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ABSTRACT: Glyphosate (GLY)-based herbicide (GBH) formulations are widely used pesticides in agriculture. The European Union recently decided to extend the use of GLY in Europe until 2034. Previously, we showed that chronic dietary GBH exposure in adult hens resulted in a reversible increase in early mortality in chicken embryos. In this present study, we investigated the GBH effects on metabolism and ovarian functions by using a transcriptomic approach in vivo in young female broilers and in vitro in ovarian explant cultures. We exposed 11-day-old female broilers to 13 mg GLY equivalent/kg body weight/d (GBH13, n = 20), 34 mg GLY equivalent/kg body weight/d (GBH34, n = 20), or a standard diet (control [CT], n = 20) for 25 d. These 2 GBH concentrations correspond to approximatively one-eighth and one-third of the no observed adverse effect level (NOAEL) as defined by European Food Safety Authority in birds. During this period, we evaluated body weight, fattening, food intake, and the weight of organs (including the ovaries). Chronic dietary GBH exposure dose dependently reduced food intake, body weight, and fattening, but increased oxidative stress and relative ovary weight. We analyzed the ovarian gene expression profile in CT, GBH13, and GBH34 broilers with RNA sequencing and showed that differentially expressed genes are mainly enriched in pathways related to cholesterol metabolism, steroidogenesis, and RNA processing. With quantitative polymerase chain reaction and western blotting, we confirmed that GBH decreased ovarian STAR and CYP19A1 messenger RNA and protein expression, respectively. Furthermore, we confirmed that GBH altered steroid production in ovarian explants. We have identified potential regulatory networks associated with GBH. These data provide valuable support for understanding the ovarian transcriptional regulatory mechanism of GBH in growing broilers.

The study of facial expressions in mammals provided great advances in the identification of their emotions and then in the comprehension of their sentience. So far, this area of research has excluded birds. With a naturalist approach, we analysed facial blushing and feather displays in domestic fowl. Hens were filmed in situations contrasting in emotional valence and arousal level: situations known to indicate calm states (positive valence / low arousal), have rewarding effects (positive valence / high arousal) or induce fear-related behaviour (negative valence / high arousal). Head feather position as well as skin redness of comb, wattles, ear lobes and cheeks varied across these situations. Skin of all four areas was less red in situations with low arousal compared to situations with higher arousal. Furthermore, skin redness of the cheeks and ear lobes also varied depending on the valence of the situation: redness was higher in situations with negative valence compared to situations with positive valence. Feather position also varied with the situations. Feather fluffing was mostly observed in positively valenced situations, except when hens were eating. We conclude that hens have facial displays that reveal their emotions and that blushing is not exclusive to humans. This opens a promising way to explore the emotional lives of birds, which is a critical step when trying to improve poultry welfare. [ABSTRACT FROM AUTHOR]

Based on next-generation sequencing, we established a repertoire of differentially overexpressed genes (DoEGs) in eight adult chicken tissues: the testis, brain, lung, liver, kidney, muscle, heart, and intestine. With 4,499 DoEGs, the testis had the highest number and proportion of DoEGs compared with the seven somatic tissues. The testis DoEG set included the highest proportion of long noncoding RNAs (lncRNAs; 1,851, representing 32% of the lncRNA genes in the whole genome) and the highest proportion of protein-coding genes (2,648, representing 14.7% of the protein-coding genes in the whole genome). The main significantly enriched Gene Ontology terms related to the protein-coding genes were "reproductive process," "tubulin binding," and "microtubule cytoskeleton." Using real-time quantitative reverse transcription-polymerase chain reaction, we confirmed the overexpression of genes that encode proteins already described in chicken sperm [such as calcium binding tyrosine phosphorylation regulated (CABYR), spermatogenesis associated 18 (SPATA18), and CDK5 regulatory subunit associated protein (CDK5RAP2)] but whose testis origin had not been previously confirmed. Moreover, we demonstrated the overexpression of vertebrate orthologs of testis genes not yet described in the adult chicken testis [such as NIMA related kinase 2 (NEK2), adenylate kinase 7 (AK7), and CCNE2]. Using clustering according to primary sequence homology, we found that 1,737 of the 2,648 (67%) testis protein-coding genes were unique genes. This proportion was significantly higher than the somatic tissues except muscle. We clustered the other 911 testis protein-coding genes into 495 families, from which 47 had all paralogs overexpressed in the testis. Among these 47 testis-specific families, eight contained uncharacterized duplicated paralogs without orthologs in other metazoans except birds: these families are thus specific for chickens/birds. NEW & NOTEWORTHY: Comparative next-generation sequencing analysis of eight chicken tissues showed that the testis has highest proportion of long noncoding RNA and protein-coding genes of the whole genome. We identified new genes in the chicken testis, including orthologs of known mammalian testicular genes. We also identified 47 gene families in which all the members were overexpressed, if not exclusive, in the testis. Eight families, organized in duplication clusters, were unknown, without orthologs in metazoans except birds, and are thus specific for chickens/birds. [ABSTRACT FROM AUTHOR]

Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets. [ABSTRACT FROM AUTHOR]

Glyphosate-based herbicides (GBHs) are massively used in agriculture. However, few studies have investigated the effects of glyphosate-based herbicides on avian species although they are largely exposed via their food. Here, we investigated the potential reversibility of the effects of chronic dietary exposure to glyphosate-based herbicides in broiler hens. For 42 days, we exposed 32-week-old hens to glyphosate-based herbicides via their food (47 mg/kg/day glyphosate equivalent, glyphosate-based herbicides, n = 75) corresponding to half glyphosate’s no-observed-adverse-effect-level in birds. We compared their performance to that of 75 control animals (CT). Both groups (glyphosate-based herbicides and control animals) were then fed for 28 additional days without glyphosate-based herbicides exposure (Ex-glyphosate-based herbicides and Ex-control animals). Glyphosate-based herbicides temporarily increased the plasma glyphosate and AMPA (aminomethylphosphonic acid) concentrations. Glyphosate and aminomethylphosphonic acid mostly accumulated in the liver and to a lesser extent in the leg muscle and abdominal adipose tissue. Glyphosate-based herbicides also temporarily increased the gizzard weight and plasma oxidative stress monitored by TBARS (thiobarbituric acid reactive substances). Glyphosate-based herbicides temporarily decreased the cecal concentrations of propionate, isobutyrate and propionate but acetate and valerate were durably reduced. The cecal microbiome was also durably affected since glyphosate-based herbicides inhibited Barnesiella and favored Alloprevotella. Body weight, fattening, food intake and feeding behavior as well as plasma lipid and uric acid were unaffected by glyphosate-based herbicides. Taken together, our results show possible disturbances of the cecal microbiota associated with plasma oxidative stress and accumulation of glyphosate in metabolic tissues in response to dietary glyphosate-based herbicides exposure in broiler hens. Luckily, glyphosate-based herbicides at this concentration does not hamper growth and most of the effects on the phenotypes are reversible. [ABSTRACT FROM AUTHOR]

Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Introduction: Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features. Objectives: In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra. Methods: A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues. Results: The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods. Conclusion: ASICS is a completely automated procedure to identify and quantify metabolites in H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles. [ABSTRACT FROM AUTHOR]

Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of 1 H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites are often designed to process one given complex mixture spectrum at a time. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present new methods that are the first to either align spectra or to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of these new methods are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.

Motivation: In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way., Results: We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition., Availability and Implementation: ASICS is distributed as an R package, available on Bioconductor., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)