To produce a protein, a stretch of DNA must first be transcribed to produce a molecule of messenger RNA (mRNA). The genetic information copied from the DNA is then read three letters at a time, in groups called codons. Each codon either encodes a particular amino acid to be added into a protein or provides further instructions: ‘start codons’ mark the beginning of a protein; ‘stop codons’ mark its end. The DNA between these two points is called an open reading frame (or ORF)—however, not all ORFs produce proteins.

Most proteins are made of several hundred amino acids, but the genomes of animals contain thousands of ORFs that would generate much smaller proteins made of fewer than 100 amino acids, if they were translated. It is, however, unclear how many of these small ORFs are converted into mRNA molecules and functional proteins.

Ribosomes are large molecular machines that translate the code in mRNA molecules and join together the appropriate amino acids in the right order to make a protein. Ribosome profiling is a technique that identifies which mRNA molecules are translated into proteins by determining the sequences of all the mRNA molecules bound to ribosomes at a particular moment. The mRNA sequences can then be compared with the sequence of the whole genome to work out which ORFs they correspond to. Ribosome profiling has been used to detect translated small ORFs, but the method yields a relatively high false positive rate as some mRNAs can bind to ribosomes without being translated.

To better detect small protein-producing ORFs, Aspden et al. developed a technique based on ribosome profiling called Poly-Ribo-Seq. The method takes advantage of the fact that during active translation, clusters of multiple ribosomes, called polysomes, bind mRNAs. Poly-Ribo-Seq isolates these polysomes and determines the sequence bound by each of the ribosomes, thereby reducing the number of false positives.

Applying Poly-Ribo-Seq to cells from the fruit fly Drosophila allowed Aspden et al. to identify two types of short ORF. The first type codes for proteins that are around 80 amino acids long and are translated with the same efficiency as larger ORFs. The sequences of these ORFs are found in other species, match at least in part sequences of known functional ORFs, and the proteins produced are found in specific locations inside cells. These small proteins may contribute to membrane integrity or function. Together, these properties suggest that these mRNAs create functional small proteins.

The second pool consists of very small ORFs (‘dwarf smORFs’) that code for around 20 amino acids, which are not translated so often and do not show many similarities with other species.

As the findings of Aspden et al. suggest that a large fraction of Drosophila small ORFs are translated into proteins, the next challenge will be to determine the roles of these small proteins in cells.

Small open reading frames (smORFs) of fewer than 100 amino acids exist in eukaryotic genomes in hundreds of thousands, but their annotation has been hindered by their size: short sequences are unable to obtain the high conservation scores that are the accepted indicator of functionality (Ladoukakis et al., 2011). This handicap, coupled with the limited numbers of experimentally proven functional smORFs in eukaryotes (Kastenmayer et al., 2006; Galindo et al., 2007; Kondo et al., 2007; Hanada et al., 2012; Magny et al., 2013; Pauli et al., 2014; reviewed in Andrews and Rothnagel, 2014), has so far precluded the reliable annotation of smORFs as coding sequences. Targeted bioinformatic approaches predict that hundreds of smORFs could be translated and functional in bacteria (Hemm et al., 2008), yeast (Basrai et al., 1997; Kastenmayer et al., 2006), plants (Hanada et al., 2012) and metazoans including Drosophila (Ladoukakis et al., 2011), mouse (Frith et al., 2006; Crappe et al., 2013), and humans (Slavoff et al., 2013), but this is in contrast to their low rate of detection in biochemical (proteomic) (Falth et al., 2006; Slavoff et al., 2013) and functional (genetic) (Kumar et al., 2002) screens. Thus, genome-wide assessments of smORF translation in a number of species are needed to determine the actual number of translated smORFs in eukaryotic genomes.

The new technique of ribosome profiling has corroborated and expanded the proteomes of yeast (Ingolia et al., 2009; Duncan and Mata, 2014; Smith et al., 2014), mouse (Ingolia et al., 2011), zebrafish (Chew et al., 2013; Bazzini et al., 2014), and Drosophila (Dunn et al., 2013). Thousands of new translated sequences have been described in each case, whether novel exons of annotated genes, alternative initiation sites, or entire ORFs. However, the application of ribosome profiling outside canonical translated sequences can lead to differing conclusions (Chew et al., 2013; Guttman et al., 2013). The problem remains that a ribosomal footprint cannot always be equated with translation; non-productive binding of single ribosomes to mRNAs and scanning 40S ribosomal subunits can result in footprints, yet do not constitute translation. It has been suggested that some smORFs associate with ribosomes in such a non-productive manner and do not undergo productive translation (Wilson and Masel, 2011). Moreover, smORF mRNAs are short and present a small target for ribosomal binding and generation of footprints, potentially making traditional ribosome profiling less suitable for the study of smORFs. To distinguish genuine translation events from background, a number of metrics, statistical treatments, and bioinfomatic analyses have been proposed (Ingolia et al., 2009; Chew et al., 2013; Guttman et al., 2013; Bazzini et al., 2014). We have taken an alternative approach by enhancing the biochemical foundation of ribosome profiling and developed ‘Poly-Ribo-Seq’, an improvement to ribosome profiling that should be of use for the study of smORFs and canonical, longer ORFs alike. Instead of profiling all ribosomal-bound mRNAs, we perform ribosome footprinting on polysomal fractions. In this way, mRNAs bound by multiple ribosomes and hence actively translated can be isolated and distinguished from mRNAs bound by sporadic, putatively non-productive single ribosomes or ribosomal subunits. Although this method may overlook very short smORFs that cannot fit multiple ribosomes, this loss should be offset by the increased stringency of discarding false positive footprints.

Application of Poly-Ribo-Seq to Drosophila S2 cells reveals extensive translation of thousands of smORFs of two putative types. ‘Longer’ smORFs encode around 80-aa-long peptides, which are translated in the same proportions (83% of transcribed coding sequences) and as efficiently as canonical long ORFs. We nearly double the number of these smORFs shown to be translated, and show that these smORF peptides further resemble canonical proteins in that they are conserved across species, show specific subcellular localisations, and display a specific amino acid composition with the potential to form transmembrane alpha-helices. ‘Dwarf’ smORFs encode peptides in 5′-UTRs and non-coding RNAs, are not detected by gene prediction programs, and are around 20-aa-long. They are less conserved, and translated at lower proportions and efficiencies than canonical proteins.

We corroborate these findings by two independent methods and observe smORF peptide expression at or near mitochondria. Extrapolation of our results suggests that thousands of smORFs are translated in higher organisms and that smORFs could have diverse functions, including expression of peptides active in cell membranes.

We have chosen to assess the translation of smORFs in Drosophila, because of the well-annotated genome of this organism and the availability of an equally well-characterised standard cell line (S2 cells) (Schneider, 1972) providing abundant and reproducible material.

The annotation of the Drosophila genome contains double the proportion of predicted smORF-encoding genes than other metazoan genomes (some 829 smORF genes, or 4% of the total, Table 1) (FlyBase, Ensembl). However, closer scrutiny reveals that although these genes have well-corroborated transcriptional data (modENCODE), less than a quarter of these have corroborated translation and peptide function. Only 164 annotated smORFs have at least two out of three markers indicating translation or peptide function: (1) molecular GO term indicating protein function (based on direct assays or presence of protein domains); (2) matches with peptides from proteomic experiments; and (3) conservation of the coding sequence beyond insects (Figure 1A). These ‘corroborated’ smORFs have in most cases a gene name (e.g., Defensin) and associated literature. The translation of the remaining 665 putative smORFs is thus not yet fully proven, and in most cases (494 smORFs) no evidence of translation is recorded. The majority of these ‘uncorroborated’ smORFs only have a cognate identifier (e.g., CG34200) and their ‘coding’ status varies between genome releases (unpublished observation). Thus, the Drosophila annotated smORFs offer an ideal framework to test for translation of smORFs and their biological importance.

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Poly-Ribo-Seq detects smORF translation

Small and large polysomes showed a marked difference in genome-wide ribosomal densities (Figure 2A), suggesting that these two fractions contain mRNAs translated at different levels. Small polysomes contain mRNAs encoding long ORFs, but these have lower TE than when isolated from large polysomes (Table 2), confirming that they were bound by fewer ribosomes. As intended, Poly-Ribo-Seq detected smORFs with translation signatures, and these were enriched in small polysomes, which contained double the number and all of the smORFs detected in large polysomes (Figure 2C). The low smORF TE values in large polysomes are similarly consistent with low levels of smORF mRNAs being present in large polysomal complexes. However, the TE of smORFs from small polysomes is similar to the TE of long ORFs from large polysomes, indicating that smORFs can be translated at similar levels to standard protein-coding ORFs (Table 2; Figure 1—figure supplement 2).

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Table 2

Median TE	Small polysomes	Large polysomes
Annotated smORFs	1.131	0.265
standard ORFs	0.829	1.110
5′-UTR	0.355	0.566
3′-UTR	0.162	0.196
uORFs	0.276	0.347
ncRNA smORFs	0.384	0.000

1. Median translational efficiency for ORFs in small and large polysomal fractions.

Altogether 191 annotated smORFs passed the cut-off values to be deemed translated in this initial Poly-Ribo-Seq experiment. This is ∼70% of the smORFs transcribed in S2 cells in the total mRNA controls (Figure 2D, small polysomes). To ensure that initial Poly-Ribo-Seq experiments sequenced to an adequate depth and to potentially extend the catalogue of translated smORFs, we repeated the experiment but exclusively sequenced small polysomes. This extensive small polysome profiling yielded nearly four times the number ORF-mapping reads obtained in the previous small polysome profiling (Supplementary file 1A) and detected translation of 224 smORFs (Figure 2D) expanding the number from both experiments to 227, which is 83% of smORFs we observed transcribed in S2 cells (Figure 2E). The genome-wide distribution of ribosome densities in two independent Poly-Ribo-Seq experiments was strongly correlated (R² = 0.83), suggesting that Poly-Ribo-Seq is highly reproducible (Figure 2B).

The majority of reads in both our experiments and previous ribosomal profiling consist of rRNA sequences released during footprinting (Supplementary file 1A; Ingolia et al., 2011). This reduces the depth of profiling and could preclude the detection of further smORFs. Therefore, we designed rRNA-depletion beads for use during footprint extraction (‘Materials and methods’, Supplementary file 2), which produced a marked improvement in the ratio of reads mapping to mRNAs (Supplementary file 1A) and increased the total number of ORFs detected (Figure 2—figure supplement 1A, −rRNA) but only expanded our overall catalogue of putatively translated smORFs by one (Figure 2D). The results of the three independent experiments are highly overlapping as 80% of putative translated smORFs were detected in all three data sets (Figure 2D). By combining the three experiments, we provide evidence that 228 smORFs are translated out of 274 transcribed in S2 cells (83%), which is very similar to the proportion of standard length protein-coding ORFs translated (81%) (Figure 2E). These similar proportions may indicate the extent of translational regulation in S2 cells. Altogether this data almost doubles the previous repertoire of translated smORFs in Drosophila from 164 to 285 (164 previously corroborated [Figure 1A] and 121 new translated smORFs).

Validation of smORF translation

The high overlap of our experiments suggests that the results do not arise from artefactual random sampling of smORFs, but most likely from the detection of the bona-fide population of annotated smORFs translated in S2 cells. To confirm this and independently validate our data, we compared our results with peptidomics data (Peptide Atlas, Brunner et al., 2007). Poly-Ribo-Seq increases nearly fourfold the number of smORFs with evidence of translation in S2 cells from 59 (Peptide Atlas) to 228 (Poly-Ribo-Seq). Poly-Ribo-Seq detects 86% of smORFs with Peptide Atlas evidence in S2 cells (51 out of 59 smORFs; Figure 2F), whilst only 8 smORFs, which have Peptide Atlas evidence are not shown to be translated by Poly-Ribo-Seq.

Detection of small peptides requires specific peptidomic methods (Boerjan et al., 2010; Slavoff et al., 2013), and this could have limited the number of smORF peptides detected in the generic proteomic experiments of Peptide Atlas. Therefore, we specifically searched for smORF peptides by performing mass spectrometry on two biological replicates of S2 cells after gel purifying small proteins 5 to 15 KDa in size, which corresponds to peptides predicted to be 45 to 130 aa in length. We detected a total of 60 annotated smORF peptides, of which 40 are not detected in Peptide Atlas S2 data sets (Figure 2F), thus bringing the combined pool of smORFs peptides detected by proteomics in S2 cells to 99 (Figure 2—figure supplement 1B). Despite this increase, Poly-Ribo-Seq was still more extensive. Poly-Ribo-Seq revealed 228 smORFs as translated, including 90 of the 99 in the combined proteomics pool (Figure 2—figure supplement 1B), and 59 of the 60 peptides detected by us (Figure 2F). The Poly-Ribo-Seq RPKM values of smORFs detected by peptidomics are over three times as high as those that are not (Figure 2—figure supplement 1B), suggesting that mass spectrometry detects peptides arising from the most highly translated smORFs, as also observed by other authors (Brunner et al., 2007; Bazzini et al., 2014).

To further validate the results of our smORF Poly-Ribo-Seq, we designed a peptide-tagging transfection assay. smORF coding sequences were tagged with a C-terminal FLAG tag lacking its own start codon. The constructs contained the full smORF 5′-UTR (which includes the Kozak and other sequences regulating translation [Kozak, 2005]) (Figure 3—figure supplement 1A). The resulting construct was transfected into S2 cells, where any FLAG signal would therefore be the result of smORF translation (Figure 3—figure supplement 1A,B). Transfection and staining of S2 cells with these FLAG-tagged smORFs confirmed the translation of all 12 smORFs tested, which exhibit a range of translational indicators (Figure 3A,B; Table 3) and peptidomics evidence, indicating that even lower levels of translation can give rise to smORF peptides detectable by this tagging method. Immunoblotting confirmed the expected sizes of the tagged peptides (Figure 3—figure supplement 1C). Tagged peptides exhibit distinct subcellular localisations, which are suggestive of different peptide functions (Figure 3A,B; Table 3). Six smORFs display a reticular distribution resembling mitochondria, an inference supported by their co-localization with the mitochondrial marker Mitotracker Red (Figure 3A and Figure 3—figure supplement 1D,E; Table 3) and the available information from homologues of two ‘corroborated’ smORFs in this group, CG32230 and CG14482 (Tripoli et al., 2005). Six smORFs exhibited other types of anisotropic cytoplasmic localisation, similarly to ER-expressed Sarcolamban smORF (Magny et al., 2013), indicating that they may localise to other cytoplasmic compartments (Figure 3B and Figure 3—figure supplement 1D,E; Table 3).

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Table 3

	Localization	Peptidomic evidence	# aa	RPKM	Coverage	TE	Phast Cons
CG32230	Mitochondrial	Yes	83	539.2	1.00	3.05	0.54
CG14482	Mitochondrial	Yes	57	600.0	1.00	1.09	0.72
CG44242	Mitochondrial	Yes	70	152.9	0.97	1.75	0.66
CG7630	Mitochondrial	Yes	90	702.2	1.00	1.05	0.64
CG33199	Mitochondrial	No	79	95.5	1.00	1.17	0.59
CG32582	Mitochondrial	No	52	16.5	0.57	2.82	0.51
sclA	Other cytoplasmic	NA	28	NA	NA	NA	NA
CG12384	Other cytoplasmic	Yes	96	205.6	1.00	1.37	0.71
CG33774	Other cytoplasmic	No	40	115.3	1.00	1.13	0.73
CG33170	Other cytoplasmic	No	71	84.2	0.84	0.75	0.60
CG34200	Limited	Yes	52	331.7	1.00	1.66	0.54
CG32267	Limited	Yes	49	82.5	0.97	1.13	0.70
CG33155	Limited	No	60	33.8	0.64	0.88	0.67
tal-B	None	NA	49	NA	NA	NA	NA

1. Details of the Poly-Ribo-Seq and transfection translation assay results for the FLAG-tagged smORFs, with RPKM, coverage and TE values. Previously corroborated smORFs (according to Figure 1A) are in bold. Scl is a positive control and tal-B is a negative control, but both are not endogenously transcribed in S2 cells, hence ‘NA’ Polysomal Ribo-Seq metrics and Peptidomic evidence.

The putative functionality of smORF peptides is further supported by over half of the translated smORFs having revealed a function in high-throughput S2 cell RNAi screens in previous studies (Schmidt et al., 2012; Figure 3—figure supplement 1F). The biological relevance of smORFs is also implied by the transcription of 196 of these translated smORFs in embryos (Supplementary file 1B). 88 of these smORFs are transcribed throughout the whole of embryogenesis, which might be indicative of a basic cellular or housekeeping role, whereas 47 have stage-specific expression, perhaps indicative of a developmental role.

Bioinformatic analysis of translated smORFs reveals specific characteristics

We scrutinised our set of annotated translated smORFs for bioinformatic markers, which might further suggest function of smORF peptides. Firstly, we used phastCons (Siepel et al., 2005) that measures conservation between 12 insect species.

We examined the phastCons values in intergenic sequences and canonical long protein-coding sequences (Figure 4A) and obtained a cut-off value of 0.55 separating them (10% FDR). 93% of S2-translated smORFs have phastCons scores above this threshold (median = 0.66), indicating a conservation level similar to that of canonical long-ORFs, and hence, a similar level of functionality for the coding sequences.

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As a further indicator of smORF translation, we studied the amino acid composition of translated smORFs, compared to canonical long proteins and expected random usage (Figure 4B). Annotated smORFs display a lower than random usage of arginine, which is a hallmark of translated proteins (King and Jukes, 1969). However, they also display differential usage of several amino acids, which are characteristic of alpha-helices in canonical proteins, being enriched for lysine and phenylalanine, and depleted of serine (Chou and Fasman, 1974; Figure 4B, Figure 4—figure supplement 1A). This finding was corroborated by an abundance of putative transmembrane alpha-helix motifs, in about a third of translated smORFs (Figure 4C) and all predicted smORFs (Figure 4D) compared to the expected 20% observed in canonical proteins (Krogh et al., 2001). This is in agreement with similar findings in bacteria (Hemm et al., 2008) and suggests that smORFs may represent a source of uncharacterised transmembrane peptides. An enrichment for molecular GO terms such as membrane transporter activity in annotated smORFs (Figure 4—figure supplement 1B), and the subcellular localisations we observe for half of the tagged smORFs, are also consistent with these findings.

The 555 annotated smORFs not transcribed in S2 cells, including 505 smORFs with uncorroborated translation, share the bioinformatic characteristics of the smORFs detected as translated by Poly-Ribo-Seq including: average peptide length (Figure 4E–F); amino acid usage (Figure 4B, Figure 4—figure supplement 1A); and abundance of putative transmembrane alpha-helices (Figure 4C,D). Therefore, our results showing translation and possible peptide function for 83% of the smORFs transcribed in S2 cells could be extrapolated to this wider pool, potentially bringing the number of smORFs encoding functional peptides in Drosophila to around 700.

The peptides encoded by uORFs and ncRNAs did not behave bioinformatically as smORFs and canonical long CDS, and thus we cannot easily extrapolate from our results to the uORFs and ncRNAs not transcribed in S2 cells. No indicator (phastCons, size, aa composition) was able to distinguish translated uORFs and ncRNAs from intergenic or random sequences (Figure 4—figure supplement 1C–D). Even though our Poly-Ribo-Seq detects translation of these uORFs and ncRNAs, perhaps the function of these smORFs is not mediated by their encoded peptides, or at least is compatible with a shorter, more variable and less canonical amino acid sequence (‘Discussion’). It appears that Poly-Ribo-Seq detects translation of two types of smORF: (a) ‘longer’ smORFs that are efficiently translated producing peptides ∼80 aa possessing bioinformatic hallmarks of peptide function; and (b) ‘dwarf’ smORFs translated from ncRNAs and 5′-UTRs, which are in general shorter (∼20 aa), less efficiently translated, and missing such bioinformatic and molecular markers.

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Author details

1. Julie L Aspden

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   JLA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Ying Chen Eyre-Walker

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   YCE-W, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

3. Rose J Phillips

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   RJP, Acquisition of data, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

4. Unum Amin

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   UA, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Muhammad Ali S Mumtaz

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   MASM, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Michele Brocard

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   MB, Conception and design, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

7. Juan-Pablo Couso

   School of Life Sciences, University of Sussex, Brighton, United Kingdom

   Contribution

   J-PC, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   j.p.couso@sussex.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

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