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      Atretic preovulatory follicles could be precursors of ovarian lutein cysts in the pig

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          Abstract

          Ovarian cysts contribute to reduced reproductive performance in pigs. Unfortunately, the mechanism of lutein cysts formation remains unknown. Here, we compared the endocrine and molecular milieus of intact, healthy preovulatory follicles (PF), gonadotropin (eCG/hCG)-induced healthy and atretic-like PF, as well as gonadotropin-provoked and spontaneous ovarian cysts in gilts. Several endocrine and molecular indicators and microRNA were compared in walls of PF and cysts. Intact and healthy PF, showed high estradiol/androstendione and low progesterone levels associated with CYP17A1, HSD17B1, and CYP19A1 elevation and reduced StAR/HSD3B1 protein expression. In contrast, low estradiol/androstendione and high progesterone concentrations, accompanied by decreased CYP17A1, HSD17B1, CYP19A1 and increased HSD3B1 protein abundance, appeared in atretic-like PF, gonadotropin-induced and spontaneous cysts. High progesterone receptor (PGR) protein abundance was maintained in intact and healthy PF, while it dropped in atretic-like PF, gonadotropins-induced and spontaneous cysts. The atretic PF showed high level of TNFα compared to healthy PF. In conclusion, follicular lutein cysts could be recruited from atretic-like PF with lost estrogenic milieu and inability to ovulate. Ovulatory cascade was presumably disrupted by a low PGR and high TNFα levels associated with earlier luteinization of follicular walls. These results suggest a novel mechanism of lutein ovarian cysts development in pigs and, perhaps, other species.

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          A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding

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            Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets.

            Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.
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              Comprehensive algorithm for quantitative real-time polymerase chain reaction.

              Quantitative real-time polymerase chain reactions (qRT-PCR) have become the method of choice for rapid, sensitive, quantitative comparison of RNA transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of mRNA levels. Calculating accurate mRNA levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold (CT) to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying qRT-PCR results using calculations based on the kinetics of individual PCR reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and CT. We use a four-parameter logistic model to fit the raw fluorescence data as a function of PCR cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For CT determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of qRT-PCR results that is independent of the specific equipment used to perform PCR reactions.
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                Author and article information

                Contributors
                a.ziecik@pan.olsztyn.pl
                m.kaczmarek@pan.olsztyn.pl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                12 May 2023
                12 May 2023
                2023
                : 13
                : 7758
                Affiliations
                [1 ]GRID grid.413454.3, ISNI 0000 0001 1958 0162, Department of Hormonal Action Mechanisms, Institute of Animal Reproduction and Food Research, , Polish Academy of Sciences, ; Tuwima 10 Str., 10-747 Olsztyn, Poland
                [2 ]GRID grid.5522.0, ISNI 0000 0001 2162 9631, Department of Endocrinology, Institute of Zoology and Biomedical Research, , Jagiellonian University, ; Kraków, Poland
                [3 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department Production Animal Medicine, Faculty of Veterinary Medicine, , University of Helsinki, ; Helsinki, Finland
                [4 ]GRID grid.13276.31, ISNI 0000 0001 1955 7966, Faculty of Veterinary Medicine, Center for Translational Medicine, , Warsaw University of Life Science, ; Warsaw, Poland
                [5 ]GRID grid.413454.3, ISNI 0000 0001 1958 0162, Molecular Biology Laboratory, Institute of Animal Reproduction and Food Research, , Polish Academy of Sciences, ; Olsztyn, Poland
                Article
                34563
                10.1038/s41598-023-34563-4
                10182091
                b1eef48c-cd25-4cba-afb1-53e104db2aec
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 November 2022
                : 3 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004281, Narodowe Centrum Nauki;
                Award ID: 2017/27/B/NZ9/02289
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                endocrinology,reproductive biology,animal physiology
                Uncategorized
                endocrinology, reproductive biology, animal physiology

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