Biochemical principles of miRNA targeting in flies

Domain: biology
Published: 2026-01-20
Editorial outcome: major_revision

Abstract
MicroRNAs direct Argonaute proteins to repress complementary target mRNAs via mRNA degradation or translational inhibition. While mammalian miRNA targeting has been well studied, the principles by which Drosophila miRNAs bind their target RNAs remain to be fully characterized. Here, we use RNA Bind-n-Seq to systematically identify binding sites and measure their affinities for five highly expressed Drosophila miRNAs. Our results reveal a narrower range of binding site diversity in flies compared to mammals, with fly miRNAs favoring canonical seed-matched sites and exhibiting limited tolerance for imperfections within these sites. We also identified non-canonical site types, including nucleation-bulged and 3′-only sites, whose binding affinities are comparable to canonical sites. These findings establish a foundation for future computational models of Drosophila miRNA targeting, enabling predictions of regulatory outcomes in response to cellular signals, and advancing our understanding of miRNA-mediated regulation in flies.

Introduction
In plants and animals, ~22-nt microRNAs (miRNAs) guide AGO-clade Argonaute proteins to repress partially complementary mRNA targets by accelerating their degradation 1–3 and inhibiting their translation 4–8, with recent mechanistic insights indicating that these effects may occur in parallel rather than as a sequential process 9. Many loss-of-function miRNA mutants display developmental, physiological, and behavioral defects 10,11.

In animals, the miRNA seed (miRNA nucleotide positions g2–g7) is the primary determinant for targeting, and pairing to the miRNA seed often suffices for target binding and repression 6,12–16. Additional pairing to the miRNA 3′ end (centered around g13–g16) can reinforce recognition of seed-matched targets and compensate for weak or imperfect seed pairing 16–20. However, such 3′-supplementary or compensatory sites appear to be rare 16,21,22. Mammalian miRNA binding tolerates single wobble, bulged or mismatched nucleotides at specific positions within the seed 23–25. Adding to this complexity, some miRNAs bind “central sites” sites bearing extensive complementarity to the miRNA center (nt 4–14 or 5–15) or 3′ nucleotides (“3′-only sites”) with affinities comparable to canonical seed-matched sites 24–26.

Partial complementarity of miRNA targets, non-canonical binding modes, local sequence-dependent interactions, and the generally modest repression of targets make prediction of animal miRNA regulatory targets difficult, despite two decades of research 24,27. The most successful computational approaches typically predict miRNA binding rather than biologically important regulation 11,24,27. In contrast to the large number of targets uncovered by cell culture or computational experiments 18,21,22,28,29, genetic experiments in Caenorhabditis elegans, Drosophila melanogaster, and mice suggest that miRNAs can act as ‘master regulators’ of specific biological processes, with repression of as few as a single mRNA explaining the primary function of the miRNA. In Caenorhabditis elegans, lin-4 represses lin-14 and is essential for transition between larval stages 30. In Drosophila melanogaster, bantam represses the pro-apoptotic gene head involution defective (hid), regulating tissue growth in post-embryonic development 31,32. In circadian cells, bantam silences a core circadian clock gene (clock) affecting circadian timekeeping 33. Transgenic flies expressing clock without 3′ UTR bantam sites are arrhythmic; introduction of a wild-type copy of clock rescues this rhythm defect. In mice, miR-9 represses the orphan nuclear receptor tailless homolog (TLX) and induces neuronal differentiation 34. Neural stem cells expressing TLX lacking miR-9 binding sites recover proliferative capacity and are not able to differentiate into astrocytes.

Much of our understanding of miRNA targeting derives from high-throughput biochemical analyses of human and mouse miRNAs 23–25,35, and these insights have improved miRNA target prediction 24. Here, we use RNA Bind-n-Seq (RBNS) to identify binding sites and obtain high-throughput affinity measurements for five abundant Drosophila miRNAs loaded into Ago1. Analyses of these data reveal that fly miRNAs bind a more restricted number of site types compared to mammals. Our experiments identify the sequence determinants for fly miRNA binding, which promise to spur the development of computational tools to accurately predict miRNA-mediated silencing in flies.

Results
Absolute equilibrium dissociation constants can be estimated from RBNS data

RNA bind-n-seq (RBNS) is a high-throughput sequencing method developed to study the affinity and specificity of RNA-binding proteins 36. When combined with maximum-likelihood analysis, RBNS recapitulates equilibrium dissociation constants (K_D) for simulated data 25, and miRNAs 25 and piRNAs 37 whose affinities have been determined by ensemble or single-molecule methods. To further test the validity of this approach, we applied the maximum-likelihood strategy to published RBNS data for human PUM 38 to obtain K_D = 0.12 ± 0.01 nM for the PUM consensus binding site, UGUAUAUA (Supplementary Data 1). This result agrees well with the reported K_D values for human PUM2, 0.17 nM 39, fly Pumilio, 0.42 ± 0.07 nM 40, and Saccharomyces cerevisiae PUF4, 0.88 nM 41.

Canonical binding can be predicted by free energy of base-pairing

miRNAs display sequence-specific differences in binding their canonical sites: those with strong predicted free energy of site pairing bind their target sites with higher affinity than miRNAs with weak seed-pairing 23–25. Nevertheless, the difference in binding affinities of mammalian miRNAs is less than might have been expected. To study the energetics of canonical binding of fly miRNAs, we loaded recombinant Ago1 with one of five different miRNAs—let-7, bantam, miR-184, miR-11, and miR-124—chosen because they play key roles in Drosophila development and homeostasis 15,16,32,42–50 and span a range of GC content within their seeds and 3′ regions (Figs. 1a and S1). In agreement with previous studies in animals, all five miRNAs bound 8mer sites (targets with complementarity to positions g2–g8 and an adenosine opposite miRNA g1, i.e., t1A) with the highest affinity: K_D = 4.54 pM (95% CI = [4.42, 4.64]) for let-7 to K_D = 29.9 pM (95% CI = [29.2, 30.6]) for bantam (Fig. 1b and Supplementary Data 1). The hierarchy of other canonical sites varied with the miRNA sequence. For let-7, miR-184 and miR-124, 7mer-m8 displayed a ~4-fold higher affinity than 7mer-A1; but for bantam and miR-11, the dissociation constants of 7mer-m8 and 7mer-A1 were almost identical. The ranking of 6mer, 6mer-m8 and 6mer-A1 was also miRNA-specific: K_D, 6mer < K_D, 6mer-m8 < K_D, 6mer-A1 for miR-184 but K_D, 6mer < K_D, 6mer-A1 < K_D, 6mer-m8 for let-7; for miR-11, the affinities of these three site types were indistinguishable. These results mirror those for mammalian miRNAs 24,25. Moreover, direct binding measurements found no substantive difference in affinity between a seed-matching (K_D = 5 ± 3 pM) and a fully complementary target (K_D = 2 ± 1 pM; Fig. 1c), suggesting that seed complementarity dominates Drosophila Ago1 target binding, just like mammalian AGO2 23,24,51–53 (Table 1).

Ago1 does not tolerate G:U pairs within the seed

Drosophila miRNAs have been proposed to repress mRNAs containing seed-matched binding sites interrupted by G:U wobble-pairs 14,32. However, these mRNAs also contain canonical binding sites free of G:U wobble pairs. Our RBNS experiments do not support the view that fly Ago1 tolerates G:U wobble pairs in the seed sequence. We measured the binding affinity of 8mer and 7mer-m8 binding sites containing one, two or three G:U wobble-pairs (Figs. 2a and S2a). A single G:U base-pair at positions t2–t7 reduced the binding affinity of 8mer and 7mer-m8 sites by at least 6-fold. Binding sites with G:U wobble-pairing at the last position of a site type had dissociation constants comparable to the corresponding shorter site type: K_D, let-7, 8mer-w8 = 15.4 pM vs. K_D, let-7, 7mer-A1 = 32.4 pM, K_D, miR-11, 8mer-w8 = 61.2 pM vs. K_D, miR-11, 7mer-A1 = 91.7 pM, K_D, let-7, 7mer-m8, w2 = 278 pM vs. K_D, let-7, 6mer-m8 = 439 pM, K_D, miR-184, 7mer-m8, w2 = 154 pM vs. K_D, miR-184, 6mer-m8 = 409 pM, and K_D, bantam, 7mer-m8, w2 = 866 pM vs. K_D, bantam, 6mer-m8 = 1356 pM. Sites with >1 G:U had K_D values comparable to RNA containing no identifiable sites (Fig. S2b). That is, binding to targets with >1 G:U is indistinguishable from background (Figs. 2a and S2a). We conclude that G:U wobbles within the seed region are detrimental for Ago1 binding.

Additional 3′ pairing compensates for weak seed complementary

Validated targets of bantam, miR-2a, and miR-7 contain compensatory sites: 8mer sites with a single G:U pair compensated by a strong pairing to the miRNA 3′ end conferred repression in vivo 14,16. RBNS is limited to studies of motifs ≤ 12-nt long, as longer motifs are poorly represented in the sequencing data 24,25. We designed a let-7 target RNA fully complementary to let-7 but for a single G:U at g4, the position at which a wobble pair is most detrimental to seed binding (Fig. 2b, middle panel), and measured its binding affinity in a competition assay with a 32P-radiolabeled target complementary to the entirety of let-7. The target bearing a single G:U pair displayed nearly the same affinity as the fully complementary target. In contrast, introduction of a second G:U pair at position g5 decreased its binding affinity 120-fold (Fig. 2b, right panel). We obtained similar results for binding sites bearing mismatched nucleotides instead of G:U pairs (Fig. 2c). Together, our data indicate that compensation by 3ʹ pairing can rescue sites with a single G:U or mismatch in the miRNA seed.

The effect of target mismatches differs across the seed

The seed region of miRNAs behaves as an RNA helix—mismatches in the center have the greatest effect, because they disrupt coaxial stacking more than mismatches at the extremities 23,25,51. Consistent with this idea, single mismatches to seed positions g3, g4, and g5 led to the largest reduction in binding for Drosophila Ago1 (Figs. 3a and S3a). At these positions mismatched nucleotides A and G are the least tolerated in half of the occurrences, likely because their purine rings are larger than pyrimidines and thereby cause more steric hindrance.

Interestingly, miR-184-guided Ago1 displays an unusual behavior in binding mismatched targets. Like let-7, bantam, and miR-11, it binds 8mer targets with single mismatches at positions t4 and t5 less tightly than fully matched sites (17–69-fold increase in K_D values). But its affinities for t4- and t5-mismatched targets were ≤ 200 pM, unexpectedly high compared to 8mer sites bearing a single mismatch at t3 or t6 (Fig. 3a). As expected, sites with t4 or t5 mismatched nucleotides have higher predicted ΔG values than sites with t3 or t6 mismatches (Fig. 3b) and are not sequestered in stable secondary structures occluding their binding (Fig. 3c). Our data suggest that Ago1 may bind sites containing one single central mismatch but identifying universal rules for predicting which miRNAs can bind 8mer-x4R sites with unexpectedly high affinity will require high-throughput target binding measurements for a larger set of miRNAs.

Nucleation bulges can confer binding affinity similar to that of canonical sites

In mammals, target insertions and deletions in the seed increase K_D 23,25,54. An exception is nucleation-bulge sites identified by high-throughput crosslinking and immunoprecipitation in mouse brain 55. These 8mer sites consist of seven nucleotides paired to miRNA positions g2–g8 and one nucleotide opposing position g6 protruding as a bulge but sharing potential complementarity to miRNA position g6. Some mammalian miRNAs effectively bind nucleation-bulge sites in vitro 24,25 and repress nucleation-bulge sites in cultured cells 55. Like mammalian AGO2, fly Ago1 does not tolerate indels within the seed: small target deletions or 1-nt bulges reduced binding affinity by ~50–1000-fold (Figs. 4 and S3b, c). Nevertheless, nucleation-bulge sites displayed binding affinities similar to those of canonical sites: K_D, let-7, 8mer-b5.6A = 120 pM vs. K_D, let-7, 6mer-A1 = 92 pM, K_D, miR-11, 8mer-b5.6G = 107 pM vs. K_D, miR-11, 6mer-A1 = 70 pM, K_D, miR-184, 8mer-b5.6C = 38 pM vs. K_D, miR-184, 7mer-A1 = 37 pM, and K_D, miR-124, 8mer-b5.6G = 75 pM vs. K_D, miR-124, 7mer-A1 = 242 pM (Figs. 4b and S3c). Moreover, the nucleation-bulge sites across the five miRNAs tested bind at greater affinity than the corresponding 6mer-A1 sites with similar flanking nucleotide composition (Fig. S3d). Like in mammals, nucleation bulges may constitute an alternative mode of Drosophila miRNA target recognition that follows a pivot-pairing rule.

Repertoire of fly Ago1 target sites

In an RBNS experiment, Ago1 is incubated with a pool of 10^12 distinct RNA sequences. Therefore, in addition to measuring binding affinities of specific sites of interest, RBNS enables a de novo search for sites of productive binding 24,25,36,56. As expected, this approach identified canonical 8mer, 7mer-m8, 7mer-A1, 6mer, and 6mer-m8 binding sites (Fig. 5a). For let-7, it also identified short seed-matched sites 6mer-A1, 5mer-A1, and 5mer-m2.6, as well as 8mer-w8 and 7mer-w8—sites bearing a G:U wobble at the distal t8 position. Because fly Ago1 does not tolerate G:U pairing in the seed, these 8mer-w8 and 7mer-w8 likely correspond to 7mer-A1 and 6mer sites with favorable, AU-rich flanking contexts (see below). Motifs complementarity to positions g13–g16 were also detected. These likely correspond to 3′ supplemental pairing for weak seeds that are too variable to be detected by our analyses: e.g., 5-mer sites separated from the supplemental pairing by loops of various sizes. The repertoire of binding sites for miR-11 resembled that of let-7, except that two additional short seed-matched sites, 6mer-m4.9 and 5mer-m4.8, could be identified. Sites with 3′ supplemental pairing were not enriched above background in miR-124, miR-184 and bantam datasets. miR-184 sites included canonical, short seed-matched and 8mer-x4C—an 8mer site bearing a mismatched t4 nucleotide with K_D ~ 130 pM (Fig. 3a). Overrepresented sites bound by bantam included only canonical and 5mer-m2.6 sites. Our de novo site discovery algorithm identified only three binding sites for fly miR-124–loaded Ago1: the canonical 8mer, 7mer-m8, and 7mer-A1 (Fig. 5a). Remarkably, AGO2 guided by human miR-124, whose sequence is identical to that in flies, binds ~15 site types within a 10-fold range of the 8mer affinity, spanning canonical and noncanonical interactions, including seed-matched sites with wobble pairs, mismatches, bulges, and 3′-only sites 24.

Considering these differences in site recognition, we examined the ΔG penalties associated with individual seed-region imperfections across the five fly miRNAs examined in this study and the mammalian miRNAs from previously published AGO2 RBNS datasets 24,25. The binding affinity for a site is predicted to be more stable at 25°C than 37°C. Contrary to expectation, for miR-124 and let-7, which are shared between the fly and mammalian datasets, binding to a perfectly matched g2–g8 target was more stable for mammalian AGO2 at 37°C than for fly Ago1 at 25°C (Fig. S4). This observation suggests that the reduced tolerance for mismatches of fly Ago1 does not arise because mismatches are inherently more destabilizing. Instead, the overall dynamic range of binding starts with weaker binding. We also note that our binding experiments were performed using 3.5 mM Mg2+. As expected, when the experiments were performed using 0.89 mM Mg2+, the concentration used previously to study binding of human AGO2 (ref. 24), we recovered fewer site-types (Fig. S5a). Together, our data suggest that fly Ago1 has lower tolerance for pairing imperfection because it binds perfect seed matches less tightly than AGO2.

Fly Ago1 cleaves central sites and binds some 3′-only sites

Unlike high-throughput analysis of mammalian AGO2 24,25, our RBNS experiments with fly Ago1 failed to detect central or 3′-only sites. Mammalian AGO2 can cleave targets with centered sites, sites with 11–12 nucleotides complementarity to the center of the miRNA 26. Drosophila Ago1 retains the ability to catalyze endonucleolytic cleavage of extensively matched target RNAs, and like other Argonaute proteins, cleaves the phosphodiester bond linking target nucleotides t10 and t11 57–59. In theory, efficient cleavage of targets with centered sites might prevent their recovery in RBNS data. To test this idea, we performed in vitro cleavage assays with synthetic RNA targets containing two distinct 11-nt centered sites complementary to miRNA nucleotides g4–g14 (11mer-m4.14) or g5–g15 (11mer-m5.15). Ago1•let-7 cleaved both centered sites at levels comparable to those observed with a fully complementary RNA (Figs. 5b and S5c, d). Therefore, cleavage of central sites likely prevents their recovery in RBNS libraries, making RBNS unsuitable for measuring their binding affinities. We note that mammalian AGO2-catalyzed mRNA cleavage of 11–12-nt-long centered sites requires Mg2+ > 2 mM 26; in animals, the intracellular Mg2+ concentration is < 1 mM 60.

By contrast, some 3′-only sites were enriched in RBNS datasets but did not pass our criteria for significance (see “Materials and Methods”). Therefore, we directly measured the affinity of sites with 10-nt segments of perfect complementarity to the miRNA sequence, scanning from position g9 to the 3′ end of the miRNA (Fig. S5b). For bantam, miR-184, miR-12, 4 and miR-11, 10-mers that base paired to the guide 3′ end bound with comparable or higher affinity than the canonical 6mer site. Except for the bantam 10mer-m14.23 site, motifs with more distal pairing had higher affinities than more seed-proximal sites: K_D, 10mer-m9.18 = 587 pM, K_D, 10mer-m10.19 = 752 pM, K_D, 10mer-m11.20 = 571 pM, K_D, 10mer-m12.21 = 217 pM, and K_D, 10mer-m13.22 = 71 pM vs. K_D, 6mer = 112 pM. For let-7, none of the 10-mers conferred stronger binding than a canonical 6mer.

Influence of flanking dinucleotide sequences

The nucleotide composition of sequences immediately flanking miRNA sites significantly influences mRNA repression in mammalian cells 18,21,23,24,61. Local sequence contexts similarly influences Ago1 binding affinity. We divided the let-7 8mer site into 4^4 = 256 different 12-nt sites according to the dinucleotide sequences immediately flanking the 5′ and the 3′ ends of the 8mer site and measured their K_D values by RBNS. We used RNAplfold 62 to estimate the accessibility of each site by computing the probability of its being unpaired at thermodynamic equilibrium. The predicted accessibility scores were negatively correlated to measured binding affinities (Pearson’s r = –0.352, p-value < 0.001; Fig. 6). For the 256 distinct flanking contexts, the K_D values spanned a ~20-fold range, with higher binding affinities corresponding to greater AU content in the flanking dinucleotides.

We repeated our analysis with 8mer binding sites for bantam, miR-184, and miR-11. As we observed for let-7, 8mer binding sites with low accessibility scores had low binding affinities for all three miRNAs (Fig. 6). Finally, for every miRNA tested, we divided 7mer-m8, 7mer-A1, 6mer, 6mer-m8, and 6mer-A1 binding sites into 256 different sites according to the dinucleotide sequences immediately flanking the 5′ and the 3′ ends of each site type and measured their K_D values by RBNS. The binding affinities for nearly all site types correlated with accessibility scores. The exceptions were let-7, bantam, miR-11, miR-124 binding to 6mer-A1 sites, miR-11 and miR-124 binding to 7mer-A1 sites, and miR-124 binding to 6mer sites. These data suggest that Drosophila Ago1, like mammalian AGO2 23,24, competes with local RNA secondary structures for site binding.

Methods
RNA

Ago1 was loaded with chemically synthesized (Sigma) guide and the passenger strands list in Table S1; each synthetic RNA was gel-purified before use. The siRNA duplexes were annealed for 1 min at 90 °C, 1 h at 37 °C and 10 min at room temperature (20–25 °C) in 30 mM HEPES-KOH, pH 7.9, 100 mM potassium acetate, 2 mM DTT using a 1:1.25 molar ratio of guide-to-passenger.

Libraries of RNA oligonucleotides, each containing 20 central random nucleotides (Table S1), were synthesized (IDT) with an equal ratio of bases for each random position, 5′ 32P-labeled and gel-purified. DNA “blocker” oligonucleotides complementary to the common sequences present in each RNA molecule were synthesized (IDT) and annealed to the RNA library in 30 mM HEPES-KOH, pH 7.5, 120 mM potassium acetate, 3.5 mM magnesium acetate using a 1:1.2 molar ratio of RNA pool to DNA blockers; 5′-end and 3′-end blocking cDNA oligonucleotides #1 (Table S1) were used in RBNS for Ago1 loaded with let-7, bantam; 5′-end and 3′-end blocking cDNA oligonucleotides #2 were used in for Ago1 loaded with miR-184 (Table S1).

Cell culture

Drosophila Schneider 2 (S2) cells (Gibco, Catalog No. R690-07) were cultured in Drosophila Schneider’s media (Gibco) supplemented with 10% FBS. Cells were maintained in a humidified incubator at 27 °C and examined regularly to ensure absence of mycoplasma contamination.

Generation of FLAG-Ago1 expressing cells

Ago1 cDNA was amplified by RT-PCR from total RNA extracted from w1118 fly heads (w1118 flies were obtained from the KYOTO Drosophila Stock Center, Kyoto Institute of Technology). The Ago1 coding sequence was cloned into pMT expression vector (Invitrogen), fusing Ago1 in-frame with an N-terminal FLAG tag. The resulting plasmid is available from Addgene (#229236). S2 cells (25 million, 70% confluent) were transfected with 10 µg pMT-FLAG-Ago1 plasmid using 20 µl TransIT-Insect Transfection Reagent (Mirus Bio) according to manufacturer’s instructions. After 18 h transfection, copper sulfate was directly added to the culture medium to a final concentration of 500 μM, and the cells were incubated for 30 h.

RISC purification

FLAG-Ago1 expressing S2 cells were harvested; cell pellets were flash-frozen and stored at −80 °C. Cell extract was essentially prepared as described 64. Briefly, the cell pellet was washed three times in ice-cold PBS and once in Buffer A (10 mM HEPES-KOH, pH 7.9, 10 mM potassium acetate, 1.5 mM magnesium acetate, 0.01% (w/v) CHAPS, 0.5 mM DTT, 1 mM AEBSF, hydrochloride, 0.3 μM Aprotinin, 40 μM Bestatin, hydrochloride, 10 μM E-64, 10 μM Leupeptin hemisulfate). Then, the pellet was resuspended in twice its volume with buffer A and incubated on ice for 20 min to allow the cells to swell. The cells were subsequently lysed with a Dounce homogenizer and a tight pestle (B type; 40 strokes) on ice. The homogenate was centrifuged at 2000 × g to remove nuclei and cell membranes. Next, 0.11 volumes (that of the clarified supernatant from the low-speed centrifugation) of Buffer B (300 mM HEPES-KOH, pH 7.9, 1.4 M potassium acetate, 30 mM magnesium acetate, 0.01% (w/v) CHAPS, 0.5 mM DTT, 1 mM AEBSF, hydrochloride, 0.3 μM Aprotinin, 40 μM Bestatin, hydrochloride, 10 μM E-64, 10 μM Leupeptin, hemisulfate) was added, followed by centrifugation at 100,000 × g for 20 min at 4 °C and the supernatant collected as the S100 extract. Finally, 0.32 volumes ice-cold Buffer C (30 mM HEPES-KOH pH 7.9, 120 mM potassium acetate, 3.5 mM magnesium acetate, 0.01% (w/v) CHAPS, 2 mM DTT, 1 mM AEBSF, hydrochloride, 0.3 μM Aprotinin, 40 μM Bestatin, hydrochloride, 10 μM E-64, 10 μM Leupeptin, hemisulfate) was added to the S100 to achieve a 20% (w/v) final glycerol concentration, followed by gentle inversion to mix. The S100 was aliquoted, frozen in liquid nitrogen, and stored at ‒80 °C.

To assemble RISC, 3 ml S100 (~40 mg total protein) was incubated with 150 nM miRNA duplex for 1.5 h at 25 °C in 30 mM HEPES-KOH pH 7.9, 120 mM potassium acetate, 3 mM magnesium acetate, 2 mM DTT, 1 mM ATP, 25 mM creatine phosphate, 30 μg/ml creatine kinase.

To capture RISC, 30 μl anti-FLAG M2 paramagnetic beads (Sigma) were used per ml of post-assembled extract, washed three times with 30 mM HEPES-KOH pH 7.9, 120 mM potassium acetate, 3 mM magnesium acetate, 0.05% (w/v) CHAPS, 2 mM DTT and incubated with assembled RISC with rotation at 4 °C overnight. Beads were washed five times with wash buffer (30 mM HEPES-KOH, pH 7.9, 120 mM potassium acetate, 3 mM magnesium acetate, 2 mM DTT, 0.05% (w/v) CHAPS), and RISC was eluted from the beads with 100 ng/ml 3X FLAG peptide (Sigma) in 300 µl wash buffer for 1 h at room temperature. Elution was repeated two more times, and the three eluates combined. RISC was further purified via the by sequence affinity capture 65. Briefly, RISC was incubated with gentle rotation for 1 h at room temperature with 800 pM biotinylated, 2′-O-methyl capture oligonucleotide (Table S1) linked to streptavidin paramagnetic beads (Dynabeads MyOne Streptavidin T1, Life Technologies). RISC was eluted with 2 nmol biotinylated competitor oligonucleotide (Table S1) in 80 µl buffer composed of 30 mM HEPES-KOH pH 7.9, 1 M potassium acetate, 3.5 mM magnesium acetate, 0.05% (w/v) CHAPS, 20% (w/v) glycerol, 2 mM DTT for 2 h at room temperature. Excess competitor oligonucleotide was removed by incubating the eluate with 3.5 mg streptavidin paramagnetic beads (Dynabeads MyOne Streptavidin T1, Life Technologies) for 15 min at room temperature. Finally, RISC was dialyzed at 4 °C against three changes (3 h each) of a 3000-fold excess of storage buffer (30 mM HEPES-KOH pH 7.9, 120 mM potassium acetate, 3.5 mM magnesium acetate, 0.05% (w/v) CHAPS, 2 mM DTT, 20% (w/v) glycerol). RISC was aliquoted, frozen in liquid nitrogen, and stored at ‒80 °C. For competition experiments (Fig. 2b, c), RISC was assembled in Drosophila embryo lysate [w[*]; AGO2[414]); Stock Number (109027); Collection (Kyoto Stock Center)] from 0 to 8-h embryos prepared as described 66; assembled RISC was purified as described above 65.

Quantification of purified RISC

Total concentration of RISC was estimated by measuring concentration of associated miRNA using Northern blotting as described 67. Briefly, miRNA guide standards and RISC were first resolved on a denaturing 15% polyacrylamide gel, followed by transfer to Hybond-XL (Cytiva) nylon membrane by semi-dry transfer at 20 V for 1 h. Next, crosslinking was performed with 0.16 M 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide in 0.13 M 1-methylimidazole, pH 8.0, at 60 °C for 1 h. The crosslinked membrane was pre-hybridized in Church buffer (1% (w/v) UltraPure BSA, 1 mM EDTA, 0.5 M phosphate buffer, and 7% (w/v) SDS) at 37 °C for 1 h. Next, 25 pmol 5′ 32P-radiolabeled RNA probe (Table S1) in Church buffer was added to the membrane and allowed to hybridize overnight at 37 °C, followed by two washes with 1× SSC containing 0.1% (w/v) SDS for 5 min and two washes with 0.1× SSC containing 0.1% (w/v) SDS at 37 °C for 15 min. The membrane was air dried and exposed to a storage phosphor screen.

The concentration of active RISC was measured by stoichiometric binding titration assays as described 51. Binding reactions were performed in 5 µl in the presence of 30 mM HEPES-KOH, pH 7.9, 120 mM potassium acetate, 3.5 mM magnesium acetate, 2 mM DTT, 0.01% (w/v) CHAPS. Concentration of 5′ 32P-RNA target complementary to the seed region of the miRNA guide (Table S1) was 0.5 nM in all reactions, and the RISC concentrations spanned from 0.05 to 5 nM. The assay also included a mock binding reaction using RISC storage buffer. Binding reactions were incubated at 25 °C for 1 h. RNA binding was measured by capturing protein-RNA complexes on Protran nitrocellulose membrane (Whatman, GE Healthcare Bioscience, Pittsburgh, PA) and unbound RNA on a Hybond-XL (Cytiva) nylon membrane with a Bio-Dot apparatus (Bio-Rad, Hercules, CA). After applying the sample under vacuum, membranes were washed with 10 µl of equilibration buffer (30 mM HEPES-KOH pH 7.9, 120 mM potassium acetate, 3.5 mM magnesium acetate, 2 mM DTT). Membranes were air-dried and signals detected by phosphorimaging. To measure concentration of active, binding-competent RISC, titration data were fit to f(r) = f_max * (r + K_D + n - sqrt((r + K_D + n)^2 - 4 * r * n)) / (2 * n) where K_D is the apparent dissociation constant, r is the molar ratio of [RISC] to [RNA], n is the stochiometric equivalence point, f is the fraction bound, f_max is the maximum fraction bound 68.

Competition assays

Competition reactions (5 µl) were performed in 18 mM HEPES-KOH, pH 7.4, 100 mM potassium acetate, 3 mM magnesium acetate, 5 mM DTT, 0.01% (v/v) IGEPAL CA-630, 0.01 mg/ml baker’s yeast tRNA. Both 5′ 32P-RNA target fully complementary to the miRNA guide (Table S1) and RISC were 0.1 nM in all reactions. Concentrations of the competitor RNA (Table S1) were spanned from 0.02 nM – 100 nM (target to let-7 with g2–21 complementarity), 0.05–1000 nM (target to let-7 with g2–21 complementarity and a G:U pair at position 4), and 0.5–10,000 nM (all the other let-7 targets). The assay also included a no-RISC competition reaction using binding buffer. All reactions were incubated at 25 °C for 1 h. The fraction bound was measured by capturing protein-RNA complexes on Protran nitrocellulose membrane (Whatman, GE Healthcare Bioscience, Pittsburgh, PA) and unbound RNA on a Nylon XL membrane (GE Healthcare Bioscience) using a Bio-Dot apparatus (Bio-Rad, Hercules, CA). The sample was placed on the top of the membranes and after applying vacuum, the membranes were washed with 10 µl of ice-cold wash buffer (18 mM HEPES-KOH, pH 7.9, 100 mM potassium acetate, 3 mM magnesium acetate, 5 mM DTT, 0.01 mg/ml baker’s yeast tRNA). Membranes were air-dried and signals detected by phosphorimaging. Data were fit to theta = (([E_T] + [S_T] + K_D + K_D/K_C * [C_T]) - sqrt(([E_T] + [S_T] + K_D + K_D/K_C * [C_T])^2 - 4 * [E_T] * [S_T])) / (2 * [S_T]) where [E_T] is total enzyme concentration, [S_T] is total RNA target concentration, K_D is the apparent equilibrium dissociation constant, theta is the fraction target bound in presence of competitor RNA with an apparent dissociation constant of K_C, [C_T] is total competitor concentration 69,70.

Target cleavage assays

Purified Ago1 miRISC (0.32 nM final concentration) was incubated at 25 °C with 5′-32P-radiolabelled target RNA. The reactions 5 nM (Figs. 5b and S5c) or 100 nM (Fig. S5d) target RNA in reaction buffer composed of 25 mM HEPES-KOH, pH 7.5, 110 mM potassium acetate, 3.5 mM magnesium acetate, 2 mM DTT, 0.02% (w/v) CHAPS, 0.01 mg/ml baker’s yeast tRNA, 1 U/μl RNasin, 8% (w/v) glycerol. At the indicated times, an aliquot of a master reaction was quenched in four volumes 100 mM Tris-HCl, pH 7.5, 200 mM NaCl, 20 mM EDTA, 2% (w/v) sodium dodecyl sulfate. Then proteinase K (1 mg/ml final concentration) was added and incubated at 55 °C for 20 min. An equal volume of formamide loading buffer (98% (w/v) formamide, 0.025% (w/v) xylene cyanol, 0.025% (w/v) bromophenol blue, 25 mM EDTA, pH 8.0) was added. Samples were incubated at 95 °C for 3 min and analyzed by electrophoresis through a denaturing 15% polyacrylamide 7 M urea gel using 0.5× Tris-borate-EDTA buffer. Gels were dried, exposed to a storage phosphor screen, and imaged on a Typhoon FLA 7000 IR phosphorimager (GE Healthcare).

Measuring equilibrium dissociation constants by double filter-binding assays

Binding assays were performed as previously described 51 in 5 μl in equilibration buffer (25 mM HEPES-KOH, pH 7.9, 110 mM potassium acetate, 3.5 mM magnesium acetate, 2 mM DTT, 0.05 μg/μl BSA, 0.01 mg/ml baker’s yeast tRNA, 8% (w/v) glycerol, 1 U/μl RNasin Plus). The 5′ [32P]-radiolabeled RNA targets (0.1 nM; Table S1) were incubated with a RISC concentration between 0.0008 and 0.8 nM RISC. The assay also included a control reaction using equilibration buffer. Binding reactions were incubated at 25 °C for 2 h. RNA binding was measured by capturing protein–RNA complexes on Protran nitrocellulose (GE, GE10600002) and unbound RNA on a Hybond-XL (Cytiva, 45001151) in a Bio-Dot apparatus (Bio-Rad, Hercules, CA) as previously described 71. After applying the sample under vacuum, membranes were washed with 10 μl of wash buffer (25 mM HEPES-KOH, pH 7.5, 110 mM potassium acetate, 3.5 mM magnesium acetate and 2 mM DTT). Membranes were air-dried and signals detected by phosphorimaging.

Because K_D < [RNA target], all binding data were fit to the following equation using Igor Pro 9.05 (WaveMetrics): f = (([E_T] + [S_T] + K_D) - sqrt(([E_T] + [S_T] + K_D)^2 - 4 * [E_T] * [S_T])) / (2 * [S_T]) where f is the fraction target bound, [E_T] is the total RISC concentration, [S_T] is the total RNA target concentration and K_D is the apparent equilibrium dissociation constant.

RNA bind-n-seq for de novo site discovery and K_D measurements

RBNS were essentially performed as described 25,71.

Each experiment included five or six binding reactions. The highest concentration of RISC used corresponded to 40% (v/v) of the stock solution and equaled 4.0–5.8 nM (f.c.) active protein. For additional reactions, the stock was serially diluted 3.2-fold in storage buffer. Each experiment also included a mock binding reaction (no-RISC control) using protein storage buffer without RISC. All binding reactions (20 μl) were performed in 20 mM HEPES-KOH, pH 7.9, 100 mM potassium acetate, 3.5 mM magnesium acetate, 0.05% (w/v) CHAPS, 2 mM DTT, 8% (w/v) glycerol, and contained 100 nM (f.c.) RNA library. To reduce non-specific binding, each reaction also included 0.05 μg/μl BSA and 0.01 mg/ml baker’s yeast tRNA. Reactions were incubated for 2 h at 25 °C and then filtered through a Protran nitrocellulose membrane (Whatman, GE Healthcare Bioscience, Pittsburgh, PA) on top of a Hybond-XL (Cytiva) nylon membrane in a Bio-Dot apparatus (Bio-Rad, Hercules, CA). To reduce retention of free single-stranded RNA, we employed pre-conditioned nitrocellulose and nylon membranes prior to use as described 72,73. Nitrocellulose filters were pre-soaked in 0.4 M potassium hydroxide for 10 min. Nylon filters were incubated in 0.1 M EDTA, pH 8.2 for 10 min, washed three times in 1 M sodium chloride for 10 min each followed by a quick rinse (~15 s) in 0.5 M sodium hydroxide. Nitrocellulose and nylon filters were then rinsed in water until the pH returned to neutral and equilibrated in wash buffer (20 mM HEPES-KOH, pH 7.9, 100 mM potassium acetate, 3.5 mM magnesium acetate, 2 mM DTT) for at least 1 h at 25 °C. After applying the sample under vacuum, membranes were washed with 100 μl wash buffer for 3 s. Membranes were air-dried and signals detected by phosphorimaging to monitor binding. The nitrocellulose membranes containing RISC-bound RNA were excised and incubated with 1 μg/μl Proteinase K (ThermoFischer) in 100 mM Tris-HCl, pH 7.5, 10 mM EDTA, 150 mM sodium chloride, 1% (w/v) SDS, and 0.05 μg/μl glycogen for 1 h at 45 °C shaking at 300 rpm. After phenol-chloroform extraction and ethanol precipitation, RNA was denatured at 90 °C for 1 min, annealed to BRTP primer (Table S1) and reverse transcribed using SuperScript III (Life Technologies). RNA was degraded by alkaline hydrolysis using 0.4 M sodium hydroxide for 1 h at 55 °C, and cDNA was recovered by ethanol precipitation. The sample was then amplified with AccuPrime Pfx DNA Polymerase (Invitrogen). The reactions were run on a 2% agarose gel, amplicons were purified, then sequenced using a NextSeq 500 (Illumina) to obtain 75-nt, single-end reads.

Only Illumina reads containing TGG (the first nucleotides of the 3′ adapter) at positions 21–23 were analyzed. Sequences were filtered (Phred quality score ≥ 20 for all nucleotides, and “N” base calls disallowed), and the 3′ adapter sequence (5′ TGGAATTCTCGGGTGCCAAGG 3′) removed.

Occurrences of all 10-nt long motifs (10-mers) were counted in all the reads of each RBNS sample. These counts were then divided by the total count of all 10-mers to give motif frequencies. Enrichment of a motif was computed as the ratio of the motif frequency in the protein-bound samples over the frequency in the RNA pool. Z-score of a motif was computed as Z = R - R_mean / S where R is enrichment of the motif, R_mean is the mean of enrichment values of all 10-mers, and S is the sample standard deviation of enrichment values of all 10-mers. A motif was considered significant if its Z-score was ≥ 99.9 percentile and was not enriched in the no-RISC control reaction.

Enrichments in the library from the binding reaction with the greatest RISC concentration were used for the following iterative procedure: (1) enrichment values of all 10-mers were calculated; (2) the hundred most enriched 10-mers were interrogated for base-pairing with the guide miRNA; (3) the most enriched site type was identified; (4) Z-scores of motifs belonging to the site type were compared to the Z-score threshold; (5) all reads containing the binding site were masked in the RISC-bound library and the RNA pool so that stepwise enrichments of subsequent 10-mers could be used to eliminate subsequent ‘shadow’ motifs; (6) all enrichment values were then recalculated on the masked read set to obtain the resulting most enriched 10-mers. This process continued until the Z-score of the most enriched binding site (calculated from the original enrichment values) was < 99.9th percentile.

To identify a binding site at each iteration, the one hundred most enriched 10-mers were tested for base-pairing with the guide RNAs. If perfect complementarity was not observed, the 10-mer was tested for any of the following in this order: (1) complementarity to nine contiguous miRNA positions, allowing a single bulged target nucleotide; (2) complementarity to ten contiguous miRNA positions while allowing for wobble pairing; (3) complementarity to ten contiguous miRNA positions while allowing a non-wobble mismatch. If none of these configurations allowed assigning the motif to a binding site, the procedure was repeated with two 9-mers within the 10-mer, the three 8-mers within the 10-mer, etc., until a configuration of base-pairing was identified.

Each sequencing read in RNA pool and RISC-bound libraries was interrogated for presence of all binding sites of interest. The entire single-stranded sequence was interrogated: the 20-nt random-sequence region flanked by constant primer-binding sequences in the case when blockers were not used and the 20-nt random-sequence region flanked by 4 or 6 nucleotides of constant primer-binding sequence on either side in the case when blockers were annealed to the RNA pool. A read was assigned to a site category if it contained one single binding motif. Reads containing multiple instances of binding sites (from the same or a different site category) and reads containing partially overlapping sites were not included in the analysis and represented ≤ 1% of libraries. Reads that did not have any of binding motifs of interest were classified as reads with a no-site.

To estimate K_D values, run the code 25 by following instructions in the README file. Bootstrapping of 95% of the data was performed ten times on sequencing reads from each binding reaction and the RNA pool. MLE of K_D values was performed on each bootstrapped sample by using 100 different combinations of 10 initial guesses of miRISC concentration (in the range 0.5–25 nM) and 10 initial guesses of K_D for RNA with no enriched site (in the range 0.5–10 nM). K_D values were initialized as the inverse of the average enrichment values. The background was initialized at 0.1 nM. All the initial guesses were partially randomized by adding a value drawn from a normal distribution with mean 0 and standard deviation 0.1. The cost function was evaluated in the presence of physically meaningful constraints on the parameters: 0.1 pM ≤ K_D, site ≤ 100 nM, 100 pM ≤ K_D, no-site ≤ 10,000 nM, 100 pM ≤ miRISC ≤ 100 nM, and 5 pM ≤ background ≤ 5 nM. Any of the fitted parameters were at the boundaries at the end of the optimization routine. K_D estimates, the background, and the stock concentration of miRISC provided by MLE were used to predict counts of each binding site type in sequencing data. These counts were compared with observed sequencing data, and MLE results were retained if Pearson correlation coefficient was > 0.90. Results from independent starting points satisfying this criterion were combined. All bootstrapped samples were combined. Finally, estimates from two independent RBNS assays were merged. Median and 95% confidence intervals on medians were reported.

To estimate K_D values of PUM2, fastq files of ENCFF761JAF, ENCFF894MLG, ENCFF090QVU, ENCFF574TBN, ENCFF266PHU, ENCFF345UJY datasets were downloaded from the ENCODE project. Reads containing one single PUM binding site—UGUAUAUA (consensus binding site), AGUAUAUA, CGUAUAUA, and GGUAUAUA—were identified. Reads containing multiple instances of the PUM binding sites were not included in the analysis and represented ≤ 0.001% of libraries. Reads that did not have any of the above binding motifs were classified as reads with a no-site. MLE of PUM2 K_D values was performed as described for miRISC.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors performed RNA Bind-n-Seq (RBNS) on four miRNAs associated with fly Ago1. Using these data they identified\nsequences corresponding to ≤10nt binding sites of these four miRNAs and reported their binding affinities. They concluded\nthat, compared to mammals, fly miRNAs have a narrower range of binding-site diversity, and that some noncanonical site\ntypes originally defined in mammals (nucleation-bulged and 3’-only sites) also function in flies.\nMajor concerns:\n1) The ability to estimate absolute Kd values from RBNS data is intriguing, but difficult to understand as described. Based on\nref. 23, the authors appear to have sampled 100 different combinations of initial values for the AGO-miRNA stock\nconcentration (in the range 0.5–25 nM) and the Kd value for the no-site sequences (in the range 0.5–10 nM). For each of\nthese combinations, the authors let the parameters converge by minimizing the cost function to obtain a best fit for the\nestimated Kd of each enriched site type, the background, and the stock concentration of AGO−miRNA. Then, these values\nwere combined and estimates from independent assays were merged. To help readers understand this last step, the authors\nshould provide additional information. First, they should provide a supplemental table listing for each of the 100\ncombinations the fitted values (Kd values for enriched sites, the background, the stock concentration of AGO−miRNA, the\nno-site Kd value, and the cost-function value) at convergence. These should be provided for at least two miRNAs (let-7a and\nmiR-184) and PUM2. Second, the authors should more clearly describe how these fitted values were combined and\nanalyzed to arrive at the final Kd estimate. Currently the manuscript says, “To estimate KD values, run the code^23 by\nfollowing instructions in the README file.\" However, the README file only lists a command without explaining how the\nKd’s were combined to arrive at a final estimate.\n2) Fig. 1C. The analysis in Fig. 1C does not support the conclusion that canonical binding can be predicted by free energy of\npairing. A more informative analysis would compare the relationship between measured Kd and predicted Kd for different\nrepresentatives of the same site type. By combining data for 7nt sites with those for 6nt sites, the apparent significance of the\nrelationship between measured Kd and predicted Kd evaluated in Fig. 1C is partially driven by the length of seed pairing\nrather than the predicted strength of seed pairing, raising the possibility that this length component is needed to achieve\nstatistical significance. Because the importance of the length of seed pairing has been observed many times, a novel\nconclusion would require an analysis that avoids this confounding component. Indeed, when looking at the correlation\nacross different miRNAs for the same site type, the predicted Kd values do not align with empirical Kd values. For example,\nthe empirical Kd for the 7mer-m8 site of miR-184 is two-fold weaker than that of let-7, while the predicted Kd for the 7mer-m8\nsite of miR-184 is 25-fold stronger than that of let-7. Another problem with including different sites of the same miRNA in the\nsame analysis is that the Pearson correlation calculation requires that the data points be independent, which is invalid for\nthe combined data, as one would expect sites from the same miRNA to behave similarly to each other across site types. Fig.\n5d and the associated analysis on 3′-only sites (page 10) have the same issues.\nThe comparison to previous results from analogous analyses for mammalian AGO2-miRNAs is also unclear. In the first\nsentence of the section the authors cite mammalian studies showing that “[miRNAs] with strong predicted free energy of site\npairing bind their target sites with higher affinity than miRNAs with weak seed-pairing” but that “the difference in binding\naffinities of mammalian miRNAs is less than might have been expected.” Doesn’t the slope of the line in Fig. 1C, which is\nmuch less than 1:1, suggest that the same is true in flies? To make a clearer comparison with mammals, the values for\nmammals and flies should both be plotted and each slope compared quantitatively to the 1:1 line.\n3) Fig. S1a. The plotted values of Fig. S1a do not agree with the reported Kd values: the Kd value of the seed-matching\ntarget appears to be around 60 pM instead of 5 pM, and that of the fully complementary target (based on the abnormally\nshaped sigmoidal curve) appears to be around 30 pM instead of 2 pM.\nHow do the authors reconcile the Kd value determined for the let-7 8mer Fig. S1a (~60 pM) with the value determined by\nRBNS in Fig. 1B (2 pM).\nAlso, the analysis of Fig. S1a may not support the conclusion that there is “no substantive difference in affinity between a\nseed-matching and a fully complementary target.” The conclusion of no substantive difference requires that the binding\nreaction(s) have reached equilibrium, which may not be the case for the fully paired target. Do the authors know the\ndissociation rate of the fully complementary target? If so, was the incubation time of experiment described in Fig. S1a (2\nhours) sufficient to achieve binding equilibrium?\n4) Fig. S2-3. The authors use AlphaFold3-predicted structures to investigate seed-mismatch tolerance of miR-184. This is\nnot a proper use of AlphaFold. AlphaFold3 fails to recognize RNA mismatches in AGO due to heavy template matching to\nexisting AGO structures, which all feature perfectly matched seed sequences, and thus shoehorns mismatched nucleotides\nacross each other as if they were complementary. As the authors noted, these predicted structures add no value to this work\nor its conclusions; they should be removed from the paper.\n5) A central and provocative claim of this manuscript is that the repertoire of miRNA binding sites is less diverse for fly Ago1-\nmiRNAs than for mammalian AGO2-miRNAs. However, the evidence for this claim is not clearly presented. Only one\nmiRNA (let-7) is shared between the fly and mammalian RBNS datasets. Are the differences observed for other miRNAs\ndue to inter-species differences or differences between different miRNA seqeunces tested? It would also help to know what\nmetric the authors are comparing. Are the authors saying that fly Ago1 has fewer unique sites in the de novo site\nidentification? This metric would be sensitive to differential noisiness of the data. Or, are there fewer sites with affinities\nwithin X-fold of the 8mer site, or fewer sites more than X-fold better than no-site sequences? The metric being used should\nbe stated and the associated value declared for each mammalian miRNA and each fly miRNA. Then, the distribution of\nvalues for the fly miRNAs should be compared to the distribution for the mammalian miRNAs, and a statistical test should be\nperformed to evaluate if the difference between species is more than that expected based on the distribution within each\nspecies.\nThe authors claim that fly Ago1-miRNAs have a lower tolerance for seed-region imperfections than mammalian AGO2-\nmiRNAs, which they invoke as a possible explanation for their more general claim (discussed above) that fly Ago1-miRNAs\nhave less diverse repertoires of binding sites. A more quantitative comparison is needed to support this claim. The authors\nshould compare the ΔG penalties associated with individual seed region imperfections, aggregating across the four fly\nmiRNAs from this study. They should then compare these to the ΔG penalties for the mammalian miRNAs with RBNS\ndatasets. If the penalties associated with a particular class of pairing imperfection (e.g. mismatches) are globally/generally\nlarger for the fly miRNAs compared to the mammalian miRNAs, and if these differences are significantly more prominent\nbetween species than within species, then it would be valid to argue that fly Ago1 has lower tolerance for that class of\npairing imperfection.\n6) The analysis of nucleation-bulge sites should be improved. Nucleation-bulge sites are also 6mer-A1 sites. Therefore, the\nnull hypothesis should be that the nucleation-bulge sites bind at the same affinity as the typical 6mer-A1 sites, and evidence\nfor the function of the nucleation-bulge would come from observation of greater affinity than that observed for other 6mer-A1\nsites with similar flanking nucleotide composition. By these criteria, only some mammalian miRNAs recognize nucleation-bulge\nsites. Is the same true in flies?\nMinor concerns:\n1) page 3. Refs 1−7 are cited in a way that suggests that some references support mRNA degradation and others support\ntranslational inhibition. However, most of them report a combination of mRNA degradation and translational inhibition, and\nso they might best be cited together. When looking at the relative contributions of mRNA degradation and translational\ninhibition, ref. 3 does not distinguish between mRNAs that have a site to the miRNA and those that do not. Therefore, ref. 3\nseems less relevant than the others for this citation.\n2) page 3. Because central sites do have some seed pairing that might still function to nucleate pairing, it might be best to\nnot characterize them as “seedless.”\n3) page 3. “The most successful computational approaches typically predict miRNA binding rather than biologically\nimportant regulation ref 9,22,25.” Actually, these approaches primarily predict site efficacy (not site binding). Some also\nprovide the option of predicting site conservation, which is one approach for predicting biologically important regulation. For\nexample, the approach of reference 22 is trained to predict site efficacy as well as site binding. The approach of reference 25\nis trained to predict site efficacy, with no training to predict site binding affinity. It also provides the option of considering only\nsite conservation. (Note that the approaches that predict site efficacy also consider site conservation to the extent that more\nconserved sites have greater efficacy.)\n4) pages 3-4. The purpose of mentioning the lin-4:lin-14", "reviewer_bantam": "clock and miR-9:TLX interactions is unclear. Is\nit to support the previous sentence, which states that prediction of animal regulatory targets is difficult? If so, these examples\ndo not all serve this purpose. The top predicted target of lin-4 is lin-14, with lin-28 (another genetically identified lin-4 target)\ntied for second (TargetScanWorm). Likewise, hid is either the top predicted target of bantam or the third-highest predicted\ntarget, depending upon whether the target sites are ranked based on evolutionary conservation or predicted site efficacy\n(TargetScanFly).\n5) To put the weaker Kd values into perspective, the no-site Kd values should also be plotted in Fig. 1b.\n6) The 10,000 mark of the x axis in Fig. 1c needs to be corrected.\n7) To better explain RBNS analysis and illustrate the quality of the new data, Fig. 1 should also have a panel showing how\nbinding enrichment varies over fly Ago1-miRNA concentrations (as in Fig. 3B of ref. 23) and also showing the fits to the\nenrichments (as in Fig. 1E of ref. 22).\n8) When comparing results from fly and mammalian RBNS studies, the authors should note that the fly study used 3.5 mM\nMg2+, whereas the original mammalian study used 1 mM Mg2+.\n9) page 11. “These data suggest that Drosophila Ago1 competes with local RNA secondary structures for site binding.” The\nauthors could mention that similar results have been reported when analyzing RBNS data for mammalian AGO2.\n10) page 12. “A reporter assay using Drosophila S2 cells monitored changes in steady-state levels of all cellular mRNAs …”\nIt is unclear what the “reporter assay” is referring to in this sentence.", "reviewer_2": "The codes of target genes recognition by mature miRNA sequences are very important for miRNA-mediated gene regulation\nand the binding affinity between miRNA and target sequences greatly determines how strong the target genes can be\nrepressed by miRNAs. The biochemical basis for mammalian miRNA interaction with targets has been well studied while\nthe biochemical rule for fly Ago targeting is still not fully known. In this manuscript “Biochemical principles of miRNA\ntargeting in flies”, Vega-Badillo et al systematically investigate the binding rules of fly Ago1-miRNA complex with RNA\ntargets using RNA Bind-n-Seq (RBNS) assay. Although most of the targeting rules are similar with mammalian miRNAs\nwhich are already known, they still find some different targeting features, for example, fly miRNAs have a narrow binding\ndiversity which favors perfect matched seed sites with limited tolerance for seed imperfections. They also identified 3’-only\nsites and nucleation-bulged sites, but no central paired sites are found due to the cleavage activity of fly Ago1 on these sites.\nThis study makes up the gap for biochemical basis of fly miRNA-target interactions and provides prediction guidance for fly\nmiRNA mediated gene regulation in the future. I would recommend this manuscript for publication in Nature\nCommunications if the following concerns can be addressed.\n1. As similar studies have been done by David Bartel lab (Ref #22 and #33) in mammalian system using same methods, the\nauthors should provide a summary figure or table to compare the similarities and differences for the targeting rules between\nfly and mammal miRNAs, and discuss the potential reasons for the differences in this summary. This will help the readers to\neasily understand the targeting rules in different species.\n2. In this study, the authors tested four different fly miRNAs on their targeting rules and showed some differences on their\ntarget binding behaviors. Can the authors summarize their major binding differences somewhere in the manuscript and\nexplain the potential reasons why different miRNA sequences can cause different binding activities?\n3. In Fig 1B, the authors showed binding activities of 6mer targets (better than 6mer-m8 and 6mer-A1), which is interesting to\nknow if these sites are really functional in vivo. Can the authors do some sensor tests for these 6mer sites to see if they can\nmediate detectable regulation?\n4. The central paired sites are not identified in this study due to Ago1 mediated cleavage of these sites as shown in Fig 5b.\nCan these central paired sites (without seed match) really mediate detectable target cleavage in cells because it’s\npresumably not involving GW182? Or these central paired sites can mediate target repression via normal miRNA way which\nis GW182 dependent? If these are real regulatory sites, it may encourage others to annotate such sites.\nMinor concerns:\n5. In Fig 1A, please indicate that the RNA oligo pool (input) is also sequenced.\n6. In Fig 1A, the blocking oligonucleotide is DNA while the sequences are showed as RNA with “U”.\n7. In Fig 1C, there is a typo in X-axis-the last “100” should be “10,000”.\n8. In this sentence “In plants and animals, ~22-nt-long microRNAs (miRNAs) guide AGO-clade Argonaute proteins to\nrepress partially complementary mRNA targets by accelerating their degradation 1–3 or inhibiting their translation 4–7.” As\nknown, in plants, miRNAs are most perfectly matched to target RNAs and guide their cleavage in an RNAi manner.\n9. Please unify the citation format. “miRNAs display sequence-specific differences in binding their canonical sites: those with\nstrong predicted free energy of site pairing bind their target sites with higher affinity than miRNAs with weak seed-pairing 21\n; McGeary et al., 2019, #16; Jouravleva et al., 2022, #143956}.”\n10. Typos in these sentences: “To study the energetics of canonical binding of fly miRNAs, we loaded recombinant Ago1\nloaded with one of four different miRNAs”; “Our data suggest that Ago1 may bind sites containing one single central\nmismatch but identifying universal rules for predicting which miRNAs can bind 8mer-x4R sites with unexpectedly high\naffinity high-throughput target binding measurements for a larger set of miRNAs”", "reviewer_3": "The manuscript by Vega-Badillo et al. describes the biochemical exploration of in vitro miRNA binding in the context of\nDrosophila Ago1. Building upon previous use of this technology for mammalian Ago2, the authors describe distinctions in\nDrosophila Ago1-mediated miRNA interactions that may help future predictive efforts in Drosophila. In general, the\nexperiments are well-done and well-described, and generates useful insights for the fly miRNA community to further explore\nthe mechanisms of how miRNAs regulate various aspects of biology in that system.\nI only really have minor comments:\n“At these positions mismatched nucleotides A and G are often the least tolerated, likely because their purine rings are larger\nthan pyrimidines and thereby cause more steric hindrance.” – can the authors provide some quantification of “often”? It\nseems true for let-7, but somewhat less for the others – I think just having some number here would be clearer than a vague\n‘often’\n“Our data suggest that Ago1 may bind sites containing one single central mismatch but identifying universal rules for\npredicting which miRNAs can bind 8mer-x4R sites with unexpectedly high affinity high-throughput target binding\nmeasurements for a larger set of miRNAs.“ – this sentence seems like it’s missing an ending (‘our data suggest … but\nidentifying universal rules for … [will be impossible? Will require more extensive profiling?]’ )\nThe analysis in Fig 6 is interesting, but it might be good to show separately in supplementary figures the 5’ and 3’ end\nversions, since the 5’ side is also impacted by whether the sequence base-pairs with the miRNA. I’m also a bit confused by\nthe conclusion of the Fig 6 section – is the overall argument in this section that secondary structure has been shown to\ninfluence repression in [[emphasis mine]]mammalian]] cells, and this section validates the same principle for Drosophila\nAgo1? Or that RBNS scores can essentially serve as a functional proxy that correlates with (and thus likely incorporates the\neffect of) local structure? I don’t think it’s clear from the manuscript whether the same finding is true (& has already been\nshown) for RBNS AGO2 data in human, or just the general principle\nI appreciate that the authors haven’t tried to over-sell the impact of this work, but I think having a little more discussion of the\nimpact of this work would be useful – I can see the obvious impact in aiding Drosophila-specific prediction tools, but I think\nhaving a touch more insight into what else the authors envision of the impact would be great"}, "author_response": "Vega-Badillo et al.\nResponse to Reviewers’ Critiques\nWe thank the Reviewers for their interest in our work and for their thoughtful and\nconstructive feedback. We address each point below.\nReviewer #1\nReviewer 1 raises many useful points, many of which underscore that we need\nto do a better job educating our readers in the fundamentals of nucleic acid\nbinding kinetics and equilibria. Their comments also helped us improve some of\nour computational analyses. However, we were surprised by the reviewer’s\naggressive tone and would like to remind the reviewer that it would have been\npossible to express all of these points without being condescending. A trainee is\non the receiving end of every manuscript review, and the adversarial review\nprocess is a significant factor in convincing young scientists to abandon\nacademia.\nThe authors performed RNA Bind-n-Seq (RBNS) on four miRNAs associated with fly\nAgo1. Using these data they identified sequences corresponding to ≤10nt binding sites\nof these four miRNAs and reported their binding affinities. They concluded that,\ncompared to mammals, fly miRNAs have a narrower range of binding-site diversity, and\nthat some noncanonical site types originally defined in mammals (nucleation-bulged and\n3’-only sites) also function in flies.\nMajor concerns:\n1) The ability to estimate absolute Kd values from RBNS data is intriguing, but difficult\nto understand as described. Based on ref. 23, the authors appear to have sampled 100\ndifferent combinations of initial values for the AGO-miRNA stock concentration (in the\nrange 0.5–25 nM) and the Kd value for the no-site sequences (in the range 0.5–10 nM).\nFor each of these combinations, the authors let the parameters converge by minimizing\nthe cost function to obtain a best fit for the estimated Kd of each enriched site type, the\nbackground, and the stock concentration of AGO−miRNA. Then, these values were\ncombined and estimates from independent assays were merged. To help readers\nunderstand this last step, the authors should provide additional information. First, they\nshould provide a supplemental table listing for each of the 100 combinations the fitted\nvalues (Kd values for enriched sites, the background, the stock concentration of\nAGO−miRNA, the no-site Kd value, and the cost-function value) at convergence. These\nshould be provided for at least two miRNAs (let-7a and miR-184) and PUM2. Second,\nthe authors should more clearly describe how these fitted values were combined and\nanalyzed to arrive at the final Kd estimate. Currently the manuscript says, “To estimate\nK values, run the code^23 by following instructions in the README file.\" However,\nD\nthe README file only lists a command without explaining how the Kd’s were combined\nto arrive at a final estimate.\nWe thank the Reviewer for pointing out that the Material and Method section\ninsufficiently described our procedure to estimate K values from RBNS\nD\ndatasets. We edited the relevant paragraph to read,\n1\n“To estimate K values, run the code (Jouravleva et al., 2022) by following\nD\ninstructions in the README file. Bootstrapping of 95% of the data was\nperformed ten times on sequencing reads from each binding reaction and\nthe RNA pool. MLE of K values was performed on each bootstrapped\nD\nsample by using 100 different combinations of 10 initial guesses of\nmiRISC concentration (in the range 0.5–25 nM) and 10 initial guesses of\nK for RNA with no enriched site (in the range 0.5–10 nM). K values were\nD D\ninitialized as the inverse of the average enrichment values. The\nbackground was initialized at 0.1 nM. All the initial guesses were partially\nrandomized by adding a value drawn from a normal distribution with\nmean 0 and standard deviation 0.1. The cost function was evaluated in\nthe presence of physically meaningful constraints on the parameters: 0.1\npM ≤ K site ≤ 100 nM, 100 pM ≤ K no-site ≤ 10,000 nM, 100 pM ≤ miRISC ≤\nD D\n100 nM, and 5 pM ≤ background ≤ 5 nM. Any of the fitted parameters\nwere at the boundaries at the end of the optimization routine. K\nD\nestimates, the background, and the stock concentration of miRISC\nprovided by MLE were used to predict counts of each binding site type in\nsequencing data. These counts were compared with observed\nsequencing data, and MLE results were retained if Pearson correlation\ncoefficient was >0.90. Results from independent starting points satisfying\nthis criterion were combined. All bootstrapped samples were combined.\nFinally, estimates from two independent RBNS assays were merged.\nMedian and 95% confidence intervals on medians were reported.\n“To estimate K values of PUM2, fastq files of ENCFF761JAF,\nD\nENCFF894MLG, ENCFF090QVU, ENCFF574TBN, ENCFF266PHU, and\nENCFF345UJY datasets were downloaded from the ENCODE project.\nReads containing one single PUM binding site—UGUAUAUA (consensus\nbinding site), AGUAUAUA, CGUAUAUA, and GGUAUAUA—were\nidentified. Reads containing multiple instances of the PUM binding sites\nwere not included in the analysis and represented ≤0.001% of libraries.\nReads that did not have any of the above binding motifs were classified\nas reads with a no-site. MLE of PUM2 K values was performed as\nD\ndescribed for miRISC"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed nearly all of my concerns. I just have a few remaining suggestions/clarifications.\nRegarding the last part of major concern 5, I apologize that my initial comments were unclear, but I was requesting that the\nauthors show the deltaG penalties calculated from the experimental values (as calculated from RBNS-derived Kd values by\nthe equation deltaG = RTln(Kd)), rather than the deltaG values predicted from the nearest-neighbor parameters. Showing the\nexperimental values in Figure S4 should speak to the claim that fly Ago1 is less tolerant of seed-region imperfections than\nhuman AGO2.\nWith respect to minor concern 1, the current model for miRNA-mediated repression centers on miRNA-mediated recruitment\n(through TNRC6) of a deadenylase complex (PAN2/PAN3 or CCR4-NOT) to accelerate mRNA poly(A) tail shortening,\nwhich in most settings enhances mRNA decay by hastening the time at which the tail becomes too short to resist mRNA\ndecapping. In addition, the CCR4-NOT deadenylation complex brings to the mRNA the proteins responsible for translational\ninhibition of the mRNA (i.e., 4EHP and 4E-T; PMID28487484 from Sonenberg lab). Because the proteins that shorten the tail\nand the proteins that inhibit translation presumably arrive concurrently, it seems likely that the early step in mRNA decay\n(i.e., accelerated deadenylation) is happening in parallel with (and at the same time as) the effects on translation. These\nmore recent mechanistic insights are not consistent with the earlier proposals that mRNA decay is a secondary effect of\ntranslational repression; for these earlier proposals to be plausible, translational repression would need to occur before\naccelerated poly(A) tail shortening (which does not appear to be the case in the settings examined). I hope this clarifies the\nconcept that miRNAs mediate a combination of mRNA degradation and translational repression.\nOne more clarification: Regarding the last part of major concern 2 (which the authors found puzzling “because the original\nversion of the manuscript stated that flies and mammals behaved similarly”), my concern was not with the quoted text but\nwith a statement in the original version of the text, saying, “Unlike in mammals, these differences can be predicted by\nexisting models of RNA duplex stability in solution.”", "reviewer_2": "I have no further comments and support the publication of this work in Nature Communications.", "reviewer_3": "The authors have addressed my comments and I support publication"}, "author_response": "Vega-Badillo et al.\nResponse to Reviewers’ Critiques\nWe thank the Reviewers for their interest in our work and their support. We address the\nremaining concerns of Reviewer #1 below.\nRegarding the last part of major concern 5, I apologize that my initial comments were\nunclear, but I was requesting that the authors show the deltaG penalties calculated\nfrom the experimental values (as calculated from RBNS-derived Kd values by\nthe equation deltaG = RTln(Kd)), rather than the deltaG values predicted from the nearest-\nneighbor parameters. Showing the experimental values in Figure S4 should speak to\nthe claim that fly Ago1 is less tolerant of seed-region imperfections than human AGO2.\nWe thank the reviewer for this clarification. In response, we plotted the ΔG\npenalties associated with individual seed-region imperfections across the five fly\nmiRNAs examined in this study, as well as the mammalian miRNAs from\npreviously published AGO2 RBNS datasets. The Reviewer’s suggestion made\nus realize that, for the miRNAs shared between the fly and mammalian\ndatasets—let-7 and miR-124—the ΔG of a perfectly matched g2–g8 target is\nlower in the mammalian datasets, while one would expect binding at 25°C to be\nmore stable. This observation suggests that the reduced tolerance for\nmismatches by fly Ago1 does not arise because mismatches are inherently more\ndestabilizing. Instead, the overall dynamic range of binding starts with weaker\nbinding. Because flies have a lower body temperature than mammals, diffusion-\nlimited association rates and target-search miRNA dynamics are slower. If the\nintrinsic biochemical properties of miRNA-loaded Ago1 were identical to those\nin mammals, fly Ago1 miRISCs would spend “too long” bound to suboptimal\ntarget sites.\nWe added the following text to the paragraph discussing the repertoire of fly\nAgo1 target sites:\n“Our de novo site discovery algorithm identified only three binding sites\nfor fly miR-124–loaded Ago1: the canonical 8mer, 7mer-m8, and 7mer-A1\n(Fig. 5a). Remarkably, AGO2 guided by human miR-124, whose sequence\nis identical to that in flies, binds ~15 site types within a 10-fold range of\nthe 8mer affinity, spanning canonical and noncanonical interactions,\nincluding seed-matched sites with wobble pairs, mismatches,\nbulges, and 3′-only sites24.\nConsidering these differences in site recognition, we examined the\nΔG penalties associated with individual seed-region imperfections across\nthe five fly miRNAs examined in this study and the mammalian miRNAs\nfrom previously published AGO2 RBNS datasets24,25. Binding affinity for a\nsite is predicted to be more stable at 25°C than 37°C. Contrary to\nexpectation, for miR-124 and let-7, which are shared between the fly and\nmammalian datasets, binding to a perfectly matched g2–g8 target was\nmore stable for mammalian AGO2 at 37°C than for fly Ago1 at 25°C (Fig.\nS4). This observation suggests that the reduced tolerance for mismatches\n1\nof fly Ago1 does not arise because mismatches are inherently more\ndestabilizing. Instead, the overall dynamic range of binding starts with\nweaker binding. We also note that our binding experiments were\nperformed using 3.5 mM Mg2+. As expected, when the experiments were\nperformed using 0.89 mM Mg2+, the concentration used previously to\nstudy binding of human AGO2 (Ref. 24), we recovered fewer site-types\n(Fig. S5a). Together, our data suggest that fly Ago1 has lower tolerance\nfor pairing imperfection because it binds perfect seed matches less\ntightly than AGO2.”\nWith respect to minor concern 1, the current model for miRNA-mediated repression\ncenters on miRNA-mediated recruitment (through TNRC6) of a deadenylase complex\n(PAN2/PAN3 or CCR4-NOT) to accelerate mRNA poly(A) tail shortening, which in most\nsettings enhances mRNA decay by hastening the time at which the tail becomes too\nshort to resist mRNA decapping. In addition, the CCR4-NOT deadenylation complex\nbrings to the mRNA the proteins responsible for translational inhibition of the mRNA\n(i.e., 4EHP and 4E-T; PMID28487484 from Sonenberg lab). Because the proteins that\nshorten the tail and the proteins that inhibit translation presumably arrive concurrently,\nit seems likely that the early step in mRNA decay (i.e., accelerated deadenylation) is\nhappening in parallel with (and at the same time as) the effects on translation. These\nmore recent mechanistic insights are not consistent with the earlier proposals that\nmRNA decay is a secondary effect of translational repression; for these earlier\nproposals to be plausible, translational repression would need to occur before\naccelerated poly(A) tail shortening (which does not appear to be the case in the settings\nexamined). I hope this clarifies the concept that miRNAs mediate a combination of\nmRNA degradation and translational repression.\nWe thank the reviewer for this clarification. We edited our text to read,\n“In plants and animals, ~22-nt microRNAs (miRNAs) guide AGO-clade\nArgonaute proteins to repress partially complementary mRNA targets by\naccelerating their degradation{Baek et al., 2008; Guo et al., 2010;\nSelbach et al., 2008} and inhibiting their translation, with recent\nmechanistic insights indicating that these effects may occur in parallel\nrather than as a sequential process {Chapat et al., 2017}.”\nOne more clarification: Regarding the last part of major concern 2 (which the authors\nfound puzzling “because the original version of the manuscript stated that flies and\nmammals behaved similarly”), my concern was not with the quoted text but with a\nstatement in the original version of the text, saying, “Unlike in mammals, these\ndifferences can be predicted by existing models of RNA duplex stability in solution.”\nUnderstood, thank you.\n2"}]

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