Literature DB >> 31401475

Optimized combination of multiple biomarkers to improve diagnostic accuracy in male fertility.

Yoo-Jin Park1, Won-Ki Pang1, Do-Yeal Ryu1, Won-Hee Song1, Md Saidur Rahman1, Myung-Geol Pang2.   

Abstract

Artificial insemination is the general method of breeding for genetic improvement in offspring. However, almost half of the insemination cases fail to achieve full-term pregnancy, due to male infertility or subfertility. To maximize the success of insemination, accurate semen quality testing is required prior to insemination. Even though basic semen analyses have been used to provide preliminary information, it cannot fully identify the superior or inferior fertility bulls. Therefore, more powerful and easy to use methods for the prediction of male fertility are required, such as proteomic or microarray chips. During past decades, omics approaches have been developed and suggested the numerous fertility-related potential biomarkers. Our previous study identified the fertility related protein markers, enolase1 (ENO1), ATP synthase, H+ transporting, mitochondrial F1 complex, beta subunit (ATP5B), voltage-dependent anion channel 2 (VDAC2), phospholipid hydroperoxide glutathione peroxide (GPx4), and ubiquinol-cytochrome-c reductase complex core protein 2 (UQCRC2) in bovine spermatozoa. In the present study, we perform a marker combination assay using the western blot data of ENO1, ATP5B, VDAC2, GPx4, and UQCRC2 from 20 individual bull semen samples. And then, we identified the predictive ability of these markers for normal (non-return rate (NRR) ≥ 70%) and normal fertility (NRR<70%) in bulls. ENO1, a single protein marker, achieved an area under the curve (AUC) of 0.86 and 90% discriminatory power between normal and below-normal fertility bulls, with 90% sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Although no meaningful changes existed in overall accuracy (70-85%) to discriminate the normal and below-normal fertility between ENO1 single marker and combined marker panels, multiple marker combination methods using ENO1, VDAC2, GPx4, and UQCRC2 provided absolute sensitivity and NPV, with higher specificity (70%) and PPV (77%). ENO1 can be used as a fertility marker candidate, but there were limitations for providing absolute information about normal and below-normal fertility. Although the combined use of fertility-related markers cannot provide absolute accuracy, it can help in indicating below-normal fertility in bulls. These results may contribute to the maintenance cost in the animal industry, via selection of bulls with inferior fertility.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bull fertility; Fertility marker; Male fertility prediction; Marker combination

Mesh:

Substances:

Year:  2019        PMID: 31401475     DOI: 10.1016/j.theriogenology.2019.07.029

Source DB:  PubMed          Journal:  Theriogenology        ISSN: 0093-691X            Impact factor:   2.740


  4 in total

Review 1.  Omics and Male Infertility: Highlighting the Application of Transcriptomic Data.

Authors:  Temidayo S Omolaoye; Victor A Omolaoye; Richard K Kandasamy; Mahmood Yaseen Hachim; Stefan S Du Plessis
Journal:  Life (Basel)       Date:  2022-02-14

Review 2.  The future of assessing bull fertility: Can the 'omics fields identify usable biomarkers?†.

Authors:  Erin K Klein; Aleona Swegen; Allan J Gunn; Cyril P Stephen; Robert John Aitken; Zamira Gibb
Journal:  Biol Reprod       Date:  2022-05-17       Impact factor: 4.161

3.  Establishment of a male fertility prediction model with sperm RNA markers in pigs as a translational animal model.

Authors:  Won-Ki Pang; Shehreen Amjad; Do-Yeal Ryu; Elikanah Olusayo Adegoke; Md Saidur Rahman; Yoo-Jin Park; Myung-Geol Pang
Journal:  J Anim Sci Biotechnol       Date:  2022-07-07

4.  Protein Identification of Spermatozoa and Seminal Plasma in Bottlenose Dolphin (Tursiops truncatus).

Authors:  Mari-Carmen Fuentes-Albero; Leopoldo González-Brusi; Paula Cots; Chiara Luongo; Silvia Abril-Sánchez; José Luis Ros-Santaella; Eliana Pintus; Sara Ruiz-Díaz; Carlos Barros-García; María-Jesús Sánchez-Calabuig; Daniel García-Párraga; Manuel Avilés; Mᵃ José Izquierdo Rico; Francisco Alberto García-Vázquez
Journal:  Front Cell Dev Biol       Date:  2021-07-16
  4 in total

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