Balanced processing of HIV-1 RNA is critical to virus replication and is regulated by host factors. In this report, we demonstrate that overexpression of either Tra2α or Tra2β results in a marked reduction in HIV-1 Gag/ Env expression, an effect associated with changes in HIV-1 RNA accumulation, altered viral splice site usage, and a block to export of HIV-1 genomic RNA. A natural isoform of Tra2β (Tra2ß3), lacking the N-terminal RS domain, also suppressed HIV-1 expression but had different effects on viral RNA processing. The functional differences between the Tra2β isoforms were also observed in the context of another RNA substrate indicating that these factors have distinct functions within the cell. Finally, we demonstrate that Tra2ß depletion results in a selective reduction in HIV-1 Env expression as well as an increase in multiply spliced viral RNA. Together, the findings indicate that Tra2α/β can play important roles in regulating HIV-1 RNA metabolism and expression. Regulation of alternative splicing is thought to play a critical role in the function of a cell. Predictions are that >90% of the mRNAs in the human genome undergo alternative splicing . ThuDrosophila melanogaster splicing regulatory factor Tra2 [Analysis of cis-acting sequences regulating HIV-1 RNA splicing have determined that use of a specific splice site is determined in part by the relative activity of adjacent exon splicing enhancers (ESEs) and exon spicing silencers (ESSs) that act in an antagonistic fashion by binding SR or hnRNP proteins, respectively , 4. Two tor Tra2 –22. Bothtor Tra2 . Isoformtor Tra2 , 24–26. tor Tra2  by binditor Tra2 , 26, 28.Given their potential to interact with known ESEs within HIV-1, we were interested in exploring whether Tra2α/β played a significant role in regulating HIV-1 RNA processing and expression. To address this question, we used both overexpression and depletion approaches. Mutagenesis was also used to examine the contribution of the various Tra2α and Tra2β protein domains to protein localization and regulation of splicing of HIV-1 and another model RNA. In this report, we demonstrate that the N- and C-terminal RS domains of Tra2α and Tra2β differ in their capacity to regulate RNA processing in the context of HIV-1. Overexpression of Tra2α/β and a variant lacking the N-terminal RS  suppressed expression of HIV-1 Gag and Env through the nuclear sequestration of the viral RNAs that was reversed upon Rev overexpression. However, the WT and ΔN constructs had distinct effects on HIV-1 RNA splicing. Functional differences among the structural variants were also observed in the context of another model RNA (doublesex). In contrast, deletion of the C-terminal RS domain or point mutations in the RRM of Tra2α/β had little or no effect on HIV-1 gene expression. Subsequent depletion studies demonstrated a role for Tra2β in the regulation of HIV-1 gene expression, reduction in its expression resulting in a selective decrease in viral Env protein and an increase in MS RNA with only limited effects on Gag protein and the accumulation of viral unspliced (US) or singly spliced (MS) RNAs.6, and pADML dsx MS2. CMVmyc3xterm was previously described [5’CCCAAGTCCA GCGACAGCGG CGAGCAGA 3’ and 5’ GCTCTAGAAG CACCAAACTA CACAAA 3’. The resulting amplicon was cloned into the EcoRV site of BLSK to create Bl-Tra2α. CMVmycTra2α was constructed by digesting Bl-Tra2α with HindIII / XbaI and cloning the resulting fragment into the respective sites in CMVmyc3xterm. CMVmycTra2αΔN was generated by digesting CMVmycTra2α with XmnI /XbaI and ligating the fragment into CMVmyc3x term cut with EcoRV/XbaI. CMVmycTra2αΔNΔC was created by digesting CMVmycTra2α with XmnI /EcoRI and ligating the fragment into CMVmyc3xterm cut with EcoRV /EcoRI. CMVmycTra2αΔC was produced by digesting CMVmycTra2α with HindIII/EcoRI and ligating into the HindIII/EcoRI sties of CMVmyc3xterm. CMVmycTra2αffdd was created by QuickChange Mutagenesis (Stratagene) using the following primers: 5’ CGATCTCGAG GAGACGCTGA CGTGTATTTT GAG 3’ and 5’CTCAAAATAC ACGTCAGCGT CTCCTCGAGA TCG 3’ and CMVmycTra2α as template DNA according to manufacturer’s protocol. CMVmycTra2β was generated by digesting pLexA-Tra2ß with XhoI followed by blunting with Vent polymerase and subsequent digestion with EcoRI. This fragment was then ligated into the EcoRI/SmaI sites of CMVmyc3xterm. CMVmycTra2βΔN was generated by PCR using the following primers 5’GGAATTCTTT AATAGCGACG AGGTGAG 3’ and 5’CCCAAGCTTA TGTCTACTCG CAGGCGTCAT 3’. The amplicon was cut with HindIII/EcoRI and cloned into the HindIII/EcoRI sites of CMVmyc3xterm. CMVmycTra2βΔNΔC was created by PCR using the following primer pair 5’CCCAAGCTTA TGTCTACTCG CAGGCGTCAT 3’ and 5’CGGAATTCGC AGCTCTCCAT CCTCCTCC 3’. The resulting amplicon was cut with HindIII/EcoRI and ligated into the HindIII/EcoRI sites of CMVmyc3xterm. CMVmycTra2βΔC was made by PCR with the following primers 5’CCCAAGCTTA TGAGCGACAG CGGC 3’ and 5’GCGAATTCTA CGCCCATCAA GCTC 3’. The amplicon was cut with HindIII/EcoRI and ligated into the respective sites of CMVmyc3xterm. To generate GFP fusions of each factor, Sac1/BamH1 fragments of the CMVmyc Tra2β, Tra2βΔN, Tra2βΔC, or Tra2βffdd were cloned into the Sac1/BamH1 sites of peGFP-C1. The SV-1 env construct was provided by H. Schaal [5’-ACA GGC CCG AAG GAA TAG GAT CCT TGG CAC TTA TCT-3’ and 5’-AGA TAA GTG CCA AGG ATC CTA TTC CTT CGG TGT-3’ to delete ESE3 and ESS3.The plasmid HIV Hxb2 R-/RI- was provided by E. Cohen and contains mutations inserting two stop codons between the protease and reverse transcriptase regions of the virus. The plasmid pNL4-3 GagzipGFP was generated by replacement of the NC, PR and RT coding regions of pNL4-3 with a leucine zipper sequence fused to GFP using an In-Fusion (Clontech) cloning strategy. Blencowe (University of Toronto) generously provided the plasmids pLexA-Tra2α, pLexA-Tra2β, pADML dxsΔ434, pADML dsx (GAA)escribed . Bl-Tra2. Schaal  and the 5 HEK293T cells were transfected with plasmids using a calcium phosphate protocol [CA hybridoma supernatants [3x10protocol . Forty-ernatants , anti-HIrnatants , anti-tuHeLa and HEK 293 were transfected with 2.0 μg of the plasmids using a calcium phosphate protocol as above. Forty-eight hours posttransfection, cells were rinsed with PBS and fixed in 4% paraformaldehyde, 1X PBS for 30 minutes at room temperature. The cells were washed twice with 10mM glycine in PBS and permeabilized with 1% Triton X-100 in PBS for 5 minutes. After 2 washes, cells were blocked in 3% BSA in 1x PBS for 1 hour at room temperature. The coverslips were then inverted over the primary antibodies and incubated for 1 hour in a humidified chamber. The cells were washed again and incubated with fluorescently labeled secondary antibodies for 1 hour. Following washing, the coverslips were incubated with 0.05 μg/ml of DAPI in PBS for 5 minutes to allow detection of the nuclei. Samples were mounted onto slides using a solution of 2-phenylenediamine in 100mM Tris pH 8.0, 90% glycerol. Images were obtained using OpenLab software on a Leica DMR microscope and captured with a Hamamatsu CCD camera. Anti-myc antibody was obtained from Invitrogen. Anti-SRm300 was provided by B. Blencowe. Staining for the HA epitope used supernatant from the 12CA5 hybridoma.6 and transfected with 1 μg of pAdML dsx plasmids or pHxb2 R-/RI- plasmid and 4 μg of the indicated Tra2 vectors. Forty-eight hours post-transfection, total RNA was isolated according to Chomczynski and Sacchi [20VN and M-MLV reverse transcriptase as outlined by the manufacturer (Invitrogen). Analysis of HIV-1 RNA splicing was performed as previously described [5’ GCT GAT GCC ACT CAT GTA TG 3’) and dsxR (5’TGA CGG GAG TAC TCA TTC AC3’). Cycling conditions for dsx amplification were 94 C for 30 s, 60 C for 30 s, and 72 C for 30 s for 30 cycles.). Amplicons were separated on native, 5% polyacrylamide gels. Quantitation of unspliced/spliced dsx RNA ratios was performed using ImageQuant software following exposure of gels to phosphor screens and scanning with a Molecular Dynamics PhosphorImager.HEK 293T cells were seeded at a density of 1.5 x 10d Sacchi . Northerd Sacchi . For RT-escribed , 37. qRTTM FISH probes consisted of a mixture of 48 Quasar 570-labelled 20-mer oligonucleotides spanning the HIV-1 Gag coding region and were used as detailed by the manufacturer (Biosearch Technologies). Images were captured using a Leica DMR epifluorescence microscope at 630X magnification.To assess the impact of the Tra2β variants on HIV-1 US RNA subcellular distribution, 293 cells were transfected with peGFP C1-fusions along with pHxb2 R-/RI-, processed, and probed with Stellaris5’-TGCCGATGTGTCTATTGTATA-3’). After 24 h, virus was removed and transduced cells selected by treatment with 1–2 μg/ml puromycin for 2 days. Following selection, HIV-1 virus expression was induced by addition of 2 μg/ml doxycycline and cells harvested for protein and RNA analysis after 24 h.To examine the effect of Tra2ß depletion on HIV-1 gene expression, the HeLa rtTA HIVΔmls cell line  was trant-test. Values determined to be significant (p<0.05) are marked by an asterisk.Data was analyzed by Microsoft Excel and expressed as means ± standard deviation. Differences relative to control were compared by two-tailed Student’s In contrast to members of the SR protein family that have only a C-terminal RS domain, Tra2α and Tra2β are distinct, having an RS domain at both termini of the protein separated by an RNA recognition motif (RRM). Given the role of RS domains in mediating protein subcellular distribution and protein-protein interactions (reviewed in ), we werTo assess what effect these domain deletions had on protein localization, we examined their steady state localization following transfection into HeLa cells. As shown in Having confirmed expression and localization of each of the variants, we examined the impact of their overexpression on HIV-1 gene expression and viral RNA processing. As shown in To explore in greater detail how these factors alter HIV-1 RNA processing, we looked for changes in splice site usage by RT—PCR in both the singly and multiply spliced viral RNAs  have the GAR-rich motif characteristic of known Tra2β binding sites , 40–42. The absence of HIV-1 Gag and Env expression in the presence of either Tra2β or Tra2βΔN, despite the presence of significant levels of the corresponding viral (US and SS) RNAs , suggestTo explore possible mechanisms by which Tra2β induced nuclear retention of HIV-1 US RNA, we tested whether Rev overexpression could rescue viral Gag expression. As shown in To complement the above overexpression studies and explore the role of the endogenous Tra2β in the regulation of HIV-1 RNA processing, targeted depletion studies were performed. To facilitate analysis, depletion experiments were performed in the context of the HeLa rtTA HIVΔmls cell line . This ceet al. [vpr) adjacent to SA2, used in the expression of Vpr. As an extension of these studies, we report here on the impact of overexpression and depletion of full length Tra2α/β as well as on the activity of variants thereof on HIV-1 RNA processing, transport, and expression.Studies to date have highlighted a significant role for SR proteins in regulating HIV-1 RNA processing and expression, different members of the SR protein family yielding distinct effects upon either overexpression or depletion , 48–51. et al.  have demet al. [et al., Tra2α/β overexpression enhanced usage of SA2, resulting in increased levels of vpr, tat3, env8, and nef4 RNAs, all containing HIV-1 exon 3. Differences between the two studies may be attributable to the virus strains used (NL4-3 versus HxB2). Analysis of MS RNA splicing patterns revealed a slight reduction of rev1/2 RNA (~70% of control) upon Tra2α/β overexpression. Such a small reduction would not be expected to result in significant changes in Rev protein that is essential for HIV-1 Gag and Env protein expression. The change in viral protein expression could be attributed to the effect on HIV-1 RNA localization, as overexpression of Tra2β resulted in nuclear retention of viral US RNA. This response is somewhat unique to Tra2β as overexpression of other SR proteins  did not have a similar effect and contrasts with the role of SRp20 and 9G8 in promoting RNA export in other systems [Overexpression of Tra2α/β yielded a marked reduction in HIV-1 Gag and Env expression which was accompanied by some changes in viral RNA abundance and alterations in the pattern of viral RNA splicing. The changes in viral RNA upon overexpression of Tra2α/β were most evident in reduced MS RNA accumulation as well as a trend to reduced US RNA abundance. Levels of SS RNA were largely unaffected. In contrast to the report by Erkelenz et al. , our exa systems , 54. One systems . In the  systems , 45. Pre systems . Overexpin vitro [et al. has shown that the ratio of Tra2β1 to Tra2β3 expression varies in a tissue specific manner [vpr , the eferaction . This sieraction . If Tra2Additional evidence in support of the important role that Tra2β plays in regulating HIV-1 RNA processing was the finding that its depletion also resulted in changes in viral protein and RNA accumulation. An effect was observed despite the continue presence of Tra2α which could buffer the effect of Tra2β depletion on HIV-1 RNA processing. However, that a response was detected suggests that Tra2β may have a distinct role in regulating viral RNA metabolism. Unlike overexpression, depletion of Tra2β was associated with an increase in HIV-1 MS RNA abundance and a decrease in Env expression. Only very limited effects were observed on US and SS RNA abundance or Gag accumulation as well as no significant changes in the pattern of splice site use within the MS RNAs possibly due to endogenous Tra2α or other factors with overlapping binding specificities substituting for the depleted Tra2β in splice site regulation. Given that Tra2β overexpression reduced MS RNA levels, our findings suggest that Tra2β has an important role in regulating the abundance of HIV-1 MS RNA. One explanation for the observed responses is that Tra2β acts to promote the processing of SS RNA to MS RNA, possibly by competing with or modulating the activity of factors interacting with the GAR ESE to regulate formation of spliceosomes using SD4 and SA7. This activity is clearly dependent on the presence of both RS domains as deletion of either resulted in a marked alteration in activity.Together, these findings provide insights into the important role that Tra2β plays in regulating HIV-1 RNA processing, in particular the demonstration that the different isoforms of Tra2β (Tra2β1 and Tra2β3) are potent regulators of HIV-1 expression but through distinct effects on viral RNA processing. Recent studies by our group and others , 66 haveS1 File(PDF)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


Systematic analysis of radiation damage within a protein–RNA complex over a large dose range (1.3–25 MGy) reveals significant differential susceptibility of RNA and protein. A new method of difference electron-density quantification is presented. trp RNA-binding attenuation protein (TRAP) bound to a single-stranded RNA that forms a belt around the protein. Over a large dose range, the RNA was found to be far less susceptible to radiation-induced chemical changes than the protein. The availability of two TRAP molecules in the asymmetric unit, of which only one contained bound RNA, allowed a controlled investigation into the exact role of RNA binding in protein specific damage susceptibility. The 11-fold symmetry within each TRAP ring permitted statistically significant analysis of the Glu and Asp damage patterns, with RNA binding unexpectedly being observed to protect these otherwise highly sensitive residues within the 11 RNA-binding pockets distributed around the outside of the protein molecule. Additionally, the method enabled a quantification of the reduction in radiation-induced Lys and Phe disordering upon RNA binding directly from the electron density.Radiation damage during macromolecular X-ray crystallographic data collection is still the main impediment for many macromolecular structure determinations. Even when an eventual model results from the crystallographic pipeline, the manifestations of radiation-induced structural and conformation changes, the so-called specific damage, within crystalline macromolecules can lead to false interpretations of biological mechanisms. Although this has been well characterized within protein crystals, far less is known about specific damage effects within the larger class of nucleoprotein complexes. Here, a methodology has been developed whereby per-atom density changes could be quantified with increasing dose over a wide (1.3–25.0 MGy) range and at higher resolution (1.98 Å) than the previous systematic specific damage study on a protein–DNA complex. Specific damage manifestations were determined within the large  Zeldin, Brockhauser et al., 2013et al., 2007et al., 2015et al., 2013With the wide use of high-flux third-generation synchrotron sources, radiation damage (RD) has once again become a dominant reason for the failure of structure determination using macromolecular crystallography (MX) in experiments conducted both at room temperature and under cryocooled conditions (100 K). Significant progress has been made in recent years in understanding the inevitable manifestations of X-ray-induced RD within protein crystals, and there is now a body of literature on possible strategies to mitigate the effects of RD , and is the metric against which damage progression should be monitored during MX data collection, as opposed to time. At 100 K, an experimental dose limit of 30 MGy has been recommended as an upper limit beyond which the biological information derived from any macromolecular crystal may be compromised  is observed in the real-space electron density, and has been detected at much lower doses than any observable decay in the intensity of reflections. Indeed, the C—Se bond in selenomethionine, the stability of which is key for the success of experimental phasing methods, can be cleaved at a dose as low as 2 MGy for a crystal maintained at 100 K  and solvent (indirect damage) can induce cascades of up to 500 secondary low-energy electrons (LEEs) that are capable of inducing further ionizations. Investigations on sub-ionization-level LEEs (0–15 eV) interacting with both dried and aqueous oligonucleotides  sugar-phosphate C—O covalent bonds within the DNA backbone and then base-sugar N1—C bonds, eventually leading to single-strand breakages  bound to a 53 bp RNA sequence (GAGUU)10GAG  to RNA segments containing 11 GAG/UAG triplets separated by two or three spacer nucleotides . To avoid the previous necessity for visual inspection of electron-density maps to detect SRD sites, a computational approach was designed to quantify the electron-density change for each refined atom with increasing dose, thus providing a rapid systematic method for SRD study on such large multimeric complexes. By employing the high 11-fold structural symmetry within each TRAP macromolecule, this approach permitted a thorough statistical quantification of the RD effects of RNA binding to TRAP.Ten successive 1.98 Å resolution MX data sets were collected from the same TRAP–RNA crystal to analyse X-ray-induced structural changes over a large dose range  in a solution containing 70 mM potassium phosphate pH 7.8 and 10 mMl-tryptophan. The reservoir consisted of 0.2 M potassium glutamate, 50 mM triethanolamine pH 8.0, 10 mM MgCl2, 8–11% monomethyl ether PEG 2000. In order to accelerate crystallization, a further gradient was induced by adding 0.4 M KCl to the reservoir after 1.5 µl protein solution had been mixed with an equal volume of the reservoir solution. Wedge-shaped crystals of approximate length 70 µm (longest dimension) grew within 3 d and were vitrified and stored in liquid nitrogen immediately after growth. The cryosolution consisted of 12% monomethyl ether PEG 2000, 30 mM triethanolamine pH 8.0, 6 mMl-tryptophan, 0.1 M potassium glutamate, 35 mM potassium phosphate pH 7.8, 5 mM MgCl2 with 25% 2-methyl-2,4-pentanediol (MPD) included as a cryoprotectant.TRAP–RNA crystals were prepared using a previously established hanging-drop crystallization protocol . For the first nine data sets the attenuated flux was recorded to be ∼5 × 1011 photons s−1. A beam refill took place immediately before data set 10, requiring a flux-scale factor increase of 1.42 to be applied, based on the ratio of observed relative intensity ID/I1 at data set 10 to that extrapolated from data set 9.Data were collected at 100 K from a wedge-shaped TRAP–RNA crystal of approximate dimensions 70 × 20 × 40 µm  on beamline ID14-4 at the ESRF using an incident wavelength of 0.940 Å (13.2 keV) and an ADSC Q315R mosaic CCD detector at 304.5 mm from the crystal throughout the data collection. The beam size was slitted to 0.100 mm  × 0.160 mm , with a uniformly distributed profile, such that the crystal was completely bathed within the beam throughout data collection. Ten successive (1.98 Å resolution) 180° data sets (with Δφ = 1°) were collected over the same angular range from a TRAP–RNA crystal at 28.9% beam transmission. The TRAP–RNA macromolecule crystallized in space group 2.4.RADDOSE-3D  of solvent heavy elements from the crystallization conditions. The beam-intensity profile was modelled as a uniform (‘top-hat’) distribution. The diffraction-weighted dose (DWD) values  was then modelled at all 11 repeats around the TRAP ring and the second spacer (U5) was omitted from the final refined structure. For the later data sets, the observed structure-factor amplitudes from each separately scaled data set (output from AIMLESS) were combined with the phases of TR1 and the resulting higher-dose model was refined with phenix.refine  with each later data set Fobs(dn) . All later data sets were scaled against the initial low-dose data set in SCALEIT. For each data set an atom-tagged .map file was generated using the ATMMAP mode in SFALL  − Fobs(d1) were calculated using FFT  − Fobs(d1) difference map at each dose, each refined atom was assigned a set of density-change values X. The maximum density-loss metric, Dloss (units of e Å−3), was calculated to quantify the per-atom electron-density decay at each dose, assigned as the absolute magnitude of the most negative Fourier difference map voxel value in a local volume around each atom as defined by the set X.The 2.7.α atom, the C-terminus of each amino acid was methylated. While the structures of the closed shell acids are well known, the same is not true of those in the oxidized state. The quantum-chemical calculations employed were chosen to provide a satisfactory description of the structure of such radical species and also provide a reliable estimation of the relative C—C(O2) bond strengths, which are otherwise not available.Model calculations were run for the simple amino acids glutamate and aspartate. In order to avoid decarboxylation at the C-terminus instead of the side chain on the CGaussian 09 computational chemistry package  carboxylates were determined using analytic energy gradients with density functional theory  − Fobs(d1) Fourier difference map peaks in terms of the sigma (σ) contour level  at which peaks become visible. However, these σ levels depend on the standard deviation values of the map, which can deviate between data sets, and are thus unsuitable for quantitative comparison of density between different dose data sets. Instead, we use here a maximum density-loss metric (Dloss), which is the per-atom equivalent of the magnitude of these negative Fourier difference map peaks in units of e Å−3. Large positive Dloss values indicate radiation-induced atomic disordering reproducibly throughout the unit cells with respect to the initial low-dose data set.To quantify the exact effects of nucleic acid binding to a protein on SRD susceptibility, a high-throughput and automated pipeline was created to systematically calculate the electron-density change for every refined atom within the TRAP–RNA structure as a function of dose. This provides an atom-specific quantification of density–dose dynamics, which was previously lacking within the field. Previous studies have characterized SRD sites by reporting magnitudes of Dloss metric successfully identified the recognized forms of protein SRD , with clear Glu and Asp side-chain decarboxylation even in the first difference map calculated . The main sequence of TRAP does not contain any Trp and Cys residues (and thus contains no disulfide bonds). The substrate Trp amino-acid ligands also exhibited disordering of the free terminal carboxyl groups at higher doses ; however, no clear Fourier difference peaks could be observed visually. Even for radiation-insensitive residues (e.g. Gly) the average Dloss increases with dose: this is the effect of global radiation damage, since as dose increases the electron density associated with each refined atom becomes weaker as the atomic occupancy decreases . Only Glu and Asp residues exhibit a rate of Dloss increase that consistently exceeds the average decay  at each dose. Additionally, the density surrounding ordered solvent molecules was determined to significantly diminish with increasing dose . The rate of Dloss (attributed to side-chain decarboxylation) was consistently larger for Glu compared with Asp residues over the large dose range ; this observation is consistent with our calculations on model systems (see above) that suggest that, without considering differential hydrogen-bonding environments, CO2 loss is more exothermic by around 8 kJ mol−1 from oxidized Glu residues than from their Asp counterparts.For each TRAP–RNA data set, the RD Fig. 2a, with es Fig. 2a; howeves Fig. 2b. Only se Fig. 2b. The r3.2.b and 3c). Only at the highest doses investigated (>20 MGy) was density loss observed at the RNA phosphate and C—O bonds of the phosphodiester backbone. However, the median Dloss was lower by a factor of >2 for RNA P atoms than for Glu and Asp side-chain groups at 25.0 MGy , and furthermore could not be numerically distinguished from Gly Cα atoms within TRAP, which are not radiation-sensitive at the doses tested here .Visual inspection of Fourier difference maps illustrated the clear lack of RNA electron-density degradation with increasing dose compared with the obvious protein damage manifestations , similar damage was apparent between the two rings within the asymmetric unit, as expected. However, TRAP residues directly on the RNA-binding interfaces exhibited greater damage accumulation in nonbound TRAP , and for residues at the ring–ring interfaces  bound TRAP exhibited enhanced SRD accumulation .For the large number of acidic residues per TRAP ring (four Asp and six Glu residues per protein monomer), a strong dependence of decarboxylation susceptibility on local environment was observed Fig. 4. For eacAP Fig. 4a, similAP Fig. 4b, and fon Fig. 4c.Dloss metric were equal for the bound and nonbound groups in Fig. 5Three acidic residues  are involved in RNA interactions within each of the 11 TRAP ring subunits, and Fig. 5Dloss is seen for Glu36 in RNA-bound compared with nonbound TRAP, indicative of a lower rate of side-chain decarboxylation . For each TRAP ring subunit, the Glu36 side-chain carboxyl group accepts a pair of hydrogen bonds from the two N atoms of the G3 RNA base. In our analysis, Asp39 in the TRAP–(GAGUU)10GAG structure appears to exhibit two distinct hydrogen bonds to the G1 base within each of the 11 TRAP–RNA interfaces, as does Glu36 to G3; however, the reduction in density disordering upon RNA binding is far less significant for Asp39 than for Glu36 .A significant reduction in 3.4.∊1) of Glu42 appears to form a hydrogen bond to a nearby water within each TRAP RNA-binding pocket, with the other (O∊2) being involved in a salt-bridge interaction with Arg58  but not for the Glu42 O∊2 atoms , indicating that the stabilizing strength of this salt-bridge interaction was conserved upon RNA binding and that the water-mediated hydrogen bond had a greater relative susceptibility to atomic disordering in the absence of RNA. The density-change dynamics were statistically indistinguishable between bound and nonbound TRAP for each Glu42 carboxyl group Cδ atom (p = 0.435), indicating that upon RNA binding the conserved salt-bridge interaction ultimately dictated the overall Glu42 decarboxylation rate.One oxygen , an indication that radiation-induced conformation disordering of Phe32 had been reduced. The extended aliphatic Lys37 side chain stacks against the nearby G1 base, making a series of nonpolar contacts within each RNA-binding interface. The Dloss for Lys37 side-chain atoms was also reduced when stacked against the G1 base . Representative Phe32 and Lys37 atoms were selected to illustrate these trends.The RNA-stabilizing effect was not restricted to radiation-sensitive acidic residues. The side chain of Phe32 stacks against the G3 base within the 11 TRAP RNA-binding interfaces  − Fobs(d1) Fourier difference map peaks between successive data sets collected from the same crystal. Here, it provided the precision required to quantify the role of RNA in the damage susceptibilities of equivalent atoms between RNA-bound and nonbound TRAP, but it is applicable to any MX SRD study.Here, MX radiation-induced specific structural changes within the large TRAP–RNA assembly over a large dose range (1.3–25.0 MGy) have been analysed using a high-throughput quantitative approach, providing a measure of the electron-density distribution for each refined atom with increasing dose, et al., 2015Fobs(dn) − Fobs(d1) map density was detected around P, O3′ and O5′ atoms of the RNA backbone, with no significant difference density localized to RNA ribose and basic subunits. RNA backbone disordering thus appears to be the main radiation-induced effect in RNA, with the protein–base interactions maintained even at high doses (>20 MGy). The U4 phosphate exhibited marginally larger Dloss values above 20 MGy than G1, A2 and G3 . Since U4 is the only refined nucleotide not to exhibit significant base–protein interactions around TRAP (with a water-mediated hydrogen bond detected in only three of the 11 subunits and a single Arg58 hydrogen bond suggested in a further four subunits), this increased U4 Dloss can be explained owing to its greater flexibility. At 25.0 MGy, the magnitude of the RNA backbone Dloss was of the same order as for the radiation-insensitive Gly Cα atoms and on average less than half that of the acidic residues of the protein . Consequently, no clear single-strand breaks could be located, and since RNA-binding within the current TRAP–(GAGUU)10GAG complex is mediated predominantly through base–protein interactions, the biological integrity of the RNA complex was dictated by the rate at which protein decarboxylation occurred.The RNA was found to be substantially more radiation-resistant than the protein, even at the highest doses investigated (∼25.0 MGy), which is in strong concurrence with our previous SRD investigation of the C.Esp1396I protein–DNA complex . For Glu36 and Asp39, no direct quantitative correlation could be established between hydrogen-bond length and Dloss . Thus, another factor must be responsible for this clear reduction in Glu36 CO2 decarboxylation in RNA-bound TRAP. The Glu36 carboxyl side chain also potentially forms hydrogen bonds to His34 and Lys56, but since these interactions are conserved irrespective of G3 nucleotide binding, this cannot directly account for the stabilization effect on Glu36 in RNA-bound TRAP. Radiation-induced decarboxylation has been proposed to be mediated by preferential positive-hole migration to the side-chain carboxyl group, with rapid proton transfer trapping the hole at the carboxyl group . However, compared with Asp39, Glu36 is strikingly less decarboxylated when bound to RNA Fig. 4. This is2. We propose that with no solvent accessibility Glu36 decarboxylation is inhibited, since the CO2-formation rate K2 is greatly reduced, and suggest that steric hindrance prevents each radicalized Glu36 CO2 group from achieving the planar conformation required for complete dissociation from TRAP. The electron-recombination rate K−1 remains high, however, owing to rapid electron migration through the protein–RNA complex to refill the Glu36 positive hole (the precursor for Glu decarboxylation). Upon RNA binding, the Asp39 side-chain carboxyl group solvent-accessible area changes from ∼75 to 35 Å2, still allowing a high CO2-formation rate K2.When bound to RNA, the average solvent-accessible area of the Glu36 side-chain O atoms is reduced from ∼15 to 0 Ået al., 2000et al., 2007et al., 2015et al., 2013et al., 2012Previous studies have reported inconsistent results concerning the dependence of the acidic residue decarboxylation rate on solvent accessibility (Weik c). However, for each of these residues the exact crystal contacts are not preserved between bound and nonbound TRAP or even between monomers within one TRAP ring. For example, in bound TRAP, Glu73 hydrogen-bonds to a nearby lysine on each of the 11 subunits, whereas in nonbound TRAP no such interaction exists and Glu73 interacts with a variable number of refined waters in each subunit. Thus, the dependence of decarboxylation rates on these interactions could not be established.Apart from these RNA-binding interfaces, RNA binding was seen to enhance decarboxylation for residues Glu50, Glu71 and Glu73, all of which are involved in crystal contacts between TRAP rings Fig. 4c. HowevDloss increased at a similar rate for both the Tyr O atoms and aromatic ring atoms, suggesting that full ring conformational disordering is more likely. Indeed, no convincing reproducible Fourier difference peaks above the background map noise were observed around any Tyr terminal –OH groups.Radiation-induced side-chain conformational changes have been poorly characterized in previous SRD investigations owing to their strong dependence on packing density and geometric strain. Such structural changes are known to have significant roles within enzymatic pathways, and experimenters must be aware of these possible confounding factors when assigning true functional mechanisms using MX. Our results show that RNA binding to TRAP physically stabilizes non-acidic residues within the TRAP macromolecule, most notably Lys37 and Phe32, which stack against the G1 and G3 bases, respectively. It has been suggested . An increase in the dose at which functionally important residues remain intact has biological ramifications for understanding the mechanisms at which ionizing radiation damage is mitigated within naturally forming DNA–protein and RNA–protein complexes. Observations of lower protein radiation-sensitivity in DNA-bound forms have been recorded in solution at RT at much lower doses (∼1 kGy) than those used for typical MX experiments [e.g. an oestrogen response element–receptor complex  are notably amongst the most susceptible residues within the asymmetric unit Fig. 4. When exDloss metric are available from the authors on request.The Python scripts written to calculate the per atom 5.et al. (2010The following references are cited in the Supporting Information for this article: Chen  al. 2010.TRAP–RNA, dose 1.3 MGy, 5eeuPDB reference: dose 3.9 MGy, 5eevPDB reference: dose 6.5 MGy, 5eewPDB reference: dose 9.0 MGy, 5eexPDB reference: dose 11.6 MGy, 5eeyPDB reference: dose 14.1 MGy, 5eezPDB reference: dose 16.7 MGy, 5ef0PDB reference: dose 19.3 MGy, 5ef1PDB reference: dose 25.0 MGy, 5ef3PDB reference: dose 21.9 MGy, 5ef2PDB reference: 10.1107/S2059798316003351/rr5121sup1.pdfSupporting Information.. DOI:


RNases H participate in the replication and maintenance of genomic DNA. RNase H1 cleaves the RNA strand of RNA/DNA hybrids, and RNase H2 in addition hydrolyzes the RNA residue of RNA–DNA junctions. RNase H3 is structurally closely related to RNases H2, but its biochemical properties are similar to type 1 enzymes. Its unique N-terminal substrate-binding domain (N-domain) is related to TATA-binding protein. Here, we report the first crystal structure of RNase H3 in complex with its RNA/DNA substrate. Just like RNases H1, type 3 enzyme recognizes the 2′-OH groups of the RNA strand and detects the DNA strand by binding a phosphate group and inducing B-form conformation. Moreover, the N-domain recognizes RNA and DNA in a manner that is highly similar to the hybrid-binding domain of RNases H1. Our structure demonstrates a remarkable example of parallel evolution of the elements used in the specific recognition of RNA and DNA. RNases H are small nucleases that specifically hydrolyze RNA moieties in various double-stranded nucleic acids that contain both RNA and DNA ,2. Two sThe mechanism of substrate recognition and cleavage by RNase H1 was elucidated based on a series of structures of bacterial and human enzymes ,8. The Rin vivo activity  RNase H3 with T. maritima (Tm) RNase H2–substrate complex. These changes resulted in abolishing its activity on RNA–DNA junctions but not on R-loops, rendering the yeast enzyme similar to an RNase H1  that structurally resembles the TATA-binding protein (TBP) but has a monopartite structure, whereas TBP contains two very similar modules for the symmetric binding of DNA . Based oe enzyme . HoweverDespite the similarity to RNase H2, the type 3 enzyme exhibits the biochemical properties of RNase H1. In order to explain this, we sought to solve a crystal structure of RNase H3 in complex with its RNA/DNA substrate. The structure revealed a remarkable example of parallel evolution of the elements required for discrimination between RNA and DNA in both domains of RNase H3. Our structure completes the family picture of RNases H and suggests a universal set of elements used by enzymes to distinguish RNA from DNA.T. ammonificans RNase H3 carrying a His-tag and a SUMO-tag was expressed in E. coli and purified on a nickel column, followed by tag cleavage and further purification on the nickel column. Crystals of Ta-RNase H3 in complex with a 19-bp RNA/DNA hybrid oligonucleotide were obtained in 0.2 M AmSO4, 0.1 M BisTris (pH 5.5), 25% PEG 3350 and 10 mM spermine tetrahydrochloride. The diffraction data for the crystals were collected at the Berliner Elektronenspeicherring-Gesellschaft für Synchrotronstrahlung (BESSY) synchrotron at beamline MX-14.1 on a Pilatus 6M detector at 100K  were performed by standard methods using fluorescently labeled substrates. A detailed description can be found in the Supplementary Information.Thermovibrio ammonificans (Ta), Thermocrinis albus (Tha) and Thermotoga lettingae (Tl). The activity of the three proteins was characterized , and all were less active on a DNA duplex with a single ribonucleotide than on a substrate with a stretch of four ribonucleotides . For Ta-RNase H3 an approximately 1000-fold higher amount of protein was needed to achieve similar activity on the single ribonucleotide substrate, confirming the preference of the enzyme for longer stretches of RNA. For the D6-R4-D14/D substrate, the first cleavage occurred between the first and second ribonucleotide and the second cut 5′ of the RNA nucleotide of the RNA–DNA junction. The exact mechanism of the two cuts is not clear at this point but may be determined by sequence preference of the enzyme. It is also possible that the presence of the first nick aids conformational changes of the substrate required for the second cut right before the RNA–DNA junction to occur.Working toward a crystal structure of RNase H3–substrate complex, we purified the enzyme from three different thermophilic bacterial species: 4. The initial experimental density maps were of poor quality but sufficiently well defined to manually place a homology model of the protein that was subsequently manually rebuilt. The model of RNA/DNA substrate was next added, and the complex structure was refined . In the structure, the helices cover one surface of the sheet Figure  and b. TA. aeolicus (Aae) RNases H3 are very similar to those in our Ta-RNase H3 structure. For example, for Aae-RNase H3 and Ta-RNase H3, the N-domains can be superimposed with a root-mean-square deviation (rmsd) of 1.1 Å (54 C-α atoms), and the catalytic domains can be superimposed with an rmsd of 1.3 Å (151 C-α atoms). However, the relative position of the two domains is different in each of the three structures, indicating the flexibility of their arrangement . In Aae-RNase H3 and Bst-RNase H3 structures, the arrangement of the domains does not allow for their simultaneous binding to the substrate, suggesting that a conformational change that involves restructuring of the linker between the two domains is required.The individual domains of the previously determined structures of Bst and In Ta-RNase H3, the linker is short and composed of three residues. The domains interact with each other through hydrophobic contacts between helix B* from the N-domain and helix A from the RNase H2/3-specific insertion in the catalytic domain. In the structure, both domains interact with the RNA/DNA substrate, and the protein covers a continuous stretch of 15 bp of the hybrid Figure  and d. AIn the Ta-RNase H3 complex structure, the RNA strand of the hybrid, as expected, adopts an A form-like conformation with C3′-endo sugar puckers. The residues of the DNA strand, with the exception of one nucleotide, adopt the A form conformation with northern sugar puckers. The minor groove has a width between 9 and 10 Å, indicating a conformation intermediate between A and B but closer to the A form Figure .The RNA strand is bound at the edge of the central β-sheet of the catalytic domain. In addition to multiple van der Waals interactions, Lys113 and Lys219 bind the phosphate backbone of the RNA Figure . The fir2+, is observed at the active site, corresponding to metal ion A. It exhibits regular octahedral coordination with six ligands: pro-Sp non-bridging oxygen of the phosphate group of nt 7, pro-Rp oxygen of nt 8, backbone carbonyl of Gly79, side chain of Asn78 and two water molecules , Glu79, Asp180 and Glu206 Figure . The imps Figure . One of s Figure  and RNass Figure  , where The RNA at the active site is distorted, with backbone torsion angles beta and gamma of the ribonucleotide 8 differing from canonical values by 90° and 120°, respectively. The deformation results from the pushing of ribonucleotide 8 from the regular helix due to the interaction of the RNA with a loop before the C-terminal helical extension. The deformation allows the phosphate of this ribonucleotide to participate in the coordination of metal ion A. Thus, in RNase H3, this deformation plays a critical role in active site assembly as has been shown for RNase H2 .The interactions between the catalytic domain and DNA are less extensive than for the RNA strand. The most notable DNA interaction is formed by the phosphate group of nucleotide 16 located across the minor groove from the scissile phosphate, two base pairs (bp) from it Figure  and d, mTo verify the importance of the substrate interactions observed in the structure, we prepared variants of Ta-RNase H3 with substitutions in the residues that are involved in nucleic acid binding in the structure Figure . The wilIn our structure, the exposed surface of the N-domain β-sheet binds the hybrid across the minor groove roughly 10 bp downstream from the scissile phosphate Figures  and 3. TThe substrate specificity of the N-domain was tested by the EMSA Figure . In agreT. ammonificans complexed with an RNA/DNA hybrid. We provided structural information on substrate binding by the N-domain, which is only the second example of a small domain that is capable of specific RNA/DNA hybrid recognition. The N-domain is a structural relative of the TBP. A detailed comparison of the N-domain and TBP can be found in the Supplementary Information . Some residues in the N-domain have been previously predicted to interact with the substrate based on comparisons with TBP–DNA complex structures. Their importance was verified in site-directed mutagenesis studies  . Our strAnother important difference between type 2 and type 3 enzymes is that bacterial type 2 enzymes can cleave substrates with RNA as the non-cleaved strand, whereas H3 enzymes require DNA as the complementary strand . Our strThe feature shared by RNase H1, H2 and H3 is the location of the deformed phosphate close to the N-terminus of the first helix of the canonical RNase H fold (helix D in Ta-RNase H3). The positive pole of the helical dipole of this helix may stabilize the deformed phosphate in all three types of RNases H. There are, however, important differences between these enzymes. In the structures of Tm-RNase H2 the binding of the deformed phosphate does not involve any protein side chains , while iThe domain organization of RNase H3 and RNase H1 is similar. Both enzymes contain, in addition to the catalytic domain, a small N-terminal domain that is responsible for substrate binding (HBD in type 1 enzymes and the N-domain in RNases H3) Figure  and c. AOur structure thus provides two examples of the apparently independent emergence of protein modules used for the specific discrimination of RNA from DNA. The fact that these elements arose independently suggests that they may constitute a universal set of elements used by proteins when discrimination between RNA and DNA is required. As expected, the contacts with 2′-OH groups of RNA are the main element for RNA recognition. However, DNA detection is more complex. Nucleic acid deformation to B form-like conformation is one strategy utilized by both RNase H1 and H3. Another recurring element for DNA recognition is stacking interactions between deoxyribonucleotide sugar rings and aromatic amino acid side chains. Several such interactions can be found in RNases H. For example, it is present at the active site of RNase H2 and used for specific RNA–DNA junction recognition . In RNasIn summary, our structure completes the picture of the mechanism of RNases H. RNase H3 represents a notable example of conversion of the substrate specificity from RNA–DNA junction recognition to RNA/DNA hybrid cleavage. This conversion seems to have involved a remarkable case of parallel evolution of the substrate recognition elements employed by RNases H. It also points to the universal principles of RNA and DNA recognition and discrimination by proteins. The recurring elements include the detection of RNA by contacts with 2′-OH groups and the detection of DNA by deformation to B form-like conformation and stacking of deoxyribose with aromatic amino acid side chains.The atomic coordinates of the Ta-RNase H3–substrate complex structure have been deposited in the Protein Data Bank under accession number 4PY5.Supplementary Data are available at NAR Online, including [33].


Demand theory can be applied to analyse how a human or animal consumer changes her selection of commodities within a certain budget in response to changes in price of those commodities. This change in consumption assessed over a range of prices is defined as demand elasticity. Previously, income-compensated and income-uncompensated price changes have been investigated using human and animal consumers, as demand theory predicts different elasticities for both conditions. However, in these studies, demand elasticity was only evaluated over the entirety of choices made from a budget. As compensating budgets changes the number of attainable commodities relative to uncompensated conditions, and thus the number of choices, it remained unclear whether budget compensation has a trivial effect on demand elasticity by simply sampling from a different total number of choices or has a direct effect on consumers’ sequential choice structure. If the budget context independently changes choices between commodities over and above price effects, this should become apparent when demand elasticity is assessed over choice sets of any reasonable size that are matched in choice opportunities between budget conditions. To gain more detailed insight in the sequential choice dynamics underlying differences in demand elasticity between budget conditions, we trained N=8 rat consumers to spend a daily budget by making a number of nosepokes to obtain two liquid commodities under different price regimes, in sessions with and without budget compensation. We confirmed that demand elasticity for both commodities differed between compensated and uncompensated budget conditions, also when the number of choices considered was matched, and showed that these elasticity differences emerge early in the sessions. These differences in demand elasticity were driven by a higher choice rate and an increased reselection bias for the preferred commodity in compensated compared to uncompensated budget conditions, suggesting a budget context effect on relative valuation. At some point, spending money on even more ice cream is not going to increase one’s subjective happiness as much  as Besides relative preferences, other factors also influence budget allocation. Classic demand theory states that the allocation of choices amongst two commodities should be influenced firstly by the relative price of those goods: the more expensive a good is, the less it is purchased. To describe how sensitive a consumer is to price changes in a commodity, the demand elasticity of such a good can be calculated from the number of purchases of this commodity made at different prices. Demand elasticity can thus be understood as how flexible a consumer is about purchasing a specific commodity. Low elasticity—a small change in purchases with rising or falling prices, is typically found for commodities that a consumer absolutely needs, such as staple food items or transportation. The converse, high elasticity, applies to those commodities for which consumers exhibit strong changes in purchases with fluctuating prices such as for seasonal or luxury food items.However, as stated in the example above, consumers usually do not consider purchases of only one commodity, but rather select a distribution of purchases amongst several alternatives on offer. In a situation where a consumer is choosing between commodities A and B, e.g. apples and oranges, the demand elasticity of apples and oranges can be evaluated concurrently by changing the price of just apples, just oranges or both commodities at the same time. In economics terms, the effect of a price change of one of the commodities on offer is twofold: firstly, the relative price of the commodities changes, which is predicted to lead to substitution effects in bundle composition . Because apples can be substituted by oranges, a decision maker may compensate the price increase of the now relatively more expensive commodity by increasing consumption of the now relatively cheaper commodity. Secondly, the budget from which the consumer makes purchases, also limits the choices she can make. Imagine that the price of apples goes up, while the price of oranges goes down. This means a consumer can now afford less apples, but more oranges. She could compensate the reduced purchasing power for apples by adding more oranges to the bundle. Assuming for the moment that our consumer prefers apples to oranges, e.g. chooses more apples than oranges when the price of both commodities is equal . If prices change and apples become more expensive (€5 per apple) and oranges become less expensive (€3 per orange) at the same time, this might affect her relative preference for the commodities. She might want to purchase some more oranges, and fewer apples, for example 25 apples and 15 oranges (substitution effect). However, it would not be possible to purchase the original nor this altered bundle from the same budget of €160 again . As such, an uncompensated price change gives rise to an income effect on choice allocation in addition to a substitution effect: in our example, the budget (income) limits the choices that can be made and forces a second change in relative consumption over and above a change that might have been made due to altered prices. To offset budget effects on choice allocation and isolate the substitution effect, the available income can be adjusted. In the above example, increasing the budget by €20 so that the adjusted budget would now be €180 would allow the consumer to purchase the same bundle of 30 apples and 10 oranges. An everyday example of a price change with budget compensation is the salary adjustment to compensate for inflation rate that allows employees to conserve their purchasing power. Hence, compensated price changes present a shift in commodity prices that is accompanied by a concurrent adjustment of the budget upward or downward so that the originally chosen bundle could in principle be reselected within the new budget constraints, isolating the substitution effect from the income effect. Typically, when budgets are adjusted to allow reselection of the previously chosen bundle under the new price regime, different choice distributions and thus demand elasticities are observed compared to conditions where prices are changed without compensating budgets, in line with a substitution effect that persists even when the income effect is cancelled by budget compensation [However, it is unclear precisely how and when budget compensation affects consumers’ choices between commodities over and above an influence of price changes. In many empirical tests of budget effects with animal consumers, subjects make sequential choices between commodities –7. That 1) to illustrate the possibility that income-effects on price elasticity estimates are merely the consequence of sampling from different choices, consider the following example (adapted from above): a consumer has a budget/income of €160 and buys 30 apples and 10 oranges, each costing €4. Now, prices change and apples cost €5, oranges €3. In an income-uncompensated condition, this consumer still has €160 to spend, and now buys, say, 20 apples and 20 oranges. In an income-compensated condition, the budget is adjusted to €180, so that the consumer has €20 more to spend relative to her original income. Assume that the consumer now buys 24 apples and 20 oranges. Comparing the elasticity measures between the income-compensated and—uncompensated conditions would yield different elasticity estimates for apples, suggesting that income-effects affect choice distributions over and above substitution effects. Now suppose that the consumer has spent the first €160 in the income-compensated condition on 20 apples and 20 oranges, and spends the remaining €20 on apples only. If this was the case, calculating price-elasticity across the budget shared between both conditions (€160) would yield exactly the same elasticity estimates in both conditions; the different elasticity estimates obtained when considering the entire choice set would merely be the result of the final €20 spent on apples. In other words, the difference in elasticity estimates when considering the entire choice set would simply be the consequence of the extra set of choices available after budget extensions.only those choices that are shared by both conditions. Thus, this theory predicts that, if only the shared choices would be considered, no difference in elasticity estimates would be expected.This possibility implies that choice allocation, and changes in choice allocation due to price changes, should be identical in compensated and uncompensated budget conditions when considering any reasonable size. Such differences in demand elasticity estimates in this case do reflect a true income effect on choice allocation between commodities. To illustrate this possibility, take again the above example: how do choices change in response to price alterations for apples (€4→€5) and oranges (€4→€3) in uncompensated (budget €160→€160) and compensated (budget €160→€180) income conditions? If relative valuation of apples and oranges depends not only on their price, but also the available budget, a difference in budget under similar price structures should thus change choice allocation. In other words, an apple for €5 may have a different relative utility for a consumer if she has a budget of €160 compared to a budget of €180. If, say, apples for €5 are valued higher when the consumer has €180 to spend (compensated income condition) than when she has €160 to spend (non-compensated income condition), she would purchase more apples in the compensated condition. Importantly, once the consumer has learned the price and budget condition she is in, she would buy relatively more apples in the compensated than uncompensated income condition from the beginning of a choice sequence on. Thus, when comparing the choice allocations within the first €160—the budget common to both conditions, our example consumer would purchase relatively more apples in the compensated than the uncompensated income condition. The income-effect on price-elasticity should therefore be detectable in any subset of choices, and should become evident long before the budget runs out.2) Alternatively, it is possible that the size of the budget immediately affects the consumer’s sequential choice structure. This could be the case if the budget context affects the relative valuation of the choice options. If consumers indeed have an internal representation of the current budget/price set and if this representation affects relative valuation of the commodities on offer, it is to be expected that this results in differences in choice allocation that are visible throughout the session and thus result in different elasticity measures (for compensated vs. uncompensated budget conditions) evaluated over choice sets of To disambiguate between these two predictions, it is therefore imperative to investigate the evolution of choice distributions across a fixed range of consecutive choices, and to use comparable choice sets when computing demand elasticities in order to isolate budget effects on choice distributions from other temporal dynamics.We approached the question whether budget adjustment (compensation) independently affects the temporal dynamics of choice distributions by training rats on a classic demand task. In this task, animal consumers spend a budget of nosepokes between two commodities, chocolate- and vanilla-flavoured soymilk ,5, in seAs the chocolate-flavoured soymilk contained more sugar than vanilla-flavoured soymilk, we expected rats to show a (baseline) preference for chocolate, reflected in choice proportions above chance level when the prices for both commodities were equal. Consequently, we expected less elasticity in demand in the face of price changes for the preferred, chocolate-flavoured soymilk compared to the vanilla-flavoured soymilk. Given that rats had access to food and water outside the experiment, we furthermore expected that the commodities would be treated as non-essential and substitute for each other, thereby predicting positive cross-price elasticity, e.g. an increase in consumption of commodity A when the price of commodity B is increased and vice versa. However, considering that animals have a tendency to explore and alternate in an option space, we predicted that substitution would be imperfect, e.g. that animals would not solely choose one option in any budget/price condition. Based on previous work ,5, we exA group of 8 male Long-Evans rats  was used in these experiments. Rats were housed in groups of four animals per cage maintained at 22°C on an inverted 12h:12h light/dark cycle (lights off at 07:00 AM). All rats weighed 240 to 260 g at the beginning of the study and were food restricted after the first four weeks to 15 g per animal per day . Weights were monitored during the whole experiment to ensure continued weight gain. Water was available ad libitum in the home cage at all time.Experiments were conducted in operant chambers  under red light conditions. Each chamber was equipped with three nosepoke units . These nosepoke units were horizontally arranged on one side of the chamber  in seven training steps with increasing complexity see  leading After initiating a trial by making a nosepoke with a duration of at least 500ms in the central nosepoke unit, the two lateral nosepoke holes became active, indicated by the illumination of the corresponding cue LEDs in the nosepoke holes.After meeting the respective FR requirement set by the current price level (a series of 3–5 nosepokes), in one of the lateral nosepoke holes (duration >100ms), separated by 50 ms delays during which the LED in the nosepoke holes turned off briefly, the liquid availability was signalled by a cue light on the opposite site turning on.A nosepoke of 100ms in the corresponding liquid access port led to the delivery of the bottle containing a either a vanilla-flavoured soymilk or chocolate-flavoured soymilk solution, depending on which side was chosen. Bottle access was available for 2s. Afterwards, the bottle was retracted out of reach of the animals.Following bottle retraction, an ITI was implemented before the next trial started, during which interaction with the operant elements had no programmed consequences. Each trial lasted exactly 32 seconds.In the final task structure, the number of nosepokes required in the choice phase was used as a proxy for price, or cost, and varied between liquids (commodities) and task conditions , with different price regimes and different budgets  adding up to a total of 70 testing days. The default price (‘baseline’ phases) for both commodities was 4 nosepokes , with a budget of N = 160 nosepokes. Consequently, rats could obtain access to vanilla and chocolate in any combination up to 40 times daily. All animals started off in a baseline phase, followed by two experimental phases . The price changes in these two phases were either compensated, for half of the animals, or uncompensated for the other half see . The fouIn the uncompensated budget conditions, rats retained the original budget of 160 nosepokes available in the baseline conditions. In the compensated budget conditions, each animal’s budget was adjusted individually so that the amount of each commodity chosen by the animal during the preceding baseline phase could theoretically be purchased again under the new price regime. For instance, imagine a rat spending 120/160 nosepokes on chocolate, and 40/160 nose-pokes on vanilla in the baseline phase, thus purchasing 30 chocolate and 10 vanilla bottle drops (each bottle drop costing 4 nosepokes). In order to allow the rat to obtain, again, a bundle of 30 chocolate and 10 vanilla bottle drops in the compensated 5:3C condition, the budget would need to be adjusted to 180 nosepokes . This example illustrates a budget extension (from 160 nose-pokes in the baseline condition to 180 nose-pokes in the compensated budget condition), but budget contractions (reduction of budget) occurred as well, namely in the 3:5C condition . From each experimental phase, the first two days were discarded to allow the rats to adapt to the new price- and budget-regime  for qi as the target variable.Own-price elasticity was calculated using linear regression on the log-transformed number-of-choices/price-ratio pairs as given by the formula:constant . Price eTo estimate the dynamic development of choice ratios with increasing trial number, we calculated running averages of the choice proportion for chocolate by dividing the number of trials on which chocolate was chosen in the past 10 trials by 10, and sliding this analysis window along the set of trials. To compare between conditions, we pooled all running average choice proportion timeseries for all sessions for rats in a condition  and ran another bootstrap analysis on the data, this time drawing N = 50 sessions, with replacement, per bootstrap iteration and taking the average. We report the median of the resulting bootstrap distributions, and their 95% confidence intervals (adjusted by Bonferroni correction for the number of time points examined) by plotting the lower and upper percentile corresponding to the adjusted two-tailed p-levels of 0.05 in the sorted bootstrap populations. Significance is tested by dividing the difference in bootstrapped means between conditions by the weighted standard deviation and evaluating the result, per time point, as |Z| > 1.96 (uncorrected). Significant differences are flagged if the Z-level exceeded 1.96 after Bonferroni-correction .All animal procedures adhered to German Welfare Act and were approved by the LANUV .t(17) = 2.34; p = 0.03, t(93) = -20.1, p < 10–30), suggesting that rats indeed experienced budget extensions.To allow the rats to experience the new, sometimes adjusted budgets for each experimental block of sessions (phase), we excluded the first 2 sessions from each phase from the analysis. Indeed, the coefficient of variation across subsequent sessions, averaged across phases and rats, almost halved from session 1–2 to session 3–4 and remained lower afterwards. A paired-sample t-test comparing the coefficient of variation (CV) of sessions 1–2 against the average of the four subsequent 2-session blocks turned out significant: (t(5) = 2.14; p<0.05, one-sided), while it was significantly higher compared to baseline in the 3:5 condition (t(5) = 3.14; p<0.05). These results confirm that price changes influence choice distributions, even in the budget condition where the original bundle could have been purchased , in all phases except for the 5:3 condition . Comparechoc/uncomp) was -0.69 ± 0.05  = -5.31; p<.005, paired-sample t-test). Both under uncompensated and compensated budget conditions, demand for chocolate was inelastic (t(5) = 5.93; p < .005 and t(5) = 14.8 p < .001, respectively, for a one-sample t-test against -1). In contrast, demand for vanilla was significantly more elastic than demand for chocolate in both uncompensated  = -5.80; p < .005 against εchoc/uncomp, paired-sample t-test) and compensated budget conditions  = -7.55; p < .001 against εchoc/comp), though elasticity at the group level neither differed significantly from unit elasticity (t(5) = -1.56 and -0.42; n.s.) nor differed between uncompensated and compensated budget conditions (t(5) = -0.43; n.s.).Using the distributions for chocolate and vanilla choices, averaged per condition per rat, we calculated price elasticity (ε) for chocolate (the preferred commodity) and vanilla. As mentioned, price elasticity values ε<-1 indicate elastic demand, and 0>ε>-1 indicate inelastic demand. Price elasticity for chocolate with budgets uncompensated (εt(5) = -5.80; p < .005) and compensated (t(5) = -7.55; p < .001) budget conditions.Cross-price elasticity was 0.72 ± 0.05 for chocolate and 1.11 ± 0.07 for vanilla under uncompensated conditions  holds that considering the entire choice set for compensated and uncompensated budget conditions might produce differences in demand elasticity that can be explained just by the act of comparing choice sets with unequal number of choices. In contrast, it is also possible that the budget context had an independent effect on commodity selection, perhaps through directly affecting relative valuation of the commodities on offer . The crucial question thus is whether the relative distribution of choices between chocolate and vanilla, presumably reflecting relative valuation, differs between compensated and uncompensated budget conditions when examining choice sets that are matched for price and number of choices. If differences in choice patterns between compensated and uncompensated conditions, and in the subsequent estimates of demand elasticity, emerge early in a session, it would support the hypothesis that the budget context has an effect in of itself on the relative valuation of the commodities, so that rats value chocolate and/or vanilla differently in the compensated than in the uncompensated condition and adjust their choices accordingly. = 12.2, p = 0.017, partial η2 = 0.71, a significant main effect for trial step F = 5.68, p < 0.001, partial η2 = 0.53 and a significant interaction F = 3.57, p = 0.002, partial η2 = 0.41. Some caution is warranted, as the cumulative nature of the data makes the incremental trial steps highly correlated. To isolate which region of the data was significantly different, we employed a paired-sample t-test with incremental alpha level correction, setting α at 0.05/1 to detect the first significant divergence between the budget conditions . Subsequent data points (60%-100%) were then tested for significance with a corrected α level of 0.05/2 for the second, 0.05/3 for the third etc. resulting in a corrected α level of 0.05/6 = 0.0083 for the 10th data point, which was passed (t(5) = 5.79, p = 0.0022). We thus found that elasticity of demand for chocolate quickly diverged between the compensated and uncompensated conditions and remained at significantly different levels throughout the rest of the session  between conditions. Using bootstrap resampling techniques (cf. ), 95% coWe have shown here that differences in budget constraints impact the evolution of choice distributions in animal consumers as they spend their budget of nosepokes between two alternative commodities, chocolate- and vanilla-flavoured soymilk. Under conditions of equal prices, rats chose chocolate at levels above chance. Rats responded to changes in the price ratio by shifting choice allocation, in line with a substitution effect. Importantly, our design allowed us to isolate this substitution effect from a co-occurring income effect on choice allocation by measuring consumption changes during income-compensated and income-uncompensated price changes. In our design, income was compensated by adjusting the budget for price changes to the revealed preferences as exhibited through commodity bundle selection. Compensating budgets affected choice distributions in comparison to uncompensated conditions both when the preferred commodity chocolate was relatively expensive and when it was relatively inexpensive. Consequently, the budget contexts (compensated vs. uncompensated) resulted in different measures of demand elasticity (ε). Importantly, these observations held for choice sets consisting of equal proportions of budget  and wereOur work replicates and extends early basic findings on budget allocation by animal consumers ,5,10. Usneed water to digest the food pellets, barring these goods from substitution. In our study, in compliance with animal welfare regulations, rats were allowed to gain weight over days by providing an adequate food supply, while yet leaving the rats non-satiated. This design resembles an open economy, as opposed to a closed economy where consumers are supposed to purchases all food and drink items from their budgets, even though the sole source of chocolate and vanilla soymilk was in the context of the daily consumption sessions in the operant cages. Under such economic constraints, it could be expected that demand for the goods would be elastic, as the additional intake of these foods was non-essential to homeostasis [Our design differs from the Kagel et al. design, however, in important ways: while their animals were living in the experimental chamber  and could spend their budget in a 24-hour period, our animals could spend their budgets only within daily session lasting up to one hour. In the essential commodity experiment, no other supply of food or water was given, thus creating a completely closed economy. As indicated by Hursh , in a cleostasis . The faceostasis  found thWe believe that the time-resolved analysis of choice distributions provides an important extension of the analyses employed in the Kagel papers: our results indicate dynamics in choice distributions that vary with budget conditions and highlight that these differences in choice distributions are not due to differences in choice set size between budget conditions, but rather are due to a difference in sequential choice structure—putatively reflecting altered relative valuation of the choice options, between budget conditions. As there are no sensory cues in the operant cage environment directly signalling the budget conditions to the rats, the current budget condition has to be experienced. In the absence of a difference in the stimulus-response-reward contingencies between sessions with or without budget compensation (e.g. 5:3C vs 5:3 with the same FR requirements), the observed differences in choice allocation suggest that rats somehow represent or at least integrate the available budget in their decision process. Interestingly, Elsmore et al.  found thwhy the rats chose to distribute their budget amongst both alternatives. More importantly, spontaneous alternation alone cannot explain why choice allocation, or indeed choice sequences, would be different for different budget sets. It is tempting to speculate, however, that spontaneous alternation between the choice options in our task would be expected most for the condition in which the choice options’ costs/benefit ratios were perceived to be closest by the animal consumers, i.e. the 5:3 condition; the only condition to exhibit choice distributions not significantly different from indifference (chance) levels. Indeed, we found that the conditional probability to reselect chocolate  after a streak of N = 1,2, or 3 chocolate choices was significantly greater in the 4:4 and 5:3C conditions as indicated by a repeated-measures ANOVA with length of chocolate chain and condition as within-subject factors: F = 5.68, p<0.05; partial η2 = 0.53. Post-hoc analyses revealed that both the 4:4 and 5:3C conditions showed significantly higher conditional probabilities of reselecting chocolate than the 5:3 condition  with a trend to a widening of the gap with increasing sequence length  commodity, which might have been expected for a situation in which chocolate and vanilla were perfect substitutes with different ratio requirements. Instead, the data is more akin to choice distributions found under variable interval schedules of reinforcement (i.e. a distribution reflecting the relative rate of reinforcement), that have been extensively studied within the Matching Law framework ,14. Why e length , suggestrelative to the budget. In the 5:3C extended budget condition, this 2-nosepoke price difference represents a relatively smaller chunk of the entire budget than in the 5:3 condition. Conversely, in the 3:5C condition it represents a larger chunk compared to the 3:5 condition. Intuitively, it makes sense why this difference in relative price should matter: a difference of 2 nosepokes is a big deal on a tight budget, but maybe not so much on a more accommodating budget. If we combine this with the notion that our rat consumers all prefer chocolate over vanilla, as shown in the 4:4 baseline conditions, we can argue that extending the budget reduced the salience of the price differences, leading to more choices of the preferred, but expensive commodity  in the Nucleus Accumbens is required for sustaining operant responding under higher effort requirements  = 2.34; p = 0.03.To assess the variation in the choice data as a function of session number, we calculated the coefficient of variation (CV) across all sessions ran for a given session pair, per rat, per phase (6 rats * 7 phases). The panel shows the average CV and SEM. The CV for sessions 1&2 per phase is significantly different from the average of the subsequent 4 session pairs. *: paired-sample t-test (EPS)Click here for additional data file.S2 FigLog-transformed choices for chocolate (left panels) or vanilla (right panels) on the y-axis are plotted as a function of the log-transformed price ratio for vanilla over chocolate (left panels) or chocolate over vanilla (right panels), per rat, for uncompensated budgets (top panels) and compensated budgets (bottom panels), respectively.(EPS)Click here for additional data file.S3 FigAs in Fig (EPS)Click here for additional data file.S4 Fig = 5.68, p<0.05; partial η2 = 0.53. Post-hoc analyses revealed that both the 4:4 and 5:3C conditions showed significantly higher conditional probabilities of reselecting chocolate than the 5:3 condition .The conditional probability of reselecting chocolate as a function of 1,2 or 3 recent chocolate choices is plotted for the averaged baseline condition , the compensated expensive chocolate condition  and the uncompensated expensive chocolate condition . The conditional probability is significantly different between conditions: rmANOVA with length of chocolate chain and condition as within-subject factors: F(EPS)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Coral reefs around US- and US-affiliated Pacific islands and atolls span wide oceanographic gradients and levels of human impact. Here we examine the relative influence of these factors on coral reef fish biomass, using data from a consistent large-scale ecosystem monitoring program conducted by scientific divers over the course of >2,000 hours of underwater observation at 1,934 sites, across ~40 islands and atolls. Consistent with previous smaller-scale studies, our results show sharp declines in reef fish biomass at relatively low human population density, followed by more gradual declines as human population density increased further. Adjusting for other factors, the highest levels of oceanic productivity among our study locations were associated with more than double the biomass of reef fishes (including ~4 times the biomass of planktivores and piscivores) compared to islands with lowest oceanic productivity. Our results emphasize that coral reef areas do not all have equal ability to sustain large reef fish stocks, and that what is natural varies significantly amongst locations. Comparisons of biomass estimates derived from visual surveys with predicted biomass in the absence of humans indicated that total reef fish biomass was depleted by 61% to 69% at populated islands in the Mariana Archipelago; by 20% to 78% in the Main Hawaiian islands; and by 21% to 56% in American Samoa. Coral reefs are frequently the focus of scientific, media, and conservation attention because of their vulnerability to human impacts, but there remain a number of extremely isolated and uninhabited coral reef areas in the Indian and Pacific Oceans. Because those places are in many cases several hundred kilometres or more from the nearest human populations, biological communities at those isolated reef ecosystems have been used as baselines against which to assess the extent of human impacts to reef ecosystems around human-populated islands. Much of that work has focused on reef fish assemblages and there is now abundant evidence that, compared to human-populated islands, remote coral reefs typically have three to four times or more standing biomass of reef fishes, with relatively large portions of biomass in upper trophic levels and made up of larger-bodied taxa –4.Extremely remote and minimally-disturbed coral reef areas are almost certainly better exemplars of natural reef ecosystems than possible alternatives, such as long established marine protected areas (MPAs) around populated islands, because protection is often incomplete or compliance uncertain within MPAs , becauseHere, we use survey data collected along wide gradients of environmental conditions and potential human impact to investigate drivers of differences in reef fish assemblages among ~40 US and US-affiliated islands and atolls spread widely across the western and central Pacific. The availability of consistent large-scale survey data, together with recently-synthesized satellite-derived environmental data from the same locations  allowed Partner agencies that contributed to field work and survey operations and provided permissions to work in local waters include the Papahānaumokuākea Marine National Monument, the US Fish and Wildlife Service, the Department of the Interior, the State of Hawaii Department of Land and Natural Resources, the Commonwealth of the Northern Mariana Islands Papahānaumokuākea Marine National Monument, US Fish and Wildlife Service, State of Hawaii Department of Land and Natural Resources, Commonwealth of the Northern Mariana Islands (CNMI) Division of Fish and Wildlife, CNMI Coastal Resources Management Office, CNMI Division of Environmental Quality, Guam Division of Aquatic and Wildlife Resources, and the American Samoa Department of Marine and Wildlife Resources.We analyzed co-located fish and benthic data collected between 2010 and 2013 as part of the Pacific Reef Assessment and Monitoring Program (Pacific RAMP), which is conducted by NOAA’s Pacific Islands Fisheries Science Center, Coral Reef Ecosystem Division (CRED) and partners. Pacific RAMP involves multidisciplinary monitoring of shallow water (<30m) coral reef habitats at US and US-affiliated islands and atolls (henceforth ‘reef areas’) in the central and western Pacific: specifically, the Hawaiian and Mariana Archipelagos, the Pacific Remote Island Areas  and American Samoa . Survey The great majority of surveys were conducted during 1–3 month long Pacific RAMP cruises, with surveys conducted by trained scientific divers working off small boats. Typically, each reef area was visited for 3–5 days, allowing for ~ 20–50 sites surveyed per each reef area per cruise. Additional site surveys came from supplemental survey efforts using the same protocols, survey design, and largely involving a consistent core group of survey divers.At each reef area, the target domain was hardbottom in <30 m depth, stratified by reef-zone  and depth bin . To maximize comparability, only data from forereef surveys were used for this study, which comprised 79% of the hardbottom habitat and 89% of the sites surveyed. At each survey location, primary and backup survey sites were selected prior to the survey visit, using a formal randomization procedure, and based on the habitat, reef-zone and bathymetric maps developed by CRED.Details of the survey protocol are given in , but in On completing the fish count, divers estimated benthic cover  and structural complexity within the SPC cylinders. For all surveys, data from the two adjacent cylinders were pooled into a mean value for the site.http://polar.ncep.noaha.gov/waves). Following [We collated and synthesized data on a range of environmental, habitat and anthropogenic covariates known to influence coral reef fish assemblages ,8,12–16 in-situ complexity measures were calibrated using data from reef areas where we had both types of data  and structural complexity (CX) come from visual estimates made by divers during surveys. Structural complexity was measured in two ways: in 2010 and 2011 divers estimated complexity on a 5-point scale ; but in 2012 and 2013 the method was revised and divers instead estimated the maximum vertical relief within their cylinder and the proportion of their cylinder in different relief bins  . Those d of data ., to allhttp://www.census.gov/2010census/). We used two measures of human population: (i) local human population density, i.e. humans resident at an island divided by reef area (HUM) and (ii) distant human population density, i.e. total humans living within 200 km but not resident on the island, again divided by reef area (HDIST). We included distant human population as a covariate in recognition of the growing body of evidence that long-range factors such as proximity to larger human populations and markets can generally be expected to have large impacts on local reef fish assemblages [Reliable and comparable socioeconomic data, including coral reef fishery data, are not available for our study locations , and we emblages ,20. The In total, we analyzed data from 1,934 sites on 37 reef areas, constituting > 2,000 hours of underwater observation. Following our survey design and standard procedure when reporting data from Pacific RAMP, we pooled data from three adjacent small Northern Mariana Islands—Sarigan, Guguan, Alamagan into a single reporting unit “SG&A” and consider that as a single reef area. We excluded three Northwestern Hawaiian Island (NWHI) reef areas from the analysis. Gardner Pinnacles was not included because it was not covered by the study we acquired oceanographic data from [http://escholarship.org/uc/item/5394f7m3). We excluded all sharks and jacks from biomass estimates because of our concern that different responses of those highly mobile roving piscivores to the presence of divers could be a source of bias among study locations. Most obviously, the ‘mobbing’ behavior of jacks and sharks appears to cause substantial overestimation of their densities in small-scale diver surveys in the Northwestern Hawaiian Islands [The core response measure used for this study was estimated biomass of fishes per unit area (hereafter “biomass”). The mass of individual fishes was calculated using length to weight conversion parameters taken from published and web-based sources ,22. Data Islands ,24.Site-level reef fish biomass, hard coral cover, and complexity values were turned into island-scale averages by first calculating within depth-strata mean and variance and then calculating weighted mean and variance using the formulas given in  with stra priori cut-off value of 3 [Prior to developing analytical models, we conducted data exploration following the recommendations of . Becauselue of 3 .mgcv packages in R [We used generalized additive models (GAM) to examine relationships between inter-island mean fish biomass and inter-island variations in the predictors using the ges in R . Models MuMIn R package v1.9.13 [dredge. We retained all models with Akaike weight > 0.05. The ‘variable importance’ output from dredge gives sum total weight of all models containing a particular variable. As total model weights sum to 1, high values of variable importance are indicative of predictor variables that occur in a large portion of highly-ranked models. Using the retained models, we then used the model.avg procedure to generate a weighted model average suitable for model prediction using the predict function. We predicted total fish biomass in the absence of humans—i.e. the complete dataset, but with HUM and HDIST set to 0 for all islands, as in Nadon et al. [We used model selection and model-averaging procedures from the  v1.9.13  based on v1.9.13 . For eacn et al. .predict procedure to generate visualizations of smoothers in the top-ranked models, setting the predictor variable of interest to values equally spaced between that variable’s min and max, and setting all other predictor variables to their means. We used the resulting output to generate smoothers with scaled biomass response shown against untransformed predictor variables.To ease interpretation of relationships between the response and predictor variables, we used the All analyses were conducted using R version 3.0.3 . Analysi-2 (SD 27.8 g m-2). Total fish biomass tended to be lower at islands with large human populations—the lowest biomass value (mean ± SE) being 8.3 ± 0.7 g m-2 at Oahu in the Hawaiian Islands—and highest at unpopulated and remote islands, particularly those in the US Line Islands—highest biomass being 129.6 ± 18.3 g m-2 at Kingman Reef  to 8.4 g m-2  to 38.4 ± 3.0% at Swains (American Samoa) with a grand mean of 21.0% (SD 9.1%). Structural complexity (mean substrate height) ranged from 0.29 ± 0.02 m at Oahu to 0.92 ± 0.09 m at Swains.ΔAICc values of > 2  for the 4 predictors in the top-ranked model was 0.990 or greater for CHL, HDIST and HUM, and 0.852 for HC; and no other predictor variable had vi > 0.256 , both local and distant human population density (HUM & HDIST), and hard-coral cover (HC) . Model ss of > 2 , indicat > 0.256 .Retained models of consumer group biomass also indicated strong support for the importance of the two human population predictors: HUM was included in all retained models  to its high of 0.21 mg m-3 (at Jarvis), but the scales of increase varied greatly among groups. For ‘all fishes’, predicted biomass at highest CHL was ~2.2 times that at lowest CHL (Chlorophyll-a (CHL) was an important predictor variable not only for all fishes, but also for secondary consumers, planktivores, and piscivores, and was included in all retained models  was an important predictor for primary consumers, being included in all retained models and having variable importance of 0.998 , and theOther predictor variables had less consistent effects. One or the other of the two correlated variables SSTL and WV was included in all retained models for planktivores , Fig. 2.-2 (mean ± SE) at Swains, to a high of 99.0 ± 20.3 g m-2 at Jarvis  averaged nearly two and a half times biomass on reefs in the Northwestern Hawaiian Islands, in spite of the fact that NWHI reefs are extremely distant from the nearest human-population centers (> 600 km). Clearly not all remote coral reefs have the same ability to sustain extremely high reef fish biomass, and, as has been shown for reef sharks , a key fdy areas . When exAn obvious explanation for the positive association between oceanic chlorophyll-a and reef fish biomass—particularly of planktivores and piscivores—is that the flux of plankton-rich oceanic waters impinging on and flowing over reefs in highly productive regions sustains large stocks and high productivity of planktivores and presumably also of other components of reef ecosystems such as filter feeders. Increased productivity in those groups in turn supports other components of reef food webs when they are preyed upon, and through defecation and other processes that subsidize reef detrital food webs –42. WhilIt has long been recognized that the invertebrate and vertebrate planktivores on reefs consume significant amounts of pelagic plankton, representing large inputs of allochthonous nutrients and organic material into the reef ,43–46. IThough wave energy, coral cover, and structural complexity did not stand out as important predictor variables in our models of reef fish biomass among islands, these factors can clearly have large impacts on the distribution of local fish biomass ,14. Our -2 and ~46 g m-2 respectively, around one third to one fifth of the reef fish biomass values reported from those locations in earlier studies [Reef fish biomass estimates in our study are considerably lower than those reported for the same islands in a number of earlier studies. For example, mean island-scale biomass values in our study for the uninhabited US Line Islands and the NWHI were ~117 g m studies ,24. Seco studies ,51,52. T-2 respectively. As we describe in the methods section, divers recorded some non-instantaneous data during surveys, specifically, counts of fishes of taxa that are observed within an initial 5-minute survey period, but where that species is not present in the survey area at the time of the instantaneous count for that group. We have previously found that the pooled non-instantaneous and instantaneous counts yield similar biomass estimates to the open belt transects used by our program before 2009 [-2; much closer to values for those region reported in earlier studies.We can quantify the impact of removing counts of sharks and jacks and, to some degree also, of using only instantaneous counts. Including sharks and jacks would have increased US Line Islands mean biomass estimates by ~50% and NWHI biomass estimates by ~ 150%, to 180 and 117 g more 2009 . IncludiMore generally, coral fish survey counts are known to vary depending on multiple factors, including observer swimming speed, survey dimensions, whether a pre-count waiting period is included, and whether a transect line is deployed –57. It iThe availability of large-scale, representative, and highly comparable biological, habitat and oceanographic datasets from 37 reef areas spread widely across the western and central Pacific allowed us to quantify associations between coral reef fish assemblages and a range of human and oceanographic factors, and to use those models to estimate the apparent extent of human impacts while accounting for those other factors. Perhaps the most important component of this study is the demonstration of the extent to which coral reefs’ capacity to support large fish populations varies among what we assume are relatively unimpacted reef areas. In our study, oceanic productivity appeared to be a key driver of those differences, but clearly there are also other factors driving differences among and within island reef ecosystems. We caution against any assumption that the spectacular high biomass fish assemblages seen at some remote reefs represent a natural level that all reefs would attain in the absence of humans.S1 FigPrior to 2011, divers estimated substrate complexity in their stationary point count cylinder on a 5-point scale (1–5). From 2012 onwards, divers estimated proportion of habitat within their survey area in different vertical height bins. In order to utilize substrate data from 2010 and 2011, we calculated the relationship mean vertical height and substrate complexity at the 15 sites where we have both complexity types, and generate a standard conversion formula using linear regression(TIF)Click here for additional data file.S1 TableNote sharks and jacks are excluded.(EPS)Click here for additional data file.S2 Table(EPS)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


An association between hammerhead ribozymes and non-autonomous, long terminal repeat retrotransposons is uncovered in plants, shedding light on the biological function of genomically encoded ribozymes. Genome Biology, reports the discovery of retrozymes, which are a novel class of transposable elements in plants that contain hammerhead ribozymes (HHRz), a class of small nucleolytic ribozymes catalyzing nucleotide-exact RNA cleavage and ligation [In recent years, research on small nucleolytic ribozymes—RNA motifs with catalytic RNA cleavage and ligation activity—has gained new momentum by the discovery of both large numbers of known catalytic RNA (catRNA) motifs encoded in various genomes  and entiligation . The charetrotransposon and ribozyme features, these plant elements were dubbed “retrozymes”. The association of mobile genetic elements and catalytic RNAs appears to constitute a recurrent trend in eukaryotic genomes; a catalytic RNA motif was previously found within the Drosophila R2 retrotransposon [Small endonucleolytic ribozymes were originally discovered in certain viroids or viral satellite RNAs, which are circular RNA (circRNA) molecules infecting plants . Since tansposon  that disansposon .Retrozymes are present as linear and circular RNAs in various plant tissues . FollowiOwing to this, no sense or antisense strands can be discerned and retrotransposon transcripts are assigned “plus” or “minus” polarities. The authors identified retrozyme transcripts of both plus and minus polarity , which mRetrozymes in the form of a circRNA are readily observed in various plants and plant tissues and the circularization reaction could also be recapitulated by proteinaceous in vitro ligation . The resTransposon replication by a rolling circle mechanism has been shown in the intriguing family of helitron transposons . DistincWhile experimental evidence is not available yet, several observations suggest that an equivalent RNA replication step might also be operational in the case of the retrozymes . For exaIt will be exciting to unravel in future work the identity of the key players involved in this process and further steps of the retrozyme life cycle. The importance of the HHRz motifs for these processes might be addressed using genetically traceable retrozyme variants in which catRNA activity is present or absent. It also will be of great interest to study whether the linear and circular retrozymes harbor ligation and cleavage activity, respectively, in both in vitro and in vivo systems.catRNA, catalytic RNA; circRNA, circular RNA; HHRz, hammerhead ribozyme; lncRNA, long non-coding RNA; LTR, long terminal repeat; RdRP, RNA-dependent RNA polymerase; SMART, small long terminal repeat retrotransposon; TRIM, terminal-repeat retrotransposon in miniature.