Eukaryotic gene expression is extensively controlled at the level of mRNA stability and the mechanisms underlying this regulation are markedly different from their archaeal and bacterial counterparts. We propose that two such mechanisms, nonsense‐mediated decay (NMD) and motif‐specific transcript destabilization by CCCH‐type zinc finger RNA‐binding proteins, originated as a part of cellular defense against RNA pathogens. These branches of the mRNA turnover pathway might have been used by primeval eukaryotes alongside RNA interference to distinguish their own messages from those of RNA viruses and retrotransposable elements. We further hypothesize that the subsequent advent of “professional” innate and adaptive immunity systems allowed NMD and the motif‐triggered mechanisms to be efficiently repurposed for regulation of endogenous cellular transcripts. This scenario explains the rapid emergence of archetypical mRNA destabilization pathways in eukaryotes and argues that other aspects of post‐transcriptional gene regulation in this lineage might have been derived through a similar exaptation route. A compelling example of this divergence is provided by mRNA destabilization mechanisms triggered by specific nucleotide and structural elements, base‐pairing between RNA molecules and unusual patterns of mRNA translation When discussing mRNA decay it is important to distinguish between the core machinery responsible for RNA degradation catalysis and controlled mechanisms that target specific subsets of mRNAs. In eukaryotes, the core machinery contains the Xrn family 5′–3′ exoribonucleases and the RNA exosome, a molecular complex combining 3′–5′ exoribonuclease and endoribonuclease activities Prokaryotes degrade their mRNAs using a distinct set of endo‐ and 5′–3′ exoriboucleases The difference between controlled mechanisms of mRNA decay in the two groups is arguably even more striking. In eukaryotes, the regulation is provided by small interfering (si), micro (mi) and Piwi‐interacting (pi) RNAs, nonsense‐mediated decay (NMD), and RNA‐binding protein‐based mechanisms targeting mRNAs with characteristic sequence or secondary structure elements for degradation Prokaryotic logic of controlled mRNA decay is fundamentally different Assuming that eukaryotes originated through symbiosis between archaea and bacteria and inherited a prokaryotic version of mRNA metabolism What could have triggered rapid rewiring of the mRNA metabolism in the newly established eukaryotic domain? Here we propose that at least some of the mechanisms regulating eukaryotic mRNA stability might have emerged as a part of cellular defense against RNA pathogens. According to this scenario, subsequent appearance of specialized innate and adaptive immunity systems allowed the host cells to repurpose, or “exapt” Viruses are exceptionally successful pathogens using their cellular hosts as a source of translation machinery, nucleotides and, occasionally, structural molecules and enzymes http://www.ictvonline.org/virusTaxonomy.asp; 2014 Release) lists 523 prokaryotic and 2666 eukaryotic viruses. Although not an exhaustive catalog of all known species, this sizeable sample suggests that RNA viruses, retroid viruses, and viroids account for ∼62% of eukaryotic viral species, whereas the corresponding figure for prokaryotes is only ∼1%. This striking over‐representation of eukaryotic RNA pathogens does not take into account eukaryotic retroelements, which appear more diverse than such elements in bacteria Although RNA‐dependent viruses have been isolated from both eukaryotic and prokaryotic hosts, an overwhelming majority of these pathogens infects eukaryotes Leviviridae and CystoviridaeEarlier phylogenetic analyses suggested that positive‐strand RNA viruses sharing picorna‐like genome architecture emerged through a “Big‐Bang” event preceding radiation of the major eukaryotic supergroups Eukaryotic retroid viruses and retrotransposable elements likely descended from prokaryotic retroelements Regardless of the exact evolutionary trajectories followed by specific groups of viruses and retroelements, it is fair to assume that eukaryotes have been exposed to an increasingly wide range of RNA‐based pathogens since their early days. If so, long‐term survival of the newly established eukaryotic lineage would have been impossible without adequate defense mechanisms.One such mechanism present in most extant eukaryotes and likely used by the LECA is RNA interference (RNAi) Metazoans use several additional levels of antiviral defense including the Piwi/piRNA system targeting retrotransposons, innate immunity based on recognition of pathogen‐associated molecular patterns (PAMPs) by host‐encoded pattern recognition receptors (PRRs), and immunoglobulin‐ and T‐cell receptor‐dependent adaptive immunity RNAi itself has important functions unrelated to immunity and might completely forego its antiviral responsibilities in many mammalian cells The animal and plant miRNA pathways appear to have evolved independently following radiation of the corresponding supergroups, Opisthokonta and Archaeplastida, from a common ancestor Below, we discuss the possibility that, similar to RNAi and the miRNA pathway, two well‐characterized eukaryotic systems regulating stability of cell‐encoded mRNAs, emerged as intrinsic defense mechanisms against RNA pathogens.NMD is a conserved eukaryotic mechanism destabilizing mRNAs with unusually positioned translation stop codons As discussed in several recent reviews Saccharomyces cerevisiae.mRNAs are typically recognized as NMD targets in the cytoplasm during initial rounds of translation. The presence of exon‐exon junctions >50–55 nt downstream of the termination codon, upstream ORFs in the 5′UTR, or an excessively long 3′UTR increases the likelihood of an mRNA to undergo NMD. Mechanistic details of this pathway differ depending on the species, and a single organism may utilize more than one distinct branch of NMD Fig. . For exastramenopiles, alveolates, Rhizaria) Notwithstanding this functional diversity, genes encoding NMD and EJC components are conserved across metazoans, fungi, plants, and members of the unicellular supergroups: Amebozoa, Excavata, and SAR  inhibited replication of +RNA viruses from the families of Alphaflexiviridae and Tombusviridae. Similar to Togaviridae, these viruses use a subgenomic strategy to express a full complement of their proteins. This gives rise to unusually long 3′UTRs in some of the virus‐encoded RNAs. The authors showed that reducing the length of the 3′UTR in the corresponding RNA species rescued them from the inhibitory effect of NMD. Moreover, a +RNA virus from the Potiviridae family containing a single ORF and a relatively short 3′UTR escaped NMD restriction Antiviral effects of NMD have been also shown in plants Smg7 and the Upf1 genes lead to retarded development and seedling death as a result of elevated expression of antibacterial defense genes A study by Gloggnitzer et al. points at a wider role of NMD in the innate immunity in plants Upf1 has been additionally identified as a cell‐encoded protein interacting with bicistronic RNA of the human non‐LTR retroelement LINE‐1 Of note, RBPs from the Staufen family can induce an NMD‐related process that destabilizes a subset of mammalian transcripts in a Upf1‐ and translation‐dependent manner Retroviridae family, human T‐lymphotropic virus type 1, inhibits the NMD machinery in part through interaction between Upf1 and the virus‐encoded Tax protein Viruses often evolve mechanisms allowing them to evade or disrupt host defenses and this trend is certainly apparent in the case of NMD. For example, Rous sarcoma retrovirus containing several ORFs in its genomic RNA contains a specialized stability element downstream of the first ORF encoding Gag and Pol proteins Flaviviridae family member, interferes with NMD by sequestering an EJC‐associated factor, PYM1/WIBG Similarly, coat protein of hepatitis C virus (HCV), a Another characteristic form of controlled mRNA decay in eukaryotes relies on recognition of specific sequence motifs and structural elements present in a subset of cellular transcripts. RBPs containing CCCH‐type zinc fingers (ZFs) have been widely implicated in this regulation One of the most extensively studied members of this protein group is mammalian Zfp36 also known as tristetraprolin (TTP) TTP has been shown to regulate the stability of several mammalian mRNAs including those encoding proto‐oncogenes, growth factors, and cytokines (including some interferons) Conversely, basal levels of TTP can transiently increase in response to toll‐like receptor (TLR) and cytokine signaling Drosophila homolog of TTP called dTIS11 has been shown to destabilize ARE‐containing targets including mRNA of the antimicrobial peptide cecropin A1 ArabidopsisProteins containing the TTP‐like TZF domain have been identified in metazoa, fungi, plants, Amebozoa, and Excavata In line with this model, a large fraction of human ARE sequences is associated with Alu elements, an abundant SINE group Liang et al. provide an important insight into the CCCH ZF protein evolution The Zc3h12 ZFs are especially closely related to the TTP ZFs see Fig. . The besZc3h12a/Regnase or Rc3h1/Roquin1 genes are somewhat evocative of the effects brought about by the Zfp36/TTP knockout Regnase recognizes secondary structure elements containing a stem and a trinucleotide loop and its PIN domain participates in RNA binding along with the ZF Flaviviridae , Picornaviridae  and Togaviridae (Sindbis virus). It may also interfere with replication of some −RNA viruses  and reverse‐transcribing lentiviruses  Flaviviridae, the ZF domain was also shown to stimulate binding of Regnase to viral RNAs Importantly, several recent studies suggest that, in addition to its role in cellular mRNA metabolism, Regnase can restrict replication of +RNA viruses from the families of Although cis‐elements mediating antiviral effects of Regnase are presently unknown, efficient replication of RNA viruses often requires conserved RNA structures that might, at least in theory, function as a Regnase “specificity code.” Interestingly, repression of viral replication by Regnase depends on the ability of this protein to form oligomers Iridoviridae family and +RNA viruses from the order of Nidovirales (mentioned in the previous chapter as viruses with a Upf1‐like RNA helicase) encode TZF‐domain proteins of unknown function , combining it with other RNA interaction domains (as in Regnase and Roquins) or forming quaternary protein units (as in Regnase and ZAP) might have extended versatility of this system allowing it to recognize a wide range of linear and folded RNA epitopes. Interestingly, no prokaryotic proteins containing more than one CCCH ZF have been identified so far  should be addressed to the authors.Table S1. Viral TZF‐containing proteins showing detectable sequence similarity to human TTP/ZFP36 Click here for additional data file.


Neurospora crassa homolog of yeast Mtr4, an essential RNA helicase that plays a central role in RNA metabolism as an activator of the nuclear RNA exosome. FRH is also a required component of the circadian clock, mediating protein interactions that result in the rhythmic repression of gene expression. Here we show that FRH unwinds RNA substrates in vitro with a kinetic profile similar to Mtr4, indicating that while FRH has acquired additional functionality, its core helicase function remains intact. In contrast with the earlier FRH structures, a new crystal form of FRH results in an ATP binding site that is undisturbed by crystal contacts and adopts a conformation consistent with nucleotide binding and hydrolysis. Strikingly, this new FRH structure adopts an arch domain conformation that is dramatically altered from previous structures. Comparison of the existing FRH structures reveals conserved hinge points that appear to facilitate arch motion. Regions in the arch have been previously shown to mediate a variety of protein-protein interactions critical for RNA surveillance and circadian clock functions. The conformational changes highlighted in the FRH structures provide a platform for investigating the relationship between arch dynamics and Mtr4/FRH function.The FRH (frequency-interacting RNA helicase) protein is the  Neurospora crassa FRH (frequency-interacting RNA helicase) is a 124 kDa homolog of Saccharomyces cerevisiae Mtr4 , a Ski2-like ATP-dependent RNA helicase that plays a central role in activating the nuclear exosome to promote processing or complete degradation of RNA substrates /; where [E] is FRH concentration, K1/2, E is functional affinity, and kunwmax, E is the unwinding rate constant at enzyme saturation.Data were collected in triplicate and fit as previously described (Fraction unwound = unw*t))) , 29. TheFRH was crystallized by hanging drop vapor diffusion in 0.2 M Sodium Citrate pH 5.6, 19% PEG 3350 at room temperature, with 10.5 mg/ml FRH (1:2 protein:well drop ratio). Crystals were transferred to a stabilization solution containing the well solution and 15% glycerol, then flash frozen in liquid Nitrogen.121 and contains one molecule in the asymmetric unit . The FRH structure was solved by molecular replacement using Mtr4 as a search model (PDB ID 4U4C). The initial maps revealed a significant repositioning of the arch domain. The domain was rebuilt and modifications to the rest of the structure were completed through iterative rounds of model building and refinement. Final refinement involved positional, individual b-factor, and TLS refinement utilizing secondary structure restraints and reference model restraints using Mtr4 (PDB ID 4U4C) as a reference model [Crystallographic data for full-length FRH crystals were collected to 3.5 Å on beamline 14–1 at the Stanford Synchrotron Radiation Lightsource SSRL, . Data wece model . Phaser-ce model  and Phence model  as implece model  was usedce model . Structuce model . Final rSedimentation velocity analytical ultracentrifugation (SV-AUC) experiments were performed using an Optima XL-I (Beckman Coulter) analytical ultracentrifuge. Purified full length FRH was analyzed at 2.9 mg/ml, 1.5 mg/ml, 0.5 mg/ml and 0.25 mg/ml. Protein solution and a reference buffer were loaded into Beckman charcoal-epon two sector cells with 12 mm path lengths and quartz windows. The samples were analyzed at 42,000 RPM and 20°C using absorbance at 280 nm until complete sedimentation was achieved. The data were regularized with a confidence interval of 0.95 and analyzed using Sedfit  with a cS1 FigN. crassa FRH and S. cerevisiae Mtr4 sequence alignments [Previous ignments , 20 have(PDF)Click here for additional data file.


The cyclooxygenase-2 is a pro-inflammatory and cancer marker, whose mRNA stability and translation is regulated by the CUG-binding protein 2 interacting with AU-rich sequences in the 3′ untranslated region. Here, we present the solution NMR structure of CUG-binding protein 2 RRM3 in complex with 5′-UUUAA-3′ originating from the COX-2 3′-UTR. We show that RRM3 uses the same binding surface and protein moieties to interact with AU- and UG-rich RNA motifs, binding with low and high affinity, respectively. Using NMR spectroscopy, isothermal titration calorimetry and molecular dynamics simulations, we demonstrate that distinct sub-states characterized by different aromatic side-chain conformations at the RNA-binding surface allow for high- or low-affinity binding with functional implications. This study highlights a mechanism for RNA discrimination possibly common to multiple RRMs as several prominent members display a similar rearrangement of aromatic residues upon binding their targets. The RNA Recognition Motif (RRM) is the most ubiquitous RNA binding domain. Here the authors combined NMR and molecular dynamics simulations and show that the RRM RNA binding surface exists in different states and that a conformational switch of aromatic side-chains fine-tunes sequence specific binding affinities. They are characterized by two conserved ribonucleoprotein (RNP) consensus sequences; RNP1 and RNP2 composed of aromatic and charged residues interspersed by hydrophobic ones. RRMs are about 90 amino acids long and adopt a βαββαβ secondary structure forming an antiparallel β-sheet stacked onto two α-helices1. Canonically, they bind two to three nucleotides on the β-sheet through the RNPs. RNA-binding proteins such as CUG-BP2 contain multiple RRM copies and are involved in several regulatory processes. To perform successfully its various functions, CUG-BP2 recognizes numerous RNA targets. The protein binds to CUG triplet repeats2, but SELEX experiments identified the UG dinucleotide and the 5′-UGUU-3′ sequence to be bound preferentially3. These motifs are found in several mRNA targets of CUG-BP2, notably in the cystic fibrosis transmembrane conductance regulator mRNA4. UV-cross linking and gel shift assays established that AU-rich sequences are also targets of CUG-BP26; these include the AU-rich sequence found upstream of the edited cytosine of apolipoprotein B mRNA and the adenine- and uridine-rich elements (AREs) mostly localized in the 3′-untranslated region (UTR) of unstable mRNAs7. The latter are 50–150 nucleotide long sequences composed of AUnA repeats. They associate with ARE-binding proteins such as CUG-BP2 that can positively or negatively modulate mRNA stability and translation levels. CUG-BP2 stabilizes the mRNA of the cyclooxygenase 2 (COX-2), which is a regulatory enzyme of prostaglandins metabolism but inhibits its translation. COX-2 is overexpressed in epithelial malignancies, where it plays a role in tumorigenesis and protection against damage due to γ-radiation5. In response to apoptotic stimuli9, the cytoplasmic concentration of CUG-BP2 increases thus down-regulating COX-2 and thereby favoring apoptosis. Owing to the variety of its functions and binding partners, studying the binding mode of CUG-BP2 to its multiple RNA sequence targets can help elucidate not only its role in ARE-mediated mRNA decay but also how proteins, and particularly the ubiquitous RRM-containing proteins discriminate various RNA targets.Messenger RNA processing is an important landmark in gene regulation and a plethora of RNA-binding proteins controls and modulates its different steps acting as essential components of ribonucleoparticles. Understanding protein-RNA recognition mechanisms in general and the interaction of the ubiquitous RNA recognition motif (RRM) protein domains with their cognate targets in particular is essential to gain insight in posttranscriptional gene regulation. RRMs are the most abundant RNA-binding domainCUG-BP2 belongs to the “CUG-BP1 and ELAV type RNA-binding protein 3-like factor” (CELF) family. All six members share structural and sequence similarities but CUG-BP1 and CUG-BP2 are the most closely related with over 90% identity in conserved regions. CELF proteins contain three RRMs Fig. ; the two11. The available crystal structures reveal that RRM1 and RRM2 bind the 5′-UGUU-3′ motif similarly. At the β-sheet RNA-binding interface of both domains, the UG dinucleotide shares structural features with the left-handed Z-RNA helix and only the last two uracils stack with the conserved phenylalanines of the RNPs. All four nucleotides are specifically recognized by hydrogen-bonding to the protein backbone and side-chains. In contrast, the solution structure of CUG-BP1 RRM3 in complex with (UG)3 shows that four out of six nucleotides stack on aromatic residues of the β-sheet surface and specific recognition is ensured by a dense network of hydrogen-bonds between RNA moieties and the protein backbone and polar side-chains. Owing to the high degree of identity between CUG-BP1 and CUG-BP2 RRMs, these structures provide very detailed insight into the binding mode to UG-rich RNAs for both proteins.Structures of the two N-terminal RRMs of CUG-BP1 bound to UG-rich RNAs containing the 5′-UGUU-3′ motif and of the third RRM bound to 5′-UGUGUG-3′ have been determined311 RNAs. However, the aromatic side-chains undergo conformational rearrangement when bound to 5′-UGUGUG-3′, whereas they remain in a conformation similar to the free protein in presence of 5′-UUUAA-3′. The two complexes differ in affinity but mutational studies revealed an unexpected affinity increase for both types of RNA sequences upon replacement of different aromatic residues by alanine. Scalar coupling measurements and molecular dynamics (MD) simulations demonstrated that the binding surface of CUG-BP2 RRM3 exists in multiple states and that a conformational switch of aromatic side-chains is at the origin of the fine tuning of the affinity for different targets.To understand how CUG-BP2 recognizes AU-rich RNAs and how the same RRM can recognize diverging targets (UG-rich and AU-rich RNAs), we focused our investigations on the C-terminal RRM, which has a particularly high density of aromatic residues at the binding interface. In addition to the three canonical aromatic residues of the RNP1 and RNP2, two phenylalanines and one histidine are exposed on the β-sheet surface. We here determined the solution structure of RRM3 bound to 5′-UUUAA-3′ RNA revealing that the RRM3 uses the same protein moieties to recognize the AU-rich and (UG)1H–15N HSQC indicating RNA binding 11.We probed the sequence-specific nature of the complex by scanning mutagenesis of 5′-UUUAA-3′ RNA. Replacing individually each nucleotide by a cytosine does not change the apparent affinity despite the loss of a few sequence-specific contacts  that describes the side-chains orientation relative to the protein backbone using spin-echo difference 2D 1H–15N-HSQC experiments14 and extracted 3J NCγ and 3J C′Cγ scalar coupling constants of aromatic residues whose magnitude are dependent on χ1   Table , which w1H–15N HSQC spectra of the non-canonical aromatic mutants of RRM3 to that of the wild-type in the free state shows that the substitutions of Tyr428, His429 and Phe455 to alanine induce large chemical shift perturbations that propagate from the mutation site through the whole β-sheet  of Y428A, H429A and F455A deviate from the wild-type values  does not have the optimal geometry. In contrast, RRM3 has significantly more affinity for the UG-rich RNA, which is reflected by the higher number of intermolecular contacts comprising four additional hydrogen-bonds and one additional stacking interaction  than that of A4 observed in the RRM3/5′-UUUAA-3′ complex. Finally, U5 can stack on Phe455 due to its trans rotamer orientation in the DOWN conformation. Our data demonstrate that CUG-BP2 RRM3′s discrimination between the various RNAs depends not only on the nature and the number of the intermolecular contacts formed in the complex, but also on the conformational states of the aromatic residues in the free protein and their ability to reorient to adopt the DOWN conformation, which is more favorable for binding purine containing RNA since better stacking and more hydrogen-bonds can be formed.Although the nucleotides of 5′-UUUAA-3′ and 5′-UGUGUG-3′ RNAs are accommodated in the same binding pockets of the protein, the conformations of the aromatic side-chains at the binding interface differ between the two complexes. RRM3 has a lower affinity for the 5′-UUUAA-3′ RNA, which is bound in the UP conformation. In this conformation, Uχ1 rotamers). The UP conformation, which is highly populated in the free state is stabilized by intramolecular interactions and is bound by AU-rich RNAs with low affinity. In contrast, high-affinity targets (UG-rich RNAs) can select the more scarcely populated DOWN conformation and stabilize it by providing more favorable intermolecular interactions. This mechanism of conformational selection allows CUG-BP2 RRM3 to discriminate efficiently between different sequences rich in uracils and purines depending on whether they are able to stabilize the DOWN conformation or not. Our results are consistent with a model in which the aromatic side-chains at the RRM-binding surface exist in an ensemble of interconverting conformations, which are tunable by the binding of an RNA target. Scalar coupling data and MD simulations indicate that although the UP and DOWN states are most prominent, interconversion to other side-chains rotamer states takes place. In the free form, mutants replacing non-canonical aromatics by the less bulky alanine exhibit a conformational shift towards a majority of the DOWN conformation compared to the wild-type RRM3 3-4 stretches and cytoplasmic polyadenylation elements through a Venus fly trap mechanism23. Overall, intra-domain rearrangements like in CUG-BP2 RRM3 may be an additional important mechanism for RNA discrimination by RRMs.Irrespective of their free conformation, most RRMs with canonical aromatic residues interact with RNA in the DOWN conformation with the notable exception of CUG-BP2 RRM3 bound to 5′-UUUAA-3′. Interestingly, RRMs that show aromatic side-chain rearrangement upon RNA binding  was cloned into pET 28a vector (Novagen) between the NdeI-XhoI restriction sites. To facilitate protein quantification, the C-terminal tyrosine was replaced by a tryptophan by Quick-changeE. coli cells (Agilent Technologies). Pre-culture and culture were carried out at 37 °C in M9 minimum media containing 1 g.L−1 of 15N NH4Cl and 8 g.L−1 of unlabelled glucose or 4 g.L−1 of 13C labelled glucose. The media was complemented with kanamycin and chloramphenicol. At an OD600 of 0.6–0.8, the culture was cooled down to 20 °C and induced with IPTG to a final concentration of 1 mM. Cells were collected after 12–16 h at 7878 x g for 30 min at 4 °C. The protein was purified by Ni-affinity chromatography using an imidazole gradient on an ÄKTA Prime purification system (Amersham Biosciences) or step-wise by gravity flow. The protein-containing eluate was concentrated and exchanged into a low salt buffer. Removal of contaminant nucleic acids was achieved by cation exchange chromatography . After another concentration step, the protein was incubated for 1 h at room temperature with Ambion RNAse inhibitor and subsequently purified by size exclusion in NMR buffer  and concentrated to 0.6–2 mM.The plasmid was transformed in BL21 DE3 Codon + Protein concentration was determined by absorbance measurement at 280 nm wavelength and application of the Lambert-Beer’s law.hnRNP A1 RRM1 (residues 416–508) cloned into pET 28a vector (Novagen) was a gift from Pierre Barraud.E. coli cells (Agilent Technologies). Pre-culture and culture were carried out at 37 °C in M9 minimum media containing 1 g.L−1 of 15N NH4Cl and 4 g.L−1 of 13C labelled glucose. The media was complemented with kanamycin and chloramphenicol. At an OD600 of 0.6–0.8, the culture was cooled down to 30 °C and induced with IPTG to a final concentration of 0.5 mM. Cells were collected after 6 h at 7878 x g for 30 min at 4 °C. The protein was purified by Ni-affinity chromatography using an imidazole gradient on an ÄKTA Prime purification system (Amersham Biosciences). The protein-containing eluate was concentrated and dialyzed against hnRNP A1 NMR buffer  and concentrated to 2 mM. Protein concentration was determined by absorbance measurement at 280 nm wavelength and application of the Lambert-Beer’s lawThe plasmid was transformed in BL21 DE3 Codon + Oligonucleotides were purchased from Dharmacon, Inc and deprotected with the provided deprotection buffer at 60 °C for 30 min. The RNA was frozen in liquid nitrogen, lyophilized and stored at −20 °C. For NMR studies, the RNA was re-suspended in NMR buffer.Kd value obtained from 15N-HSQC titrations.To determine the solution structure of CUG-BP2 RRM3 in complex with 5′-UUUAA-3′, the protein and the RNA were mixed at ratios of 1:1 or 1:3. At the latter ratio, 96% of the protein is saturated based on calculation of the expected fraction of free protein by using the The flag-tag sequence was inserted between NheI and HindIII in the pcDNA3.1 vector. CUG-BP2 full length, RRM12 and RRM3 coding sequences were cloned into the modified pcDNA3.1 + vector between the HindIII and XhoI restriction sites. The F426A and Y428A mutants were obtained by site directed mutagenesis.24, by using the 5′-CTAGGCGACTCGAGGATCGCCGTGTAATTCTA-3′ and 5′-TGGCCGGCGGCCGCTATCATGTCTGCTCGAAG-3′ forward and reverse primers, respectively to introduce XhoI and NotI sites. The PCR insert was cloned into the 3′-UTR of the renilla luciferase gene in the psiCHECK 2 vector (Promega).The first 60 nucleotides of the murine COX-2 3′-UTR (which has 80% sequence identity to the human COX-2 3′-UTR) were amplified from a pGL3 plasmid which was a generous gift from Aubrey Morrison2.HEK-293T (ECACC No. 85120602) cells were grown in Gibco D-MEM medium supplemented with GlutamaxTM, 10% fetal bovine serum in a humidified chamber at 37 °C with 5% COt-test. Assays were performed in three replicates and experiment were repeated 3 times.For the luciferase reporter gene assay, HEK-293T cells were seeded in 24 wells plates at day0. At day1, they were transfected transiently using lipofectamine 2000 (Invitrogen) with 0.1 µg of the luciferase reporter gene and 0.3 µg of the relevant CUG-BP2 construct. After 24 h, cell lysis and luciferase activity determination were done as per the manufacturer’s instructions  using a Berthold Mithras LB 940. The renilla activity was normalized to the luciferase activity, is presented as relative luciferase units relative to control with  ±  the standard error of the mean (SEM) values. The significance of the differences between the samples was tested by a Western blot analysis of the whole-cell lysate was performed by using the monoclonal anti-FLAG M2 antibody produced in mouse (Sigma) (1:2500 in TBST- 5% milk blotting powder) and monoclonal anti-α-Tubulin antibody produced in mouse (1:20000 in TBST- 5% milk blotting powder). After secondary antibody incubation (Anti-Mouse IgG (whole molecule) Peroxidase antibody produced in rabbit, in 1:50,000 in TBST- 5% milk blotting powder), western was detected by using the Amersham ECL Prime Western Blotting Detection Reagent . Western blots of two biological replicates were done.2O (v/v). The data were processed using Topspin 2.1 and 3.0 (Bruker) and analysed using SPARKY 3.11325.NMR experiments were performed on AVANCE III  and AVANCE III HD 600 MHz Bruker spectrometers. For NMR measurements with sample volumes below 450 μl 5 mm NMR tubes from Shigemi Inc. were used . For larger samples, 5 mm NMR tubes from ARMAR AG  were used. Typical NMR protein concentrations were 0.6−2 mM in NMR buffer and 10% D15N–1H HSQC, 2D 13C–1H HSQC, 3D HNCA, 3D CBCACONH, 3D 3D (H)C(CCO)NH TOCSY, 3D H(CCCO)NH TOCSY, 3D NOESY 15N–1H HSQC and 3D NOESY 13C–1H HSQC, all recorded in H2O at 40 °C. To sequentially assign 5′-AUUUAAUU-3′ unlabeled RNA bound to CUG-BP2 RRM3, we recorded the following experiments in D2O at 40 °: 2D 1H–1H TOCSY, 2D F1 13C-filtered, F2 13C-filtered 1H–1H NOESY and natural abundance 2D 13C–1H HSQC. Intermolecular NOEs of the CUG-BP2 RRM3-RNA complex were obtained from a 2D 1H–1H NOESY, 2D F2 13C-filtered 1H–1H NOESY and 3D F1 13C-filtered 1H–1H NOESY, all recorded in D2O.The backbone resonance assignments of free CUG-BP2 RRM3 were previously determined by Fred Damberger and Neel Bavesh. The backbone and side-chain assignments CUG-BP2 RRM3 in complex with 5′-AUUUAAUU-3′ were obtained with the following experiments: 2D15N–1H HSQC and two 3D NOESY 13C–1H HSQCs centered on aliphatic or aromatic 13C signals, all recorded in H2O at 25 °C. Protein backbone and side-chain assignments were derived from the assignments obtained for the complex with the longer RNA and adapted to the NOESY spectra recorded on CUG-BP2 RRM3 in complex with 5′-UUUAA-3′. To determine the protonation state of the histidines, we recorded long-range 2D 15N–1H HSQCs26. We measured 3D HNHA experiments to obtain backbone quantify the 3JHN–HA coupling constant and determined ψ dihedral angles28. To sequentially assign 5′-UUUAA-3′ RNA and obtain intermolecular NOEs, we recorded the following experiments in D2O at 25°: 2D 1H–1H TOCSY, 2D F2 13C-filtered 1H–1H NOESY and natural abundance 2D 13C–1H HSQC. Intermolecular NOEs were obtained by using a 2D 1H–1H NOESY, 2D F2 13C-filtered 1H–1H NOESY and 3D F1 13C-filtered 1H–1H NOESY 13C–1H HSQC.For the CUG-BP2 RRM3 in complex with 5′-UUUAA-3′, we recorded a 3D NOESY 3J NCγ and 3J C′Cγ coupling constants was carried out by recording spin-echo difference constant time 15N–1H HSQC and HN(CO)CG experiments29, respectively. The pulse sequences were modified compared to the published version. The 180° 13C shaped pulses were changed from G330 to Q331 to improve selectivity. The 90° C’ pulses were changed from rectangular to Q531 shape. At 600 MHz, the durations of the Q3 C’ and Cγ 180° pulses, of the Q3 Caliphatic 180° pulses and of the Q5 C’ 90° pulses are 760 µs, 512 µs and 760 µs, respectively. The reference and the coupled experiments were interleaved and all the measurements were repeated at least three times.Measurements of the aromatic Ib/Ia = cos (2πJCγτ), where Ia is intensity of an H–N correlation in the reference spectrum and Ib is the intensity of the same peak in the spectrum where the relevant coupling was active for either the amide signal of the i + 1 residue in the 3J C′Cγ experiment or for the i residue in the 3J NCγ experiment, where i is the residue whose coupling is determined. In 3J C′Cγ experiments, τ was set to 40 or 50 ms for CUG-BP2 RRM3 and hnRNP A1 RRM1, respectively. In 3J NCγ experiments τ was set to 65 ms. The values of τ used were determined empirically to maximize the intensity for signals of interest when calculating the difference between reference and coupled spectrum.The coupling constants were calculated from the relationship 15N–1H HSQC, and the 3D NOESY 13C–1H HSQCs performed by atnoscandid34. The peak lists generated by atnoscandid but not the manually assigned aromatic NOEs were carried over to the next step. Automated NOE assignment of intra-protein NOEs was achieved using the macro noeassign within cyana 3.035 and manual curation.In NOESY spectra, NOEs originating from the aromatics at the binding interface were manually assigned to derive upper limit restraints. Those were input for the iterative process of automated peak picking of the 3D NOESY 6 relationship. Structure calculation was carried out using cyana 3.0 and included distance restraints, TALOS36 derived dihedral angle constraints in agreement with the determined 3J HN-HA coupling constants, intra-protein hydrogen-bond constraints derived from hydrogen–deuterium exchange experiments on the amide protons and RNA dihedral angle constraints based on 1H1′–1H2′ coupling efficiency in homonuclear 2D TOCSYs measured with short TOCSY mixing times. 200 structures were calculated with 20000 torsion angle dynamics steps per conformer. The best 50 conformers were subsequently refined in implicit water using the force field rna.ff99SB in the SANDER module of AMBER 1238. Finally, the 20 conformers with the lowest amber energy were submitted to CING39 for structure validation.RNA intramolecular and intermolecular NOEs were manually assigned and calibrated using a 1/dITC experiments. ITC was performed using VP-ITC calorimeter . Protein was injected into the microcalorimeter cell containing the RNA. The protein and RNA samples were dialysed against the same buffer . Wild-type and mutant RRM3 proteins (0.13–0.47 mM) were titrated by injection of 6 μl into 2 ml of 8 or 9 µM RNA. The experiments were carried out at 25 °C. The raw data were analysed using Microcal Origin 7.0 software with the single binding site model.1H–15N HSQC spectra were quantified using the following equation:δHN and δN are the proton and nitrogen chemical-shift differences of the same signal.NMR titration. Chemical shift changes in P]0 and [L]0 are the concentrations of total protein and RNA, respectively, Kd the dissociation constant, ΔCS,max the maximum frequency difference, ΔCSobs the observed frequency at a given titration point and c a correction coefficient for the protein concentration determined by absorbance measurements with a calculated extinction coefficient.Subsequently, the NMR titration data were fitted using the following equation in Matlabχ1 rotamer populations were determined under the assumption that the values of the 3J-couplings reflect a population mixture of the three staggered states using the Karplus parametrization published previously15. Accordingly:3Jcalc, NCγ and 3Jcalc, C′Cγ are the calculated coupling constants, p180p−60 and p+60 are the populations of the respective χ1 rotamer states trans, gauche + and gauche- respectively; and Jt, Jg +, and Jg- for NCγ and C′Cγ are the expected coupling values for the fully populated staggered rotamer states with χ1 equal to 180°, −60°, and + 60°, respectively. Populations were fitted in Mathematica by minimization of the squared difference between the experimental and calculated 3J NCγ and/or 3J C′Cγ coupling constants:σJexp is the standard deviation based on three or more 3J measurementsThe Structure building and force field selection. We have used the first conformers of the NMR ensembles of the free CUG-BP1 RRM3 domain structure (PDB: 2rq4)11, its complex with (UG)3 RNA (PDB: 2rqc)11 and 5′-UUUAA-3′ RNA (this work), respectively, as the starting structures for our MD simulations. The coordinate and topology files were created using the tleap module of Amber 1440. We have used the ff99bsc0χOL344 and ff12SB46 force fields to describe the RNA and the protein, respectively.System solvation. The biomolecules were solvated in an octahedral box of SPC/E waters47 with a minimal distance of 10 Å between the solute and the edge of the box. The systems were neutralized by addition of KCl salt48, achieving a 150 mM excess-salt concentration.Simulation protocol. Prior to the production simulations, the systems were minimized and equilibrated using a standard equilibration protocol for protein/RNA systems49. We have then used the initial portions of the production simulations to stabilize the structure using the experimental NMR restraints50. This was followed by free unrestrained simulations.52. The cut-off distance for Lennard-Jones interactions was 9 Å. We have used the SHAKE algorithm53 to constrain the covalent bonds involving hydrogen along with the HMR scheme54, allowing a 4 fs integration step to be used. Berendsen thermostat and barostat32 were used to maintain the systems at temperature and pressure of 300 K and 1 bar, respectively.The particle mesh Ewald was used for calculation of the electrostatic interactionsSimulation analyses. We have used the cpptraj module of Amber1440 to analyze the simulation trajectories. The VMD program was used for visualization55. To evaluate the simulation agreement with the experimental data, we have calculated (r-6)(−1/6) weighted averages of the NOE distances in the simulation ensembles. These values were then compared with the experimentally measured upper bound distances of the individual NOEs.A list of the simulation can be found in Supplementary Table Coordinates of CUG-BP2 RRM3 in complex with 5′-UUUAA-3′ have been deposited in the Protein Data Bank under the accession code 5M8I. The corresponding chemical shift assignments and scalar couplings have been deposited in the BioMagResBank (BMRB) under the accession number 34057. The scalar couplings of CUG-BP2 RRM3 wild-type free or in complex with 5′-UGUGU-3′, of CUG-BP2 RRM3 Y428A free or in complex with 5′-UUUAA-3′, of CUG-BP2 RRM3 H429A free or in complex with 5′-UUUAA-3′, and of CUG-BP2 RRM3 F455A free or in complex with 5′-UUUAA-3′ have been deposited under the BMRB accession numbers, 27140, 27154, 27142, 27155, 27152, 27156, 27153 and 27157, respectively. The scalar coupling constants of hnRNPA1 RRM1 free or in complex with 5′-UUAGGCU-3′ have been deposited under the BMRB accession numbers 27163 and 27164, respectively. Other data supporting the findings of this manuscript are available from the corresponding author upon reasonable request.Supplementary InformationPeer Review file


Mesophotic coral ecosystems, which occur at depths of ~40 to 150 m, have received recent scientific attention as potential refugia for organisms inhabiting deteriorating shallow reefs. These ecosystems merit research in their own right, as they harbor both depth-generalist species and a distinctive reef-fish fauna. Reef ecosystems just below the mesophotic are globally underexplored, and the scant recent literature that mentions them often suggests that mesophotic ecosystems transition directly into those of the deep sea. Through submersible-based surveys in the Caribbean Sea, we amassed the most extensive database to date on reef-fish diversity between ~40 and 309 m at any single tropical location. Our data reveal a unique reef-fish assemblage living between ~130 and 309 m that, while taxonomically distinct from shallower faunas, shares strong evolutionary affinities with them. Lacking an existing name for this reef-faunal zone immediately below the mesophotic but above the deep aphotic, we propose “rariphotic.” Together with the “altiphotic,” proposed here for the shallowest reef-faunal zone, and the mesophotic, the rariphotic is part of a depth continuum of discrete faunal zones of tropical reef fishes, and perhaps of reef ecosystems in general, all of which warrant further study in light of global declines of shallow reefs. This is due in part to the global decline of shallow coral reefs having sparked interest in the potential for deep reefs to act as refugia for shallow-water organisms stressed by warming surface waters or deteriorating reefs. Variously called the “coral-reef twilight zone” or “deep reefs,” mesophotic coral ecosystems are light-dependent coral communities at tropical and some higher latitudes that generally are considered to extend from 30–40 m to as deep as 150 m11. Although the boundary between shallow and mesophotic ecosystems initially was established based on the lower limit for conventional scuba diving rather than substantial turnovers in species composition13, there is supporting biological evidence for both corals14 and reef fishes15. The lower limit of the mesophotic is defined as the maximum depth at which there is sufficient penetration of sunlight to support photosynthesis and, hence, the growth of zooxanthellate coral reefs2. Some coral biologists divide the mesophotic into upper and lower portions, with a faunal transition of species around 60 m16.Studies of deep tropical-reef ecosystems have surged during the past decade13. A faunal break among reef fishes occurs between ~60 and 90 m, thus also resulting in the recognition of upper and lower mesophotic zones for such fishes18. The species composition of reef fishes inhabiting the upper mesophotic is similar to that on shallow coral reefs, whereas species inhabiting the lower mesophotic constitute an assemblage that is taxonomically distinct from shallow-reef taxa17. Abundance and biomass of fish species in the upper and lower mesophotic typically vary significantly3.Fish biologists have adopted the term “mesophotic” for tropical reef fishes  at similar depths just below shallow areas, at ~40 to 150 m depending on the method of study and maximum depth investigated21. Two studies that recorded fishes to depths of 300 m only distinguished shallow (<30 m) from “deep-reef” (>50 m) faunas23, and several recent publications depict the mesophotic transitioning directly into aphotic deep-sea ecosystems26. Tropical ecosystems below 150 m thus have received little targeted scientific attention, a deficiency attributable to depth limits imposed by the rebreather/mixed-gas technology commonly used to explore mesophotic reefs, to the scarcity and high costs of deep-diving ROVs and submersibles, and to the inability of trawls to sample deep rocky habitat.Maximum depths of investigation between 70 and 150 m in most studies of tropical mesophotic fish communities have precluded recognition of the lower boundary of the mesophotic zone, the nature of the reef-fish fauna below that zone, and the location of any deeper faunal breaksOur intensive efforts over the past six years using a manned submersible to study the diversity of mesophotic and deeper tropical fish communities at Curaçao Island in the Caribbean Sea have resulted in the most extensive database to date on the diversity and depth distributions of reef fishes between ~40 and 309 m at a single location anywhere in the tropics. This data set provided the opportunity to analyze variation in faunal assemblages (species composition and abundance) at mesophotic and greater depths, the results of which we report here.30 or remain undescribed. As there is no appropriate term in existing coral-reef literature for a demersal zone that is immediately below the mesophotic but above the deep aphotic regions, we propose “rariphotic” (rarus = scarce), to reflect the scarcity of light at sub-mesophotic depths. Below we discuss the boundaries and taxonomic composition of this fish-faunal zone off Curaçao. Providing a name for this faunal zone, as biologists have for the mesophotic, draws attention to its existence, emphasizes that the mesophotic is not the only “deep-reef” faunal zone below shallow coral reefs, and provides terminology with which to discuss it. As there is also no equivalent term in general use for shallow coral reefs above the mesophotic that follows the same naming scheme, we propose “altiphotic”  to complete this classification of coral-reef faunal zones: altiphotic, mesophotic, rariphotic.Our data set comprises 4,436 depth observations of 71 reef-fish species observed and unambiguously identified between 40 and 309 m off Curaçao Fig. . Many co−7) from the others , with a division between the two most dissimilar sections at ~190 m delineating upper (130–189) and lower (190–309) sections. Faunal breaks in reef-fish assemblages at ~120–130 m also have been identified off Hawaii and the Marshall Islands in the Pacific, as well as in the Gulf of Mexico31, and the ~190 m break between upper and lower rariphotic faunas at Curaçao is at the same depth as a faunal break recently identified in a study of Gulf of Mexico fishes based on depth maxima and minima10. The displacement of the 260–279 m depth group outside the 240–309 m cluster  analysis based on the Bray-Curtis dissimilarity metric revealed eight clusters, each significantly different  are significantly distinct (p < 0.01) and highly dissimilar (95–99%) from one another , Paranthias furcifer (Serranidae), Chromis cyanea, and C. multilineata (Pomacentridae), all of which are common altiphotic species, dominate the upper mesophotic relative to the lower mesophotic. Bullisichthys caribbaeus, Pronotogrammus martinicensis, Liopropoma mowbrayi (Serranidae), Antilligobius nikkiae (Gobiidae), and Chromis cf. enchrysura predominantly define the lower mesophotic relative to the upper mesophotic. Pronotogrammus martinicensis becomes more abundant in the upper rariphotic and, along with Palatogobius incendius (Gobiidae) and Symphysanodon octoactinus (Symphysanodontidae), drives the separation between the upper rariphotic relative to the lower mesophotic. Key lower-mesophotic species driving the separation from the upper rariphotic include some of the same species that separate the upper and lower mesophotic  but also C. insolata, C. scotti, and Serranus luciopercanus (Serranidae). Relative to the lower rariphotic, recognition of the upper rariphotic is driven by some species that also distinguish the upper rariphotic and lower mesophotic  but also by several additional species of Serranidae . Chrionema squamentum (Percophidae) and Symphysanodon berryi drive the separation of the lower and upper rariphotic.The key fish species responsible for divisions between the upper and lower mesophotic, lower mesophotic and upper rariphotic, and upper and lower rariphotic at Curaçao are listed in Table 24.Although we lack the data necessary to identify the depth of a faunal break between rariphotic reef fishes and deep-sea fishes at Curaçao, the lower limit of the rariphotic may lie below 309 m. The global depth ranges of many of the Curaçao rariphotic reef fishes extend to 300–500 m, whereas few occur much deeper , Liopropoma (2), Pontinus (2), Serranus (2), and Symphyanodon (2) are the most common rariphotic genera , Haptoclinus (Labrisomidae), and both Decodon and Polylepion (Labridae). In each case, the rariphotic species are nested deep within clades of altiphotic or mesophotic species38 (supplemented with our unpublished sequence data for the rariphotic genus Haptoclinus). Other genera of rariphotic reef fishes, such as Pristipomoides (and closely related species in the Lutjanidae subfamily Etelinae), and Corniger and Ostichthys (Holocentridae subfamily Myripristinae), belong to clades primarily containing rariphotic and mesophotic taxa that, collectively, are sister groups to clades of altiphotic taxa40. Few studies have investigated historical depth transitions between shallow- and deep-reef taxa. Most research on the general structure of tropical reef-fish faunas at the regional level has focused on the phylogeography of shallow faunas and the relationship between faunal structure and the usage of reefs vs. other habitats44. However, research attention needs to be expanded to resolving phylogenetic relationships among reef-fish assemblages inhabiting different depth zones, as that is central to understanding the evolutionary origins and current composition of local and regional reef-fish faunas inhabiting shallow as well as deep reefs.Thus, deep-sea taxa contribute little to the rariphotic reef-fish assemblage at Curaçao, which is dominated overwhelmingly by primarily altiphotic and mesophotic families. Furthermore, at least some rariphotic fishes not only are members of predominantly altiphotic taxa but are evolutionarily derived from shallow-reef ancestors. A recent study of ours indicates that several Caribbean rariphotic goby lineages represent recent (~10–14 mya) evolutionary transitions from shallow ancestors, with subsequent species radiations by rariphotic lineages in some genera23 (and see species range-maps34) have observed the same for the Caribbean mesophotic fish fauna. Other ocean basins also appear to accommodate a rariphotic fish fauna; for example, in the Hawaiian Archipelago and Indo-west Pacific, some species of Chromis (Pomacentridae), Parapercis (Pinguipedidae), Bodianus (Labridae), Scorpaena (Scorpaenidae), and Anthias, Odontanthias, and Sacura (Serranidae) have the upper limits of their depth ranges between 130 and 180 m32.The rariphotic reef-fish fauna described here is not restricted to Curaçao. Similarly large data sets are needed to determine the depth breaks between mesophotic and rariphotic fish faunas at other Caribbean sites, and establish the lower limit of the rariphotic zone. However, preliminary observations and collections between ~40 and 300 m off Bonaire, Dominica, Honduras, and St. Eustatius indicate that much of the Curaçao rariphotic fauna is widespread throughout the Caribbean. Not surprisingly, we and others11. However, the lower limits of zooxanthellate coral-reef development  range from ~55 to 133 m46 in the wider Caribbean area, and the lower limits of such reef corals in our study area at Curaçao are ~85–90 m47  between the edge of the continental shelf and slope10. However, while that Gulf possesses a broad continental shelf, no equivalent exists at Curaçao, where the reef drops off rapidly from near the surface to at least 300 m.The boundary between mesophotic and rariphotic fish communities occurs at Curaçao at ~130 m, shallower than the 150-m depth typically considered as the lower boundary of the mesophotic zone24. We assessed potential correlations between temperature and fish-faunal structure from temperature data recorded during 319 sub dives to depths as great as 309 m in 2011–2014  do not delineate tropical altiphotic, mesophotic, and rariphotic faunal zones and cannot be adapted to fully describe major depth-related changes in the Curaçao reef-fish assemblage between the surface and 309 m. Open-ocean ecosystems have yet another, different classification  that is not appropriate for benthic communities. Our classification, derived from analyzing fish-faunal assemblages between 40 and 309 m, accommodates a broad rariphotic zone below the mesophotic that is populated largely by deep-living representatives of shallow-water higher taxa at Curaçao and elsewhere in the Caribbean Sea. The extent to which this classification can accommodate depth changes in assemblages of zooxanthellate and other types of corals, as well as other reef taxa at Curaçao and elsewhere, remains to be investigated. Formally classifying mesophotic and rariphotic assemblages improves the ability of scientists and the public to communicate about tropical ocean biodiversity and directs attention to one of the poorly studied marine environments, in this case a highly diverse one. Recent investigations of just a few Caribbean mesophotic and rariphotic reef ecosystems have revealed so many new species62 that the true amount of biodiversity in both of those zones is likely far from adequately known. Dedicated global biodiversity exploration of such ecosystems should be a major research priority. Because altiphotic, mesophotic, and rariphotic zones form a tropical depth continuum, further study of their biological and ecological interactions is needed in light of changing global ocean conditions. As mesophotic depths may serve as refugia for altiphotic inhabitants, so may rariphotic depths for mesophotic life.A graphical representation of reef-faunal zones based on the present study at Curaçao is shown in Fig. 49. That summary agrees well with our observations from sub dives at both Islands. Coral-cover estimates for shallow reefs (<20 m) on the leeward side of Curaçao range between 16–40%63. Overall, gradually sloping shallow-reef areas drop more steeply at ~7–15 m, followed by additional vertical drop-offs beginning at 70–90 m depending on location66. Most of the deep-reef area to ~300 m at Curaçao is characterized by steep to moderately sloped cliffs interrupted by rubble and sand beaches. Cliffs were largely formed by erosion of terraces formed during periods of low sea-level during Pleistocene67. Curaçao experiences less than 500 mm of annual precipitation (http://www.meteo.cw/climate.php), and, in general, water visibility is high year-round. There were no observable differences in visibility along the deep-reef slope during our study period.Observations and collections of deep-reef fishes were conducted off the coast of Curaçao near 12.083197N, 68.899058W. The study site was chosen based on accessibility to deep-reef ecosystems very close to shore, obviating the need (and associated costs) of a research support vessel for submersible operations. The general reef characteristics of Curaçao and Bonaire are very similar and have been summarized elsewhereCurasub (http://www.substation-curacao.com/) off the coast of Curaçao. Each dive typically lasted ~4 hours and many reached a depth near 310 m (the maximum to which Curasub is rated). Fish-sighting records and collections commenced at 40 m on each dive. Dives generally involved roving surveys, with the submersible facing the reef and moving slowly (<2 knots) laterally while simultaneously descending very slowly down the slope to a variable maximum depth. Periodic pauses were made throughout each dive for collecting specimens. Most species depth records were obtained by a pair of us seated in the front of the submersible linking our sightings of identifiable fishes to readouts from an internal digital depth gauge. These observations were supplemented with representative specimens captured using an anesthetic ejection system coupled to a suction hose that empties into a collecting chamber55. A total of 202 specimens was collected, with a special emphasis on species that are difficult to identify visually (e.g. cryptobenthic species), unfamiliar species likely to be new, and species for which tissue samples were needed for ongoing studies involving genetic analyses. Identifications of captured specimens were made using both morphology and DNA barcoding67. Fishes that could not be identified confidently visually from the sub or from investigation of captured specimens  were excluded from the analysis presented here. We estimated relative sampling effort by examining the time spent at each 10-m depth band on 53 dives in 2011–2013  were followed for all field-collecting activities, and fish specimens collected as part of this study were done so under Smithsonian Animal Care and Use Committee (ACUC) approval to C. C. Baldwin (ACUC #2011–07 and #2014–13).Observations and collections of deep-reef fishes were a major objective of ~80 dives between 2011 and 2016 made by the manned submersible 69. The 4,436 observations on 71 species were separated into 10-m bins from 40 to 309 m. The number of observations per species ranged from 1 to 559 (mean = 62.4). Sampling effort (dive time) across each 10-m depth interval was relatively uniform across most depths, but somewhat shorter at the deepest intervals. We therefore standardized species observations in each depth interval by applying a multiplier equal to the average dive time in the most heavily sampled depth interval divided by the average dive time in each respective interval. In some cases, few observations of certain species may be due to sampling bias, as small, solitary, cryptobenthic species are often overlooked in visual surveys. To avoid overemphasizing the importance of rare species in our analysis, while also controlling for extremely abundant species that sometimes occur in large schools, we applied a square-root transformation to our raw abundance data. The clustering analysis based on the Bray-Curtis metrics used the complete-linkage clustering algorithm, which seeks to maximize distance between clusters by calculating the proximity between clusters based on the two most distant objects in each cluster. Non-metric multidimensional scaling (NMDS) ordination and a hierarchical cluster dendrogram were used to visualize community structure. The number of significantly distinct depth clusters was determined using Analysis of Similarity Profiles (SIMPROF) with a conservative value of alpha = 10−7 (see72). To confirm the statistical significance of depth zones that were delineated a posteriori , we used PERMANOVA73. A similarity percentage analysis (SIMPER)74 was used to determine which species contributed most to differences between adjacent depth zones   to as deep as 309 m.The Raw fish-depth data analyzed during this study are included in Supplementary Table Supplementary Materials


However, on problems that are mostly regular, but contain some irregularities, which describes most real-world problems, indirect encodings struggle to handle the irregularities, hurting performance. Direct encodings are better at producing irregular phenotypes, but cannot exploit regularity. An algorithm called HybrID combines the best of both: it first evolves with indirect encoding to exploit problem regularity, then switches to direct encoding to handle problem irregularity. While HybrID has been shown to outperform both indirect and direct encoding, its initial implementation required the manual specification of when to switch from indirect to direct encoding. In this paper, we test two new methods to improve HybrID by eliminating the need to manually specify this parameter. Auto-Switch-HybrID automatically switches from indirect to direct encoding when fitness stagnates. Offset-HybrID simultaneously evolves an indirect encoding with directly encoded offsets, eliminating the need to switch. We compare the original HybrID to these alternatives on three different problems with adjustable regularity. The results show that both Auto-Switch-HybrID and Offset-HybrID outperform the original HybrID on different types of problems, and thus offer more tools for researchers to solve challenging problems. The Offset-HybrID algorithm is particularly interesting because it suggests a path forward for automatically and simultaneously combining the best traits of indirect and direct encoding.Many challenging engineering problems are regular, meaning solutions to one part of a problem can be reused to solve other parts. Evolutionary algorithms with  As noted previously, HyperNEAT consistently produced larger genomes than Offset-HybrID on the target weights problem , and thus no local optima. In the periment , a feed-The target network consists of two layers, each with 49 nodes geometrically arranged in seven rows and seven columns. Links in the target network can be constrained to columns or rows within the two-dimensional layout of the layer. Phenotype links are enabled if the absolute magnitude of the CPPN weight output is greater than 0.2, and links weights are normalized to have an absolute magnitude within the range 0 to 3. Just before publication, we noticed a small error in the code that allowed Offset-HybrID treatments to have weights outside of this prescribed range. We do not believe this would qualitatively change our conclusions.epistasis and makes the problem more difficult to solve.To evaluate fitness, an individual network is given a random set of bits as input that propagate through the network and the output is checked for correctness. The individual’s fitness value is incremented for each node in the output layer that mirrors the correct input bit according to the target solution. Because the network input is random, each network is tested ten times with different inputs and fitness is summed across all tests in order to ensure that fit networks are truly solving the problem rather than producing a solution for one random input by chance. In contrast with the target weights experiment, which evolves ANNs toward a goal by comparing network topologies, the bit mirroring experiment evolves ANNs toward a goal by comparing network input-output mappings. Many differently wired networks may produce similar output, which is another reason that the fitness space of the bit mirroring problem is more complex than target weights. Correctly mirroring a bit requires that the link between the input and target nodes be turned on while other connections to the output node are turned off. The output value for a node at the endpoint of a correctly wired link may be incorrect due to the presence of other links, which is an example of The most-regular networks have each node in the input layer directly wired to the corresponding node in the same row and column of the output layer. Regularity can be reduced by first relaxing the constraint that input nodes are wired to the same column in the output layer, then by relaxing the within-row constraint so that the most-irregular networks require that bits be mirrored to random nodes in the output layer (i.e. 0% column-constrained and 0% row-constrained) . We perfp > 0.0002, NSABC) except 90% and 70% column-regular treatments  different at the 100th generation  compared to the 2,000th and final generation , demonstrating that few fitness gains occur after initial generations in which HyperNEAT exploits underlying problem regularities. The plateau effect on fitness becomes more pronounced for irregular problems compared to regular problems, resulting in decreasing final median fitness as problem irregularity increases  shows that Preset-Switch-HybrID  significantly (p < 0.05) outperforms HyperNEAT  on the bit mirroring experiment . The switch points are roughly clustered together soon after 100 generations, which is the earliest that Auto-Switch-HybrID may switch. The indirect encoding’s performance on most problems quickly reaches a plateau, triggering the switch to the direct encoding. Early switch points appear to be most beneficial for irregular problems, which the direct encoding is better suited to solving . On the p < 0.05) outperforms HyperNEAT , and is not significantly different (p > 0.05) than Preset-Switch-HybrID   shows that Auto-Switch-HybrID  significantly (304.97]) .p > 0.0002, NSABC) greater than other regularities, except 90% and 70% column-regular treatments  HyperNEAT on 10%, 20%, and 30% row-regular treatments (p < 0.0001) less than HyperNEAT , Preset-Switch-HybrID , and Auto-Switch-HybrID .Offset-HybrID is not as effective on the bit mirroring experiment as the target weights experiment . Offset-eatments . Switch-eatments : The finOffset-HybrID must separately mutate both genomes to account for epistasis in the bit mirroring problem, which makes the problem difficult. Individual ANN phenotypes are produced by summing the patterns encoded in the offsets and the CPPN genome and evaluated according to the ANN output. The patterns from each genome must compliment each other to produce fitter individuals. The bit mirroring fitness landscape is much harder for the concurrently-evolving genomes to navigate than the fitness landscape for the target weights experiment, where weights are directly evaluated for fitness without epistatic effects.ρ = 0.32, p < 0.0001), which is consistent with the results of the target weights experiment and further demonstrates how HyperNEAT attempts to deal with irregularity through increasing genome complexity. Small genomes are useful for producing repeated weight patterns that aid in solving regular bit mirroring problems. Larger genomes are needed to avoid reuse of genomic information, which produces regular phenotypes that are unable to solve irregular problems.Comparing HyperNEAT genome sizes across regularities shows a subtle and significant trend toward larger genomes as problem regularity decreases  smaller than HyperNEAT  for all regularities. The presence of a concurrently-evolving, directly encoded genome limits growth of the CPPN genome. The fact that Offset-HybrID produces small CPPN genomes for the bit mirroring problem while failing to outperform HyperNEAT suggests that the direct encoding appears to be unable to effectively encode bit mirroring problem irregularities.Final champion indirectly encoded genome sizes of Offset-HybrID  are significantly  units wide, 0.3 units long, and 0.05 units tall, parallel to the ground plane . Each leThe ANN controller is a three-layer feed-forward network with input, hidden, and output layers each consisting of twenty nodes arranged in rows and columns . Links mx and y are the distance in ODE units traveled in the x and y dimensions along the ground plane from the starting location. The fitness metric encourages evolution of walking patterns that avoid falling and maximize movement speed through the environment, and it is exponential to emphasize small gains in distance traveled.Following , 24, theFollowing , 24, theHyperNEAT is able to exploit the regularities present within the problem to a great degree. On regular problems HyperNEAT can quickly find a stable gait allowing the robot to walk greater distances without falling . As was n = 200) reveals that Preset-Switch-HybrID  significantly (p < 0.05) outperforms HyperNEAT . The final champion fitness values for Preset-Switch-HybrID on the quadruped problem are greater  than HyperNEAT for treatments with four, eight, and twelve noisy joints  . As withn = 200) for Auto-Switch-HybrID  are not significantly (p > 0.05) different from Preset-Switch-HybrID , but are significantly (p < 0.05) greater than HyperNEAT . Auto-Switch-HybrID and Preset-Switch-HybrID increasingly outperform the other encodings as regularity decreases   were not significantly different (p > 0.05) from HyperNEAT , but were significantly (p < 0.05) worse than Auto-Switch-HybrID  and Preset-Switch-HybrID   . Offset-3D visualizations of the fittest ANNs produced by HyperNEAT display evolved structural regularity in the form of repetition, symmetry, and interesting patterns in the network link weights . The netThe evolved Offset-HybrID networks exhibit some notable features resulting from the concurrent evolution of multiple encodings . The indtransfer learning [Part of the challenge of evolving ANNs for robots operating in the physical world is handling a dynamic environment where changes may fundamentally alter the steps necessary to accomplish an action, or may result in an entirely new set of goals. Individuals transferred to a new environment are most effective if they preserve existing knowledge and skills and adapt them to the current task, which is known as learning , 41. Prelearning .Because Offset-HybrID evolves both indirectly and directly encoded genomes, it should be able to transfer to new tasks easier than switching-HybrID methods that preserve only a directly encoded genome after evolution. To demonstrate how encodings transfer to new tasks, a second test of the quadruped robot experiment was performed on previously-evolved populations from Auto-Switch-HybrID and Offset-HybrID. Physical parameters for the robot and environment are identical to the previous quadruped experiments. All populations are evolved in a new quadruped robot experiment for 3,000 generations across the spectrum of regularity, and each level of regularity is tested with 40 treatments of evolution that continue from where the prior quadruped experiment ended. The populations evolve for three times as long as the previous quadruped robot experiments in order to highlight the differences in how Offset-HybrID and Auto-Switch-HybrID (post-encoding-switch) behave over time.In the new experiment, a regular change is made to the amount of error present in the robot’s joint angles from the previous evolutionary environment by adding a random uniform amount of error to all joints. The added error acts as a new problem that controllers must evolve to solve, and it is intended to model challenges faced by physical robots that affect all joins simultaneously, such as failing power supplies, weak batteries, or altered environmental conditions. Since the change made to joint angle error is uniform and regular, the relationships between joints from the previous problem are preserved. An indirect encoding should be able to quickly adjust to the regular change, unlike a direct encoding. Therefore we hypothesize that Offset-HybrID will outperform Auto-Switch-HybrID (post-encoding-switch) when transferred to the new problem.Offset-HybrID is capable of retaining irregularities solved in the pre-transfer experiment, while evolving the indirectly encoded genome to solve the new, regular change in the transfer environment. We would also expect HyperNEAT-alone treatments to handle the regular change in the new, transfer environment effectively, however the irregularities from the original problem, which HyperNEAT struggles to solve, remain in the transfer experiment, which could harm HyperNEAT’s performance. Since the point of this transfer experiment is to only test whether maintaining an indirect encoding benefits transferability rather than find the best performing encoding, and due to restricted computational resources, HyperNEAT-alone is not tested on the transfer experiment.The starting point for evolution on the pre-transfer problem is notably different from the transfer problem. In the most-regular treatments of the pre-transfer problem, the robot has no joints with error, and the problem of controlling the robot is thus simpler. Even in the most-regular treatments of the transfer problem, all joints contain some amount of error that must be corrected in order for the robot to contact the ground properly, which results in a problem that is potentially harder than the pre-transfer problem.n = 200)  is still significantly (p < 0.0001) below what Auto-Switch-HybrID was capable of achieving during the pre-transfer problem in 1,000 generations   Figs  vs 17.Similar to the Auto-Switch-HybrID results, Offset-HybrID gaits evolved for the pre-transfer problem perform poorly on the first generation of the transfer problem . Howevern = 200)  are significantly (p < 0.05) greater than Auto-Switch-HybrID , showing that the indirect encoding is helpful when solving the regularities present in the new environment   is significantly (p < 0.0001) greater than Auto-Switch-HybrID  and Preset-Switch-HybrID , but is significantly (p < 0.0001) less than HyperNEAT . Offset-HybrID still outperforms other encodings on the target weights problem  CPPN genomes  than Offset-HybrID  as the CPPN must evolve to solve irregularities without the directly encoded offsets before the switch point. On the other hand, Auto-Offset-HybrID treatments greater than 60% regular  produce smaller  genomes than Offset-HybrID , which provides more evidence that concurrently-evolving offsets interfere with the evolution of regularities.Auto-Offset-HybrID treatments with less than 40% regularity produce larger (n = 880)  is significantly (p < 0.0001) greater than Offset-HybrID , and HyperNEAT  showing that the bit mirroring experiment, which exhibits epistasis, benefits from early evolution of regularity and later evolution of irregularities. The final fitness of Auto-Offset-HybrID champions (n = 880) is not significantly (p > 0.05) different from Auto-Switch-HybrID  but is significantly (p < 0.05) less than Preset-Switch-HybrID . Because all experiments are run with identical offset mutation settings, it is possible that the parameters might be fine-tuned to match the performance of Preset-Switch-HybrID.The final fitness of Auto-Offset-HybrID run champions after 2,000 generations (n = 200)  are significantly (p < 0.05) less than Auto-Switch-HybrID , but are significantly (p < 0.05) greater than Offset-HybrID  and HyperNEAT . The results show that the Auto-Offset-HybrID method effectively combines indirect and direct encodings, but that the direct encoding mutation parameters used by Auto-Switch-HybrID are more effective. The evolved Auto-Offset-HybrID quadruped ANN controllers appear visually similar to the evolved Offset-HybrID ANNs   are significantly (p < 0.0001) greater than Auto-Switch-HybrID . However, no significant (p = 0.0658) difference in overall fitness exists between the transferred Auto-Offset-HybrID population and Offset-HybrID .For the quadruped robot transfer experiment, the Auto-Offset-HybrID population evolved for the previous quadruped robot problem is transferred to the new environment and evolved with the Offset-HybrID algorithm. This experiment shows the potential advantage of Auto-Offset-HybrID having the CPPN available post-switch, which is not true for Auto-Switch-HybrID. The transferred Auto-Offset-HybrID population quickly evolves to compensate for the new environment . ChampioWe could have also tested evolving the Auto-Offset-HybrID (or Offset-HybrID) population on the transfer experiment by discarding the offset values, keeping the CPPNs, and restarting the Auto-Offset-HybrID (or Offset-HybrID) algorithm initialized with the transferred CPPNs, but where the new offsets evolve from scratch. However, because in this type of problem there are irregularities in the base environment that remain in the target environment, those methods would require evolution to re-solve the same irregularities that were solved in the pre-transfer experiment in addition to solving the regular change in the transfer environment. Nevertheless, such experiments would help identify the extent to which transferring the offsets are helpful, the degree to which simply having a CPPN in the target environment is helpful, and the degree to which the CPPN and the offsets have co-evolved in an intertwined way that affects their ability to be effectively transferred separately. Such experiments thus represent a fruitful opportunity for future research, and an opportunity to compare these new variants to the algorithms presented in this paper, including vanilla HyperNEAT. In the next section we outline other intriguing areas for future work.The new HybrID methods presented in this paper allow for many potentially interesting variations on their implementation. The effectiveness of automatic encoding switching might be improved by using different methods to measure fitness stagnation, or by allowing multiple switch points. The concurrently evolving genomes of Offset-HybrID allow for multiple implementations of crossover, such as performing crossover on the indirectly or directly encoded genome according to a probability value specified in the input parameters. Additionally, one could try co-evolving separate populations of direct and indirect encodings that get paired randomly (or in some other way) each generation. Due to limitations of time and space, we are unable to test all potential variations in this paper.This paper demonstrates that indirect encodings handle irregularity most effectively when combined with a method for introducing directly encoded mutations. Learning algorithms directly manipulate ANN weights similar to the mutation of a direct encoding. One advantage of learning is that it is targeted, whereas mutation is random. The effectiveness of concurrently evolving direct and indirect encodings to address irregular problems might be reproduced or augmented by allowing learning to take place during fitness evaluation . A key dOn the easiest problem, target weights, Offset-HybrID greatly outperforms all other encoding types, followed by Auto-Switch-HybrID, Preset-Switch-HybrID, Auto-Offset-HybrID and then HyperNEAT. HyperNEAT must use genome complexification to generate the irregular phenotypic structural patterns that are necessary to solve irregular problems, while Offset-HybrID is also capable of representing irregularities in the concurrently-evolving directly encoded genome. The directly encoded genome of Offset-HybrID becomes less-compressible as target weights problem irregularity increases, which shows that the offsets encode problem irregularities.On the bit mirroring problem, Auto-Switch-HybrID, Preset-Switch-HybrID, and Auto-Offset-HybrID outperform Offset-HybrID and HyperNEAT. The switch-based encodings are able to fine-tune performance with the direct encoding, while concurrently evolving encodings produces little benefit. Comparing the size of indirectly encoded genomes evolved with HyperNEAT and Offset-HybrID shows that the concurrently-evolved offsets are unable to encode the bit mirroring problem irregularities as effectively as the target weights irregularities.Preset-Switch-HybrID, Auto-Switch-HybrID, and Auto-Offset-HybrID also outperform HyperNEAT and Offset-HybrID on the quadruped problem. Auto-Switch-HybrID is able to successfully automate the encoding switch point while performing as well as Preset-Switch-HybrID on the quadruped robot and bit mirroring experiments, while outperforming Preset-Switch-HybrID on the target weights experiment. The successful tests verify that automatically switching from indirect to direct encoding is useful for solving irregular problems.It is interesting that Offset-HybrID is least-effective when fitness is determined by the output of the phenotype, such as the bit mirroring and quadruped experiments, instead of the evolved topology, as in the target weights experiment. The differences in performance could be due to the increased difficulty of the bit mirroring and quadruped robot experiments resulting from epistasis. Preset-Switch-HybrID, Auto-Switch-HybrID, and Auto-Offset-HybrID show that a direct encoding is able to optimize solutions on problems with epistasis after an indirect encoding solves problem regularities.The concurrent evolution of indirect and direct encodings can be preferable to switching from an indirect encoding to a direct encoding in situations where populations are to be transferred between related tasks. The indirectly encoded genome allows for faster, large-scale, regular changes in the phenotype via mutation while a directly encoded genome can only be altered by slower small-scale fine-tuning that is not suited to produce the types of mutations needed to solve the transfer problem. The most-regular treatments of the quadruped transfer experiment show that Offset-HybrID accounts for regular changes to the problem through coordinated mutation effects produced by the indirectly encoded genome. Auto-Switch-HybrID (post-encoding-switch) is unable to take advantage of regularity in the changes to the problem and is ineffective on regular treatments compared to Offset-HybrID.Our work has shown that Offset-HybrID and Auto-Switch-HybrID are capable of out-performing HyperNEAT to evolve solutions for certain irregular problems. Auto-Switch-HybrID improves on Preset-Switch-HybrID to reduce the level of human intervention needed for switch-based HybrID methods, while Offset-HybrID quickly solves simple irregular problems and evolves to account for regular changes in the problem description. Work with Offset-HybrID also suggests possible designs for more effectively combining encoding methods. Overall, the encoding methods developed for this paper are effective tools for automatically generating solutions to difficult engineering problems.


Despite the increasing number of protein-RNA complexes in structure databases, few data resources have been made available which can be readily used in developing or testing a method for predicting either protein-binding sites in RNA sequences or RNA-binding sites in protein sequences. The problem of predicting protein-binding sites in RNA has received much less attention than the problem of predicting RNA-binding sites in protein. The data presented in this paper are related to the article entitled “PRIdictor: Protein-RNA Interaction predictor”   Specifications TableValue of the data•Few data resources have been available which can be readily used in developing or assessing a method for predicting protein-binding sites in RNA sequences or RNA-binding sites in protein sequences.•Protein-RNA binding sites at the nucleotide and residue levels can facilitate to develop a new method for predicting protein-RNA binding sites.•Protein-RNA binding sites provided here can be used as a useful resource to evaluate and compare different methods for predicting protein-binding nucleotides in RNAs and/or RNA-binding residues in proteins.1The four datasets 2From the Protein Data Bank (PDB) As of September 2013, there were a total of 542 protein-RNA complexes, which contained 546 protein-RNA sequence pairs between 376 protein sequences and 439 RNA sequences.We defined a protein-RNA binding site using three types of protein-RNA interactions . A nucleotide (or amino acid) involved in at least one of the interactions was classified as a protein-binding (or RNA-binding) site. For each of the protein–RNA complexes from PDB, we obtained the three types of interactions from the Nucleic acid–Protein Interaction DataBase (NPIDB) 1.Protein sequences were included in the dataset 2.In the dataset In order to reduce overlap between training and test datasets, we ran CD-HIT-EST on the RNA sequences and selected RNA sequences with a similarity of 80% or lower from other RNA sequences and constructed test datasets The dataset The test datasets