In Silico Identification of lncRNAs Regulating Sperm Motility in the Turkey (Meleagris gallopavo L.)

Genome-Wide Analysis of Genetic Predispositions Linked to Damaged Membranes and Impaired Fertility as Indicators of Compromised Sperm-Egg Interaction Mechanisms in Frozen-Thawed Rooster Semen

BACKGROUND

Cryopreservation cannot be widely used for rooster sperm due to high incidences of cryoinjury, including damage to sperm membranes. Thus, cryopreserved rooster sperm has limited use due to low sperm motility and reduced fertilizing ability, which disrupts the mechanisms involved in sperm-egg interactions. Previously, we used an Illumina 60K single-nucleotide polymorphism (SNP) array to search for genes associated with rooster sperm quality, before and after freeze-thawing. As a continuation of these genome-wide association studies (GWAS), the present investigation used a denser 600K SNP chip. Consequently, the screen depth was expanded by many markers for cryo-resistance in rooster sperm while more candidate genes were identified. Thus, our study aimed to identify genome-wide associations with ejaculate quality indicators, including those concerning sperm membrane damage.

METHODS

We selected sperm quality indicators after freezing-thawing using samples from a proprietary cryobank collection created to preserve generative and germ cells of rare and endangered breeds of chickens and other animal species. A total of 258 ejaculates from 96 roosters of 16 different breeds were analyzed. Moreover, 96 respective DNA samples were isolated for genotyping using a 600K Affymetrix® Axiom® high-density genotyping array.

RESULTS

In total, 31 SNPs and 26 candidate genes were associated with characteristics of sperm membrane damage, progressive motility, and sperm cell respiration induction using 2,4-dinitrophenol. In particular, we identified the ENSGALG00000029931 gene as a candidate for progressive motility, PHF14 and ARID1B for damaged sperm membranes, and KDELR3, DDX17, DMD, CDKL5, DGAT2, ST18, FAM150A, DIAPH2, MTMR7, NAV2, RAG2, PDE11A, IFT70A, AGPS, WDFY1, DEPDC5, TSC1, CASZ1, and PLEKHM2 for sperm cell respiration induction.

CONCLUSIONS

Our findings provide important information for understanding the genetic basis of sperm membrane integrity and other traits that can potentially compromise the mechanisms involved in sperm-egg interactions. These findings are relevant to the persistence of fertility after thawing previously frozen rooster semen.

Importance and variability of the paternal component in sow reproductive traits

Reproductive traits are an integral part of the goals of the breeding programs that contribute to the economic success of production. Reproductive phenotypes such as litter size, number of piglets born alive, or litter weight at birth are mainly attributed to females. Thus, the maternal components can be found by default in quantitative genetics' animal models. Still, paternal contribution to variance components should not be discarded. In this review, we indicate the importance of paternal effects in pig breeding by describing both the biology and genetics of boars' traits, the use of (non-)genetic service sire effects in quantitative genetic models for traits measured on females, and genes involved in male reproduction. We start by describing the important biological traits of boars that have the most important effect on their reproductive abilities, i.e., sexual maturity, sperm quality, and testes parameters. Then we move to the possible environmental effects that could affect those traits of boars (e.g., feed, temperature). The main part of the review in detail describes the genetics of boars' reproductive traits (i.e., heritability) and their direct effect on reproductive traits of females (i.e., genetic correlations). We then move to the use of both genetic and non-genetic service sire effects in quantitative models estimated as their percentage in the total variance of traits, which vary depending on the breed from 1 to 4.5% or from 1 to 2%, respectively. Finally, we focus on the description of candidate genes and confirmed mutations affecting male reproduction success: IGF2, Tgm8, ESR1, ZSWIM7, and ELMO1. In conclusion, the observed variance of paternal effects in female reproduction traits might come from various attributes of boars including biological and genetic aspects. Those attributes of boars should not be neglected as they contribute to the success of female reproductive traits.

Sperm long non-coding RNAs as markers for ram fertility

It is critical in sheep farming to accurately estimate ram fertility for maintaining reproductive effectiveness and for production profitability. However, there is currently a lack of reliable biomarkers to estimate semen quality and ram fertility, which is hindering advances in animal science and technology. The objective of this study was to uncover long non-coding RNAs (lncRNAs) in sperm from rams with distinct fertility phenotypes. Mature rams were allocated into two groups: high and low fertility (HF; n = 31; 94.5 ± 2.8%, LF; n = 25; 83.1 ± 5.73%; P = 0.028) according to the pregnancy rates sired by the rams (average pregnancy rate; 89.4 ± 7.2%). Total RNAs were isolated from sperm of the highest- and lowest-fertility rams (n = 4, pregnancy rate; 99.2 ± 1.6%, and 73.6 ± 4.4%, respectively) followed by next-generation sequencing of the transcripts. We uncovered 11,209 lncRNAs from the sperm of rams with HF and LF. In comparison to each other, there were 93 differentially expressed (DE) lncRNAs in sperm from the two distinct fertility phenotypes. Of these, 141 mRNAs were upregulated and 134 were downregulated between HF and LF, respectively. Genes commonly enriched for 9 + 2 motile cilium and sperm flagellum were ABHD2, AK1, CABS1, ROPN1, SEPTIN2, SLIRP, and TEKT3. Moreover, CABS1, CCDC39, CFAP97D1, ROPN1, SLIRP, TEKT3, and TTC12 were commonly enriched in flagellated sperm motility and sperm motility. Differentially expressed mRNAs were enriched in the top 16 KEGG pathways. Targets of the differentially expressed lncRNAs elucidate functions in cis and trans manner using the genetic context of the lncRNA locus, and lncRNA sequences revealed 471 mRNAs targets of 10 lncRNAs. This study illustrates the existence of potential lncRNA biomarkers that can be implemented in analyzing the quality of ram sperm and determining the sperm fertility and is used in breeding soundness exams for precision livestock farming to ensure food security on a global scale.

IncRna: The R Package for Optimizing lncRNA Identification Processes

In silico identification of long noncoding RNAs (lncRNAs) is a multistage process including filtering of transcripts according to their physical characteristics (e.g., length, exon-intron structure) and determination of the coding potential of the sequence. A common issue within this process is the choice of the most suitable method of coding potential analysis for the conducted research. Selection of tools on the sole basis of their single performance may not provide the most effective choice for a specific problem. To overcome these limitations, we developed the R library lncRna, which provides functions to easily carry out the entire lncRNA identification process. For example, the package prepares the data files for coding potential analysis to perform error analysis. Moreover, the package gives the opportunity to analyze the effectiveness of various combinations of the lncRNA prediction methods to select the optimal configuration of the entire process.