Figure S5 Repression effects of dme-mir-313 twin miRs. (A) Box plot ... A1 ovary. (Rozhkov et al. 2010). GSM280082. Celera sequencing strain ovary. (Czech et ...
Supplementary Text
The issue we wish to address in this supplement is as follows:
“Is it sufficient to study miRNA functionality by using transcriptome data, rather than proteome or translation measurements?”
The answer depends on the exact question being asked. Obviously, for questions explicitly about protein output, the answer would be “no”. However, for many questions about gene regulation and phenotypic consequence, mRNA abundance data are sufficient to provide at least partial and sometimes nearly full answer to the question. This is because mRNA abundance and protein quantity are correlated (Baek, et al. 2008; Guo, et al. 2010; Schwanhausser, et al. 2011). There have been many “high-impact” papers that specifically address this question (Baek, et al. 2008; Eulalio, et al. 2008; Filipowicz, et al. 2008; Selbach, et al. 2008; Hendrickson, et al. 2009; Guo, et al. 2010; Djuranovic, et al. 2012; Eichhorn, et al. 2014).
The major conclusions are as follows. First, mRNA abundance can substantially reflect the interactions between miRNAs and their targets. Huntzinger and Izaurralde (2011) concluded that “target degradation is the predominant mode of regulation by miRNAs in mammalian cell cultures”. Second, in a more recent review (Jonas and Izaurralde 2015), the authors summarized the detailed mechanisms of both translational repression and mRNA destabilization and concluded that “It is now well established that the miRISCs directly recruit the cellular mRNA decay machinery and that mRNA degradation is the dominant effect of miRNAs at steady state.” In short, measuring mRNA abundance is sufficient to reveal a substantial part of miRNA repression. At the protein level, as stated, the sum effects of both mRNA decay and translation inhibition might be even larger.
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References: Baek D, Villen J, Shin C, Camargo FD, Gygi SP, Bartel DP. 2008. The impact of microRNAs on protein output. Nature 455:64-71. Djuranovic S, Nahvi A, Green R. 2012. miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science 336:237-240. Eichhorn SW, Guo H, McGeary SE, Rodriguez-Mias RA, Shin C, Baek D, Hsu SH, Ghoshal K, Villen J, Bartel DP. 2014. mRNA destabilization is the dominant effect of mammalian microRNAs by the time substantial repression ensues. Mol Cell 56:104-115. Eulalio A, Huntzinger E, Izaurralde E. 2008. Getting to the root of miRNA-mediated gene silencing. Cell 132:9-14. Filipowicz W, Bhattacharyya SN, Sonenberg N. 2008. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9:102-114. Guo H, Ingolia NT, Weissman JS, Bartel DP. 2010. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835-840. Hendrickson DG, Hogan DJ, McCullough HL, Myers JW, Herschlag D, Ferrell JE, Brown PO. 2009. Concordant regulation of translation and mRNA abundance for hundreds of targets of a human microRNA. PLoS Biol 7:e1000238. Huntzinger E, Izaurralde E. 2011. Gene silencing by microRNAs: contributions of translational repression and mRNA decay. Nat Rev Genet 12:99-110. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. 2011. Global quantification of mammalian gene expression control. Nature 473:337-342. Selbach M, Schwanhausser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N. 2008. Widespread changes in protein synthesis induced by microRNAs. Nature 455:58-63.
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Supplementary Figures A
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miRBase high confidence miRNAs (149 precursors, 290 matures)
remove duplicated miRNAs sharing mature sequences
remove lowly expressed miRNAs (average RPM 30% of the total read counts of the precursor in two libraries of a given tissue
It's a twin-miR if it meets the criterion above in any one of the tissues.
It's a solo-miR if it is not defined as twinmiR in any one of the tissues.
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Supplementary Figure Legends Figure S1 miRNA hairpin structure and the workflow of solo and twin miRNAs identification. (A) The hairpin structure of dme-mir-10. Two mature miRNAs originate from a single precursor. (B) The workflow of solo and twin miRNAs identification in D. melanogaster. Figure S2 Principal component analysis of the miR-5p expression ratio Principal component analysis of the ratio of 5p/(3p+5p) in RPM for 123 miRNAs from 45 small RNA libraries of D. melanogaster. The first and second principal components explain 84.6% and 3.2% of the total variance, respectively. Figure S3 Dynamic arm expression of miRNAs in different libraries. (A) dme-mir-10, a typical twin-miR in D. melanogaster. (B) Solo-miR dme-let-7, 5p arm-specific expression. (C) Solo-miR dme-bantam, 3p arm-specific expression. The vertical axis shows RPM proportion of each arm. Black bars represent the 5p arm, white bars represent the 3p arm, and dashed red lines mark the 50% fraction. Figure S4 Example estimation of miRNA sequence conservation The mir-8 precursor is used as an example here. The precursor is divided into 4 parts - seed regions (2-8nt, shaded in yellow) and non-seed regions (the remaining 14nt, shaded in green) from both the major and minor miRs. dme-miR-8-5p and dme-miR-8-3p as annotated by miRBase are underlined. Note that only the first 21nts in each mature miR were used in the conservation analysis, where the number of substitutions between D. melanogaster and D. pseudoobscura were counted. dme: D. melanogaster, dps: D. pseudoobscura. Figure S5 Repression effects of dme-mir-313 twin miRs (A) Box plot shows expression fold change (log2 scaled) of miRNA targets and non-targets between the miR310 cluster knockout and control flies in testes. Outliers are not shown. (B) dme-miR-310/311/312/312 cluster member mature sequences. Seed regions are shaded in yellow. Figure S6 Predicted target number comparison between solo- and twin-miRs in D. melanogaster Major and minor miRs of 74 solo- and 31 twin-miRs were used for target prediction using microT-CDS (Reczko et al. 2012; Paraskevopoulou et al. 2013). The boxplot shows the number of target genes with a threshold of 0.7 (default) for the major miR of solo-miRs (A), the minor miR of solo-miRs (A*), the major miR of twin-miRs (B), and the minor miR of twin-miRs (B*). The zoom-in layout is shown to allow for a better comparison, with an inset on the top for the global distribution. Statistical significance was determined by two-sided Mann-Whitney U test. **, P < 0.01. 9
Supplementary Tables Table S1 Small RNA libraries used in this study GEO accession genetic background tissue citations GSM1000609 MG derived ovary (Grentzinger et al. 2014) GSM1000610 MG derived ovary (Grentzinger et al. 2014) GSM1000611 MG derived ovary (Grentzinger et al. 2014) GSM327620 w[1118] ovary (Brennecke et al. 2008) GSM327621 wK ovary (Brennecke et al. 2008) GSM327622 Harwich ovary (Brennecke et al. 2008) GSM327623 NA ovary (Brennecke et al. 2008) GSM327624 LK ovary (Brennecke et al. 2008) GSM548583 A2 ovary (Rozhkov et al. 2010) GSM548585 yw67c23(2) ovary (Rozhkov et al. 2010) GSM548592 hs-Penelope ovary (Rozhkov et al. 2010) GSM548593 A1 ovary (Rozhkov et al. 2010) GSM280082 Celera sequencing strain ovary (Czech et al. 2008) GSM2562977 Canton S ovary this study GSM2562974 Z56 ovary this study GSM466487 Oregon R head (Ghildiyal et al. 2010) GSM180328 head (Ruby et al. 2007) GSM1278631 w[1118] head (Abe et al. 2014) GSM1278633 w[1118] head (Abe et al. 2014) GSM286601 head (Chung et al. 2008) GSM322543 Canton S head (modENCODE) (Contrino et al. 2012) GSM322533 Canton S head (modENCODE) (Contrino et al. 2012) GSM246084 head (Lu et al. 2008) GSM240749 head (Chung et al. 2008) GSM1373331 Oregon R head (Fagegaltier et al. 2014) GSM1373332 Oregon R head (Fagegaltier et al. 2014) GSM278695 Oregon R head (Ghildiyal et al. 2008) GSM278706 Oregon R head (Ghildiyal et al. 2008) GSM1373333 Oregon R body (Fagegaltier et al. 2014) GSM1373334 Oregon R body (Fagegaltier et al. 2014) GSM286602 body (Chung et al. 2008) GSM286603 body (Chung et al. 2008) GSM399107 body (modENCODE) (Contrino et al. 2012) GSM399106 body (modENCODE) (Contrino et al. 2012) GSM180329 body (Ruby et al. 2007) GSM2562976 Canton S body this study GSM909277 Oregon R testis (Toledano et al. 2012) GSM909278 Oregon R testis (Toledano et al. 2012) GSM280085 Oregon R testis (Czech et al. 2008) GSM548591 hs-Penelope testis (Rozhkov et al. 2010) GSM548589 A1 testis (Rozhkov et al. 2010) 10
GSM548584 GSM548582 GSM2562978 GSM2562975
yw67c23(2) A2 Canton S Z56
testis testis testis testis
(Rozhkov et al. 2010) (Rozhkov et al. 2010) this study this study
Reference: Abe M, Naqvi A, Hendriks GJ, Feltzin V, Zhu Y, Grigoriev A, Bonini NM. 2014. Impact of age-associated increase in 2'-O-methylation of miRNAs on aging and neurodegeneration in Drosophila. Genes & development 28(1): 4457. Brennecke J, Malone CD, Aravin AA, Sachidanandam R, Stark A, Hannon GJ. 2008. An epigenetic role for maternally inherited piRNAs in transposon silencing. Science 322(5906): 1387-1392. Chung WJ, Okamura K, Martin R, Lai EC. 2008. Endogenous RNA interference provides a somatic defense against Drosophila transposons. Current biology : CB 18(11): 795-802. Contrino S, Smith RN, Butano D, Carr A, Hu F, Lyne R, Rutherford K, Kalderimis A, Sullivan J, Carbon S et al. 2012. modMine: flexible access to modENCODE data. Nucleic Acids Res 40(Database issue): D1082-1088. Czech B, Malone CD, Zhou R, Stark A, Schlingeheyde C, Dus M, Perrimon N, Kellis M, Wohlschlegel JA, Sachidanandam R et al. 2008. An endogenous small interfering RNA pathway in Drosophila. Nature 453(7196): 798-802. Fagegaltier D, Konig A, Gordon A, Lai EC, Gingeras TR, Hannon GJ, Shcherbata HR. 2014. A genome-wide survey of sexually dimorphic expression of Drosophila miRNAs identifies the steroid hormone-induced miRNA let-7 as a regulator of sexual identity. Genetics 198(2): 647-668. Ghildiyal M, Seitz H, Horwich MD, Li C, Du T, Lee S, Xu J, Kittler EL, Zapp ML, Weng Z et al. 2008. Endogenous siRNAs derived from transposons and mRNAs in Drosophila somatic cells. Science 320(5879): 1077-1081. Ghildiyal M, Xu J, Seitz H, Weng Z, Zamore PD. 2010. Sorting of Drosophila small silencing RNAs partitions microRNA* strands into the RNA interference pathway. RNA 16(1): 43-56. Grentzinger T, Armenise C, Pelisson A, Brun C, Mugat B, Chambeyron S. 2014. A user-friendly chromatographic method to purify small regulatory RNAs. Methods 67(1): 91-101. Lu J, Shen Y, Wu Q, Kumar S, He B, Shi S, Carthew RW, Wang SM, Wu CI. 2008. The birth and death of microRNA genes in Drosophila. Nature genetics 40(3): 351-355. Rozhkov NV, Aravin AA, Zelentsova ES, Schostak NG, Sachidanandam R, McCombie WR, Hannon GJ, Evgen'ev MB. 2010. Small RNA-based silencing strategies for transposons in the process of invading Drosophila species. RNA 16(8): 1634-1645. Ruby JG, Stark A, Johnston WK, Kellis M, Bartel DP, Lai EC. 2007. Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome research 17(12): 1850-1864. Toledano H, D'Alterio C, Czech B, Levine E, Jones DL. 2012. The let-7-Imp axis regulates ageing of the Drosophila testis stem-cell niche. Nature 485(7400): 605-610.
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Table S2 RNA-seq libraries used in this study GEO accession GSM2562970 GSM2562971 GSM2562972 GSM2562973
sample miR-310s knock out miR-310s knock out control control
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tissue testis testis testis testis
description biological replicate 1 biological replicate 2 biological replicate 1 biological replicate 2
