Feasibility of a liver transcriptomics approach to assess bovine treatment with the prohormone dehydroepiandrosterone (DHEA)

Investigating Differential Expressed Genes of Limosilactobacillus reuteri LR08 Regulated by Soybean Protein and Peptides

Soybean protein and peptides have the potential to promote the growth of Lactobacillus, but the mechanisms involved are not well understood. The purpose of this study is to investigate differentially expressed genes (DEGs) of Limosilactobacillus reuteri (L. reuteri) LR08 responding to soybean protein and peptides using transcriptome. The results showed that both digested protein (dpro) and digested peptides (dpep) could enhance a purine biosynthesis pathway which could provide more nucleic acid and ATP for bacteria growth. Moreover, dpep could be used instead of dpro to promote the ABC transporters, especially the genes involved in the transportation of various amino acids. Interestingly, dpro and dpep played opposite roles in modulating DEGs from the acc and fab gene families which participate in fatty acid biosynthesis. These not only provide a new direction for developing nitrogen-sourced prebiotics in the food industry but could also help us to understand the fundamental mechanism of the effects of dpro and dpep on their growth and metabolisms and provides relevant evidence for further investigation.

Do Dehydroepiandrosterone (DHEA) and Its Sulfate (DHEAS) Play a Role in the Stress Response in Domestic Animals?

In animal husbandry, stress is often associated with poor health and welfare. Stress occurs when a physiological control system detects a state of real or presumptive threat to the animal's homeostasis or a failure to control a fitness-critical variable. The definition of stress has mostly relied on glucocorticoids measurement, even though glucocorticoids represent one stress-response system, the hypothalamus-pituitary-adrenocortical axis, which is not precise enough as it is also related to metabolic regulation and activated in non-stressful situations (pleasure, excitement, and arousal). The mammal adrenal can synthesize the androgenic steroid dehydroepiandrosterone (DHEA) and its sulfate metabolite (DHEAS), which have been associated to the stress response in several studies performed mostly in humans and laboratory animals. Although the functions of these steroids are not fully understood, available data suggest their antagonistic effects on glucocorticoids and, in humans, their secretion is affected by stress. This review explores the scientific literature on DHEA and DHEAS release in domestic animals in response to stressors of different nature (inflammatory, physical, or social) and duration, and the extra-adrenal contribution to circulating DHEA. Then, the potential use of DHEA in conjunction with cortisol to improve the definition of the stress phenotype in farmed animals is discussed. Although the focus of this review is on farmed animals, examples from other species are reported when available.

Gene expression profiles in bovine granulocytes reflect the aberration of liver functions

Liver performs several important functions; however, predicting its functions is difficult. Methods of analyzing gene expression profiles, for example, microarray, provide functional information of tissues. Liver and peripheral blood leukocytes (PBLs) were collected from Holstein cows subjected to two different physiological conditions (non-pregnant and pregnant), and PBLs were fractionated by gradient cell separation. RNA from PBLs and liver were applied to oligo-DNA microarray and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). It revealed a group of stable bovine liver genes under constant physiological conditions. When they applied to physiological conditions including non-pregnant and pregnant, the profiles of some genes in liver were consistent with those in PBLs. Microarray data subjected to a principal component analysis (PCA) showed that the hepatic gene expression profiles were more consistent with those of granulocytes than mononuclear cells. The relationship of gene profiles in liver with granulocytes was confirmed by RT-qPCR and hierarchical cluster analysis. Gene profiles of granulocytes were more reliable than those of mononuclear cells, which reflected liver functions. These results suggest that the genes expressed in PBLs, particularly granulocytes, may be convenient bioindicators for the diagnosis of clinical disorder and/or detecting aberration of liver functions in cows subjected to different physiological conditions.

New surveillance concepts in food safety in meat producing animals: the advantage of high throughput 'omics' technologies - A review

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the ‘transcriptome’ has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern ‘omics’ and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid ‘biomarker signature’ can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional ‘biomarker signatures’ based on the physiological response triggered by illegal substances.

Canonical discriminant analysis and meat quality analysis as complementary tools to detect the illicit use of dexamethasone as a growth promoter in Friesian bulls

A screening method based on meat quality parameters and production traits for detecting the effects of illegal administration of dexamethasone in Friesian bulls was assessed. Twenty finishing bulls were divided into an untreated control group (n=8) and two treatment groups receiving dexamethasone orally at dosages of 1.4 (n=6) or 0.7 (n=6)mg per head per day for 60 days. The animals were slaughtered 26days after cessation of treatment. Thirty-six parameters were measured on live animals, carcasses and samples of the longissimus thoracis muscle. The production traits were similar between groups, but there were significant differences in meat quality between treatment groups. The higher dosage of dexamethasone improved meat tenderness, while the lower dosage resulted in more saturated red meat, with increased meat cooking shrinkage and cooking loss. The use of a portable 'electronic nose' as a screening tool was not successful in discriminating between treated and untreated meat. These results indicate that a multivariable approach using canonical discriminant analysis may be a complementary tool to identify meat from animals illegally treated with dexamethasone, based on several parameters (meat flavour, cooking and thawing loss, tenderness, colour and live weight gain), which are part of the normal analysis of meat quality.

Morphological Examination and Transcriptomic Profiling To Identify Prednisolone Treatment in Beef Cattle

In livestock production corticosteroids are licensed only for therapy; nevertheless, they are often illegally used as growth promoters. The aim of this study was to identify morphological or biomolecular alterations induced by prednisolone (PDN) in experimentally treated beef cattle, because PDN and its metabolites are no longer detectable by LC-MS/MS methods in biological fluids. Moreover, PDN does not induce any histological alterations in the thymus, different from dexamethasone treatments. Therefore, a marker of illicit treatment for this growth promoter could be useful. Eight male Italian Friesian beef cattle were administered prednisolone acetate 30 mg day^-1 per os for 35 days, and seven beef cattle represented the control group. Six days after drug withdrawal, the animals were slaughtered. Morphological and morphometric modifications were evaluated in the epididymis and testis, whereas transcriptomic changes induced by PDN administration were investigated in peripheral blood mononuclear cells (PBMCs) at different sampling times and in skeletal muscle and testis sampled at slaughtering. In the epididymis, spermatozoa number decreased in PDN-treated animals, and in some cases they were totally absent. Correspondingly, in the testis of treated animals, down-regulation for serine/threonine kinase 11 (STK11) gene expression was detected (p < 0.01). DNA microarray analysis revealed a total of 133 differentially expressed genes in skeletal muscle and testis, and 907 and 1416 in PBMCs after 33 days of treatment and at slaughtering, respectively. Histological investigations on epididymal content could represent a promising marker for PDN treatment in beef cattle and could be used as a screening method to identify animals worthy of further investigation with official methods. Moreover, the clear transcriptomic signature of PDN treatment evidenced in PBMCs supported the possibility of using this matrix to monitor the illicit treatment in vivo during ranching.

Transcriptomic profiling as a screening tool to detect trenbolone treatment in beef cattle

The effects of steroid hormone implants containing trenbolone alone (Finaplix-H), combined with 17β-oestradiol (17β-E; Revalor-H), or with 17β-E and dexamethasone (Revalor-H plus dexamethasone per os) on the bovine muscle transcriptome were examined by DNA-microarray. Overall, large sets of genes were shown to be modulated by the different growth promoters (GPs) and the regulated pathways and biological processes were mostly shared among the treatment groups. Using the Prediction Analysis of Microarray program, GP-treated animals were accurately identified by a small number of predictive genes. A meta-analysis approach was also carried out for the Revalor group to potentially increase the robustness of class prediction analysis. After data pre-processing, a high level of accuracy (90%) was obtained in the classification of samples, using 105 predictive gene markers. Transcriptomics could thus help in the identification of indirect biomarkers for anabolic treatment in beef cattle to be applied for the screening of muscle samples collected after slaughtering.

Application of Top-Down and Bottom-up Systems Approaches in Ruminant Physiology and Metabolism

Systems biology is a computational field that has been used for several years across different scientific areas of biological research to uncover the complex interactions occurring in living organisms. Applications of systems concepts at the mammalian genome level are quite challenging, and new complimentary computational/experimental techniques are being introduced. Most recent work applying modern systems biology techniques has been conducted on bacteria, yeast, mouse, and human genomes. However, these concepts and tools are equally applicable to other species including ruminants (e.g., livestock). In systems biology, both bottom-up and top-down approaches are central to assemble information from all levels of biological pathways that must coordinate physiological processes. A bottom-up approach encompasses draft reconstruction, manual curation, network reconstruction through mathematical methods, and validation of these models through literature analysis (i.e., bibliomics). Whereas top-down approach encompasses metabolic network reconstructions using ‘omics’ data (e.g., transcriptomics, proteomics) generated through DNA microarrays, RNA-Seq or other modern high-throughput genomic techniques using appropriate statistical and bioinformatics methodologies. In this review we focus on top-down approach as a means to improve our knowledge of underlying metabolic processes in ruminants in the context of nutrition. We also explore the usefulness of tissue specific reconstructions (e.g., liver and adipose tissue) in cattle as a means to enhance productive efficiency.

Transcriptomic markers meet the real world: finding diagnostic signatures of corticosteroid treatment in commercial beef samples

Background

The use of growth-promoters in beef cattle, despite the EU ban, remains a frequent practice. The use of transcriptomic markers has already proposed to identify indirect evidence of anabolic hormone treatment. So far, such approach has been tested in experimentally treated animals. Here, for the first time commercial samples were analyzed.

Results

Quantitative determination of Dexamethasone (DEX) residues in the urine collected at the slaughterhouse was performed by Liquid Chromatography-Mass Spectrometry (LC-MS). DNA-microarray technology was used to obtain transcriptomic profiles of skeletal muscle in commercial samples and negative controls. LC-MS confirmed the presence of low level of DEX residues in the urine of the commercial samples suspect for histological classification. Principal Component Analysis (PCA) on microarray data identified two clusters of samples. One cluster included negative controls and a subset of commercial samples, while a second cluster included part of the specimens collected at the slaughterhouse together with positives for corticosteroid treatment based on thymus histology and LC-MS. Functional analysis of the differentially expressed genes (3961) between the two groups provided further evidence that animals clustering with positive samples might have been treated with corticosteroids. These suspect samples could be reliably classified with a specific classification tool (Prediction Analysis of Microarray) using just two genes.

Conclusions

Despite broad variation observed in gene expression profiles, the present study showed that DNA-microarrays can be used to find transcriptomic signatures of putative anabolic treatments and that gene expression markers could represent a useful screening tool.

Transcriptional biomarkers--high throughput screening, quantitative verification, and bioinformatical validation methods

Molecular biomarkers found their way into many research fields, especially in molecular medicine, medical diagnostics, disease prognosis, risk assessment but also in other areas like food safety. Different definitions for the term biomarker exist, but on the whole biomarkers are measureable biological molecules that are characteristic for a specific physiological status including drug intervention, normal or pathological processes. There are various examples for molecular biomarkers that are already successfully used in clinical diagnostics, especially as prognostic or diagnostic tool for diseases. Molecular biomarkers can be identified on different molecular levels, namely the genome, the epigenome, the transcriptome, the proteome, the metabolome and the lipidome. With special "omic" technologies, nowadays often high throughput technologies, these molecular biomarkers can be identified and quantitatively measured. This article describes the different molecular levels on which biomarker research is possible including some biomarker candidates that have already been identified. Hereby the transcriptomic approach will be described in detail including available high throughput methods, molecular levels, quantitative verification, and biostatistical requirements for transcriptional biomarker identification and validation.