray study [4]. However, it is important to note that none of the genes passed FDR correction in the microarray study, and therefore would not have been selected as critical drivers of gain or intake from the previous study. These results highlight the importance of evaluating more than one group of animals in which thousands of genes are being evaluated for specific phenotypes.

Conclusions

The current study, using 5 cohorts of steers in conjunction with a meta-analysis, has produced a number of highly specialized and functional candidate genes that may be responsible for gain and feed intake in cattle. Genes clustering into lipid metabolism, cellular development, growth/proliferation biological functions and inflammatory and protein processing pathways were identified. Many genes were identified that passed 4–5 jackknife analyses, as well as two DEG with the same direction of expression across all cohorts and four that were also identified in the muscle tissue of these sample animals [5]. These may be promising candidate genes to validate for gain and feed intake phenotypes in additional populations of animals.

DEGs associated with the gain main effect in the individual cohort and meta-analyses.

Genes are ordered by adjusted meta-P-value. The individual cohort cells for DEGs identified in the meta-analysis are colored according to the sign of their log2 fold change, where green indicates up-regulation and red indicates down-regulation in high gain. Genes with all gray cell indicate those that were excluded because they were also significant for the gain by intake interaction term.

(PDF)

Click here for additional data file.

DEGs associated with the intake main effect in the individual cohort and meta-analyses.

Genes are ordered by adjusted meta-P-value. The individual cohort cells for DEGs identified in the meta-analysis are colored according to the sign of their log2 fold change, where green indicates up-regulation and red indicates down-regulation in high intake. Genes with all gray cell indicate those that were excluded because they were also significant for the gain by intake interaction term.

(PDF)

Click here for additional data file.

DEGs associated with the gain by intake interaction effect in the individual cohort and meta-analyses.

Genes are ordered by adjusted meta-P-value. The individual cohort cells for DEGs identified in the meta-analysis are colored according to the sign of their log2 fold change, where green indicates up-regulation and red indicates down-regulation in low gain, low intake.

(PDF)

Click here for additional data file.

Jackknife sensitivity analysis results for the DEGs associated with the gain main effect.

Genes with all gray cell indicate those that were excluded because they were also significant for the gain by intake interaction term. Jackknife 1 P-value gives the adjusted P-value for the meta-analysis with Cohort 1 removed, Jackknife 2 P-value gives the adjusted P-value for the meta-analysis with Cohort 2 removed, and so on. Yellow cells indicate jackknife analyses where the P-value was insignificant, i.e. the gene failed to pass the jackknife analysis.

(PDF)

Click here for additional data file.

Jackknife sensitivity analysis results for the DEGs associated with the intake main effect.

Genes with all gray cell indicate those that were excluded because they were also significant for the gain by intake interaction term. Jackknife 1 P-value gives the adjusted P-value for the meta-analysis with Cohort 1 removed, Jackknife 2 P-value gives the adjusted P-value for the meta-analysis with Cohort 2 removed, and so on. Yellow cells indicate jackknife analyses where the P-value was insignificant, i.e. the gene failed to pass the jackknife analysis.

(PDF)

Click here for additional data file.

Jackknife sensitivity analysis results for the DEGs associated with the gain by intake interaction.

Jackknife 1 P-value gives the adjusted P-value for the meta-analysis with Cohort 1 removed, Jackknife 2 P-value gives the adjusted P-value for the meta-analysis with Cohort 2 removed, and so on. Yellow cells indicate jackknife analyses where the P-value was insignificant, i.e. the gene failed to pass the jackknife analysis.

(PDF)

Click here for additional data file.

21 Oct 2019

PONE-D-19-25581

Genes associated with body weight gain and feed intake identified by meta-analysis of the mesenteric fat from crossbred beef steers

PLOS ONE

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Reviewer #1: Dear Authors:

This MS finally made it clear to me that my approach to genomics in the early 2000 s with 5 animals per treatment group and single marker genes for lipogenesis, fat oxidation etc gave us little information. On the other hand this work also did not pinpoint too many DE genes one would think of from a biological-function perspective.

This was a big project and authors and ever new emerging bioinformatic tools allowed you to do extensive analyses and assessment. The overall assessments are interesting since there are other data sets point to CNS, immune function system/inflammation etc. Once again I see no DE for genes involved in protein turnover and of course protein synthesis is under transnational control. There are also many other factors that effect gene expression qualitatively and quantitatively. Such would include epi-genetic effects, nucleosomes and nucleic acid post transcriptional remodeling, mi RNA, long non-coding RNA etc.

I like the way the paper is written, but it may be a bit wordy and all those gene abbreviations do not make for pleasant reading (there appear to be no alternatives either).

I have a few comments/queries:

Line 54 High fat diets various various biological reasons in ruminants seldom exceed 5% fat and in comparison to chickens and pigs such diet could easily contain 20% fat while in humans in the US many consume a 50% plus fat calorie diet. So this high fat is only applicable to ruminants.

Line 112 says: Length of time between the feeding trial end and slaughter were 12-18 etc dates. Gene expression can be pretty sensitive to type and amount of feed. Once the trial was ended, were the animals continued on the same diet and at the same intake level until tissue sampling? Was feed efficiency maintained post feeding trial until slaughter. Would a time course sampling strategy at intervals throughout the feeding trail result more representative data. Often when a trial is over and all animals are to the same finish and weight there are no more differences. That is a problem of sampling only at the end of the trial.

Much of the phenotypic expression that is observed also depends on regulation of enzyme catalysis by covalent modifications and allosteric factors.

Reviewer #2: Manuscript PONE-D-19-25581 aimed to identify genes that may be differentially expressed in steers with high and low BW gain and feed intake. The study is technically and scientifically sound but authors need to improve the main messages that can be taken from their nice research scope.

Line 52 any idea on how much a farmer can save? Please give an estimate in %

Line 53 what is usable? Please elaborate

Lines 68-75 please describe your study hypothesis. It seems that it is somehow encrypted in this paragraph, could you please be more precise on your research expectations

Line 89 is it 64 to 92 days. Is that the sampling period? Please clarify

Lines 88-94 please provide forage to concentrate ratio and the rationale behind the diet formulation i.e., NRC… here and later for the other diet descriptions

Line 91 after steakmater add (protein supplement; Land..

Line 122 add city and country and also catalogue number here and anywhere appropriate

Line 278 how can you overcome the fact that you have 5 cohort animals – does that influence or not your results? Please elaborate

Lines 417 to 425 what would be the take-home message? Please try to deliver more precise. You have 5 cohorts of animals, each of them with precise feeding managements can you relate those variables with your transcriptomic results and BW gain and feed intake. The main issue perhaps is that in its present version your conclusion statements have not been landed in a way that the answered your initial objectives.

Reviewer #3: This is a very well-written manuscript describing a meta-analysis of RNAseq in mesenteric fat tissue in regards to body weight gain and feed efficiency. While I agree with the choice of using an adjusted p-value of 0.01, I would appreciate an acknowledgement of the false positive errors that the lack of concordance between cohorts can be due to genes being identified as DE by chance.

**********

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26 Nov 2019

Dear Dr. Loor,

We want to sincerely thank you for the opportunity to revise our manuscript for potential publication in PLOS ONE. We are also grateful to the three peer-reviewers for their comments, questions and suggestions. The comments and questions that we received from the peer-reviewers were excellent points and as such, we have attempted to address each one and edit the manuscript accordingly. With the incorporation of these suggestions, we believe the manuscript has been substantially improved.

Sincerely,

Amanda Lindholm-Perry, Ph.D.

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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Reviewer's Responses to Questions

Response: We have modified our manuscript according to the PLOS ONE guidelines.

2. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

Response: We have altered the sentence on Lines 340-342 to include a list of the three differentially expressed heat shock proteins and removed reference to “data not shown”.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Response: We have included a caption for each file of Supporting information that is denoted by bold type, and have chosen to leave the legend.

4. We note that you are reporting an analysis of a microarray, next-generation sequencing, or deep sequencing data set. PLOS requires that authors comply with field-specific standards for preparation, recording, and deposition of data in repositories appropriate to their field. Please upload these data to a stable, public repository (such as ArrayExpress, Gene Expression Omnibus (GEO), DNA Data Bank of Japan (DDBJ), NCBI GenBank, NCBI Sequence Read Archive, or EMBL Nucleotide Sequence Database (ENA)). In your revised cover letter, please provide the relevant accession numbers that may be used to access these data. For a full list of recommended repositories, see http://journals.plos.org/plosone/s/data-availability#loc-omics or http://journals.plos.org/plosone/s/data-availability#loc-sequencing.

Response: Our sequencing data has been uploaded to the NCBI Sequence Read Archive and is accessible under accession number PRJNA528040. This is shown on Lines 149-150.

Reviewer #1:

This MS finally made it clear to me that my approach to genomics in the early 2000 s with 5 animals per treatment group and single marker genes for lipogenesis, fat oxidation etc gave us little information. On the other hand this work also did not pinpoint too many DE genes one would think of from a biological-function perspective.

Response: We appreciate this comment. Our lab has previously performed several microarray and RNA-Seq experiments using one group of animals and then have gone to validate the genes identified in a separate population of animals. We have been surprised with how few genes overlap. Our thoughts were that a meta-analysis may help us identify genes that are involved in these phenotypes in more than just one group of animals. It is unfortunate that we did not detect more genes with biological functions that we recognized, but not altogether surprising considering that we are evaluating the entire transcriptome rather than using a candidate gene approach. We also believe that not all of the functions of the genes expressed are known. The key really is to test the genes we have identified in an unrelated population to determine whether they hold up. This is our next step.

This was a big project and authors and ever new emerging bioinformatic tools allowed you to do extensive analyses and assessment. The overall assessments are interesting since there are other data sets point to CNS, immune function system/inflammation etc. Once again I see no DE for genes involved in protein turnover and of course protein synthesis is under transnational control. There are also many other factors that effect gene expression qualitatively and quantitatively. Such would include epi-genetic effects, nucleosomes and nucleic acid post transcriptional remodeling, mi RNA, long non-coding RNA etc.

Response: The only genes that have a role in protein turnover that we consistently identified among multiple cohorts were heat shock proteins. We did, however, identify many genes involved in lipid metabolism, which was something that we had expected to see. However, we do agree with this comment and have included information in the discussion regarding other factors that could alter gene expression. Please see Lines 303-308.

Line 54 High fat diets various various biological reasons in ruminants seldom exceed 5% fat and in comparison to chickens and pigs such diet could easily contain 20% fat while in humans in the US many consume a 50% plus fat calorie diet. So this high fat is only applicable to ruminants.

Response: We agree that high fat diets in ruminants are lower than in humans and other species. However, these diets, which are high in fat for ruminants, have been shown to increase mesenteric fat accumulation.

Line 112 says: Length of time between the feeding trial end and slaughter were 12-18 etc dates. Gene expression can be pretty sensitive to type and amount of feed. Once the trial was ended, were the animals continued on the same diet and at the same intake level until tissue sampling? Was feed efficiency maintained post feeding trial until slaughter. Would a time course sampling strategy at intervals throughout the feeding trail result more representative data. Often when a trial is over and all animals are to the same finish and weight there are no more differences. That is a problem of sampling only at the end of the trial.

Response: These are notable observations and we fully agree with the reviewer. Unfortunately, we do not have the data regarding feed efficiency until the end of the study, and we do not have the means to take a time course sample on a tissue like mesenteric fat until slaughter. We do maintain the steers on the same ad libitum feed study ration from the end of the study when we evaluate gain and feed intake through the 12-18 days up to slaughter. While these animals did finish the study on the same date, they do have variation in their intakes and gains even through the end of the study. This information can be viewed in our previous manuscript: Keel BN, Zarek CM, Keele JW, Kuehn LA, Snelling WM, Oliver WT, Freetly HC, and Lindholm-Perry AK. RNA-Seq meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers. BMC Genom. 2018; 19:430. We are moving to the direction of sampling tissues that are less invasive by biopsy during a feeding trial, unfortunately, we do not think that mesenteric fat will be possible to sample by biopsy.

Much of the phenotypic expression that is observed also depends on regulation of enzyme catalysis by covalent modifications and allosteric factors.

Response: We agree with the reviewer and recognize that this is a limitation of the transcriptomic research. We have added information to the Discussion to address this. Please see Lines 303-308.

Reviewer #2: Manuscript PONE-D-19-25581 aimed to identify genes that may be differentially expressed in steers with high and low BW gain and feed intake. The study is technically and scientifically sound but authors need to improve the main messages that can be taken from their nice research scope.

Line 52 any idea on how much a farmer can save? Please give an estimate in %

Response: This is a great question and I can appreciate the interest in trying to quantify the potential cost savings to the producer. However, this is a challenging request because the actual savings due to improved feed efficiency are going to be specific to the producer and depend on the environment and available feed resources. Unfortunately, we do not think there is a way to address this well.

Line 53 what is usable? Please elaborate

Response: This has been changed to “meat products for consumption”, Line 54.

Lines 68-75 please describe your study hypothesis. It seems that it is somehow encrypted in this paragraph, could you please be more precise on your research expectations

Response: We have added additional information to the Introduction to better describe our hypothesis. Please see lines 77-82.

Line 89 is it 64 to 92 days. Is that the sampling period? Please clarify

Response: We have revised this section by reordering sentences to better clarify the study, see Lines 104-124. Animals were on the feeding trial for a total of 64-92 days. At the end of the study, average daily gain and feed intake phenotypes were calculated for all the animals on the trail. A total of 16 animals were selected from each cohort. Those animals were then comingled and allowed to consume the same study ration ad libitum until slaughter, which occurred 11 to 28 days after the feeding trial ended.

Lines 88-94 please provide forage to concentrate ratio and the rationale behind the diet formulation i.e., NRC… here and later for the other diet descriptions

Response: Thank you for identifying this oversight. All diets were formulated to meet or exceed nutrient requirements to express maximum growth potential (NRC 2000). This sentence has been added to the manuscript at Lines 104-105. We have included the exact ration that animals were fed, rather than the ratio of forage to concentrate. With the information provided the ration can be calculated.

Line 91 after steakmaker add (protein supplement; Land..

Response: This has been included on Line 98.

Line 122 add city and country and also catalogue number here and anywhere appropriate

Response: Catalog #, City and country have been added to the Methods.

Line 278 how can you overcome the fact that you have 5 cohort animals – does that influence or not your results? Please elaborate

Response: We do believe that having 5 cohorts of animals influences our results and have previously published the meta-analysis procedure as a method to handle multiple cohorts.

Lines 417 to 425 what would be the take-home message? Please try to deliver more precise. You have 5 cohorts of animals, each of them with precise feeding managements can you relate those variables with your transcriptomic results and BW gain and feed intake. The main issue perhaps is that in its present version your conclusion statements have not been landed in a way that the answered your initial objectives.

Response: Thank you for this comment. We agree that the prior version of the manuscript did not deliver an acceptable conclusion. We have moved that paragraph out of the Conclusions and into a paragraph of the Discussion and modified it, Lines 294-308. We have also included a new Conclusions paragraph, Lines 447-454.

Reviewer #3: This is a very well-written manuscript describing a meta-analysis of RNAseq in mesenteric fat tissue in regards to body weight gain and feed efficiency. While I agree with the choice of using an adjusted p-value of 0.01, I would appreciate an acknowledgement of the false positive errors that the lack of concordance between cohorts can be due to genes being identified as DE by chance.

Response: We actually used an adjusted P-value of 0.05 for the meta-analysis. We have included the following sentences in the first paragraph of the Discussion (Lines 290-293) to acknowledge that there are still false positive errors within the genes identified as differentially expressed with the following statements: “This allowed us to identify a several genes among the 80 animals tested that may be involved in gain and feed intake. Although we did correct the meta-analysis P-values for multiple testing and reported the data using PADJ<0.05, this still implies that 5% of the significant tests are false positives.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

13 Dec 2019

Genes associated with body weight gain and feed intake identified by meta-analysis of the mesenteric fat from crossbred beef steers

PONE-D-19-25581R1

Dear Dr. Lindholm-Perry,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Juan J Loor

Academic Editor

PLOS ONE

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Very good job of revision.

Line 166... developed in (5) was used; give a name of the authors

Line 290 This allowed us to identify a several genes; remove the "a"

Reviewer #2: authors have made all suggested corrections

Reviewer #3: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

20 Dec 2019

PONE-D-19-25581R1

Genes associated with body weight gain and feed intake identified by meta-analysis of the mesenteric fat from crossbred beef steers

Dear Dr. Lindholm-Perry:

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