Comparison of NUCLEOCOUNTER, ANDROVISION with Leja chambers and the newly developed ANDROVISION eFlow for sperm concentration analysis in boars

Exact analysis of sperm concentration in raw and diluted semen is of major importance in swine artificial insemination, as sperm concentration is one of the most important characteristics of an ejaculate determining the value of the ejaculate and the productive life of the boar. The study compares different methods for sperm concentration analysis in raw and diluted boar semen: NUCLEOCOUNTER SP-100, the ANDROVISION with Leja chambers and the new ANDROVISION eFlow system. The Concordance Correlation Coefficient (CCC) between NUCLEOCOUNTER and ANDROVISION eFlow was 0.955 for raw (n = 185 ejaculates) and 0.94 for diluted semen (n = 109 ejaculates). The CCC between NUCLEOCOUNTER and ANDROVISION with Leja chambers was 0.66. A Bland-Altman plot of split-sample measurements of sperm concentration with NUCLEOCOUNTER and ANDROVISION eFlow showed that 95.1% of all measurements lay within ± 1.96 standard deviation. The coefficients of variance were 1.6 ± 1.3%, 3.6 ± 3.6% and 7.3 ± 6.3% for NUCLEOCOUNTER, ANDROVISION eFlow and ANDROVISION with Leja chambers in diluted semen, respectively. NUCLEOCOUNTER and ANDROVISION eFlow are comparable tools to measure the concentration of raw and diluted boar semen. In comparison to ANDROVISION with Leja chambers, concentration analyses of diluted semen using NUCLEOCOUNTER or ANDROVISION eFlow show a higher repeatability within and a higher concordance between the methods.

Relations between the time of ovulation and fecal estrogen concentration in sows

Artificial insemination (AI) is the most important biotechnology in pig reproduction. To achieve the best possible fertility results, appropriate timing of the insemination is essential. The optimal time for AI is 12 h before to 4 h after ovulation. This time-frame, unlike in estrus, is not recognizable through external indicators. It would, therefore, be beneficial to find simple and economical methods that support manual estrus checks and are able to determine the time of ovulation more accurately. On this basis, starting 80 h after weaning, 14 DanBred sows (parity: 5.2 ± 2.4) were checked for ovulation via ultrasound scans every 8 h over a period of 72 h. Additionally, rectal fecal samples were taken and analyzed for their estrogen concentration to assess possible relations to ovulation time. On average, sows ovulated 121 ± 10 h after weaning and 16 ± 9 h after onset of heat. There was a prominent drop in fecal estrogen levels 4 h before ovulation when compared to almost all other points in time (before ovulation: 20 h (P = 0.056), 12 h (P = 0.006); after ovulation: 4 h and 12 h (P < 0.001)). There are, however, significant differences in the sow-individual fecal estrogen concentrations for which several influencing factors must be considered.

Boar Semen Shipping for Artificial Insemination: Current Status and Analysis of Transport Conditions with a Major Focus on Vibration Emissions

In the modern pig reproduction system, artificial insemination (AI) doses are delivered from AI centers to sow farms via logistics vehicles. In this study, six breeding companies in three countries (Brazil, Germany, and the USA) were interviewed about their delivery process. It was found that there is currently no comprehensive monitoring system for the delivery of semen. The entire process “shipping of boar semen” was documented using Business Process Model and Notation (BPMN). Although it is not currently known which vibrations occur at all, it is suspected that vibration emissions affect the quality of boar semen. For this reason, a prototype of a measuring system was developed to calculate a displacement index (Di), representing vibration intensities. Vibrations were analyzed in standardized road trials (n = 120) on several road types (A: smooth asphalt pavement, B: rough asphalt pavement, C: cobblestone, and D: dirt road) with different speeds (30, 60, 90, 120, and 150 km/h). A two-way ANOVA showed significant differences in mean Di, depending on road surface and speed as well as an interaction of both factors (p < 0.001). A field study on a reference delivery from a German AI center to several sow farms indicated that 33% of the observed roads are in good quality and generate only a few vibrations (Di ≤ 1), while 40% are of a moderate quality with interrupted surfaces (Di = 1−1.5). However, 25% of the roads show markedly increased vibrations (Di ≥ 1.5), as a consequence of bad conditions on cobblestones or unpaved roads. Overall, more attention should be paid to factors affecting sperm quality during transport. In the future, an Internet of Things (IoT) based solution could enable complete monitoring of the entire transport process in real time, which could influence the courier’s driving behavior based on road conditions in order to maintain the quality of the transported AI doses.

The Munich MIDY Pig Biobank - A unique resource for studying organ crosstalk in diabetes

OBJECTIVE

The prevalence of diabetes mellitus and associated complications is steadily increasing. As a resource for studying systemic consequences of chronic insulin insufficiency and hyperglycemia, we established a comprehensive biobank of long-term diabetic INSC94Y transgenic pigs, a model of mutant INS gene-induced diabetes of youth (MIDY), and of wild-type (WT) littermates.

METHODS

Female MIDY pigs (n = 4) were maintained with suboptimal insulin treatment for 2 years, together with female WT littermates (n = 5). Plasma insulin, C-peptide and glucagon levels were regularly determined using specific immunoassays. In addition, clinical chemical, targeted metabolomics, and lipidomics analyses were performed. At age 2 years, all pigs were euthanized, necropsied, and a broad spectrum of tissues was taken by systematic uniform random sampling procedures. Total beta cell volume was determined by stereological methods. A pilot proteome analysis of pancreas, liver, and kidney cortex was performed by label free proteomics.

RESULTS

MIDY pigs had elevated fasting plasma glucose and fructosamine concentrations, C-peptide levels that decreased with age and were undetectable at 2 years, and an 82% reduced total beta cell volume compared to WT. Plasma glucagon and beta hydroxybutyrate levels of MIDY pigs were chronically elevated, reflecting hallmarks of poorly controlled diabetes in humans. In total, ∼1900 samples of different body fluids (blood, serum, plasma, urine, cerebrospinal fluid, and synovial fluid) as well as ∼17,000 samples from ∼50 different tissues and organs were preserved to facilitate a plethora of morphological and molecular analyses. Principal component analyses of plasma targeted metabolomics and lipidomics data and of proteome profiles from pancreas, liver, and kidney cortex clearly separated MIDY and WT samples.

CONCLUSIONS

The broad spectrum of well-defined biosamples in the Munich MIDY Pig Biobank that will be available to the scientific community provides a unique resource for systematic studies of organ crosstalk in diabetes in a multi-organ, multi-omics dimension.

Comparison of FACSCount AF system, Improved Neubauer hemocytometer, Corning 254 photometer, SpermVision, UltiMate and NucleoCounter SP-100 for determination of sperm concentration of boar semen

Current research aims at reducing the number of sperm per insemination dose thereby making measurement of sperm concentration in raw semen and the production of uniform insemination doses much more crucial. The present study evaluated the determination of sperm concentration using FACSCount AF System (FACS), Improved Neubauer hemocytometer (HEMO), Corning 254 photometer (Photo C254), SpermVision CASA System (SpermVision), UltiMate CASA System (UltiMate) and NucleoCounter SP-100 (SP-100). The instruments were evaluated with respect to repeatability and to establishing the regression curve towards both HEMO and FACS. Repeatability for the instruments was 2.7, 7.1, 10.4, 8.1, 5.4 and 3.1% for FACS, HEMO, Photo C254, SpermVision, UltiMate and SP-100, respectively. Correlation between instruments was highest between FACS and SP-100. This was made possible due to the high repeatability for both instruments. The agreement between the instruments and HEMO as the gold standard was lower than expected as the largest difference in estimation of concentration was -25 to +50%. The largest percentage difference was observed for measurements of dilute semen. It was clear that percentage difference between instruments depended on sperm concentration. In comparison to the gold standard, agreement was highest between SpermVision and HEMO for dilute semen, but for concentrated semen, agreement was highest between SP-100 and HEMO. However, the agreement between HEMO and all other instruments was not as good as expected. The reason may lie within the presence of agglutinated sperm, preventing proper HEMO counts.