Chicken adaptive response to low energy diet: main role of the hypothalamic lipid metabolism revealed by a phenotypic and multi-tissue transcriptomic approach.

BACKGROUND: Production conditions of layer chicken can vary in terms of temperature or diet energy content compared to the controlled environment where pure-bred selection is undertaken. The aim of this study was to better understand the long-term effects of a 15%-energy depleted diet on egg-production, energy homeostasis and metabolism via a multi-tissue transcriptomic analysis. Study was designed to compare effects of the nutritional intervention in two layer chicken lines divergently selected for residual feed intake. RESULTS: Chicken adapted to the diet in terms of production by significantly increasing their feed intake and decreasing their body weight and body fat composition, while their egg production was unchanged. No significant interaction was observed between diet and line for the production traits. The low energy diet had no effect on adipose tissue and liver transcriptomes. By contrast, the nutritional challenge affected the blood transcriptome and, more severely, the hypothalamus transcriptome which displayed 2700 differentially expressed genes. In this tissue, the low-energy diet lead to an over-expression of genes related to endocannabinoid signaling (CN1R, NAPE-PLD) and to the complement system, a part of the immune system, both known to regulate feed intake. Both mechanisms are associated to genes related polyunsaturated fatty acids synthesis (FADS1, ELOVL5 and FADS2), like the arachidonic acid, a precursor of anandamide, a key endocannabinoid, and of prostaglandins, that mediate the regulatory effects of the complement system. A possible regulatory role of NR1H3 (alias LXRα) has been associated to these transcriptional changes. The low-energy diet further affected brain plasticity-related genes involved in the cholesterol synthesis and in the synaptic activity, revealing a link between nutrition and brain plasticity. It upregulated genes related to protein synthesis, mitochondrial oxidative phosphorylation and fatty acid oxidation in the hypothalamus, suggesting reorganization in nutrient utilization and biological synthesis in this brain area. CONCLUSIONS: We observed a complex transcriptome modulation in the hypothalamus of chicken in response to low-energy diet suggesting numerous changes in synaptic plasticity, endocannabinoid regulation, neurotransmission, lipid metabolism, mitochondrial activity and protein synthesis. This global transcriptomic reprogramming could explain the adaptive behavioral response (i.e. increase of feed intake) of the animals to the low-energy content of the diet.

Immunome differences between porcine ileal and jejunal Peyer's patches revealed by global transcriptome sequencing of gut-associated lymphoid tissues.

The epithelium of the intestinal mucosa and the gut-associated lymphoid tissues (GALT) constitute an essential physical and immunological barrier against pathogens. In order to study the specificities of the GALT transcriptome in pigs, we compared the transcriptome profiles of jejunal and ileal Peyer's patches (PPs), mesenteric lymph nodes (MLNs) and peripheral blood (PB) of four male piglets by RNA-Seq. We identified 1,103 differentially expressed (DE) genes between ileal PPs (IPPs) and jejunal PPs (JPPs), and six times more DE genes between PPs and MLNs. The master regulator genes FOXP3, GATA3, STAT4, TBX21 and RORC were less expressed in IPPs compared to JPPs, whereas the transcription factor BCL6 was found more expressed in IPPs. In comparison between IPPs and JPPs, our analyses revealed predominant differential expression related to the differentiation of T cells into Th1, Th2, Th17 and iTreg in JPPs. Our results were consistent with previous reports regarding a higher T/B cells ratio in JPPs compared to IPPs. We found antisense transcription for respectively 24%, 22% and 14% of the transcripts detected in MLNs, PPs and PB, and significant positive correlations between PB and GALT transcriptomes. Allele-specific expression analyses revealed both shared and tissue-specific cis-genetic control of gene expression.

KIRREL is differentially expressed in adipose tissue from 'fertil+' and 'fertil-' cows: in vitro role in ovary?

We have previously shown that dairy cows carrying the 'fertil-' haplotype for one quantitative trait locus affecting female fertility located on the bovine chromosome three (QTL-F-Fert-BTA3) have a significantly lower conception rate and body weight after calving than cows carrying the 'fertil+' haplotype. Here, we compared by Tiling Array the expression of genes included in the QTL-F-Fert-BTA3 in 'fertil+' and 'fertil-' adipose tissue one week after calving when plasma non-esterified fatty acid concentrations were greater in 'fertil-' animals. We observed that thirty-one genes were overexpressed whereas twelve were under-expressed in 'fertil+' as compared to 'fertil-' cows (P < 0.05). By quantitative PCR and immunoblot we confirmed that adipose tissue KIRREL mRNA and protein were significantly greater expressed in 'fertil+' than in 'fertil-'. KIRREL mRNA is abundant in bovine kidney, adipose tissue, pituitary, and ovary and detectable in hypothalamus and mammary gland. Its expression (mRNA and protein) is greater in kidney of 'fertil+' than 'fertil-' cows (P < 0.05). KIRREL (mRNA and protein) is also present in the different ovarian cells with a greater expression in granulosa cells of 'fertil+' than 'fertil-' cows. In cultured granulosa cells, recombinant KIRREL halved steroid secretion in basal state (P < 0.05). It also decreased cell proliferation (P < 0.05) and in vitro oocyte maturation (P < 0.05). These results were associated to a rapid increase in MAPK1/3 and MAPK14 phosphorylation in granulosa cells and to a decrease in MAPK1/3 phosphorylation in oocyte. Thus, KIRREL could be a potential metabolic messenger linking body composition and fertility.

Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis.

BACKGROUND: Efforts to improve sustainability in livestock production systems have focused on two objectives: investigating the genetic control of immune function as it pertains to robustness and disease resistance, and finding predictive markers for use in breeding programs. In this context, the peripheral blood transcriptome represents an important source of biological information about an individual's health and immunological status, and has been proposed for use as an intermediate phenotype to measure immune capacity. The objective of this work was to study the genetic architecture of variation in gene expression in the blood of healthy young pigs using two approaches: an expression genome-wide association study (eGWAS) and allele-specific expression (ASE) analysis. RESULTS: The blood transcriptomes of 60-day-old Large White pigs were analyzed by expression microarrays for eGWAS (242 animals) and by RNA-Seq for ASE analysis (38 animals). Using eGWAS, the expression levels of 1901 genes were found to be associated with expression quantitative trait loci (eQTLs). We recovered 2839 local and 1752 distant associations (Single Nucleotide Polymorphism or SNP located less or more than 1 Mb from expression probe, respectively). ASE analyses confirmed the extensive cis-regulation of gene transcription in blood, and revealed allelic imbalance in 2286 SNPs, which affected 763 genes. eQTLs and ASE-genes were widely distributed on all chromosomes. By analyzing mutually overlapping eGWAS results, we were able to describe putative regulatory networks, which were further refined using ASE data. At the functional level, genes with genetically controlled expression that were detected by eGWAS and/or ASE analyses were significantly enriched in biological processes related to RNA processing and immune function. Indeed, numerous distant and local regulatory relationships were detected within the major histocompatibility complex region on chromosome 7, revealing ASE for most class I and II genes. CONCLUSIONS: This study represents, to the best of our knowledge, the first genome-wide map of the genetic control of gene expression in porcine peripheral blood. These results represent an interesting resource for the identification of genetic markers and blood biomarkers associated with variations in immunity traits in pigs, as well as any other complex traits for which blood is an appropriate surrogate tissue.

Rapid Discovery of De Novo Deleterious Mutations in Cattle Enhances the Value of Livestock as Model Species.

In humans, the clinical and molecular characterization of sporadic syndromes is often hindered by the small number of patients and the difficulty in developing animal models for severe dominant conditions. Here we show that the availability of large data sets of whole-genome sequences, high-density SNP chip genotypes and extensive recording of phenotype offers an unprecedented opportunity to quickly dissect the genetic architecture of severe dominant conditions in livestock. We report on the identification of seven dominant de novo mutations in CHD7, COL1A1, COL2A1, COPA, and MITF and exploit the structure of cattle populations to describe their clinical consequences and map modifier loci. Moreover, we demonstrate that the emergence of recessive genetic defects can be monitored by detecting de novo deleterious mutations in the genome of bulls used for artificial insemination. These results demonstrate the attractiveness of cattle as a model species in the post genomic era, particularly to confirm the genetic aetiology of isolated clinical case reports in humans.

RACK1 cooperates with NRASQ61K to promote melanoma in vivo.

Melanoma is the deadliest skin cancer. RACK1 (Receptor for activated protein kinase C) protein was proposed as a biological marker of melanoma in human and domestic animal species harboring spontaneous melanomas. As a scaffold protein, RACK1 is able to coordinate the interaction of key signaling molecules implicated in both physiological cellular functions and tumorigenesis. A role for RACK1 in rewiring ERK and JNK signaling pathways in melanoma cell lines had been proposed. Here, we used a genetic approach to test this hypothesis in vivo in the mouse. We show that Rack1 knock-down in the mouse melanoma cell line B16 reduces invasiveness and induces cell differentiation. We have developed the first mouse model for RACK1 gain of function, Tyr::Rack1-HA transgenic mice, targeting RACK1 to melanocytes in vivo. RACK1 overexpression was not sufficient to initiate melanomas despite activated ERK and AKT. However, in a context of melanoma predisposition, RACK1 overexpression reduced latency and increased incidence and metastatic rate. In primary melanoma cells from Tyr::Rack1-HA, Tyr::NRasQ61K mice, activated JNK (c-Jun N-terminal kinase) and activated STAT3 (signal transducer and activator of transcription 3) acted as RACK1 oncogenic partners in tumoral progression. A sequential and coordinated activation of ERK, JNK and STAT3 with RACK1 is shown to accelerate aggressive melanoma development in vivo.

Comparison of whole-genome (13X) and capture (87X) resequencing methods for SNP and genotype callings.

The number of polymorphisms identified with next-generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole-genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down-sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture-related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.