FUNCTIONAL GENOMICS FOR SPERMATOZOA OF STRIPED BASS: IMPLICATIONS FOR MALE FISH FERTILITY

• Sponsoring Institution: State Agricultural Experiment Station
• Project Status: TERMINATED
• Funding Source: STATE
• Reporting Frequency: Annual
• Accession No.: 1005983
• Project No.: MD-ANSC-8793
• Project Start Date: Feb 11, 2015
• Project End Date: Jun 30, 2016

Project Director
Woods, L.

Recipient Organization
UNIV OF MARYLAND
(N/A)
COLLEGE PARK,MD 20742

Performing Department
Animal and Avian Sciences

Non Technical Summary
Striped bass (Morone saxatilis) spermatozoa are used to fertilize in vitro the eggs of white bass (Morone chrysops) to produce the preferred hybrid for the striped bass aquaculture industry. Currently, few sources of striped bass juveniles are available to growers that aren't obtained from wild-caught parents and are thus devoid of any genetic improvement in phenotypic traits of importance to the aquaculture industry. Our recent isolation of RNA and DNA from sperm obtained from domesticated striped bass brood stocks, selected for superior survival and growth rate under intensive culture conditions; provides a unique opportunity to develop spermatozoal methylation profiles which may then be correlated with fertility. We hypothesize that the DNA methylation profile of striped bass sperm can be directly determined by deep sequencing and that changes in sperm epigenetics can be detected and correlated to sub-fertility or infertility in male striped bass. This proposed work, in combination with a recent study that demonstrated that striped bass ovarian transcript profiles were predictive of egg quality and the recently completed sequencing of the striped bass genome; will together provide powerful tools to maximize reproductive efficiencies for this important aquaculture finfish species.

Animal Health Component

0%

Research Effort Categories

Basic

100%

Applied

(N/A)

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
304	0810	1080	100%

Knowledge Area
304 - Animal Genome;

Subject Of Investigation
0810 - Finfish;

Field Of Science
1080 - Genetics;

Keywords

striped bass

sperm transriptome

Goals / Objectives
1. Evaluate striped bass sperm DNA methylation profiles from males with different fertility.2. Identify gene loci and functional pathways for these observed differences in fertility.

Project Methods
Objective 1. Procedures - DNA methylation profiles:MBD-seq (Methyl Binding Domain combined with Sequencing) will be used to obtain the striped bass methylated DNA sequences from a range of high to low fertility males (previously sequenced male spermatozoa with phenotypic fertility estimates from an earlier USDA/NIFA award). We will utilize a MethylCap kit (Diagenode, NJ, USA) to obtain DNA containing methylated CpG dinucleotide sites by first extracting and shearing the DNA into fragments of 300-500 bp. These sheared DNA fragments will be incubated with MethylCap protein so that the methylated DNA can be subsequently captured with magnetic beads. After capture, the DNA not bound to the magnetic beads will be washed off. DNA elutions, using sequentially: 150 μl each of low, medium and high elution buffers will be utilized for each capture reaction. Finally, qPCR will be implemented to test the enrichment efficiency of our methylated DNA. Transcriptome resources critical to our proposed research have recently been developed and include an ovarian transcriptome (Reading et al. 2012, Chapman et al. 2014) and a multi-tissue transcriptome (Li et al. 2014) that collectively represent nearly all of the 23,525 protein-coding genes in the striped bass reference genome. The short sequencing reads we develop will be aligned to the newly available striped bass reference genome to generate tag counts for both methylated (denoted as Mil) and unmethylated (denoted Uil) sequences at each specific genomic locus in the sample. From these counts an estimate of relative methylation mil for sample i and locus l will be obtained as the log-ratio of methylated counts to unmethylated counts for sample i and locus l. We will verify that the genomic sequence composition (GC-content, for instance) does not induce biased estimates of relative methylation and will normalize relative methylation estimates accordingly. Once an estimate of relative methylation is obtained for each genomic locus l, we will determine differentially methylated genomic regions (DMR) between groups of interest. To determine DMR between two groups (e.g., g and h), we will construct a smooth moderated t-statistic, where SE(l) is the standard error of the differencein relative methylation estimated from variance functions. Finally, regions will be defined as those contiguous loci l where the moderated t-statistic is greater than a determined critical value. The sum of the moderated t-statistic for each region will be used as a test statistic to determine if regions are differentially methylated. We will use permutation methods to obtain p-values for region statistics, and will use the q-value method to control for multiple testing. We will define new methods to discover regions of differentially-variable methylation using Methyl-MAPS data. The principle is to treat the log-ratio of variances between groups g and h as a test statistic to define regions where hyper- or hypo-variability is detected between groups. Using this method, the ratio can be assumed to be normally distributed and a similar approach, to the methods described above for differences in mean methylation, can be used.Objective 2. Procedures - Integrative analysis to identify gene loci and functional pathways:We recently obtained striped bass spermatozoal transcriptomic profiles for males with high and low fertility rates through a USDA/NIFA/NRAC mini-grant. We will integrate the "omics" data from DNA methylation and the previously generated RNA-sequencing data to elucidate relationships between epigenetic alterations and differential expression related to fertility and to locate candidate functional loci. Once these loci are obtained, we will carry out a Gene Set Enrichment Analysis (GSEA) to find biopathways and functional gene categories that are altered in a way that is mediated by epigenetic modifications. The procedures will include: 1) estimates of differential expression for each gene g 2) regions of hypo- or hyper-methylation modeled as a smooth functionover genomic locations; and 3) small regions indicating differential enrichment from ChIP-seq data; also modeled as smooth function of genomic location.The goal of this integrative analysis is to devise a new statistical algorithm to estimate a model of differential expressionas a function of differential methylation and enrichment in a genomic window surrounding the gene. Specifically, for each gene we will define a model as follows: 1) a genomic window spanning 2,000 bp upstream of the gene's transcription start site to the end of the coding region; 2) a cubic spline model will be used to represent smooth functions and using finite basis expansions: where and are truncated polynomials of the appropriate order which form the basis of the expansions; and 3) the coefficients of the spline models over genomic windows that are then used as our input to a regression model of the differential expression measurement . We will use the fused lasso penalized regression method to estimate the regression function parameters (in a sparse manner using the spatial relationship of the expansion coefficients to select a small number of sequences of expansion coefficients and to include as terms in the regression function. We will use permutation tests to compute p-values for the selection of expansion coefficients into the differential expression regression measurement and use the q-value to avoid Type I error. Then, regions of epigenetic modification can be classified by their differential methylation or enrichment profile, distance to transcription start site, length in genomic space, and their effect on differential expression, to produce a list of epigenetic modification patterns. By ranking these modifications by both the size of their effect on differential expression and the statistical significance of their inclusion in the regression function, we will construct a catalog of epigenetic modification patterns that will serve as the basis for further investigation into how epigenetic modifications mediate alterations in gene expression. Using the catalog of epigenetic modification patterns that show significant effects on differential expression that are related to fertility; we will construct sets of genes that show similar differential expression profiles and similar patterns of epigenetic modifications. We will discover functional categories and pathways, that are significantly enriched for each of these genes sets, which will serve as guides for further development of genomic targets.

Progress 02/11/15 to 06/30/16

Outputs
Target Audience:We were able to reach theUSDA ARS Center Administrator (Dr. Caird Rexroad III) and USDA ARS Center Directors who have important aquaculture finfish species as a priority. We also had the opportunity to present our research findings at the most recent meeting of the USDA's NRSP-8 Projectthis year.(presentation reference below). Reading, B.J., Baltzegar, D.A., Salger, S.A., Schilling, J.D., Clark, R.W., Rajab, S., Hopper, M.S., McGinty, A.S., Berlinsky, D.L., Kenter, L., Kovach, A., Woods III, L.C., and Song, J. 2016. Current Status of Striped Bass Genome Assembly, QTL Work and Other Genome Resources. The Strategic Planning Workshop for Aquaculture Genomics, Genetics and Breeding. United States Department of Agriculture NRSP-8 Project: National Animal Genome Research Program. March 24-25. Auburn, Alabama. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?We were given the opportunity to present details of our research findings, at the USDA NRSP-8 Workshop, to our targeted stakeholders. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Goal 1. We successfully isolated RNA and DNA from striped bass sperm; one of the first to do so with fish spermatozoa. We were able to identify differentially methylated regions (DMRs) of DNA in striped bass sperm of high and low fertility using the methyl-CpG binding domain, protein-enriched genome sequencing methodology (MBD-Seq). We further identified 171 DMRs across the entire genome for striped bass sperm related to fertility with 94 being downregulated and 77 being upregulated. These results clearly demonstrate the potential use of DNA methylation as biomarkers of sperm quality. Goal 2. We were able to conduct RNA-Seq transcriptomic analysis of the striped bass sperm of low and high quality. We identified 77 expressed genes that significantly differed between the two groups. We also annotated the differing DMRs of the high and low fertility sperm, regarding the methylation region's proximity to annotated genes;and performed both enrichment and pathway analyses. We have initially discovered protein coding genes present on 171 contigs (i.e. a set of overlapping DNA segments that together represent a consensus region of DNA) that contained DNA methylation markers; annotated from the 27,485 predicted loci in the striped pass genome. Of the 171 contigs, 116 contained one or more of the predicted gene loci and 68 of these had annotations.

Publications