Identification of sperm proteins as biomarkers of field fertility in Holstein-Friesian bulls used for artificial insemination

Rabaglino, María Belén; Danvic, C Le; Schibler, Laurent; Kupisiewicz, Kasia; Perrier, Jean‐Philippe; O’Meara, C.M.; Kenny, David A.; Fair, Seán; Lonergan, P.

doi:10.3168/jds.2022-22273

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…On the other hand, proteomic analyses allow us to identify potential biomarkers associated with sperm quality and fertility. Several previous studies have used a DDA method for proteomic data collection; − however, few fertility markers identified were common in all of them. These discrepancies could be due to different animal samples, different procedures, or the data reproducibility of procedures.…”

Section: Discussionmentioning

confidence: 99%

“…New advances in technology have improved proteomics techniques in the last years, and mass spectrometry (MS) has become the method of choice due to its unrivaled ability to analyze complex protein mixtures like spermatozoa. Data-dependent acquisition (DDA) is a widely used mode of data collection in tandem with mass spectrometry (MS/MS) sperm proteins from Holstein Friesian − or crossbreed bulls. − In 2013, Gillet et al proposed a new proteomic strategy using a data-independent acquisition (DIA) method called SWATH-MS (sequential window acquisition of all theoretical mass spectra). The SWATH-MS method is particularly innovative among the DIA methods and presents several advantages (reproducibility and consistency, data acquisition, and quantifiable proteins) compared with DDA-based methods and targeted proteomics. , Sperm proteomic analysis using SWATH-MS provides highly valuable information on cellular components or sperm function, which, together with the data obtained from the in vitro parameters obtained by FC or CASA, offers a more complete picture of biological events.…”

Section: Introductionmentioning

confidence: 99%

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Bull Sperm SWATH-MS-Based Proteomics Reveals Link between High Fertility and Energy Production, Motility Structures, and Sperm–Oocyte Interaction

Gacem,

Castello-Ruiz,

Hidalgo

et al. 2023

J. Proteome Res.

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The prediction of male or semen fertility potential remains a persistent challenge that has yet to be fully resolved. This work analyzed several in vitro parameters and proteome of spermatozoa in bulls cataloged as high-(HF; n = 5) and low-field (LF; n = 5) fertility after more than a thousand artificial inseminations. Sperm motility was evaluated by computer-assisted sperm analysis. Sperm viability, mitochondrial membrane potential (MMP) and reactive oxygen species (mROS) of spermatozoa were assessed by flow cytometry. Proteome was evaluated by the SWATH-MS procedure. Spermatozoa of HF bulls showed significantly higher total motility than the LF group (41.4% vs 29.7%). Rates of healthy sperm (live, high MMP, and low mROS) for HF and LF bull groups were 49% and 43%, respectively (p > 0.05). Spermatozoa of HF bulls showed a higher presence of differentially abundant proteins (DAPs) related to both energy production (COX7C), mainly the OXPHOS pathway, and the development of structures linked with the motility process (TPPP2, SSMEM1, and SPAG16). Furthermore, we observed that equatorin (EQTN), together with other DAPs related to the interaction with the oocyte, was overrepresented in HF bull spermatozoa. The biological processes related to protein processing, catabolism, and protein folding were found to be overrepresented in LF bull sperm in which the HSP90AA1 chaperone was identified as the most DAP. Data are available via ProteomeXchange with identifier PXD042286.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bull Sperm SWATH-MS-Based Proteomics Reveals Link between High Fertility and Energy Production, Motility Structures, and Sperm–Oocyte Interaction

Gacem,

Castello-Ruiz,

Hidalgo

et al. 2023

J. Proteome Res.

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“…The abundance or absence of these sperm proteins in bulls may also have an effect on fertility by decreasing or increasing the developmental competence of embryogenesis at the molecular level (DeJarnette, 2005;Klein, et al 2022), and influence later developmental phases of the embryo. A comprehensive analysis of proteins present in bull sperm can provide essential insight into their functional importance in the regulations of various processes of fertilization and reproduction (Peddinti et al 2008;Rabaglino et al 2022;Klien, et al 2022). Therefore, helping cattle breeders make informed herd improvement decisions for reproductive efficiency and reduce premature disposal of breeding bulls.…”

Section: Introductionmentioning

confidence: 99%

Proteome of bull fertility: a comparative study between resting and in-service Nellore bulls

Udoekong

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This study aimed to analyses sperm proteome of Nellore bulls by comparing their sperm protein profiles and identifying differentially abundant proteins during their in-service and resting periods. A total of twenty bulls aged between 19.8 and 22.7 months were investigated at their resting period and at the breeding season (in-service). Semen samples were collected from bulls by electroejaculatation and preserved in liquid nitrogen. A total of 155 sperm proteins were identified from the protein analysis using SDA-PAGE. 84 of these proteins were specific to both resting bulls and in-service bulls while 61 were found only in resting bulls and five found only in the samples from in-service bulls. The differentially expressed sperm proteins observed in sodium dodecyl sulfate polyacrylamide gel electrophoresis were identified using LC-MS/MS. In resting bulls, albumin was differentially expressed and significantly (p<0.05) lower, down regulated in in-service bulls. SRN, bovin proacrosine, GAPDHS, ASRGL1, BSP1, CES5A, SPACA3, SPAM1, ADAM32 and ACE were significantly (p<0.05) lower and down regulated in in-service bulls. SPADH1 and LOC101907989 were differentially expressed in in-service bulls, significantly (p<0.05) lower and down regulated in resting bulls; SPADH1 (spermadhesin-1) associates with seminal plasma proteins BSP-30k Da, seminal plasma PDC-109, seminal plasma A3, ANG1, RNASE4, SRN and carbohydrate-binding proteins (uncharacterized protein). These findings suggest that these sperm proteins play significant role and are associated with increased fertility in bull during in-service periods rather than when they are at rest. This study suggests prior clean-up of breeding bulls before breeding season to allow for biological processes and favorable correlations of other sperm protein like SPADH1 in the regulation of sperm maturation, functions, fertility and oocyte-sperm interaction. Keywords: Fertility. Seminal plasma proteins. Spermadhesin-1.

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The role of machine learning in decoding the molecular complexity of bovine pregnancy: a review

van Rumpt,

Rabaglino

2024

Reprod. Fertil. Dev.

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Pregnancy establishment and progression in cattle are pivotal research areas with significant implications for the industry. Despite high fertilization rates, ~50% of bovine pregnancies are lost, pinpointing the need to keep studying the biological principles leading to a successful pregnancy. The increasing access to and generation of omics data have aided in defining the molecular characteristics of pregnancy, i.e. embryo and fetal development and communication with the maternal environment. Large datasets generated through omics technologies are usually analyzed through pipelines that could lack the power to deeply explore the complexity of biological data. Machine learning (ML), a branch of artificial intelligence, offers a promising approach to address this challenge by effectively handling large-scale, heterogeneous and high-dimensional data. This review explores the role of ML in unraveling the intricacies of bovine embryo–maternal communication, including the identification of biomarkers associated with pregnancy outcome prediction and uncovering key genes and pathways involved in embryo development and survival. Through discussing recent studies, we define the contributions of ML towards advancing our understanding of bovine pregnancy, with the final goal of reducing pregnancy losses and enhancing reproductive efficiency while also addressing current limitations and future perspectives of ML in this field.

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Identification of sperm proteins as biomarkers of field fertility in Holstein-Friesian bulls used for artificial insemination

Cited by 5 publications

References 42 publications

Bull Sperm SWATH-MS-Based Proteomics Reveals Link between High Fertility and Energy Production, Motility Structures, and Sperm–Oocyte Interaction

Bull Sperm SWATH-MS-Based Proteomics Reveals Link between High Fertility and Energy Production, Motility Structures, and Sperm–Oocyte Interaction

Proteome of bull fertility: a comparative study between resting and in-service Nellore bulls

The role of machine learning in decoding the molecular complexity of bovine pregnancy: a review

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