Stability, delivery and functions of human sperm RNAs at fertilization

Abstract

Increasing attention has focused on the significance of RNA in sperm, in light of its contribution to the birth and long-term health of a child, role in sperm function and diagnostic potential. As the composition of sperm RNA is in flux, assigning specific roles to individual RNAs presents a significant challenge. For the first time RNA-seq was used to characterize the population of coding and non-coding transcripts in human sperm. Examining RNA representation as a function of multiple methods of library preparation revealed unique features indicative of very specific and stage-dependent maturation and regulation of sperm RNA, illuminating their various transitional roles. Correlation of sperm transcript abundance with epigenetic marks suggested roles for these elements in the pre- and post-fertilization genome. Several classes of non-coding RNAs including lncRNAs, CARs, pri-miRNAs, novel elements and mRNAs have been identified which, based on factors including relative abundance, integrity in sperm, available knockout data of embryonic effect and presence or absence in the unfertilized human oocyte, are likely to be essential male factors critical to early post-fertilization development. The diverse and unique attributes of sperm transcripts that were revealed provides the first detailed analysis of the biology and anticipated clinical significance of spermatozoal RNAs.

Figure 1.

The distribution of sequencing reads across ACSBG2 in all libraries. The green arrow indicates transcript orientation. The number of reads corresponding to each read start at each base position is represented on the vertical axis. The scale for each sample is based on maximum read count within the displayed region. Four Total Sperm samples—Sp_D62[Tt], Sp_D64[Tt], Sp_D66[Tt] (individual donors) and Sp_P1[Tt] (mixed pool of three random donors)—were not subject to any of the commonly used RNA-selection methods. Sample D62 was additionally separated into Poly(A⁺) (Sp_D62[A⁺]) and Poly(A^-) (Sp_D62[A⁻]) fractions by oligo(dT) selection. Commercially obtained pooled testes RNA—Te_PAm[A⁺] (Applied Biosystems/Ambion, Austin, TX, USA, Lot 054P010702031A) and Te_PCl[A⁺] (ClonTech, Mountain View, CA, USA, Lot 3090051)—were subject to Poly(A⁺) selection. Sequences from Single Primer Isothermal Amplification (SPIA—Nugen Ovation Nugen Inc., San Carlos, CA, USA) (Sp_D62[SP], Sp_D64[SP], Sp_D66[SP]), a single pooled sperm sample (Sp_P2[SP]) and a single pooled testes (Te_PAm[SP]) prepared libraries are compared.

Figure 2.

Novel sperm transcript expression profiles: The green arrow indicates transcript orientation. The number of reads corresponding to each base position is represented on the vertical axis. The scale for each sample is based on maximum read count within the displayed region. (A) The complete annotated 3′ UTR of EFHD2 is 1.6 kb in length (pink box). In contrast to testes the 3′ UTR of this RNA in sperm is truncated to ∼100 nt due to alternative polyadenylation. (B) An isoform of PKM2 specific to sperm contains exons (pink ovals) not observed together in other tissues. Examination of paired reads from these regions indicates splicing between these neighbouring exons and that these regions are transcribed at levels comparable with the remainder of the transcript. Previously reported isoforms containing either the M1 or M2 exon are presented below.

Figure 3.

Average level of miR-181C targets as a function of preimplantation embryonic development. The average level of 27 high-confidence predicted miR-181C targets through preimplantation embryonic development OO: Oocyte, M:Morula, BL:blastocyst, ST:Stem cell is shown in blue. This is compared to the relative level of non-targeted genes indicated in red.

Figure 4.

The most abundant sperm RNAs exhibit differential levels of stability. (A) Normalized RNA-seq coverage across the coding regions and UTRs of the 1000 most abundant sperm transcripts at a 100-bin resolution was used to identify intact RNAs. Transcripts are ranked in descending order of intactness and were divided into quintiles prior to ontological analysis. Profiles corresponding to stable RNAs exhibit uniform sequence coverage across the transcript (top, yellow). Profiles exhibiting increasing heterogeneity of coverage (bottom, red) are indicative of truncation. (B) A scaling function was applied to the above integrity scores to rank transcripts by their directionality and degree of fragmentation. The average sequencing coverage profiles for decile bins are presented. The expected level of coverage for an ideal transcript is shown as a dashed line. To highlight their likely functional importance, the flattest transcript deciles are prioritized to the foreground.

Figure 5.

Sperm RNAs are correlated with the genomic positioning of histones and epigenetic elements. A total of 19 521 highly expressed RNA-seq clusters were identified in the promoter and exonic regions of sperm sample Sp_D62[Tt]. Each distance correlation is centered on the genomic coordinates of elements within one of the four following classes, H3K4me3 and H3K27me3 enriched regions (n = 34 912 and 38 337, respectively; 95), histone-enriched regions (n = 25 114; 33) and hypomethylated DNA regions (n = 79 124; 34).