Enhanced fertility prediction of cryopreserved boar spermatozoa using novel sperm function assessment

Daigneault, Bradford W.; McNamara, K. A.; Purdy, P. H.; Krisher, Rebecca L.; Knox, Robert V.; Rodriguez-Zas, Sandra L.; Miller, David J.

doi:10.1111/andr.12035

Cited by 21 publications

(13 citation statements)

References 49 publications

(67 reference statements)

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“…Evidence has shown that the evaluation of various sperm traits rather than a single trait analysis provides a better fertility prediction of FT semen [27][28][29]. Several sperm attributes have been used to assess the post-thaw quality of boar semen, and among these sperm motility is widely used in semen quality assessment because it correlates with fertility outcomes [30,31]. Sperm motility is assessed subjectively or with the computer-assisted semen analysis (CASA) system; however, subjective motility assessment has been shown to provide reliable estimates in association studies [26,32,33].…”

Section: Snp Associations With Post-thaw Semen Qualitymentioning

confidence: 99%

Gene Polymorphisms in Boar Spermatozoa and Their Associations with Post-Thaw Semen Quality

Mańkowska

Brym

Paukszto

et al. 2020

IJMS

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Genetic markers have been used to assess the freezability of semen. With the advancement in molecular genetic techniques, it is possible to assess the relationships between sperm functions and gene polymorphisms. In this study, variant calling analysis of RNA-Seq datasets was used to identify single nucleotide polymorphisms (SNPs) in boar spermatozoa and to explore the associations between SNPs and post-thaw semen quality. Assessment of post-thaw sperm quality characteristics showed that 21 boars were considered as having good semen freezability (GSF), while 19 boars were classified as having poor semen freezability (PSF). Variant calling demonstrated that most of the polymorphisms (67%) detected in boar spermatozoa were at the 3’-untranslated regions (3’-UTRs). Analysis of SNP abundance in various functional gene categories showed that gene ontology (GO) terms were related to response to stress, motility, metabolism, reproduction, and embryo development. Genomic DNA was isolated from sperm samples of 40 boars. Forty SNPs were selected and genotyped, and several SNPs were significantly associated with motility and membrane integrity of frozen-thawed (FT) spermatozoa. Polymorphism in SCLT1 gene was associated with significantly higher motility and plasma membrane integrity of FT spermatozoa from boars of the GSF group compared with those of the PSF group. Likewise, polymorphisms in MAP3K20, MS4A2, and ROBO1 genes were significantly associated with reduced cryo-induced lipid peroxidation and DNA damage of FT spermatozoa from boars of the GSF group. Candidate genes with significant SNP associations, including APPL1, PLBD1, FBXO16, EML5, RAB3C, OXSR1, PRICKLE1, and MAP3K20 genes, represent potential markers for post-thaw semen quality, and they might be relevant for future improvement in the selection procedure of boars for cryopreservation. The findings of this study provide evidence indicating that polymorphisms in genes expressed in spermatozoa could be considered as factors associated with post-thaw semen quality.

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Section: Snp Associations With Post-thaw Semen Qualitymentioning

confidence: 99%

Gene Polymorphisms in Boar Spermatozoa and Their Associations with Post-Thaw Semen Quality

Mańkowska

Brym

Paukszto

et al. 2020

IJMS

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“…The obtained results are confirmed by the indicator of intervals between collections (Table 2). Searching for tools and information supporting the decision making process concerning the selection of boars for a station encompasses the possibility of using regression analysis (Daigneault et al, 2015;Knecht et al, 2017b;Quintero-Moreno et al, 2004). In the present study, the possibility of obtaining additional data based on multiple regression analysis was proposed.…”

Section: Discussionmentioning

confidence: 99%

“…Our solution forms part of the discussion undertaken by Knecht et al (2017b), who showed that regression equations, in which boar's age, the interval between semen collections, and the time of semen collection were included as independent variables, allow the assessment of semen parameters with a high degree of precision. In other studies, (Daigneault et al, 2015), it was stated that regression equations in which quality parameters of the semen were used as independent variables, allow the prediction of insemination efficiency (r 2 =0.87), litter size (r 2 =0.57), and allow an improvement of insemination efficiency performed by means of frozen semen. We believe it contributes to rare literature data concerning the possibility of using regression in planning insemination boars.…”

Section: Discussionmentioning

confidence: 99%

“…Boars' productive capacity was also predicted on the basis of data concerning the reproduction of sows, comprising the indicators of insemination efficiency, fertility, and litter size (Gadea et al, 2004). Research also involved the possibility of applying the parameters of the semen used for insemination to predict the indicators of sows' fertility (Daigneault et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation and prediction of semen parameters of breeding boars and their use in stock management

Tereszkiewicz¹,

Pokrywka²,

Herbalists³

2019

Int. j. adv. appl. sci.

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Artificial insemination constitutes the main method of pig breeding in Europe. According to literature, statistical models which allow prediction of productive capacity can be used as additional tools which may be helpful in making selection decisions concerning breeding boars. The aim of the research was to evaluate the influence of boar's age, standardized daily growth rate, standardized meat content in the carcass, the number of nipples, the age when the boar starts to be used for breeding, the number of semen collections per month, and the interval between semen collections on selected qualitative and quantitative parameters of the semen and to calculate multiple regression equations, which would allow prediction of productive capacity of AI landrace boars. The material for the analysis comprised 47596 ejaculates obtained over the period 1995-2014 from 306 Polish Landrace boars. The obtained results show that semen parameters of the examined boars depend on physiological and organizational factors included in the analysis. It was observed that the following factors, measured with the number of produced semen doses, were of particular significance for the efficiency of use of the boars: boar's age, the daily growth rate, meat content in the carcass, as well as the number of collections and the interval between collections. The conducted statistical analysis points to the possibility of predicting semen parameters accurately using regression analysis. The models are characterized by very high accuracy, which points to their potential for application. The calculated determination coefficients for the multiple regression equations assumed the R 2 value of above 0.92. The highest value of the determination coefficient (R 2-0.99) was obtained for the percentage of sperm cells showing progressive motility. The calculated regression equations may be used in the management of the stock of breeding boars and when making selection decisions.

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“…Using a combination of traditional and functional assays can allow for fertility prediction with greater accuracy. Previous work has identified a correlation between sperm-oviduct epithelial cell binding and fertility (Waberski et al, 2005;Daigneault et al, 2015). This correlation is likely due to the function of the oviduct isthmic epithelium in binding and retaining sperm to form a reservoir (Gualtieri and Talevi, 2003;Rodríguez-Martínez et al, 2005;Töpfer-Petersen et al, 2008).…”

Section: Introductionmentioning

confidence: 96%

Porcine sperm binding to oviduct cells and glycans as supplements to traditional laboratory semen analysis1

Winters

Hamilton²,

Bhatnagar³

et al. 2018

Journal of Animal Science

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Accurate semen evaluation is necessary to maintain high reproductive efficiency but difficult to accomplish. The objective was to determine if the ability to bind oviduct cells or oviduct glycans are useful supplements to traditional semen analyses. Measuring binding to specific soluble glycans is less laborious than assessing binding to oviduct cell aggregates and more suitable for routine use. Previous work has shown that sperm binding to oviduct cells improves fertility prediction, possibly by estimating the ability of sperm to form an oviduct reservoir. The two oviduct glycan motifs, biantennary 6-sialylated N-acetyllactosamine (bi-SiaLN) and Lewis X trisaccharide (Le X), that bind boar spermatozoa with high affinity and specificity were tested. Semen from 30 boars was shipped overnight for laboratory analysis and for inseminations to determine fertility (n = 3 replicates). Oviduct cell binding and traditional sperm analyses including motility and morphology were completed. Additionally, binding to soluble fluoresceinated glycans bi-SiaLN, sulfated Le X (suLe X), and the control lactosamine disaccharide (LacNAc) was measured. Inseminations were at 15 farms (>50 matings per boar) in the Midwest and farrowing data from all matings were used. Pregnancy rate (PR) and litter size (LS) were adjusted to account for different farms, number of services, number of doses inseminated, and sow parity, using the MIXED procedure in SAS 9.4. A fertility index (FI) was generated, consisting of PR × LS, to estimate boar overall fertility. Finally, the GLMSELECT procedure was used to select variables having a significant impact on PR, LS, and FI. The predictive models constructed were further analyzed using the REG procedure and accounted for 58% or more of the variation in PR, LS, and FI [PR (P < 0.001, r 2 = 0.60), LS (P < 0.001, r 2 = 0.58), and FI (P < 0.001, r 2 = 0.63)]. The final model for PR includes oviduct cell binding as well as boar age, % distal droplets, head morphology, tail morphology, beat/cross frequency, and curvilinear velocity. The final model for LS includes boar age, % distal droplets, tail morphology, and overall morphology. Finally, the FI model included boar age, % distal droplets, head morphology, tail morphology, curvilinear velocity, and semen volume per ejaculate. Although binding to intact oviduct cells was impactful as a means to predict PR, binding to specific soluble oviduct glycans was not a useful supplement to traditional semen analysis.

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Enhanced fertility prediction of cryopreserved boar spermatozoa using novel sperm function assessment

Cited by 21 publications

References 49 publications

Gene Polymorphisms in Boar Spermatozoa and Their Associations with Post-Thaw Semen Quality

Gene Polymorphisms in Boar Spermatozoa and Their Associations with Post-Thaw Semen Quality

Evaluation and prediction of semen parameters of breeding boars and their use in stock management

Porcine sperm binding to oviduct cells and glycans as supplements to traditional laboratory semen analysis1

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