Piwi proteins and piRNAs in mammalian oocytes and early embryos: From sample to sequence

Abstract

The role of the Piwi/piRNA pathway during mammalian oogenesis has remained enigmatic thus far, especially since experiments with Piwi knockout mice did not reveal any phenotypic defects in female individuals. This is in striking contrast with results obtained from other species including flies and zebrafish. In mouse oocytes, however, only low levels of piRNAs are found and they are not required for their function. We recently demonstrated dynamic expression of PIWIL1, PIWIL2, and PIWIL3 during mammalian oogenesis and early embryogenesis. In addition, small RNA analysis of human, crab-eating macaque and cattle revealed that piRNAs are also expressed in the female germline and closely resemble piRNAs from testis. Here, we thoroughly describe the experimental and computational methods that we applied for the generation, processing and analyses of next generation sequencing (NGS) data associated with our study on Piwi proteins and piRNAs in mammalian oocytes and embryos (Roovers et al., 2015). The complete sequence data is available at NCBI's Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under the accession GSE64942.

Specifications
Organism/cell line/tissue	Human (adult and fetal ovary), marmoset (adult testis and ovary), cattle (adult ovary, oocytes, cumulus and in vitro fertilized 2–4 cell stage embryos)
Sex	Male and female
Sequencer or array type	Illumina HiSeq 2500
Data format	Raw and analyzed
Experimental factors	Normal and untreated tissues/cells
Experimental features	Sequencing of small RNA transcriptomes. Comparison of small RNA libraries with and without a NaIO4 oxidation step (for selected samples).
Consent	Human adult ovary samples were from cancer patients that underwent unilateral oophorectomy for fertility preservation and have signed informed consent. The human fetal material used was from elective abortions and donated for research with informed consent. The research on human material was approved by the Medical Ethical Committee of the Leiden University Medical Center (CME P08.087 and CME 05/03 K/YR).
Sample source location	Leiden, Netherlands (human samples), Göttingen, Germany (macaque samples), Utrecht, Netherlands (bovine samples)

1. Direct link to deposited data

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64942.

2. Experimental design, materials and methods

The Gene Expression Omnibus (GEO) Series GSE64942 comprises 41 high-throughput sequencing datasets (raw and processed) associated with the Roovers et al. study [1]. The following table lists all samples and provides the most relevant data associated with each sample (Table 1).

Table 1.

Key features of GEO Series GSE64942 samples (hsap: Homo sapiens, mfas: Macaca fascicularis, and btau: Bos taurus).

GEO id	Sample name	Species	Tissue or cell type	NaIO4 treatment
GSM1584495	Bovine_GV_a	btau	Oocytes at GV stage	No
GSM1584496	Bovine_GV_b1	btau	Oocytes at GV stage	No
GSM1584497	Bovine_MI_1	btau	Oocytes at MI stage	No
GSM1584498	Bovine_MI_2	btau	Oocytes at MI stage	No
GSM1584499	Bovine_MII_a1	btau	Oocytes at MII stage	No
GSM1584500	Bovine_MII_a2	btau	Oocytes at MII stage	No
GSM1584501	Bovine_MII_b1	btau	Oocytes at MII stage	No
GSM1584502	Bovine_MII_b2	btau	Oocytes at MII stage	No
GSM1584503	Bovine_ovary_1	btau	Adult ovary	No
GSM1584504	Bovine_ovary_2	btau	Adult ovary	No
GSM1584505	Bovine_oxidized_1	btau	Adult ovary	Yes
GSM1584506	Bovine_oxidized_2	btau	Adult ovary	Yes
GSM1584507	Bovine_testis_1	btau	Adult testis	No
GSM1584508	Bovine_testis_2	btau	Adult testis	No
GSM1584509	Bovine_cumulus_MI_1	btau	Cumulus cells from MI oocytes	No
GSM1584510	Bovine_cumulus_MI_2	btau	Cumulus cells from MI oocytes	No
GSM1584511	Bovine_cumulus_MII_1	btau	Cumulus cells from MII oocytes	No
GSM1584512	Bovine_cumulus_MII_2	btau	Cumulus cells from MII oocytes	No
GSM1584513	Bovine_IVF_1	btau	IVF embryos in 2–4 cell stages	No
GSM1584514	Bovine_IVF_2	btau	IVF embryos in 2–4 cell stages	No
GSM1584515	Macaque_ovary_1	mfas	Adult ovary	No
GSM1584516	Macaque_ovary_2	mfas	Adult ovary	No
GSM1584517	Macaque_oxidized_1	mfas	Adult ovary	Yes
GSM1584518	Macaque_oxidized_2	mfas	Adult ovary	Yes
GSM1584519	Macaque_testis_1	mfas	Adult testis	No
GSM1584520	Macaque_testis_2	mfas	Adult testis	No
GSM1584521	Human_ovary_1	hsap	Adult ovary	No
GSM1584522	Human_ovary_2	hsap	Adult ovary	No
GSM1584523	Human_ovary_1_oxidized	hsap	Adult ovary	Yes
GSM1584524	Human_ovary_2_oxidized	hsap	Adult ovary	Yes
GSM1584525	Fetal_1st_1	hsap	Ovary from 1st trimester fetus	No
GSM1584526	Fetal_1st_2	hsap	Ovary from 1st trimester fetus	No
GSM1584527	Fetal_1st_1_oxidized	hsap	Ovary from 1st trimester fetus	Yes
GSM1584528	Fetal_1st_2_oxidized	hsap	Ovary from 1st trimester fetus	Yes
GSM1584529	Fetal_2nd_1	hsap	Ovary from 2nd trimester fetus	No
GSM1584530	Fetal_2nd _2	hsap	Ovary from 2nd trimester fetus	No
GSM1584531	Fetal_2nd _1_oxidized	hsap	Ovary from 2nd trimester fetus	Yes
GSM1584532	Fetal_2nd _2_oxidized	hsap	Ovary from 2nd trimester fetus	Yes
GSM1614231	Bovine_oxidized_2_repeat	btau	Adult ovary	Yes
GSM1614232	Bovine_cumulus_GV_a	btau	Cumulus cells from oocytes at GV stage	No
GSM1614233	Bovine_cumulus_GV_b	btau	Cumulus cells from oocytes at GV stage	No

2.1. RNA isolation and NaIO₄ treatment

RNA was extracted from the ovary and testis tissues by adding 750 μl TRIzol LS reagent (Ambion) and the tissues were mashed and sonicated (3 times 30 s, low power). The oocyte samples of all stages (~ 1000 oocytes per sample) and the IVF samples (450 embryos per sample) were treated similarly, but instead of adding 750 μl TRIzol LS, only 325 μl TRIzol LS was used (for following steps half amounts were used as well). First trimester fetal samples were taken up in 325 μl TRIzol LS and sonicated 3 times 30 s, whereas second trimester ovary samples were first grinded under liquid N₂ and then taken up in 325 μl TRIzol LS. The RNA extraction was performed according to instructions of the manufacturer with small adjustments: Precipitation was performed overnight at − 80 °C with the addition of 1 μl glycoblue (Ambion). Secondly, samples were spun down for 1 h at 16,000 × g at 4 °C before washing with 70% ethanol.

Samples that were sequenced directly without NaIO₄ treatment were taken up in 6 μl RNase free MQ followed by library preparation. Samples that were oxidized were either first enriched for > 200 nt RNA molecules (bovine, macaque and human ovary) using the mirVana kit (Ambion), or were oxidized directly (low input samples: GV oocytes and fetal ovary tissues). In the case of GV oocytes, to control for successful oxidation and subsequent RNA isolation, we added 1/10th concentration of macaque testis RNA, since the testis piRNAs are methylated and the methylation state of the piRNAs from GV stage oocytes was still unknown. For the oxidation, we divided the samples into two and performed NaIO₄ treatment or mock treatment as follows: prepare 5 × borate buffer (148 mM borax, 148 boric acid, adjust pH to 8.6) and prepare freshly 200 mM NaIO₄. Mix 4 μl 5 × borate buffer, 2.5 μl 200 mM NaIO₄ or 2.5 μl MQ (mock samples), RNA, MQ to 20 μl. Leave the reaction at RT for 10 min and add 2 μl glycerol and incubate 1 more minute at RT. Continue with RNA precipitation by adding 2.2 μl 3 M NaAc pH 5.5, 25 μl isopropanol and 1 μl glycoblue. Mix and precipitate overnight at − 80 °C. Spin down samples 1 h at 16,000 × g at 4 °C and wash once with 70% ethanol. Spin 20 more minutes, remove supernatant and take up the pellet in 6 μl RNase free MQ and continue with library preparation.

2.2. Library construction and high-throughput sequencing

Total RNA was subjected to 15% TBE-urea gel for size selection of 15–35 nt. This excised gel fraction was eluted in 0.3 M NaCl for > 16 h and precipitated with 100% isopropanol and Glycoblue for > 1 h at − 20 °C. The precipitated RNA pellet was washed once with 75% ethanol and dissolved in nuclease-free water. The purified RNA fraction was confirmed by Bioanalyzer Small RNA assay (Agilent). Library preparation was based on the NEBNext® Small RNA Library Prep Set for Illumina® (New England Biolabs) with minor modifications. To counteract ligation bias and to remove PCR duplicates, small RNA was first ligated to the 3′ adapter and then the 5′ adapter, both of which contained four random bases at the 5′ and 3′ end, respectively. Adapters with random bases were chemically synthesized by Bioo Scientific. Adapter-ligated RNA was reverse-transcribed and PCR amplified for 14 cycles using index primers. The PCR amplified cDNA construct was checked on the Bioanalyzer (Agilent) using High Sensitivity DNA assay. We performed a size selection of the small RNA library on LabChip XT instrument (PerkinElmer) using the DNA 300 assay kit. All libraries were pooled to obtain 10 nM, which was denatured to 9 or 10 pmol with 5% PhiX spiked-in and sequenced as single-read for 50 cycles on an Illumina MiSeq or HiSeq 2500 instrument in either rapid or high-output mode.

2.3. Computational processing and analysis of sequence data sets

2.3.1. Software deposition

For the processing, filtering and analyses of sequence datasets we applied a set of Perl scripts that are available using the following download link: http://www.smallrnagroup-mainz.de/software/scripts_Roovers-et-al.zip. Many of these Perl scripts are part of the NGS toolbox, which is subject to constant updating and debugging. The NGS toolbox collection as well as the latest sRNAmapper and proTRAC [2] software version including detailed documentations are available at http://www.smallrnagroup-mainz.de/software.html.

2.3.2. Data filtering, mapping and annotation

The 5′ and 3′ RNA adapters used for library construction were tagged with a short stretch of four random nucleotides at their 3′ or 5′ end, respectively. As a result, all 50 bp raw sequence reads comprise the original small RNA sequence flanked by four random nucleotides, and end with the 3′ adapter sequence (Fig. 1).

Fig. 1.

Scheme of sequencing construct. The cloned RNA molecule is flanked by 5′ and 3′ random tags. The read length is 50 nt and therefore, depending on the cloned RNA length, typically ends with an incomplete 3′ adapter sequence.

In order to avoid any PCR-induced bias we clipped adapter sequences and excluded raw sequence reads with identical small RNA sequences and flanking random tags from the sequence data sets as these may constitute cloning products that each represents the same molecule (cliplinker_random.pl). Remaining sequence reads within the size range of 18–34 nt (NGS toolbox: length-filter) were collapsed to non-identical sequences while storing information on read counts for each sequence in the FASTA headers (NGS toolbox: collapse). We then mapped the sequences to the genome of Homo sapiens (assembly: GRCh38, GenBank accession: GCA_000001405.15), Macaca fascicularis (assembly: Macaca_fascicularis_5.0, GenBank accession: GCA_000364345.1) and Bos taurus (assembly: UMD3.1, GenBank accession: 6 GCA_000003055.3) using piRmapper_1.0.pl which is now replaced by the sRNAmapper software that is available at http://www.smallrnagroup-mainz.de/software.html. The mapping process was performed requiring a perfect match for positions 1 to 18 and allowing one internal mismatch in the following part of the sequence. In addition, we allowed up to two non-template 3′ nucleotides in order to map piRNAs that may carry post-transcriptionally modified 3′ ends. Fig. 2 displays the results of the initial processing and mapping procedures.

Fig. 2.

Results of initial processing and mapping.

Sequences that were successfully mapped to the genome were also mapped (in sense orientation) to non-coding (nc) RNA datasets from Ensembl database (release 77), miRBase (release 21 [3]), Genomic tRNA database [4], and Silva rRNA database (release 119, [5]) using SeqMap [6] in order to identify miRNAs or fragments of miRNA precursors, lincRNA (no lincRNAs are described for Bos taurus), miscRNA, rRNA, snoRNA, snRNA and tRNA. In addition, we scanned for low complexity reads discarding reads with ≥ 75% of their sequence being composed of the same 1–4 nt motif (kill_simples.pl. = NGS toolbox: duster). In case of multiple annotation for one sequence, we apportioned the read counts accordingly to the different ncRNA types. Fig. 3 displays the results of this annotation step. Exact values for the information provided in the Fig. 2, Fig. 3 are available in supplementary file 1 (Excel sheet). A set of FASTA-formatted sequence files with sequences sorted according to the described annotation are available for each sample in supplementary file 2 (zip-compressed folder).

Fig. 3.

Results of ncRNA annotation for successfully mapped reads.

2.3.3. Cloning efficiency of different random tags

Besides the possibility to minimize a putative sequence bias induced by PCR amplification, the usage of randomly tagged RNA adapters also ensures a high cloning efficiency due to providing all possible ligation substrates in order to antagonize putative ligation bias resulting from unknown or obscure ligation preferences of the applied RNA ligase. Indeed, we observed a bias for specific random tags in both 5′ and 3′ adapters across the different sequence datasets (Fig. 4).

Fig. 4.

Positional nucleotide composition of 5′ and 3′ random tags.

While all positions of the 5′ random tag are biased towards G, only the first position of the 3′ random tag is biased towards G, whereas the following positions are biased towards T (U). Interestingly, this pattern is essentially the same comparing 3′ uridylated testis and 3′ adenylated oocyte and IVF derived sequences [1]. This suggests equal ligation efficiency and adapter bias for 3′ U and 3′ A RNA populations. However, we cannot exclude a difference in the population composition of cloned RNAs for different random adapters as observed by Jayaprakash and coworkers [7].

2.3.4. Prediction of genomic piRNA clusters

We predicted genomic piRNA clusters with proTRAC version 2.0.4 using the following command line arguments: -clstrand 0.5, -pimin 24, -pimax 31, -distr 1-90, -nr, -nh, and -mmr. The option -mmr (per million mapped reads) is renamed -rpm (reads per million) in proTRAC version 2.0.5 and later. The original proTRAC output is available for download at http://www.smallrnagroup-mainz.de/data/proTRAC_Roovers-et-al.zip as these results are not attached to the original study. Selected datasets from this GEO Series have also been added to the piRNA cluster database that can be accessed under the following URL: http://www.smallrnagroup-mainz.de/piRNAclusterDB.html.

Appendix A. Supplementary data

Supplementary material 1.

Supplementary material 2.