Angus sire field fertility and in vitro sperm characteristics following use of different sperm insemination doses in Brazilian beef cattle

Journal Pre-proof Angus sire field fertility and in vitro sperm characteristics following use of different sperm insemination doses in Brazilian beef cattle

Saulo Menegatti Zoca, Bahman Shafii, William Price, Matthew Utt, Bo Harstine, Kristina McDonald, Leandro Cruppe, Mel DeJarnette, Lon Peters, Jose Luiz Moraes Vasconcelos, Joseph Dalton PII:

S0093-691X(19)30521-7

DOI:

https://doi.org/10.1016/j.theriogenology.2019.11.021

Reference:

THE 15256

To appear in:

Theriogenology

Received Date:

24 May 2019

Accepted Date:

17 November 2019

Please cite this article as: Saulo Menegatti Zoca, Bahman Shafii, William Price, Matthew Utt, Bo Harstine, Kristina McDonald, Leandro Cruppe, Mel DeJarnette, Lon Peters, Jose Luiz Moraes Vasconcelos, Joseph Dalton, Angus sire field fertility and in vitro sperm characteristics following use of different sperm insemination doses in Brazilian beef cattle, Theriogenology (2019), https://doi.org/10.1016/j.theriogenology.2019.11.021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof

REVISED

Angus sire field fertility and in vitro sperm characteristics following use of different sperm insemination doses in Brazilian beef cattle Saulo Menegatti Zocaa, Bahman Shafiib, William Priceb, Matthew Uttc, Bo Harstinec, Kristina McDonaldc, Leandro Cruppec, Mel DeJarnettec, Lon Petersc, Jose Luiz Moraes Vasconcelosd, and Joseph Daltone, * aAnimal

and Veterinary Science Department, University of Idaho, Moscow, ID, 83844, USA

bStatistical

Programs, College of Agricultural and Life Sciences, University of Idaho,

Moscow, ID, 83844, USA cSelect

Sires, Inc., Plain City, OH, 43064, USA

dDepartamento

de Produçáo Animal, Faculdade de Medicina Veterinária e Zootecnia-

UNESP, Botucatu, São Paulo, 18618-681, Brazil eAnimal

and Veterinary Science Department, University of Idaho, Caldwell, 83605, USA

*Corresponding author. University of Idaho, Animal and Veterinary Science Department, Caldwell, ID, 83605, USA E-mail address: [email protected] (Joseph Dalton)

Journal Pre-proof

2 1 2 3

ABSTRACT The primary objective was to determine if Angus bull fertility varied by number of

4

sperm inseminated. A secondary objective was to characterize the potential impact of random

5

variation on fertility using two identical sperm per dose treatments, which differed only in

6

straw color. Computer-assisted sperm analysis (CASA) and flow cytometry (FC) were used

7

to identify post-thaw sperm characteristics associated with field fertility differences between

8

bulls. Ejaculates from five Angus bulls were collected, extended, and cryopreserved at 10, 20,

9

20 or 40 x 106 sperm per dose in color-coded 0.5-mL French straws. Multiparous cows (n =

10

4,866) from ten Brazilian farms were synchronized for first-service timed artificial

11

insemination (TAI). Bull identification and straw color were recorded at TAI. Pregnancy per

12

TAI (P/TAI) did not differ between sperm doses (43.8, 45.3, 43.8 and 47.1% for 10, 20, 20 or

13

40 x 106 sperm respectively; P = 0.31) nor was there an interaction between bull and dose (P

14

= 0.53). The P/TAI differed between bulls and ranged from 40.7 to 48.1 % (P < 0.01). The

15

overall P/TAI between the two control groups were not different (45.3 vs 43.8%); however,

16

the numerical variation within bull ranged from 0.5 to 4.9 percentage points. Numerous

17

CASA and FC post-thaw sperm characteristics differed among bulls (P < 0.05), but these

18

characteristics did not explain the fertility difference between bulls. Principal component

19

analysis provided a multivariate description of the CASA and FC data, where three principal

20

components (Prin1, Prin2, and Prin3) accounted for a combined total of 88.7% of the data

21

variability. The primary components of each PCA axis were flow cytometric measures of

22

sperm viability and DNA fragmentation (Prin1), and CASA-derived sperm movement

23

patterns (Prin2) and motility (Prin3); however, the relative influence of these characteristics

24

varied by bull. Although fertility differences between bulls were detected, neither sperm per

25

dose nor post-thaw in vitro sperm analyses (CASA and FC) were able to explain the observed

Journal Pre-proof

3 26

differences in field fertility between bulls, further illustrating the difficulties in predicting bull

27

fertility.

28

Keywords: Beef cattle, Bull fertility, Computer-assisted sperm analysis, Flow cytometry,

29

Sperm dose

30

1. Introduction

31

The AI industry strives to maximize fertility of each bull while avoiding excess sperm

32

per dose that may limit supply [1,2]. The minimum number of viable sperm required for

33

maximum fertility differs among bulls, as does the rate at which maximum fertility is

34

achieved with increasing sperm dosage [3,4]. As described by Saacke et al. [5], differences in

35

fertility among males responsive to increased sperm dosage are considered “compensable,”

36

whereas those not responsive to increased sperm dosage are considered “uncompensable.”

37

Saacke and White [6] argued a single laboratory test to estimate fertility of a semen

38

sample was unlikely. Indeed, fertility is a multifactorial phenomenon [7-9]. Consequently, the

39

use of in vitro analyses focused on multiple sperm characteristics [10] to estimate the fertility

40

of a semen sample is reasonable, despite the apparent difficulties in association of field

41

fertility with assay results [11,12].

42

The primary objective of the field fertility study was to determine if Angus bull

43

fertility varied by number of sperm inseminated. A secondary objective was to characterize

44

the potential impact of random variation using two 20 x 106 sperm per dose treatments. The

45

hypothesis was that Angus bull fertility to first-service timed artificial insemination (TAI)

46

would not vary by the number of sperm inseminated (10, 20, 20 or 40 x 106 sperm per dose).

47

The objective of the in vitro post-thaw sperm characterization study was to identify sperm

48

characteristics associated with fertility differences between bulls using computer-assisted

49

sperm analysis (CASA) and flow cytometry (FC). The hypothesis was that greater fertility

Journal Pre-proof

4 50

bulls would exhibit the most favorable values for all sperm analyses, e.g., the smallest DNA

51

fragmentation index (DFI).

52

2. Materials and methods

53

All procedures were approved by the University of Idaho Animal Use and Care

54

Committee.

55

2.1 Field fertility trial

56

2.1.1 Semen collection, extension and cryopreservation

57

Ejaculates (2 per collection day) from Angus bulls (n = 5) ranging from 2 to 4 yr of age

58

and housed at Select Sires, Inc. (Plain City, OH, USA) were collected by artificial vagina.

59

Semen collection of all bulls was performed between May 13 and May 30, 2014. To fulfill

60

the treatments of 4 sperm dosages per bull, 2 bulls required 2 collection days, 2 bulls required

61

3 collection days and 1 bull required 4 collection days. The semen collection procedure

62

included the use of teaser mount animals, and 2 min of active restraint before each of two

63

false mounts prior to ejaculate collection. Two ejaculates, collected in succession using the

64

described protocol, were pooled after each ejaculate met the minimum criteria of 65%

65

motility in a subjective assessment.

66

Semen was initially extended to 80 × 106 sperm per mL using a two-step, proprietary

67

whole milk-glycerol extender (Select Sires, Inc., Plain City, OH, USA). Strategic aliquots

68

were removed from the 80 × 106 sperm per mL preparation to enable further dilution to 40

69

and 20 x 106 sperm per mL using the combined A and B-fraction extender. The 40 x 106

70

sperm per mL preparation contained twice the total volume of 20 or 80 × 106 sperm per mL

71

dilutions, such that 2 straw colors could be filled from the same sample. The ultimate target

72

was to yield approximately equal numbers of straws for each treatment within each collection

73

(split ejaculate technique). Semen was packaged in 0.5-mL French straws (Instruments de

Journal Pre-proof

5 74

Médecine Vétérinaire, l’Aigle, France) marked with each bull’s identification and freezing

75

date code. Straws were color-coded (brown, purple, red, yellow) and straw color was

76

randomized between sperm dose (10, 20-a, 20-b and 40 × 106 sperm per straw) by bull to

77

ensure AI personnel were blind to sperm dosage. Straws were frozen in liquid nitrogen vapor

78

using the optimum procedure described by Robbins et al. [13] before being plunged and

79

stored in liquid nitrogen. The use of two 20 × 106 (20-a and 20-b) sperm per straw treatments

80

was an attempt to illustrate the potential impact of random variation on P/TAI, as these

81

treatments differed only in straw color.

82

2.1.2 Semen quality evaluation

83

Sample straws were thawed in a 37oC water bath for 60 s before post-thaw evaluation

84

of progressive sperm motility [14]. Motility was evaluated subjectively with phase-contrast

85

optics (× 200), using a minimum of 5 random fields of view (avoiding slide perimeter) and

86

rounded to the nearest 5% [14]. A minimum of 60% post-thaw motility was required for

87

inclusion in the study. To evaluate post-thaw sperm viability with flow cytometry, sperm

88

were stained with Hoechst 33342 (H33342) (H342; Invitrogen, Thermo Fisher Scientific -

89

Life Technologies Co., Eugene, OR, USA) and propidium iodide (PI; Sigma-Aldrich, Inc.,

90

St. Louis, MO, USA) as described by Garner et al. [15]. Extended semen (2 µL) was diluted

91

in buffer (198 µL; 0.14 M TRIS, 0.14 M Citric Acid, 10% BSA w/v, pH 7) containing

92

H33342 (15.5 µg/mL) and PI (5 µg/mL). Subsequently, samples were incubated for 20

93

minutes at 35oC in the absence of light [15]. Samples were prepared in round bottom 96-well

94

plates and a total of 5000 cells/sample were analyzed using a MACSQuant Analyzer 10

95

(Miltenyi Biotec, Auburn, CA, USA). Hoechst 33342 was excited via a 405 nm laser and

96

emission spectra were collected with a 450/50 nm band pass filter. A 488 nm laser was used

97

to excite PI and emission spectra were collected with a 655-730 nm long pass filter. Flow

98

cytometric data was analyzed with FLOWJo (FLOWJo, LLC, Ashland, OR, USA).

Journal Pre-proof

6 99

Sperm morphology was evaluated post-thaw using differential interference contrast

100

(DIC) microscopy (× 1000) after fixing sperm with 0.2% formalin and wet mount preparation

101

[16]. One hundred cells were evaluated per bull. Sperm head defects were classified as

102

primary defects, while sperm tail defects were classified as secondary defects [17]. A

103

minimum of 65% normal sperm morphology was required for inclusion in the study. Each

104

farm received all bull and dose combinations; however, collection date was randomly

105

assigned for each location.

106

2.1.3 Animals and reproductive management

107

This study was conducted across ten farms located in Acre, Goiás, and Mato Grosso,

108

Brazil. All cows were maintained on tropical grass pasture with ad libitum access to water

109

and mineralized salt during the experimental period. Multiparous Bos indicus or Bos indicus x

110

Bos taurus cows (n = 4,866), 40 to 60 days postpartum, were evaluated for body condition

111

score (BCS; 1 to 5 scale) [18,19] and enrolled in a first-service TAI program. All cows were

112

synchronized for TAI using an intravaginal progesterone insert and estradiol-based protocol

113

as previously described [20-22].

114

Detection of estrus was performed using Estrotect patches (Estrotect, Spring Valley,

115

WI, USA) on two farms (n = 1,061 cows). Estrotect patches were administered to cattle

116

immediately upon removal of the intravaginal progesterone insert to facilitate detection of

117

estrus and comparison of fertility between estrus and non-estrus cows. All cows received TAI

118

regardless of Estrotect patch activation status.

119

Within bull, technicians were instructed to use 5 straws of the same color, and then

120

move to the next color. After twenty inseminations (5 straws, 4 dosages), technicians were

121

instructed to move to the next bull and repeat the procedure until all cows were inseminated.

122

Count of AI by bull and farm, and dose and farm are shown in Tables 1 and 2 respectively.

Journal Pre-proof

7 123

All straws were thawed in a 37ºC water bath for a minimum of 30 s. At TAI, straw color and

124

sire identification were recorded. Pregnancy was diagnosed by transrectal ultrasonography 30

125

to 90 d after TAI, according to each farm’s standard operating procedure. A total of 1,228

126

cows were diagnosed between 30 to 45 d, 3,424 cows between 50 to 70 d, and 214 cows at 90

127

d after TAI (Table 3).

128

2.14 Statistical analyses

129

With 1200 observations per treatment, the estimated power to detect a 6.5-percentage

130

point difference in proportion pregnant (P/TAI) was 80%. The proportion pregnant (P/TAI)

131

were analyzed using generalized linear mixed models (GLIMMIX procedure; SAS version

132

9.4; SAS Inst. Inc., Cary, NC, USA) to estimate the fixed effects of bull, dose, and bull by

133

dose interaction. Farms were considered as a random blocking effect. The BCS response was

134

assumed to be distributed normally and analyzed using the GLIMMIX procedure. Estimates

135

(P/TAI and BCS) were reported as least squares means and compared using pair-wise

136

comparisons. Statistical significance was declared when P ≤ 0.05.

137

2.2 In vitro sperm characterization study

138

Representative semen samples from each bull, dose and collection date were analyzed

139

for subjective motility (SM), CASA, FC and morphology. For all analyses, two straws from

140

each bull, dose and collection date (used for the field trial) were thawed simultaneously in a

141

water bath at 37oC for a minimum of 60 s [14]. Contents of the two straws were pooled in a

142

1.5 mL Eppendorf vial (Eppendorf Safe-Lock Tube, USA Scientific, Ocala, FL, USA),

143

vortexed for 2 s to homogeneity, and analyzed in duplicate.

144

2.2.1 Reagents

145 146

Reagents (not mentioned previously) used for FC analyses were obtained from Sigma-Aldrich, Inc. (St. Louis, MO, USA) and included acridine orange (AO). Fluorescein

Journal Pre-proof

8 147

isothiocyanate-conjugated peanut agglutinin-647 (PNA) and fluo-3-acetomethoxy ester

148

(Fluo-3) were obtained from Invitrogen (Thermo Fisher Scientific - Life Technologies

149

Corporation, Eugene, OR, USA).

150

2.2.2 Flow cytometry

151

Samples were prepared in round bottom 96-well plates and assayed in duplicate. A

152

total of 5000 cells per duplicate were analyzed using a MACSQuant Analyzer 10 (Miltenyi

153

Biotec, Auburn, CA, USA). Flow cytometric analysis was conducted using four stains (PI,

154

PNA, Flou-3, and H33342) simultaneously. Briefly, extended semen (2 μL) was diluted in

155

TRIS buffer solution containing 5 ng/μL PI, 15.5 ng/μL H33342, 12.5 ng/μL PNA, and 11

156

μM Fluo-3 and incubated for 20 min at 35oC without exposure to light [adapted from 15,23-

157

26]. Hoechst 33342 was excited via a 405 nm laser and detected with a 450/50 nm band pass

158

filter. A 488 nm laser was used to excite PI and Fluo-3 which were detected with 655-730 nm

159

long pass and 525/50 nm band pass filters, respectively. A red 635 nm laser was used to

160

excite PNA which was detected with a 655-730 nm long pass filter.

161

The proportion of sperm with intact plasma membranes (viable; PI negative), intact

162

acrosome (acrosome; PNA negative), viable with acrosome (VA; PI and PNA negative),

163

viable with normal Ca influx (VNCa; PI and Fluo-3 negative), and acrosome-intact with

164

normal calcium within viable (ANCa; PNA and Flou-3 negative within PI negative) were

165

calculated by FLOWJo for each sample. To determine the proportion of total sperm analyzed

166

that possess all the desirable characteristics assayed, the overall population of sperm with

167

intact plasma and acrosomal membranes with normal Ca influx (VANCa) were calculated by

168

multiplying viable and ANCa (VANCa = viable × ANCa).

169

A modified Sperm Chromatin Structure Assay (SCSA) [27,28] was used to evaluate

170

DNA stability via FC. Semen (20 µL) was diluted in TNE buffer (180 µL; 0.01 M TRIS-HCl,

171

0.15 M NaCl, and 1mM disodium EDTA, pH 7.4). Next, acid detergent solution (400 µL;

Journal Pre-proof

9 172

0.1% Triton X-100, 0.08 N HCl, and 0.15 M NaCl, pH 1.2) was added followed by

173

incubation for 30 s at room temperature. A staining solution was added next (1.2 mL; 6 g/mL

174

AO, 0.2 M disodium phosphate, 1 mM disodium EDTA, 0.15 M NaCl, 0.1 M citric acid

175

monohydrate, pH 6.0) followed by incubation for 3 min at room temperature [29]. A 488 nm

176

laser was used to excite AO, and red fluorescence was detected with a 630/50 nm long pass

177

filter while green fluorescence was detected with a 515/30 nm band pass filter. The

178

proportion of single (red fluorescence) and double (green fluorescence) DNA were calculated

179

by FLOWJo. DNA stability was reported as the percentage of single-stranded DNA and

180

calculated as % DFI = (Single-Stranded) / (Single-Stranded + Double-Stranded) × 100 [29,

181

30].

182

2.2.3 Subjective motility and CASA

183

Subjective motility was estimated using phase contrast optics (×200) by two trained

184

technicians (in duplicate), blindly, and the estimates were averaged [31]. Aliquots from the

185

same samples used for SM were also used for CASA. Computer-assisted sperm analysis was

186

accomplished with an IVOS II (Hamilton Thorne, Beverly, MA, USA). Semen samples used

187

for CASA were first standardized for concentration prior to further preparation by diluting to

188

approximately 20 × 106 sperm per mL using TRIS buffer at 37oC. A proprietary whole milk-

189

glycerol based extender was used for cryopreservation; therefore, all samples were incubated

190

with Hoechst 33342 (H33342) dye (H3570; Invitrogen, Eugene, OR, USA) at 80 µg/mL for

191

10 min in the absence of light, to allow for the detection of individual sperm by CASA [16].

192

Samples were loaded in chambers of Leja slides (Leja Standard Count 4-Chamber 20

193

µm chamber depth, Ref# SC 20-01-04-B, Nieuw-Vennep, The Netherlands). Samples were

194

analyzed in duplicate, and 10 fields from each chamber were analyzed using auto-capture.

195

The CASA program settings were designed for bovine semen using the IDENT option with

196

LED illumination (IDENT Fluorescence System; Hamilton Thorne, Beverly, MA, USA) with

Journal Pre-proof

10 197

the following parameters: temperature: 37ºC; number of frames: 30; capture speed: 60 Hz;

198

minimum cell size: 5 µm2; minimum head brightness: 160 units. For analysis, minimum cell

199

detection and classification requirements were: Cell travel max: 15 µm; progressive

200

straightness: 70%; progressive average path velocity: 50 µm/s; slow average path velocity: 20

201

µm/s; slow straight-line velocity: 30 µm/s.

202

The variables analyzed were total motility (TM, %), progressive motility (PM, %),

203

amplitude of lateral head displacement (ALH, μm), beat cross frequency (BCF, Hz), straight-

204

line velocity (VSL, μm/s), average path velocity (VAP, μm/s), curvilinear velocity (VCL,

205

μm/s), linearity (LIN, %: the ratio between VSL and VCL), straightness (STR, %: the ratio

206

between VSL and VAP) and wobble (WOB, %: the ratio between VAP and VCL).

207

2.2.4 Sperm morphology

208

Sperm morphology was evaluated post-thaw using DIC microscopy (×1000) after

209

fixing sperm with 0.2% formalin and wet mount preparation [16]. One hundred cells were

210

evaluated per bull and collection date. Sperm head abnormalities were classified as primary

211

defects, while sperm tail abnormalities were classified as secondary defects [17].

212

2.2.5 Statistical analyses

213

Data were analyzed using GLIMMIX with bull, duplicate, and bull by duplicate

214

interaction considered as fixed effects, while collection date, and collection date by sperm

215

concentration per duplicate were considered as random effects [32]. Following univariate

216

analyses, proportions (SM, TM, PM, LIN, STR, WOB and flow cytometer parameters) were

217

assumed to follow a beta distribution, whereas VSL, VAP, VCL, ALH and BCF were

218

considered as normally distributed responses. Least squares mean comparisons were assessed

219

using pair-wise tests. An additional analysis, assuming a beta distribution, was used to

220

analyze morphology, with bull as a fixed effect, and collection date and collection date by

Journal Pre-proof

11 221

bull as random effects. Least squares means were compared using pair-wise tests. Statistical

222

differences were noted at P ≤ 0.05.

223

To investigate the multivariate pattern and variability of in vitro sperm characteristics,

224

a principal component analysis (PCA) was used. In this analysis, 16 CASA and FC

225

characteristics were investigated: TM, PM, ALH, BCF, VAP, VCL, VSL, LIN, STR, WOB,

226

viable, acrosome, VA, VNCa, ANCa and DFI. Subjective motility was not included in this

227

analysis, as it was a redundant parameter. Further, because VANCa was calculated based on

228

the analyses of viable and ANCa, it was also excluded from PCA. Principal component axes

229

with eigenvalues > 1.0 were retained for further consideration and interpretation [33].

230

3. Results and discussion

231

In beef cows, TAI is an efficient management strategy to administer AI in large groups.

232

Parity and BCS at TAI are critical factors related to TAI outcomes [20,21], hence, only

233

multiparous cows were included in this study. Body condition score was not different

234

between sperm dose (P = 0.91), bull (P = 0.19) or bull by dose interaction group (P = 0.35).

235

Sperm dose BCS (LSM ± SEM) ranged from 2.85 ± 0.06 to 2.86 ± 0.06, and bull BCS (LSM

236

± SEM) ranged from 2.83 ± 0.06 to 2.86 ± 0.06. Bull by treatment interaction BCS (LSM ±

237

SEM) ranged from 2.80 ± 0.06 to 2.90 ± 0.06. Consequently, BCS was not included in the

238

statistical model for P/TAI.

239

Salisbury and VanDemark [3] first proposed that fertility increases with increasing

240

number of viable sperm inseminated up to a certain threshold at which the fertility level of

241

the female population becomes the limiting factor. With 1200 observations per treatment in

242

the present study, the estimated power to detect a 6.5-percentage point difference in P/TAI

243

was 80%. The proportion pregnant (P/TAI) was not different between sperm dose (P = 0.31)

244

and there was no bull by dose interaction (P = 0.53; Table 4).

12 245

Journal Pre-proof Differences in fertility among males responsive to increased sperm dosage are

246

considered “compensable,” whereas those not responsive to increased sperm dosage are

247

considered “uncompensable,” as originally described by Saacke et al. [5]. For the sperm

248

dosages used in this study, it appears the bulls had few, if any, compensable traits as no

249

difference was detected among doses [34,35]. Den Daas et al. [4], in a study with 20 mature

250

dairy bulls, estimated a dose of approximately 4.0 × 106 total sperm (range 1 to 11.8 × 106

251

sperm/dose) was necessary to satisfy the compensable component for bulls used in their

252

study. Further, Den Daas et al. [4] reported only one of the 20 bulls required more than 10 ×

253

106 total sperm to achieve 95% of the optimum conception rate. Our results provide evidence

254

the lowest prepared treatment of 10 × 106 sperm per dose was sufficient to achieve similar

255

fertility among bulls at all dosages used in this study (Table 4). Similar to Den Daas et al. [4],

256

however, there was one bull in the present study that appeared to need more sperm in the

257

insemination dose. Although no significant bull by dose interaction was detected, P/TAI for

258

bull E was numerically less at 10 × 106 sperm (36.6%) as compared to 20-a (44.7%), 20-b

259

(45.2%) and 40 × 106 sperm (45.9%), respectively. It is possible Bull E has a higher threshold

260

level of compensable traits, as P/TAI appeared to increase from 10 × 106 (36.6%) and plateau

261

at 20 to 40 × 106 sperm per dose (approximately 45%).

262

Harstine et al. [36] demonstrated more than 90% of semen from commercial bulls (n =

263

2,062) are within ± 3% of average fertility, in agreement with previous reports [34,37]. Our

264

results, however, demonstrate a significant difference between bulls A, B, and C (P < 0.01) in

265

which Bulls A and B have greater P/TAI as compared to Bull C (48.1 and 47.7 vs. 40.7 %,

266

respectively). In contrast, Oliveira et al. [12] reported no difference in P/TAI among three

267

Angus bulls inseminated into Nelore cows (n = 944), perhaps highlighting binomial variation

268

associated with pregnancy success or failure leading to difficulty in bull fertility estimation

269

with a small number of inseminations [3,10].

13

Journal Pre-proof

270

All ejaculates in the present study were collected from bulls that had previously

271

produced semen acceptable for use in AI. Semen used in this experiment underwent similar

272

pre-freeze and post-thaw quality control analyses, including motility, morphology, and

273

viability (membrane integrity) assessments; therefore, it was not expected that Bull C would

274

exhibit lower overall fertility as compared to the other bulls. DeJarnette [34] argued

275

prediction of bull fertility should be considered questionable even when sufficient levels of

276

known semen characteristics are reached, because of unknown or unmeasured characteristics

277

that could affect fertility. For example, Bull C may have had uncompensable seminal traits

278

which affected fertility at all doses studied [8,34].

279

A secondary objective of the field fertility trial was to characterize the potential impact

280

of random variation using two 20 × 106 sperm per dose treatments, which differed only by

281

straw color. Although the overall P/TAI between the two control groups (20-a: 45.3%; 20-b:

282

43.8%; Table 4) were not different, the numerical variation within bull averaged 2.6

283

percentage points and ranged from 0.5 (Bull E) to 4.9 percentage points (Bull D), providing

284

evidence that random variation in reproductive field trials should not be ignored.

285

Estrotect patches were used on a subset of animals to facilitate detection of estrus.

286

During the period immediately following progesterone insert removal and TAI, 64.4%

287

(683/1,061) were detected in estrus. The P/TAI for cows detected in estrus was 44.7%, as

288

compared to 20.9% for cows not detected in estrus (P < 0.01). In similar studies [22,38]

289

57.4% and 57.8% of cows were detected in estrus, with P/TAI for cows detected in estrus

290

prior to TAI of 61.9% and 67.7%, compared to P/TAI for cows not detected in estrus of

291

41.4% and 36.2%, respectively. In the present study, a greater percentage of cows appears to

292

have been detected in estrus as compared to Sá Filho et al. [22,38]; however, fertility in the

293

present study appears to be depressed compared to Sá Filho et al. [22,38]. A difference in

294

time to pregnancy determination may play a role in understanding the relative fertility

14

Journal Pre-proof

295

reported in these studies, as pregnancy status was determined by Sá Filho et al. [22,38] 30 d

296

after TAI compared to between 50 and 70 d after TAI in the estrous detection subset in the

297

present study, allowing increased time for pregnancy loss to occur [39,40].

298

Sperm morphology has been reported to affect embryonic survival [41,42] and

299

embryo quality [43,44]. Morphologic defects of the sperm tail and acrosomal membrane are

300

considered compensable traits, as deficiencies in fertility may be overcome with an increase

301

in sperm number per insemination dose [34]. In contrast, abnormalities in sperm head shape

302

are generally considered uncompensable traits, which limit the maximum fertility threshold

303

[8,34]. Saacke et al. [45], however, reported that morphologically abnormal sperm are

304

excluded from the accessory sperm population in the ovum based on the severity of sperm

305

head shape distortion. Given the importance of morphology and its association with fertility,

306

semen from all bulls in this study was required to have a minimum of 65% morphologically

307

normal sperm to be included in the field fertility study, which was conducted before the in

308

vitro study. Consequently, it is not surprising that the proportion of morphologically normal

309

and abnormal sperm did not differ among bulls (P = 0.7; Table 5).

310

There was no effect of duplicate or duplicate by bull interaction (P > 0.1) in both

311

CASA and FC analyses. In addition, no differences in BCF, LIN and WOB were found (P >

312

0.1; Table 5). All other in vitro sperm characteristics were significantly different among bulls

313

for CASA and SM and FC (P < 0.05; Table 5). Field fertility (P/TAI) of each bull is reported

314

at the top of Table 5 to facilitate comparison.

315

The visual assessment of sperm motility, i.e. subjective motility, is a classic measure

316

of sperm quality [34]. Seidel [31] suggested progressive motility is the most appropriate way

317

to evaluate motility and a good measure of potential fertility. In the present study, we used

318

two well-trained evaluators who estimated progressive motility in blind samples, and in

319

duplicate, as suggested by Seidel [31]. Farrell et al. [10] compared SM and CASA motility

Journal Pre-proof

15 320

for fresh semen and reported CASA motility had greater variation (52-82% PM) than SM

321

(62- 69% PM). In the present study, SM appears to overestimate motility as compared to

322

CASA results (TM and PM; Table 5). Surprisingly, SM results appeared to be numerically

323

closer to TM of CASA, rather than PM, which is the goal of the visual subjective assessment.

324

This apparent discrepancy may be related to the inherent differences in the estimation of

325

motility by each method, i.e., human subjectivity (SM) and computer objectivity (CASA TM

326

and PM).

327

Based on CASA characteristics, four sperm subpopulations have been reported in

328

Holstein bulls [46]. The first subpopulation had relatively low velocity (medium VCL, VSL

329

and VAP) but exhibited greater progressive traits (greater LIN, STR, WOB and BCF, and

330

smaller ALH). The second sperm subpopulation was characterized as very active but non-

331

progressive (greater values of VCL and ALH with smaller values of LIN and STR, and

332

moderate BCF), suggesting hyperactivated-like motility [46]. The third subpopulation

333

contained poorly motile and non-progressive sperm (smaller VAP, VCL, VSL, BCF, ALH,

334

LIN, STR and WOB values). The fourth subpopulation exhibited mostly rapid and

335

progressive sperm movement (greatest values of VCL, VSL, VAP, BCF, VCL, VSL, VAP

336

and BCF and moderate ALH) [46]. Bull E appears to exhibit characteristics of the second and

337

fourth sperm subpopulations (Table 5) as evidenced by greater values of VCL, ALH, VAP,

338

and VSL and a small TM value. When taken together with the smallest percentage of intact

339

acrosomes (53%; Table 5), these sperm characteristics may be indicative of early capacitation

340

[7], hyperactivated-like motility [46], and shortened lifespan in the reproductive tract. Bull E,

341

however, does not exhibit all characteristics of either the second or fourth sperm

342

subpopulations, perhaps highlighting the heterogeneous nature of sperm contained in an

343

ejaculate and frozen-thawed semen [47].

Journal Pre-proof

16 344

Cryopreservation may cause irreversible damage to sperm, which may affect the

345

ability of sperm to fertilize the ovum [16,46,47]. An intact plasma membrane is an important

346

sperm characteristic; however, a wide range in the correlation between fertility and plasma

347

membrane integrity (as measured with fluorescent probes) has been reported (r = 0.05 to r =

348

0.56) [48-51]. In the present study, fertility appears to be associated closely with FC results

349

for viable, VNCa and VANCa, in which Bulls A, B and D have the greatest values. Further,

350

Bulls B and D also exhibit the greatest values for acrosome and VA, and the smallest for DFI,

351

which may be related to their greater fertility as compared with Bull C. Unfortunately,

352

comparison of FC results for bulls of greatest (Bull A) and least (Bull C) fertility is difficult

353

as these bulls appear to be similar in 4 of 7 FC values (Table 5). Unknown or unmeasured

354

characteristics could affect fertility [34], and therefore may provide a plausible explanation of

355

the fertility achieved by Bull C.

356

Several studies have reported an inverse relationship between DFI and fertility in a

357

variety of species (as DFI increases, fertility decreases) [8,30,50,52-54]. The results reported

358

here do not agree, however, as Bull A and C, the greatest and least fertility bulls,

359

respectively, showed similar DFI values (37.7 vs. 36.6%, respectively). Further, Evenson [30]

360

argued bulls with DFI greater than 10 – 20% would have reduced fertility. In the present

361

study, however, Bulls A and B had acceptable field fertility (48.1 and 47.7% P/TAI) with

362

37.7 and 26.1% DFI, respectively. Richardson et al. [16] also described acceptable fertility

363

while reporting greater values of DFI (33 and 41%) for two Angus bulls.

364

From the field fertility trial, we initially proposed Bull C may have had greater levels

365

of uncompensable traits because of the lower fertility exhibited across dosages. In fact, the

366

level of uncompensable characteristics assessed (DFI and morphology) for Bull C were not

367

different from the greatest fertility bull (Bull A). Although Bull D exhibited the smallest DFI

368

coupled with the greatest FC values, fertility for Bull D was not greater than Bulls A and B,

Journal Pre-proof

17 369

again corroborating the concerns of DeJarnette [34] relative to the potential importance of

370

unknown or unmeasured traits to fertility.

371

When many measurements are available, PCA may be used to evaluate the possibility

372

of their replacement with fewer measurements without losing valuable information [55].

373

Principal component analysis has been used previously for grouping sperm subpopulations

374

within CASA analyses in rams [56]; to evaluate the relationship of sperm analyses with

375

recurrent pregnancy loss in humans [57]; and to create an index for CASA and morphological

376

parameters to evaluate their influence in dog semen freezeability [58].

377

In our study, PCA revealed 88.7% of total data variability was accounted for by three

378

principal components. The first principal component (Prin1) accounted for 47.1% of total

379

data variability, and was most influenced by sperm viability (TM, viable, acrosome, VA,

380

VNCa) and DFI. The second principal component (Prin2) accounted for 25.9% of total data

381

variability and was most influenced by sperm movement pattern (BCF, LIN, STR and WOB).

382

The third principal component (Prin3) represented 15.7% of the total data variability and was

383

most influenced by motility (PM, VAP, VCL and VSL). Across the three principal

384

components, ALH and ANCa were consistently not influential in accounting for variability;

385

thus, ALH and ANCa were not included in the final estimation of PCA components Prin1,

386

Prin2 or Prin3.

387

A biplot of Prin1 vs. Prin2 (Fig. 1), accounting for 73% of the total data variability,

388

shows patterns related to each bull. In Prin1, variation along the X axis (sperm viability)

389

shows changes across bulls, whereas in Prin2 (sperm movement pattern) the variation is

390

primarily within bulls, with the exception of Bull D which had both high viability and high

391

movement PCA scores. Hence, bulls with greater values of TM, viable, acrosome, VA and

392

VNCa, and lower values of DFI are seen at the right of Fig. 1. Bulls B and D are in the top

393

right quadrant with the lowest variability as samples cluster together, followed by Bull A in

Journal Pre-proof

18 394

the lower right quadrant. Bull E exhibited the largest variability in Prin1 and Prin2 covering

395

portions of both the lower and upper left quadrants. In contrast to Bull E, Bull C showed

396

smaller variability in Prin1 and similar variability in Prin2.

397

When examining PCA results (Fig. 1) with field fertility data, bulls with greater fertility

398

(A, B and D) were in the right quadrants of the plot and had smaller variability in both Prin1

399

and Prin2. The lowest fertility bull (C) was in the left quadrants, displayed the smallest

400

variation in Prin1 (which accounted for the greatest proportion of total data variability), and

401

had larger variability in Prin2. Interestingly, Bull E showed large variability in both Prin1 and

402

Prin2, was located primarily in the left quadrants, yet had fertility similar to Bulls C and D.

403

Technological methods of semen evaluation, such as CASA and FC, have been

404

included in routine quality control analyses at many AI centers [36,59]. Our PCA results

405

provide evidence progress can be made in understanding variability within and between bulls,

406

as PCA simplified the complexity of CASA and FC data allowing for the grouping and

407

identification of key sperm characteristics. Unfortunately, we currently do not know how

408

“much” of each sperm characteristic is “enough” for successful completion of fertilization

409

and an acceptable fertility outcome [60].

410

4. Conclusions

411

The influence of bull, but not sperm dosage (10, 20 and 40 × 106 sperm per dose) on

412

P/TAI was detected in Brazilian beef cattle synchronized for first service TAI. The use of two

413

20 × 106 sperm per dose treatments, which differed only by straw color, revealed numerical

414

variation within bull ranged from 0.5 to 4.9 percentage points. Morphology, CASA and FC

415

were not able to explain the difference in field fertility between bulls, further illustrating the

416

difficulties in predicting bull fertility. Principal component analysis, however, simplified the

417

complexity of CASA and FC data allowing for the grouping and identification of key sperm

Journal Pre-proof

19 418

characteristics. The greatest variability was accounted for by sperm viability and DFI,

419

followed by movement patterns and motility; however, the relative influence of these

420

characteristics varied by bull. The use of PCA should be investigated further, as it allows for

421

the grouping and identification of key sperm characteristics and provides a visual aspect to

422

understanding variability.

423

Acknowledgments

424

The authors thank the collaborating farms, employees, and veterinarians for

425

participating in this research. This project was funded by Select Sires, Inc. (Plain City, OH,

426

USA) and facilitated by Select Sires do Brasil (Porto Alegre, RS, Brazil).

427 428

References

429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454

[1]

Amann RP, DeJarnette JM. Impact of genomic selection of AI dairy sires on their likely utilization and methods to estimate fertility: A paradigm shift. Theriogenology 2012; 77:795–817. [2] Dalton JC, DeJarnette JM, Saacke RG, Amann RP. The male component of dairy herd fertility. In: Beede DK, editor. Large dairy herd management, third edition, American Dairy Science Association, Champaign, IL, 2017, p. 565-78. [3] Salisbury GW, VanDemark NL. Significance of semen quality. In: Physiology of reproduction and artificial insemination in cattle, first edition, W.H. Freeman and Co., San Francisco, CA, 1961, p. 358-79. [4] Den Daas JHG, De Jong G, Lansbergen LMTE, Van Wagtendonk-De Leeuw AM. The relationship between the number of spermatozoa inseminated and the reproductive efficiency of individual dairy bulls. J Dairy Sci 1998;81:1714–23. [5] Saacke RG, Nadir S, Nebel RL. Relationship of semen quality to sperm transport, fertilization, and embryo quality in ruminants. Theriogenology 1994;41:45-50. [6] Saacke RG, White JM. Semen quality tests and their relationship to fertility. In: Proc. Nat’l Assoc. Anim. Breeders 4th Tech. Conf. Artif. Insem. and Reprod., Columbia, MO, 1972, p. 22-7. [7] Rodríguez-Martínez H. Laboratory semen assessment and prediction of fertility: still Utopia? Reprod Domest Anim 2003;38(4):312-318. [8] Saacke RG. Sperm morphology: Its relevance to compensable and uncompensable traits in semen. Theriogenology 2008;70:473-8. [9] Vincent P, Underwood SL, Dolbec C, Bouchard N, Kroetsch T, Blondin P. Bovine semen quality control in artificial insemination centers. Anim Reprod 2012;9(3):153-65. [10] Farrell PB, Presicce GA, Brockett CC, Foote RH. Quantification of bull sperm characteristics measured by computer-assisted sperm analysis (CASA) and the relationship to fertility. Theriogenology 1998;49:871-9.

20 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504

Journal Pre-proof

[11] Mocé E, Graham JK. In vitro evaluation of sperm quality. Anim Reprod Sci 2008;105: 104–18. [12] Oliveira LZ, de Arruda RP, de Andrade AFC, Celeghini ECC, dos Santos RM, Beletti, ME, et al. Assessment of field fertility and several in vitro sperm characteristics following the use of different Angus sires in a timed-AI program with suckled Nelore cows. Livestock Sci 2012;146:36-46. [13] Robbins RK, Saacke RG, Chandler PT. Influence of freeze rate, thaw rate and glycerol level on acrosomal retention and survival of bovine spermatozoa frozen in French Straws. J Anim Sci 1976;42:145–54. [14] DeJarnette JM, McCleary CR, Leach MA, Moreno JF, Nebel RL, Marshall CE. Effects of 2.1 and 3.5 × 106 sex-sorted sperm dosages on conception rates of Holstein cows and heifers. J Dairy Sci 2010;93:4079–85. [15] Garner DL, Dobrinsky JR, Welch GR, Johnson LA. Porcine sperm viability, oocyte fertilization and embryo development after staining spermatozoa with SYBR-14. Theriogenology 1996;45:1103-13. [16] Richardson BN, Larimore EL, Walker JA, Utt MD, DeJarnette JM, Perry GA. Comparison of fertility of liquid or frozen semen when varying the interval from CIDR removal to insemination. Anim Reprod Sci 2017;178:61-6. [17] Barth AD, Oko J. Abnormal morphology of bovine spermatozoa. Iowa: Iowa State University Press; 1989. [18] Houghton PL, Lemenager RP, Moss GE, Hendrix KS. Prediction of postpartum beef cow body composition using weight to height ratio and visual body condition score. J Anim Sci 1990;68:1428–37. [19] Ayres H, Ferreira RM, Torres-Júnior JRS, Demétrio CGB, de-Lima CG, Baruselli PS. Validation of body condition score as a predictor of subcutaneous fat in Nelore (Bos indicus) cows. Livestock Sci 2009;123:175–9. [20] Meneghetti M, Sá Filho OG, Peres RFG, Lamb GC, Vasconcelos JLM. Fixed-time artificial insemination with estradiol and progesterone for Bos indicus cows I: Basis for development of protocols. Theriogenology 2009;72:179–89. [21] Sá Filho OG, Meneghetti M, Peres RFG, Lamb GC, Vasconcelos, JLM. Fixed-time artificial insemination with estradiol and progesterone for Bos indicus cows II: Strategies and factors affecting fertility. Theriogenology 2009;72:210–18. [22] Sá Filho MF, Santos JEP, Ferreira RM, Sales JNS, Baruselli PS. Importance of estrus on pregnancy per insemination in suckled Bos indicus cows submitted to estradiol/progesterone-based timed insemination protocols. Theriogenology 2011;76:455-63. [23] Landim-Alvarenga FC, Graham JK, Alvarenga MA, Squires EL. Calcium influx into equine and bovine spermatozoa during in vitro capacitation. Anim Reprod 2004;1:96– 105. [24] Purdy PH, Graham JK. Effect of adding cholesterol to bull sperm membranes on sperm capacitation, the acrosome reaction and fertility. Biol Reprod 2004;71:522–7. [25] Gualtieri R, Boni R, Tosti E, Zagami M, Talevi R. Intracellular calcium and protein tyrosine phosphorylation during the release of bovine sperm adhering to the fallopian tube epithelium in vitro. Reproduction 2005;129:51-60. [26] Odhiambo JF, Sutovsky M, DeJarnette JM, Marshall C, Sutovsky P. Adaptation of ubiquitin-PNA based sperm quality assay for semen evaluation by a conventional flow cytometer and a dedicated platform for flow cytometric semen analysis. Theriogenology 2011;76:1168-76. [27] Evenson DP, Darzynkiewicz Z, Melamed MR. 1980. Relation of mammalian sperm chromatin heterogeneity to fertility. Science 1980;210:1131–3.

21 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552

Journal Pre-proof

[28] Ballachey BE, Saacke RG, Evenson DP. The sperm chromatin structure assay: relationship with alternate tests of sperm quality and heterospermic performance of bulls. J Androl 1988;9:109-15. [29] Evenson DP. Sperm Chromatin Structure Assay (SCSA®). In: Carrell D, Aston K, editors. Spermatogenesis. Methods in molecular biology (Methods and Protocols), Vol 927, New Jersey: Humana Press; 2013. [30] Evenson DP. The sperm chromatin structure assay (SCSA®) and other sperm DNA fragmentation tests for evaluation of sperm nuclear DNA integrity as related to fertility. Anim Reprod Sci 2016;169:56-75. [31] Seidel Jr GE. Several insights on evaluation of semen. Anim Reprod 2012;9(3):329-32. [32] Stroup WW. Rethinking the analysis of non-normal data in plant and soil science. Agron J 2014;106:1–17. [33] Kaiser HF. The application of electronic computers to factor analysis. Educ and Psych Measurement 1960;20:141–51. [34] DeJarnette JM. The effect of semen quality on reproductive efficiency. Vet Clin Food Anim 2005;21:409-18. [35] Dalton JC. Semen evaluation and fertility in the field. In: Proc. 4th Int’l Symp. Appl. Anim. Reprod., Londrina, Paraná, Brazil, 2010, p. 101-16. [36] Harstine BR, Utt MD, DeJarnette, JM. Review: Integrating a semen quality control program and sire fertility at a large artificial insemination organization. Animal 2018;22:1-12. [37] Clay JS, McDaniel, BT. Computing mating bull fertility from DHI nonreturn data. J Dairy Sci 2001;84:1238-45. [38] Sá Filho MF, Crespilho AM, Santos JEP, Perry GA, Baruselli, PS. Ovarian follicle diameter at timed insemination and estrous response influence likelihood of ovulation and pregnancy after estrous synchronization with progesterone or progestin-based protocols in suckled Bos indicus cows. Anim Reprod Sci 2010;120:23–30. [39] Aono FH, Cooke RF, Alfieri AA, Vasconcelos JLM. 2013. Effects of vaccination against reproductive diseases on reproductive performance of beef cows submitted to fixed-timed AI in Brazilian cow-calf operation. Theriogenology 2013;79:242-8. [40] Pohler KG, Pereira MHC, Lopes FR, Lawrence JC, Keisler DH, Smith MF, et al. Circulating concentration of bovine pregnancy-associated glycoproteins and late embryonic mortality in lactating dairy herds. J Dairy Sci 2016;99:1584-94. [41] Kidder HE, Black WG, Wiltbank JN, Ulberg LC, Casida LE. Fertilization rates and embryonic death rates of cows bred to bulls of different levels of fertility. J Dairy Sci 1954;37:691-7. [42] Bearden HJ, Hansel WM, Bratton RW. Fertilization and embryonic mortality rates of bulls with histories of either low or high fertility in artificial breeding. J Dairy Sci 1956;39:312-8. [43] DeJarnette JM, Saacke RG, Bame J, Vogler CJ. 1992. Accessory sperm: Their importance to fertility and embryo quality and attempts to alter their numbers in artificially inseminated cattle. J Anim. Sci 1992;70:484-91. [44] Saacke RG, Nadir, S, Dalton, J. Accessory sperm evaluation and bull fertility (an update). In: Proc. Nat’l Assoc. Anim. Breeders 15th Tech. Conf. Artif. Insem. and Reprod. Columbia, MO, 1994, p. 57-67. [45] Saacke RG, DeJarnette JM, Bame JH, Karabinus DS, Whitman SS. Can spermatozoa with abnormal heads gain access to the ovum in artificially inseminated super- and singleovulating cattle? Theriogenology 1998;50:117-28.

22 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594

Journal Pre-proof

[46] Muiño R, Tamargo C, Hidalgo CO, Peña AI. 2008. Identification of sperm subpopulation with defined motility characteristics in ejaculates from Holsteins bulls: Effect of cryopreservation and between bull variation. Anim Reprod Sci 2008;109:27-39. [47] Rodríguez-Martínez H. Sperm function in cattle and pigs: morphological and functional aspects. Arch Animal Breed 2001;44:102-13. [48] Alm K, Taponen J, Dahlbom M, Tuunainen E, Koskinen E, Andersson M. A novel automated fluorometric assay to evaluate sperm viability and fertility in dairy bulls. Theriogenology 2001;56:677-84. [49] Januskauskas A, Johannisson A, Rodríguez-Martínez H. Assessment of sperm quality through fluorometry and sperm chromatin structure assay in relation to field fertility of frozen-thawed semen from Swedish AI bulls. Theriogenology. 2001;55:947-61. [50] Januskauskas A, Johannisson A, Rodríguez-Martínez H. Subtle membrane changes in cryopreserved bull semen in relation to sperm viability, chromatin structure and field fertility. Theriogenology 2003;60:743-58. [51] Anzar M, He L, Buhr MM, Kroetsch TG, Pauls KP. 2002. Sperm apoptosis in fresh and cryopreserved bull semen detected by flow cytometry and its relationship with fertility. Biol Reprod 2002;66:354-60. [52] Ballachey BE, Hohenboken WD, Evenson DP. Heterogeneity of sperm nuclear chromatin structure and its relationship to bull fertility. Biol Reprod 1987;36:915-25. [53] Waterhouse KE, Haugan T, Kommisrud E, Tverdal A, Flatberg G, Farstad W, et al. Sperm DNA damage is related to field fertility of semen from young Norwegian Red bulls. Reprod Fertil Devel 2006;18:781-8. [54] Gliozzi TM, Turri F, Manes S, Cassinelli C, Pizzi F. 2017. The combination of kinetic and flow cytometric semen parameters as a tool to predict fertility in cryopreserved bull semen. Anima 2017;11:11:1975-82. [55] Rao CR. The use and interpretation of principal component analysis in applied research. Sankhyā: The Indian Journal of Statistics, Serie A (1961-2002). 1964;26:4:329.58. [56] Luna C, Yeste M, Rivera del Alamo MM, Domingo J, Casao A, Rodriguez-Gil JE, et al. Effect of seminal plasma proteins on the motile sperm subpopulations in ram ejaculates. Reprod Fert Devel 2017;29:394-405. [57] Gil-Villa AM, Cardona-Maya W, Agarwal A, Sharma R, Cadavid A. Assessment of sperm factors possibly involved in early recurrent pregnancy loss. Fertil Steril 2010;94:4:1465-72. [58] Núñez Martínez I, Morán JM, Peña FJ. Two-step cluster procedure after principal component analysis identifies sperm subpopulations in canine ejaculates and its relation to cryoresistance. J Androl 2006;27:596-603. [59] DeJarnette JM, Semen quality control and quality assurance in AI centers. In: Proc. Assoc. Appl. Anim. Androl., Vancouver, BC, Canada, 2012. [60] Amann RP, Hammerstedt, RH. In vitro evaluation of sperm quality: An opinion. J Androl 1993;14(6):397-406.

23 595 596 597 598

Table 1 Count of artificial insemination (AI) by bull and farm. Bull A B C D AI, (no.) AI, (no.) AI, (no.) AI, (no.) Farm 1 140 139 151 92 2 83 75 88 50 3 72 155 165 63 4 149 112 144 80 5 131 118 223 122 6 122 91 80 33 7 23 30 24 23 8 28 27 26 27 9 254 271 266 234 10 48 40 39 23 Total AI 1,050 1,058 1,206 747

E AI, (no.) 73 63 58 89 106 48 31 27 242 68 805

Total AI 595 359 513 574 700 374 131 135 1267 218 4,866

24 599 600 601

Table 2 Count of artificial insemination (AI) by dose and farm. Dose, × 106 10 20 20 40 AI, (no.) AI, (no.) AI, (no.) AI, (no.) Farm 1 143 155 146 151 2 89 81 104 85 3 122 140 117 134 4 165 139 116 154 5 152 188 185 175 6 125 71 61 117 7 33 32 33 33 8 34 35 32 34 9 320 311 326 310 10 48 52 62 56 Total AI 1,231 1,204 1,182 1,249

Total AI 595 359 513 574 700 374 131 135 1,267 218 4,866

25 602 603

604

Table 3 Count of cows within pregnancy diagnosis interval by farm. Pregnancy diagnosis interval, days after AI 30-45 50-70 90 Farm 1 595 2 359 3 513 4 574 5 700 6 374 7 131 8 135 9 588 465 214 10 218 Total 1,228 3,424 214

26 605 606

607 608 609 610 611

Table 4 Proportion of cows pregnant per timed AI (P/TAI; %; LSM ± SEM) across sperm dosages and bulls. Dose, × 106 Bull 10 n1 20-a2 n 20-b2 n 40

Bull mean3

n

n

A

48.5 ± 3.3

274

50.2 ± 3.5

240

48.7 ± 3.4

261

45.1 ± 3.3

275

48.1 ± 2.0a

1,050

B

47.2 ± 3.3

270

44.4 ± 3.5

241

46.7 ± 3.3

275

52.7 ± 3.3

272

47.7 ± 2.0a

1,058

C

41.4 ± 3.2

297

41.1 ± 3.0

334

37.3 ± 3.3

263

43.1 ± 3.1

312

40.7 ± 1.9c

1,206

D

45.2 ± 4.0

179

46.4 ± 3.8

195

41.5 ± 3.8

190

48.9 ± 4.0

183

45.5 ± 2.3ab

747

E

36.6 ± 3.6

211

44.7 ± 3.8

194

45.2 ± 3.8

193

45.9 ± 3.7

207

43.1 ± 2.2bc

805

Dose mean4

43.8 ± 2.0

1,231

45.3 ± 2.0

1,204

43.8 ± 2.0

1,182

47.1 ± 2.0

1,249

-

4,866

a,b,c

Values within a column with different superscript letters differ (P < 0.05) of cows that received timed AI 220-a and 20-b treatments differ only in straw color 3Bull mean = least squares mean of P/TAI per bull 4Dose mean = least squares mean of P/TAI per sperm dosage 1Number

Journal Pre-proof

27 612 613 614 615 616 617

Table 5 Fertility, morphology, subjective motility (SM; LSM ± SEM), computer-assisted sperm analyses (CASA; LSM ± SEM), and flow cytometer (LSM ± SEM) analyses of representative semen samples (n = 92: except for morphology n =14) from Angus bulls used in a timed AI program. Variables

Bull A

B

C

D

E

48.1a

47.7a

40.7c

45.5ab

43.1bc

Normal, %

67.4

72.5

70.5

76.0

71.1

Primary, %

29.6

13.5

18.5

16

26.5

Secondary, %

3.7

14

11

8.3

2.7

42.1 ± 2.3c

54.8 2.5ab

50.8 ± 2.2b

60.6 ± 1.9a

38.3 ± 1.5c

TM, %

31.8 ± 2.2bc

33.0 ± 2.4b

26.5 ± 2.0cd

51.6 ± 2.0a

24.2 ± 1.4d

PM, %

21.8 ± 2.0b

23.7 ± 2.2b

19.8 ± 1.8b

36.5 ± 2.0a

19.0 ± 1.3b

ALH, μm BCF, Hz VAP, μm/s

7.2 ± 0.3c 30.0 ± 0.9 87.3 ± 2.4d

8.2 ± 0.3b 31.0 ± 0.8 107.1 ± 2.2b

7.7 ± 0.2bc 30.8 ± 0.7 99.5 ± 2.0c

9.1 ± 0.2a 29.6 ± 0.6 114.0 ± 1.6a

VCL, μm/s

154.3 ± 5.3d

192.4 ± 5.0b

177.5 ± 4.5c

206.4 ± 3.7a

VSL, μm/s

73.7 ± 2.1c

7.6 ± 0.3bc 28.4 ± 0.9 93.3 ± 2.5cd 165.3 ± 5.7cd 76.9 ± 2.2c

89.9 ± 2.0a

84.1 ± 1.7b

92.8 ± 1.4a

82.4 ± 1.0bc 49.1 ± 1.3 58.2 ± 0.9

85.3 ± 0.8a 51.0 ± 1.1 58.8 ± 0.8

84.6 ± 0.7ab 50.4 ± 1.0 58.4 ± 0.7

82.1 ± 0.6c 48.4 ± 0.8 57.9 ± 0.6

Fertility1, % Morphology2

SM3, % CASA4

STR, % 83.8 ± 0.9abc LIN, % 50.9 ± 1.2 WOB, % 59.2 ± 0.9 Flow Cytometer5

618 619 620 621 622 623

Viable, %

48.4 ± 2.0c

55.1 ± 2.1b

41.6 ± 1.8d

66.2 ± 1.6a

30.5 ± 1.2e

Acrosome, %

59.7 ±2.1b

76.7 ± 1.8a

64.3 ± 1.9b

77.5 ± 1.4a

53.2 ± 1.5c

VA, %

49.4 ± 2.0c

58.1 ± 2.2b

44.8 ± 1.9c

68.1 ± 1.6a

31.8 ± 1.3d

VNCa, %

45.9 ± 2.1c

53.2 ± 2.3b

40.1 ± 2.0d

62.9 ± 1.7a

29.1 ± 1.3e

ANCa, %

91.6 ± 0.9a

92.1 ± 0.9a

91.7 ± 0.8a

91.8 ± 0.7a

87.7 ± 0.8b

VANCa, %

44.1 ± 2.0c

50.7 ± 2.2b

38.1 ± 1.8d

60.6 ± 1.7a

27.1 ± 1.2e

DFI, %

37.7 ± 2.0c

26.1 ± 1.8b

36.6 ± 1.9c

21.5 ± 1.4a

47.8 ± 1.5d

a-e

Values within the same row not sharing a common superscript differ (P ≤ 0.05) proportion of cows pregnant per timed AI in ten Brazilian beef farms 2 Morphology: estimated percentage sperm morphology assessed with differential interference contrast (DIC) microscopy of one hundred sperm per bull and collection date (n = 14). Normal: total normal sperm (LSM, P = 0.7). Primary: Sperm head defects (Mean). Secondary: Sperm tail defects (Mean)[17] 1 Fertility:

28 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641

3Subjective

Journal Pre-proof

motility (SM): Motility was evaluated subjectively with phase-contrast optics (× 200), using a minimum of 5 random fields of view (avoiding slide perimeter) and rounded to the nearest 5% 4CASA variables analyzed: TM = Total motility, PM = Progressive motility, ALH = Amplitude of lateral head displacement, BCF = Beat-cross frequency, VAP: Average path velocity, VCL = Curvilinear velocity, VSL = Straight-line velocity, STR = Straightness, LIN = Linearity, WOB = Wobble 5Flow cytometric data were analyzed with FLOWJo software. The proportion of viable and acrosome for each sample were determined based on the histogram generated. The proportion of viable with intact acrosome (VA) and viable with normal calcium influx (VNCa) for each sample were determined based on a dot plot. The proportion with intact acrosome and normal calcium (ANCa) was determined by a dot plot within the viable population, which was determined based on a histogram. Viable = Membrane integrity, Acrosome = Acrosome integrity, VA: Viable with intact acrosome, VNCa = Viable with normal Ca influx, ANCa = Within viable with intact acrosome and normal Ca, VANCa = Overall population of sperm with intact plasma and acrosome membranes with normal Ca influx (VANCa); calculated by multiplying viable and ANCa (VANCa = viable × ANCa), DFI = proportion of single stranded DNA

29

642

Journal Pre-proof

30

Journal Pre-proof

643

Fig. 1. Principal component 1 (Prin1) and 2 (Prin2) account for 73% of the total variability

644

associated with 16 in vitro sperm characteristics of five Angus bulls with different fertility

645

used in a first-service timed AI program (total motility, progressive motility, amplitude of

646

lateral head displacement, beat cross frequency, average path velocity, curvilinear velocity,

647

straight line velocity, linearity, straightness, wobble, membrane integrity, acrosome integrity,

648

membrane and acrosome intact, membrane integrity with normal Ca influx, membrane and

649

acrosome intact with normal Ca influx and DNA fragmentation index). Symbols (as

650

described in the legend) represent observations (n = 92) from the five bulls for each sample.

651

X axis: Prin1 (47.1% of total variability) most influenced by sperm viability and DNA

652

fragmentation index. Y axis: Prin2 (25.9% of total variability) most influenced by sperm

653

movement pattern.

654

Journal Pre-proof

Author Contribution Statement Saulo Menegatti Zoca: Investigation, Formal analysis, Project Administration, Writing-Original draft preparation, Writing-Review and editing. Bahman Shafii: Formal analysis, Writing-Review and editing. William Price: Formal analysis, Writing-Review and editing. Matthew Utt: Conceptualization, methodology, Resources, Writing-Review and editing, Supervision. Bo Harstine: Conceptualization, Methodology, Resources, Validation, Writing-Review and editing, Supervision. Kristina McDonald: Supervision, Validation. Leandro Cruppe: Conceptualization, Methodology, Resources, Validation, Supervision, Project Administration. Mel DeJarnette: Conceptualization, Methodology, Resources, Validation, Writing-Review and editing, Supervision, Project administration, Funding acquisition. Lon Peters: Supervision, Resources. Jose Luiz Moraes Vasconcelos: Supervision, Project administration. Joseph Dalton: Funding Acquisition, Project Administration, Supervision, Writing-Original draft preparation, WritingReview and editing.

Journal Pre-proof

The objective was to determine if bull fertility varied by sperm dose inseminated. Treatments were 10, 20 and 40 x 106 sperm per dose. Pregnancy per timed AI did not differ between sperm doses. Pregnancy per timed AI was different between bulls. In vitro sperm analyses were unable to identify traits associated with field fertility.