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      Transcriptome profiles in peripheral white blood cells at the time of artificial insemination discriminate beef heifers with different fertility potential

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          Abstract

          Background

          Infertility is a longstanding limitation in livestock production with important economic impact for the cattle industry. Female reproductive traits are polygenic and lowly heritable in nature, thus selection for fertility is challenging. Beef cattle operations leverage estrous synchronization in combination with artificial insemination (AI) to breed heifers and benefit from an early and uniform calving season. A couple of weeks following AI, heifers are exposed to bulls for an opportunity to become pregnant by natural breeding (NB), but they may also not become pregnant during this time period. Focusing on beef heifers, in their first breeding season, we hypothesized that: a- at the time of AI, the transcriptome of peripheral white blood cells (PWBC) differs between heifers that become pregnant to AI and heifers that become pregnant late in the breeding season by NB or do not become pregnant during the breeding season; and b- the ratio of transcript abundance between genes in PWBC classifies heifers according to pregnancy by AI, NB, or failure to become pregnant.

          Results

          We generated RNA-sequencing data from 23 heifers from two locations (A: six AI-pregnant and five NB-pregnant; and B: six AI-pregnant and six non-pregnant). After filtering out lowly expressed genes, we quantified transcript abundance for 12,538 genes. The comparison of gene expression levels between AI-pregnant and NB-pregnant heifers yielded 18 differentially expressed genes (DEGs) ( ADAM20, ALDH5A1, ANG, BOLA-DQB, DMBT1, FCER1A, GSTM3, KIR3DL1, LOC107131247, LOC618633, LYZ, MNS1, P2RY12, PPP1R1B, SIGLEC14, TPPP, TTLL1, UGT8, eFDR≤0.02). The comparison of gene expression levels between AI-pregnant and non-pregnant heifers yielded six DEGs ( ALAS2, CNKSR3, LOC522763, SAXO2, TAC3, TFF2, eFDR≤0.05). We calculated the ratio of expression levels between all gene pairs and assessed their potential to classify samples according to experimental groups. Considering all samples, relative expression from two gene pairs correctly classified 10 out of 12 AI-pregnant heifers ( P = 0.0028) separately from the other 11 heifers (NB-pregnant, or non-pregnant).

          Conclusion

          The transcriptome profile in PWBC, at the time of AI, is associated with the fertility potential of beef heifers. Transcript levels of specific genes may be further explored as potential classifiers, and thus selection tools, of heifer fertility.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-4505-4) contains supplementary material, which is available to authorized users.

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          Most cited references50

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          Minireview: kisspeptin/neurokinin B/dynorphin (KNDy) cells of the arcuate nucleus: a central node in the control of gonadotropin-releasing hormone secretion.

          Recently, a subset of neurons was identified in the arcuate nucleus of the hypothalamus that colocalize three neuropeptides, kisspeptin, neurokinin B, and dynorphin, each of which has been shown to play a critical role in the central control of reproduction. Growing evidence suggests that these neurons, abbreviated as the KNDy subpopulation, are strongly conserved across a range of species from rodents to humans and play a key role in the physiological regulation of GnRH neurons. KNDy cells are a major target for steroid hormones, form a reciprocally interconnected network, and have direct projections to GnRH cell bodies and terminals, features that position them well to convey steroid feedback control to GnRH neurons and potentially serve as a component of the GnRH pulse generator. In addition, recent work suggests that alterations in KNDy cell peptides may underlie neuroendocrine defects seen in clinical reproductive disorders such as polycystic ovarian syndrome. Taken together, this evidence suggests a key role for the KNDy subpopulation as a focal point in the control of reproductive function in health and disease.
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            Activating and inhibitory receptors of natural killer cells.

            Natural killer (NK) cells are potent immune effector cells that can respond to infection and cancer, as well as allowing maternal adaptation to pregnancy. In response to malignant transformation or pathogenic invasion, NK cells can secrete cytokine and may be directly cytolytic, as well as exerting effects indirectly through other cells of the immune system. To recognize and respond to inflamed or infected tissues, NK cells express a variety of activating and inhibitory receptors including NKG2D, Ly49 or KIR, CD94-NKG2 heterodimers and natural cytotoxicity receptors, as well as co-stimulatory receptors. These receptors recognize cellular stress ligands as well as major histocompatibility complex class I and related molecules, which can lead to NK cell responses. Importantly, NK cells must remain tolerant of healthy tissue, and some of these receptors can also prevent activation of NK cells. In this review, we describe the expression of prominent NK cell receptors, as well as expression of their ligands and their role in immune responses. In addition, we describe the main signaling pathways used by NK cell receptors. Although we now appreciate that NK cell biology is more complicated than first thought, there are still facets of their biology that remain unclear. These will be highlighted and discussed in this review.
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              Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

              , , (2013)
              RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC.
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                Author and article information

                Contributors
                sed0029@auburn.edu
                bag0028@auburn.edu
                elmormf@auburn.edu
                kriesla@auburn.edu
                elmorjb@auburn.edu
                pwd0003@auburn.edu
                rodnisp@auburn.edu
                334 844 1680 , fbiase@auburn.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                9 February 2018
                9 February 2018
                2018
                : 19
                : 129
                Affiliations
                [1 ]ISNI 0000 0001 2297 8753, GRID grid.252546.2, Department of Animal Sciences, , Auburn University, ; 559 Devall Dr, Auburn, AL 36839 USA
                [2 ]Alabama Cooperative Extension System, Auburn, AL USA
                Author information
                http://orcid.org/0000-0001-7895-0223
                Article
                4505
                10.1186/s12864-018-4505-4
                5807776
                29426285
                98603710-fb38-4d75-a250-678b44dec21d
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 13 September 2017
                : 29 January 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008745, Alabama Agricultural Experiment Station;
                Funded by: Hatch program of the National Institute of Food and Agriculture, U.S. Department of Agriculture
                Funded by: Alabama Cattlemen's Association
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics
                biomarkers,infertility,pregnancy
                Genetics
                biomarkers, infertility, pregnancy

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