Mullen, A. | Search bioRxiv

bioRxiv2023

Hormonal induction, quality assessments and the influence of seasonality on male reproductive viability in a long-term managed ex situ breeding colony of Southern Rocky Mountain Boreal toads, Anaxyrus boreas boreas.

The Southern Rocky Mountain Boreal Toad (Anaxyrus boreas boreas) is an ex situ managed species which relies heavily on the use of assisted reproductive technologies to augment existing in situ populations. Despite the use of ARTs to manipulate reproduction of this species, the ex situ program continues to face challenges with annual reproduction. Human chorionic gonadotropin (hCG) at 10 IU/gbw singly or in combination with 0.4 ug/gbw GnRH-A have been successfully reported for this species, however, with a number of other available hormones, it is not clear if these are the most effective protocols for this species. Moreover, there is no information on how exogenous hormone administration is affected by other factors such as seasonality. Therefore, in the first part of this study, we compare the efficacy of the 10 IU/g hCG and 0.4 Lg/g Gonadotropin Releasing Hormone agonist (GnRH-A) administered singly or in combination, as well as GnRH-Apd + Amphiplex (0.4 Lg/g GnRH-A + 10 Lg/g Metoclopramide [MET] - a dopamine antagonist), or 10 Lg/g MET alone. Spermiation responses and sperm viability were compared across treatments with hormonal curves then correlated to seasonality. The results of this study suggest that the optimal hormonal stimulation protocol, across all treatments, in terms of sperm quality was 10 IU/g hCG + 0.4 Lg/g GnRH-A. Further optimization is required, in particular the exploration of higher doses of GnRH-A. Lastly, we observed that the effect of seasonality influenced the hormonal efficacy and magnitude of the spermiation response. As was expected, quality and concentration parameters were affected by the month in which hormone administration occurred. HIGHLIGHTSO_LISpermiation in the Southern Rocky Mountain Boreal toad, Anaxyrus boreas boreas, is elicited most effectively by hCG singly or in combination with GnRH-A. C_LIO_LISpermiation responses lasted up to 96 hours post injection (hpi) though quantity and quality parameters were highest in the first 12 hpi. C_LIO_LITotal motility, forward progressive motility and percentage live cells all indicated A. b. boreas sperm quality was in a good range. C_LIO_LIAcrosome integrity requires more research as it was comparatively lower than expected. C_LIO_LIResponses to hormone inductions are seasonally affected in this species but suggest semi-discontinuous cycling. C_LIO_LIEx situ breeding should perform better sperm assessments before designing breeding strategies. C_LI

zoology↗

bioRxiv2022

Singletrome: A method to analyze and enhance the transcriptome with long noncoding RNAs for single cell analysis

Single cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression in individual cell types, but scRNA-seq studies have focused primarily on expression of protein-coding genes. Long noncoding RNAs (lncRNAs) are more diverse than protein-coding genes, yet remain underexplored in part because they are under-represented in reference annotations applied to scRNA-seq. Merging annotations containing protein-coding and lncRNA genes is not sufficient, because the addition of lncRNA genes that overlap in sense and antisense with protein-coding genes will affect how reads are counted for both protein-coding and lncRNA genes. Here, we introduce Singletrome, a Singularity image that integrates protein-coding and lncRNA gene transfer format (GTF) annotations to generate enhanced annotations that take into account the sense and antisense overlap of annotated genes, maps scRNA-seq data, and produces files for downstream analysis and visualization. With Singletrome, we observed an increase in the number of reads mapped to exons, detected thousands of lncRNAs not included in GENCODE, and observed a decrease in uniquely mapped reads, indicating improved mapping specificity. Moreover, we were able to cluster cell types based solely on lncRNAs expression, and lncRNAs alone were able to predict cell types and human disease pathology through machine learning. This comprehensive annotation will allow mapping of lncRNA expression across cell types of the human body, facilitating the development of an atlas of human lncRNAs in health and disease with the ability to integrate new lncRNA annotations as they become available.

bioinformatics↗