Sperm-borne tsRNAs and miRNAs analysis in relation to dairy cattle fertility.

Sperm small non-coding RNAs (sncRNAs), such as microRNAs (miRNAs) and tRNA-derived small RNAs (tsRNAs), have been found to have implications for male fertility and play a role in the intergenerational transmission of specific phenotypes by influencing the early embryo's physiological processes in various animal species. This study postulates that there exists a correlation between sperm small non-coding RNAs (sncRNAs) and bull fertility, which in turn can influence the fertility of offspring through the modulation of early embryo development. To investigate this hypothesis, we generated comparative libraries of sperm sncRNAs from sires exhibiting high (n = 3) versus low bull fertility (n = 3), as well as high (n = 3) versus low daughter fertility (n = 3), as determined by the industry-standard Bull fertility index and Daughter fertility index. In total, 12 tsRNAs carried by sperm (11 down-regulated and 1 up-regulated) were found to be associated with bull fertility, while 19 tsRNAs (11 down-regulated and 8 up-regulated) were found to be associated with daughter fertility (q < 0.05, Log2foldchange>±1.5, base mean > 50). Notably, tRX-Glu-NNN-3811 exhibited potential as a biomarker for predicting fertility in both male and female dairy cattle. Moreover, a total of six miRNAs sperm-borne (two up-regulated and four down-regulated) and 35 miRNAs (27 up-regulated and eight down-regulated) exhibited a significant correlation with both bull fertility and daughter fertility individually (p < 0.05, base mean > 50, log2foldchange>±1.5), two microRNAs, namely miR-2385-5p (down-regulated) and miR-98 (up-regulated), exhibit a significant association (p < 0.05, base mean > 50, log2foldchange>±1.5) with the fertility of both bulls and daughter. The targets of these two microRNAs were subsequently identified and integrated with the transcriptomic database of the embryonic cells at the two-cell stage, which is known to be indicative of embryonic competence. The KEGG analysis revealed a potential correlation between these targets and choline metabolism, a crucial factor in embryonic epigenetic programming. In summary, the findings of this study indicate that sperm-borne small non-coding RNAs (sncRNAs) hold promise as biomarkers for predicting and enhancing fertility in dairy cattle. Furthermore, it is plausible that these sncRNAs may exert their effects on daughter fertility by targeting genes in the early embryo.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data.

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

DNA methylation profiles in bovine sperm are associated with daughter fertility.

The current decline in dairy cattle fertility has resulted in significant financial losses for dairy farmers. In the past, most efforts to improve dairy cattle fertility have been focused on either management or genetics, while epigenetics have received less attention. In this study, 12 bulls were selected from a provided 100 bull list and studied (High daughter fertility = 6, Low daughter fertility = 6) for Enzymatic methylation sequencing in the Illumina HiSeq platform according to the Canadian daughter fertility index (DFI), sires with high and low daughter fertility have average DFI of 92 and 112.6, respectively. And the bull list provided shows a mean DFI of 103.4. 252 CpGs with methylation differences greater than 20% (q < 0.01) were identified, as well as the top 10 promising DMRs with a 15% methylation difference (q < 1.1e-26). Interestingly, the DMCs and DMRs were found to be distributed more on the X chromosome than on the autosome, and they were covered by gene clusters linked to germ cell formation and development. In conclusion, these findings could enhance our ability to make informed decisions when deciding on superior bulls and advance our understanding of paternal epigenetic inheritance.

Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data.

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets.