In this current work plant succus was prepared from fresh plant leaves. Inhibition of three major CYPs was determined using human liver microsomes and enzyme-selective model substrates. Of 15 species tested, succus from 6/15 was found to inhibit CYP1A2, 7/15 inhibited CYP3A4, and 4/15 inhibited CYP2D6. Chi-squared tests determined differences in inhibitory activity between succus and methanol preparations. Over 80% agreement was found. Thus, fresh juice from PNG medicinal plants does exhibit the potential to complicate drug therapy in at risk populations. Further, the general reproducibility of these findings suggests that methanol extraction of dried material is a reasonable surrogate preparation method for fresh plant samples. The hypothesis underlying this current work is that fresh juice expressed from Papua New Guinea (PNG) medicinal plants (succus) will inhibit human Cytochrome P450s (CYPs). The CYP inhibitory activity identified in fresh material was compared with inhibition in methanol extracts of dried material. Succus is the most common method of traditional medicine (TM) preparation for consumption in PNG. There is increasing concern that TMs might antagonize or complicate drug therapy. We have previously shown that methanol extracts of commonly consumed PNG medicinal plants are able to induce and/or inhibit human CYPs In Papua New Guinea (PNG) approximately 0.6% of the 7 million population is living with human immunodeficiency virus (HIV)  while ov in vitro [ARV and anti-TB therapies are often complicated by drug-drug interactions , most coin vitro . Close tp < 0.05) observed in their abilities to inhibit CYPs in vitro. Elements of this work were presented at the American Society of Pharmacognosy 2015 annual meeting [In PNG the most common method of medical plant preparation for consumption is to express the juice, often using a little water to facilitate capture . Recent  meeting .A team from the University of Papua New Guinea (UPNG) consisting of trained botanists, pharmacognosists, and students interviewed traditional healers in the rural communities of Drekikir in the East Sepik Province, Dogura in the Milne Bay Province, Arawa and Namatoa in Bougainville, and National Capitol District. Fresh medicinal plant materials indicated in the traditional healer interview were collected, and expressed juices were prepared and refrigerated until return to the BioDiscovery laboratory at UPNG School of Medicine and Health Sciences, Port Moresby, for rotary evaporation. In accordance with our established protocols which require informed consent prior to interviews , plant iPlant succus prepared from ~200 g fresh plant leaves was evaporated to dryness and dissolved in 100 mL of 100% methanol (MeOH) overnight. Extracts (50 mL) were then transferred to clean vessels, evaporated to dryness, and redissolved in dimethyl sulfoxide (DMSO) to yield a final concentration of approximately 10 mg/mL.Km) or saturating (Vmax) concentrations. Substrates selective for their respective CYPs are as follows: methoxyresorufin (MR), CYP1A2; 7-benzyloxyquinoline (7-BQ), CYP3A4; and 7-methoxy-4-(aminomethyl)-coumarin (MAMC), CYP2D6. NADPH was added in excess (60 mM) and the formation of fluorescent product was measured using a Biotek-Synergy 2 Microplate Reader [Km and/or Vmax concentrations were deemed inhibitory .p < 0.05. Pairwise comparisons between of medicinal plants of like genus and species from the methanol study and current succus study were used to calculate percent agreement.To investigate statistical significance between TM preparation methods, data from the previous methanol study and current succus study was organized into a 2 × 2 contingency table and Chi-squared tests performed for each CYP subtype. Results were considered statistically significant when  Calophyllum inophyllum L., Cassia alata L., Casuarina equisetifolia L., Passiflora foetida L., Morinda citrifolia L. (one of 2 collections), and Terminalia catappa L. was found to inhibit CYP 1A2. Plant succi that inhibited CYP 3A4 were Calophyllum inophyllum L., Cassia alata L., Casuarina equisetifolia L., Ipomea pes-caprae L., Morinda citrifolia L., Passiflora foetida L., Sida rhombifolia L., and Terminalia catappa L. For CYP 1A2 inhibition, only Calophyllum inophyllum L., Cassia alata L., Passiflora foetida L., and Terminalia catappa L. were active.Succus was prepared in the field from 17 species of medicinal plants that were previously analysed for inhibition of CYPs . Some of Ipomea pes-caprae L., Morinda citrifolia L. (one of two preparations), Passiflora foetida L., Premna obtusifolia R.Br., Sida rhombifolia L., and Terminalia catappa L. For CYP3A4, out of 20 pairwise comparisons, nonagreement was found for 3: Cassia alata L. (two of two preparations) and Sida rhombifolia L. For CYP2D6, out of 20 pairwise comparisons, nonagreement was found for 4: Bidens pilosa L. (one of two preparations), Casuarina equisetifolia L., and Passiflora foetida L. (two of two preparations). Furthermore, several of the nonagreements were likely due to the magnitude of inhibition being close to the adopted “significant” cutoff of 50% for moderately active inhibitors [ Bidens pilosa L. (51% inhibition at 40 μM substrate), one of the two Morinda citrifolia L. samples (56% inhibition at 40 μM substrate), and so forth. In any case, there was no statistical difference for CYP inhibition found in the succus study compared to the methanol study for CYP1A2, 3A4, or 2D6 (p < 0.05), overall agreement being approximately 84%. Therefore, methanol preparations may serve as a surrogate for traditional preparation methods, when fresh plant material is difficult to obtain.Overall, CYP inhibitory activity (or lack thereof) determined for the succus plant preparations agreed with the previously determined inhibitory activity determined for the methanol preparations. For CYP1A2, out of 20 pairwise comparisons, nonagreement was found for 5:hibitors . ExampleIn PNG the most common method of medicinal plant preparation for oral consumption is squeezing of juice from the fresh plant (succus). In contrast, our previous work measured CYP inhibition by methanol extracts of dried medicinal plant parts. Therefore, this work investigated CYP inhibition by succus, the more traditional method preparation. in vitro [Antiretroviral and anti-TB therapies employ combinations of drugs to reduce disease burden. Metabolism of the majority of these drugs is performed by CYP3A4. Protease inhibitors (PIs) are the most common ARV component associated with drug-drug interactions. PIs inhibit CYP metabolism, a phenomenon which is often utilized to enhance pharmacokinetic profiles of other PIs taken concomitantly –24. Commin vitro . Excessiin vitro , 27. Uncaria tomentosa (Willd.), an herbal medicine taken to boost the immune system, has been observed to elevate protease inhibitor levels in the blood through CYP3A4 inhibition [Traditional medicines have the potential to exacerbate adverse drug effects through plant-drug interactions. For instance, cat's claw,hibition , 29. IdeMany factors can contribute to the irreproducibility of plant extract activity when extracts from independently collected plants of the same species are compared. It is well accepted that the chemical composition of recollected plant extracts can vary with location of collection, season of collection, varieties or cultivars sampled, and even age of the plant parts collected –34. The The ability of fresh juice from PNG medicinal plants to inhibit any of three human CYP enzymes was tested here in 17 samples from 15 species. Significant inhibition was determined in 7 of the 1A2 analyses, 8 of the 3A4 analyses, and 5 of the 2D6 analyses. Within this relatively limited dataset, statistical analysis could find no significant difference in the CYP inhibitory activity between the two extraction methods. This implies that the compounds likely responsible for drug-drug interaction appear to be, on average, equally accessed by traditional preparation of fresh plant material and by methanol extraction of dried material. The data indicate that methanol extraction of dried plant material is probably a reasonable surrogate preparation method when fresh plant samples cannot be obtained for analysis.


The speeds of both Arctic surface warming and sea-ice shrinking have accelerated over recent decades. However, the causes of this unprecedented phenomenon remain unclear and are subjects of considerable debate. In this study, we report strong observational evidence, for the first time from long-term (1984–2014) spatially complete satellite records, that increased cloudiness and atmospheric water vapor in winter and spring have caused an extraordinary downward longwave radiative flux to the ice surface, which may then amplify the Arctic wintertime ice-surface warming. In addition, we also provide observed evidence that it is quite likely the enhancement of the wintertime greenhouse effect caused by water vapor and cloudiness has advanced the time of onset of ice melting in mid-May through inhibiting sea-ice refreezing in the winter and accelerating the pre-melting process in the spring, and in turn triggered the positive sea-ice albedo feedback process and accelerated the sea ice melting in the summer. Summer sea ice is retreating at an accelerated rate5, and surface temperatures in this region are rising at a rate double that of the global average, a phenomenon known as Arctic amplification7. Several major competing hypotheses have been proposed to explain the causes of Arctic amplification and summer sea-ice retreat. For instance, an enhanced Atlantic Meridional Overturning Circulation (AMOC) transporting extraordinary amounts of heat northward to the Arctic Ocean has been hypothesized to substantially amplify Arctic warming and accelerate summer sea-ice melting in this region11. However, evidence has instead shown significant slowing of the AMOC over the last few decades13. Sea-ice albedo feedback caused by the continuously shrinking summer sea ice potentially increasing the absorption of shortwave radiation is also believed to play a critical role in recent Arctic amplification17. But certain model simulations have indicated that sea-ice albedo feedback was likely not the dominant factor19 - robust warming amplification still occurs in the Arctic in the absence of albedo feedback21. Recently, downward longwave radiation (LWD) at the surface has been suggested as an important driver of Arctic winter warming and summer sea-ice dynamics26. Because LWD is affected by several highly correlated climatic factors, including atmospheric temperature, and the amounts of water vapor and cloudiness27, which of these factors has been the fundamental force driving Arctic amplification is still under debate. Based on model simulations, several studies have claimed that atmospheric temperature feedback , which is associated with wintertime temperature inversions in the Arctic boundary layer, is the dominant factor responsible for amplifying Arctic surface warming28. The other two greenhouse effect factors, water vapor and cloudiness, have very limited (even negative) effects on Arctic amplification18. However, other analyses, based on observations29 and atmospheric reanalysis25, have pointed in the opposite direction and demonstrated that the cloud- and water vapor-induced greenhouse effect is crucial to Arctic winter warming and the development of summer sea ice. In fact, LWD is much more sensitive to water vapor anomalies at high latitudes33, which suggests that the water vapor effect in the Arctic may be considerably stronger than that at lower latitudes. However the findings that support this hypothesis are either from small regions23 or very short spans of time (early winter25 or late spring34), and therefore are insufficient and problematic for fully interpreting the mechanism that drives variation in LWD at the surface and its effects on Arctic amplification and sea-ice variations. Strong and comprehensive additional evidence is therefore required to clarify the relationship between LWD at the surface and Arctic amplification.Despite an apparent hiatus in global warming36 which largely determines the minimum sea-ice coverage reached in the fall37, and is asynchronous with Arctic amplification, which occurs mainly in the wintertime15, it is important to chronologically investigate the causal relationships between Arctic wintertime amplification and initial sea-ice melting to elucidate the physical mechanisms of recent Arctic warming.Furthermore, because the state of sea ice at the onset of the melting season is crucial for evaluating surface energy uptakeFor this study, we use for the first time, long-term (1984–2014) spatially complete satellite data to provide strong observational evidence that the enhanced greenhouse effect from increased atmospheric water vapor and cloudiness in both winter and spring may reinforce the amplification of Arctic wintertime warming, which in turn has triggered the accelerated sea-ice melting in the summer.37 between May 16 and June 5 (days of year 136 to 156). In addition, we identified the correlations between these variables from records of the last 30 years (1985–2014) , and between wintertime LWD and CFC over 2001–2014  and wintertime (including both winter and spring) temporally averaged surface downward longwave radiative flux (LWD), water vapor (WV), cloud area fraction (CFC), skin temperature (SKT), and sea ice concentration (SIC) for the melting onset periodtic Fig. , the str41 before entering an ice pre-melting state until the onset of melting in mid-May40. The gradual increase in correlation coefficients between the integrated surface LWD and onset SIC 42. After sea-ice melting begins, shortwave albedo feedback becomes more important on driving variations in ice coverage over the subsequent months36. The conclusions of this study, based on long-term observational evidence, are partially consistent with previous studies that have reported isolated cases of atmospheric conditions influencing ice melting in either the late spring34 or winter25. However, our results indicate that the downward longwave radiation from early winter to late spring is continually affecting both the inter-annual and long-term changes of the Arctic sea ice state.To investigate the influence of wintertime surface LWD on the state of sea ice at the onset of melting, we directly calculated the correlation between wintertime LWD and the melting onset SIC in late spring. As shown in Fig. SIC Fig.  demonstr−2 per decade (0.61 W m−2 per decade from cloudiness and 2.38 W m−2 per decade from water vapor) in additional longwave radiative forcing, and the sum of the two totally contributed 81% of the linear trend of LWD (3.70 W m−2 per decade). This finding indicates that the increase of wintertime surface LWD in the Arctic is mainly resulted from the enhanced greenhouse effect by increased amounts of water vapor and cloudiness. Therefore, temperature feedback mechanisms, particularly thermal inversions during the winter, are unlikely to be the dominant factor driving the amplification of Arctic wintertime warming43. The trend during winter  is close to that of spring , which indicates that the atmospheric greenhouse effects in the winter and spring seasons are both important to the sea-ice initial melting in late spring.To quantify the contributions of greenhouse effects associated with water vapor and cloudiness to the long-term trend of LWD, and in turn Arctic wintertime warming and initial sea-ice melting, we have calculated the statistical linear trends of all variables for both the winter and spring seasons and estimated the surface downward longwave radiative forcing caused by changes in water vapor and cloudiness. As shown in Table −2 per decade over the last 30 years resulted from the significant increases in water vapor and cloudiness have warmed the ice surface by 1.5–3 K per decade, and highly likely then induced a decline in onset SIC by 10–20% per decade. Over the last 14 years  extra downward longwave radiative forcing, which is almost equal to the surface LWD anomaly of 3.22 W m−2. These findings indicate that the greenhouse effect caused by water vapor and cloudiness also plays a dominant role in the inter-annual variation of Arctic wintertime LWD, and then skin temperature and initial sea-ice melting. The contribution of the winter season  is similar to that of the spring season , which indicates that the atmospheric greenhouse effects in both the winter and spring are important to initial sea-ice melting in the Arctic.We also explored the relationships among all of these variables based on inter-annual variations in the de-trended time series. With a threshold of ±0.5 standard deviations for the de-trended melting onset SIC time series, we detected eight low-onset ice years (LOIYs) and nine high-onset ice years (HOIYs). We then calculated the averaged anomalies of all variables for LOIYs and HOIYs and estimated the surface downward longwave radiative forcing from changes in water vapor and cloudiness Table . In LOIY24, and therefore the state of sea ice at the onset of the melting season. Since only the thin ice over coastal regions begin to melt in late spring40, and the sea-ice state is also influenced by surface wind10, ocean currents9, ice albedo feedback35 and other factors18, there are still some differences in the spatial patterns of both long-term trend and de-trended anomalies between onset SIC and other four variables.The results for four representative years, comprising two low-onset ice years, 1990 and 2006, and two high-onset ice years, 1999 and 2013, were selected to explore the coupling characteristics of the spatial patterns of the de-trended anomalies for all five variables  Synoptic Radiative Fluxes and Clouds (SYN) product, version 3 A, has been evaluated on a global scale, and is considered to be the best source of observed surface downward longwave radiative fluxes currently availablens Table , that inlts Fig. –S9, the 50) derived from the International Satellite Cloud Climatology Project (ISCCP) DX product51 are used in this study. The monthly 0.67° × 0.50° precipitable water vapor dataset from Modern Era Retrospective-Analysis for Research and Applications (MERRA) used in this study was produced by NASA’s Global Modeling and Assimilation Office Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5) using a three-dimensional variational data assimilation (3D-Var) framework with an emphasis on the hydrologic cycle52. An incremental analysis update (IAU) procedure and a variety of recent satellite observations, such as NASA’s Earth Observing System, SSM/I radiances, TIROS Operational Vertical Sounder (TOVS) radiances, Atmospheric Infrared Sounder (AIRS) radiances and scatterometer wind retrievals, were incorporated into the assimilation system to improve estimates of the global energy and water budgets52.Cloud area fraction data for 2000–2014 from the CERES SYN 1.0° product derived using the CERES-MODIS (Moderate Resolution Imaging Spectroradiometer) cloud retrieval algorithm, and 1.0° cloud fraction data for 1984–2007 from the Global Energy and Water Cycle Experiment Surface Radiation Budget  2.0 weekly snow cover and sea ice extent and the daily sea-ice concentration product, derived from the Scanning Multichannel Microwave Radiometer (SMMR) onboard Nimbus-7, and the Special Sensor Microwave Imager and the Special Sensor Microwave Imager Sounder (SSM/I-SSMIS) onboard the Defense Meteorological Satellite Program (DMSP), provided by the National Snow and Ice Data Center (NSIDC) are both used in this study55, which has been successfully utilized in many previous studies57. The ERA-Interim skin temperature dataset used here has similar properties to the surface 2-meter air temperature dataset58. The ERA-Interim surface temperature dataset was extensively validated with in-situ measurements at high latitudes, and was found to outperform all other available reanalysis products60. The quality of this dataset is comparable even to observations from satellites over the Arctic land surface58.We also used the ERA-Interim monthly temperature data productTo calculate spatially averaged statistics for the Arctic Ocean, two separate phases were executed. The first phase was mask generation; the mask of maximum sea-ice coverage for the area north of 60°N between 1984 and 2014 (180°W−180°E for the Arctic Ocean) were derived using the Equal Area Scalable Earth Grid (EASE-Grid) 2.0 Weekly Sea Ice Extent data product. The mask was used to define the study area. In the second phase, all grid datasets  were first re-projected onto an equal-area projection, and then the maximum sea-ice coverage mask was applied to calculate spatially averaged values.in-situ radiation flux measurements and water vapor datasets33:WV and To quantify the contributions of greenhouse effects from precipitable water vapor and cloudiness, we calculated the surface LWD forcing generated by both precipitable water vapor and cloudiness separately. For the estimation of water vapor radiative forcing, we apply a formula for this relationship based on 23. For each iteration, we first randomly generate 10,000 LOIY/HOIY artificial composites, and then compare to the original LOIY/HOIY anomaly composite. The confidence level of an original composite is calculated as one minus the percentage of the absolute values of the artificial composites larger than the original anomaly composite. This method is thought to be more robust than a Student t-test approach that requires data follow a normal distribution.The confidence level of a statistical anomaly in Table Supplementary Information


The tumor form of pyruvate kinase M2 (PKM2) undergoes tyrosine phosphorylation and gives rise to the Warburg effect. The Warburg effect defines a pro-oncogenic metabolism switch such that cancer cells take up more glucose than normal tissue and favor incomplete oxidation of glucose, even in the presence of oxygen. Retinal photoreceptors are highly metabolic and their energy consumption is equivalent to that of a multiplying tumor cell. In the present study, we found that PKM2 is the predominant isoform in both rod- and cone-dominant retina, and that it undergoes a light-dependent tyrosine phosphorylation. We also discovered that PKM2 phosphorylation is signaled through photobleaching of rhodopsin. Our findings suggest that phosphoinositide 3-kinase activation promotes PKM2 phosphorylation. Light and tyrosine phosphorylation appear to regulate PKM2 to provide a metabolic advantage to photoreceptor cells, thereby promoting cell survival. Almost a century ago, Otto Warburg found that the retina had the highest oxygen consumption of any tissue in the body, and he compared retinas to rapidly growing tumor cells12456trans-retinal released by photobleaching of photopigmentstrans-retinal reduction.In addition, maintenance of the “dark current” requires huge amounts of ATP generated through the Krebs cycle, creating reactive oxygen species (ROS) byproducts. NADPH generated by the pentose phosphate pathway (PPP) is necessary for glutathione recycling for antioxidant protection and for lipid synthesis, as well as for reducing all-Most of the glucose in fetal tissues and cancer cells has been shown to be used for anabolic processes, mainly to synthesize lipids, proteins and nucleic acids. A limited amount of glucose is used for the generation of ATP through oxidative phosphorylation. The switch that redirects glucose to anabolic processes is mediated by a glycolytic enzyme, pyruvate kinase M2 (PKM2). PKM2 is the predominant form of pyruvate kinase expressed in retinal photoreceptors12via PI3K/Akt activationPKM2 expression has been documented in the retinaPolyclonal pPKM2 (Y105), PKM2, pAkt (S473), and Akt antibodies were obtained from Cell Signaling . Rabbit polyclonal anti-red/green cone opsin (M-opsin) antibody was obtained from Millipore . α-3 Na/K ATPase antibody was obtained from Novus Biologicals . DAPI stain used for nuclear staining and secondary antibodies were purchased from Invitrogen-Molecular Probes . Monoclonal anti-arrestin antibody was a kind gift from Dr. Paul Hargrave . Monoclonal anti-opsin (1D4) was kindly provided by Dr. Robert Molday .ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and the NIH Guide for the Care and Use of Laboratory Animals. The protocols were approved by the IACUC at the University of Oklahoma Health Sciences Center. Animals were born and raised in our vivarium and kept under dim cyclic light . The Nrl−/− mice were kindly provided by Dr. Anand Swaroop . The Rpe65−/− mice were a kind gift from Dr. Jing-Xing Ma . We previously reported the generation of Nrl/cone-p85α double knockout mice2 asphyxiation. These tissues were subjected to biochemistry or immunohistochemistry.All animals were treated in accordance with the The OptiPrep™ density gradient was obtained from Sigma . The PI3K inhibitor, LY294002, was procured from Cell Signaling . The NADP/NADPH quantification kit (MAK038) was obtained from Sigma. All other reagents were of analytical grade and purchased from Sigma .Nrl−/− retinas, both dark- and light-adapted, were placed in Ringer’s solution  containing 8% OptiPrep™ and gently vortexed for 1 min. We repeated this process five times. The pooled crude lysate was placed on top of the 10–40% OptiPrep™ step gradient. We spun the gradients at 19,210 × g for 60 min at 4 °C. Fractions were collected, and protein localization was examined using immunoblot analysis. We repeated these experiments more than three times. Each time, we observed consistent results, in terms of fractionation and phosphorylation.We prepared the isolated photoreceptor cells by the method described earlier2, 1 mM ADP, 0.5 mM PEP, 0.2 mM NADH (reduced form of NAD+)] and 8 U of LDH. The PK activity was measured spectrophotometrically by monitoring the reduction in the absorbance at 340 nm from the oxidation of NADH.Lactate dehydrogenase (LDH) coupled enzyme assay was used to measure pyruvate kinase (PK) enzyme activityPrefer solution  was used to fix the mouse eyes for 15 min at room temperature, followed by 70% ethanol overnight. The tissue was paraffin-embedded, and 5-μm-thick sections were cut and mounted onto slides. These sections were subjected to immunohistochemistry, as described elsewhere2, 1 mM phenylmethylsulfonyl fluoride, 0.2 mM Na3VO4, 10 μg/ml leupeptin, and 1 μg/ml aprotininMouse retinas were homogenized in a lysis buffer containing 1% Triton X-100, 137 mM NaCl, 20 mM Tris-HCl (pH 8.0), 10% glycerol, 1 mM EGTA, 1 mM MgClNrl−/− (cone-dominant) mice were stained with PKM1 and PKM2 antibodies. Arrestin immunolocalization was used to confirm the adaptability of mice to dark- and light-adapted conditions. Under dark-adapted conditions, arrestin predominantly localized to rod inner segments and the outer plexiform layer  and rm layer . Upon lirm layer . Our resrm layer . We did rm layer . The locNrl−/− mice as we cannot study their expression in the wild type mouse retina since it contains only 3–5% of cones. The Nrl−/− mice do not express rods due to a block in the differentiation of rod precursor cells, which results in the expression of all cone-like cells that are indistinguishable biochemically from normal cones222324Nrl−/− mouse retina has a characteristic feature of large undulations of the outer nuclear layer (ONL), commonly known as rosettes. These rosettes arise due to defects in the outer limiting membrane and delayed maturation of a subset of photoreceptorsNrl−/− mouse retinal sections stained with PKM2 and PKM1 antibodies showed that PKM2 was predominantly expressed in photoreceptor inner segments and the outer plexiform layer; a weak immunoreactivity was also observed in the inner plexiform layer under both dark- and light-adapted conditions , which results in the inhibition of PKM2 activity. This regulation of signaling diverts glucose metabolites from energy production to anabolic processes , in addition to generating NADPH to support antioxidant metabolismNrl−/− mouse retinal sections stained with PKM2-Y105 antibody also showed light-dependent tyrosine phosphorylation in photoreceptor inner segments and the outer plexiform and inner plexiform layers in cone-dominant retinas  in the retinal pigment epithelium (RPE) results in the absence of 11-cis-retinal regeneration, leading to the absence of rhodopsin photobleaching or activationRpe65−/− mice, arrestin does not translocate to outer segments under light-illuminationTo determine whether the observed light-dependent tyrosine phosphorylation of PKM2 signals through rhodopsin activation, we used the TM density gradient (8–40%) that yields rod outer segments with large portions of the rod inner segments attached  or DMSO (vehicle), followed by light exposure. Proteins that differentially migrated on 2D-electophoresis were identified using mass spectrometer analysis. We found that the differentially migrated proteins belonged to glycolytic and energy metabolism pathways, which also include pyruvate kinase (data not shown). To determine whether PKM2 phosphorylation is under the control of PI3K activation, we incubated ex vivo mouse retinal explants prepared in the dark in DMSO or PI3K-inhibitor (LY294002) for 10 min before exposing them to room light for 30 min. Light-dependent phosphorylation of both Akt (downstream effector of PI3K) and PKM2 was reduced in the presence of PI3K inhibitor ; the kinase that phosphorylates glucose, hexokinase II (HK-II); and the two key enzymes that shunt glucose into PPP for NADPH generation, glucose 6-phosphate dehydrogenase (G6PD)Nrl−/−/cone-p85α−/− retinas compared with Nrl−/− retinas , which regulates the transcription of many glycolytic genes , reduction of GSSG to GSH (the substrate for many of the cellular thioreductases necessary for maintaining a reducing environment in the cell), and detoxification of all-trans-retinal (produced in the light) by retinol dehydrogenase18303038In photoreceptor cells, NADPH is required for in vitro phosphorylation by FGF1-R. Running a standard curve with different amounts of phospho-PKM2, we could measure the phospho-signal by ELISA using phospho-PKM2 antibody. These studies are underway in our laboratory.The functional consequence of enhanced light-dependent tyrosine phosphorylation of PKM2 is unknown. In the present study, we showed that light increases the level of phosphorylation by about 40%, and causes an approximate 50% reduction in enzyme activity. However, there is no interpretable quantitative relationship between these numbers. Due to the limitation of reagents, we cannot quantify the percentage of the protein that is phosphorylated. The ideal way to quantify the PKM2 phosphorylation under dark- and light-adapted conditions would be expression and purification of PKM2, followed by Rpe65−/− mouse retinas, a mouse model that lacks rhodopsin photobleaching due to an inability to regenerate the chromophore, 11-cis-retinalIn the present study, we found reduced phosphorylation of PKM2 in Our biochemical characterization of pyruvate kinase in isolated rod and cone photoreceptors clearly shows the expression of both PKM1 and PKM2 isoforms. It is interesting to note that PKM1 isoform localization remains the same under both dark- and light-adapted conditions, whereas, there are distinct differences in PKM2 localization in rods isolated under dark- and light-adapted conditions. Under light-adapted conditions, we found increased PKM2 phosphorylation in fractions that co-eluted with rhodopsin, a phenomenon that is lost in mice lacking bleachable rhodopsin. Studies to examine whether PKM2 has increased affinity for light-adapted membranes upon phosphorylation, or whether phosphorylation causes PKM2 translocation between rod inner and outer segments, are needed.p85α/Nrl double knockout micevia PI3K/Akt activation35Interestingly, we observed that PKM2 phosphorylation is PI3K-dependent. Loss of PI3K in cones resulted in decreased NADPH production and reduced expression of genes involved in glucose uptake, preservation, and divergence. In this experiment, phosphorylation of PKM2 may or may not be what is causing the lower levels of NADPH. PI3K is known to regulate glucose uptake and Glut1 glucose transporter expression. Further studies are required to examine the role of phosphorylation of PKM2 by generating phospho-mutant of PKM2 (Y105F) and examining NADPH production in the presence of a functional PI3K. We have not performed experiments on rods lacking the p85α subunit of PI3K. When we deleted p85α in rods, we did not observe any retinal phenotype due to the expression of p85β in rodsin vitro and in vivoIn addition to phosphorylation, PKM2 has been shown to undergo acetylation on K305Recent works suggest non-canonical (non-metabolic) function of PKM2 serves to regulate gene expression, and these effects are based on PKM2 protein-kinase function or protein-protein interactions involving PKM2PKM2, but not PKM1, has been reported to activate gene expression by binding and transactivating the transcriptional factor HIF1αHow to cite this article: Rajala, R. V. S. et al. The Warburg Effect Mediator Pyruvate Kinase M2 Expression and Regulation in the Retina. Sci. Rep.6, 37727; doi: 10.1038/srep37727 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Hemoglobin is the natural carrier of oxygen in red blood cells (RBCs). While intracellular hemoglobin provides life-sustaining oxygen transport, extracellular free hemoglobin displays toxicity due to inherent peroxidase activity generating reactive oxygen species that subsequently react with the hemoglobin molecule to produce toxic heme degradation products resulting in free radicals, oxidative stress damage, and lipid peroxidation. We have recently demonstrated that Peptide Inhibitor of Complement C1 (PIC1) inhibits peroxidase activity of the heme-based enzyme myeloperoxidase. To elucidate whether PIC1 could inhibit peroxidase activity of hemoglobin, we evaluated the consequence of PIC1 on RBC lysates, methemoglobin, and myoglobin using tetramethylbenzidine (TMB) as an oxidation target. PIC1 reversibly and dose-dependently prevented TMB oxidation to tetramethylbenzidine diimine by RBC lysates, methemoglobin, and myoglobin, having comparable activity to the inhibitor 4-aminobenzoic acid hydrazide. PIC1 inhibited TMB oxidation of RBC lysates similar to L-cysteine suggesting that the two cysteine residues contained in PIC1 may mediate peroxidase activity. PIC1 also inhibited heme destruction by NaOCl for RBC lysates, hemoglobin, and myoglobin as assayed by preservation of the Soret absorbance peak in the presence of NaOCl and reduction in free iron release. In conclusion, PIC1 inhibits peroxidase activity of hemoglobin and myoglobin likely via an antioxidant mechanism. Hb consrricHb Fe+ also knrrylHb Fe+ and the2O2 forms a potent oxidant that is damaging to lipids, nucleic acids, and amino acids. Finally, Hb protein radical formation can result in crosslinking of heme and amino acids [Hb peroxidase activity and its degradation are extremely toxic to organs and tissues, having serious pathological ramifications , 6. In aOur laboratory has characterized a family of peptides know as Peptide Inhibitor of Complement C1 (PIC1) that inhibit the classical pathway of complement by binding the initiator molecule C1q and inhibiting activation of the cognate serine protease tetramer C1s-C1r-C1r-C1s that is normally associated with C1q , 8. We hHuman blood was obtained from four healthy volunteers for the generation of RBC lysates used in these studies, per the Eastern Virginia Medical School IRB approved protocol 02-06-EX-0216. The study participants provided written informed consent. Human blood was collected for these experiments from May 2016 through November 2016.2HPO4 buffer to 15 mM. dPEG24 was purchased from PolyPeptide Group and dissolved in the same buffer. Pure human methemoglobin (metHb), myoglobin from equine skeletal muscle, and L-cysteine were purchased from Sigma . 3,3′,5,5′-Tetramethylbenzidine (TMB) as a solution containing H2O2 was purchased from Thermo Fisher Scientific . 4-Aminobenzoic acid hydrazide (ABAH), O-dianisidine, and NaOCl were purchased from Sigma-Aldrich . 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) solution was purchased from Thermo Fisher Scientific .PIC1 (IALILEPICCQERAA-dPEG24) was manufactured by PolyPeptide Group  to ≥95% purity as verified by HPLC and mass spectrometry analysis. Lyophilized PIC1 was solubilized in normal saline with 0.01 M Na−−  and then incubated at 37°C to allow for spontaneous lysis. The cells were then washed twice again with GVBS++  and resuspended in a volume which set the concentration to 1 × 109 cells/ml (absorbance reading of lysed RBCs at 541 nm of 0.210 = 1 × 109 cells/ml). An aliquot of these cells was combined with an equal aliquot of water at room temperature and spun at 21,000 ×g for 5 minutes to separate the released hemoglobin from the cell debris.RBC lysates were prepared from the blood of healthy donors as previously described . Cells wμl of TMB in a nontreated 96-well plate for 15 seconds followed by 100 μl of 2.5 N H2SO4 (stop buffer) and absorbance was read at 450 nm in a Synergy HT BioTek plate reader [μl of ABTS solution was added to each sample and incubated for 15 seconds before adding 100 μl of 1% SDS to stop the reaction [2O2 added immediately before use [μl of the prepared O-dianisidine solution was added to each sample and incubated for 2 minutes before adding 100 μl of 2.5 N H2SO4 to stop the reaction. Absorbance was read at 405 nm.Inhibition of peroxidase activity was assessed by combining the RBC lysate , metHb (0.5 mg/ml), or myoglobin (0.5 mg/ml) with increasing concentrations of PIC1, with a constant final volume, for 5 minutes before adding TMB as described below. Additionally, increasing amounts of RBC lysates were incubated with increasing amounts of PIC1, with a constant final volume, for 5 minutes before adding TMB. As a positive control for inhibition of peroxidase activity, increasing amounts of ABAH or L-cysteine were included in select experiments. After the 5-minute incubation, samples were combined with 100 reaction . Absorbafore use . 100 μl 7 RBCs) followed by incubation for 1 hour at 37°C to allow for RBC hemolysis via antibody mediated complement lysis [2SO4 as above to evaluate peroxidase activity from the cell-free Hb.To assess if PIC1 could inhibit peroxidase activity of Hb released from hemolyzed RBCs in human serum, type O sera at 15% in GVBS++ buffer was mixed with type AB RBCs at titrating concentrations , 2 mg/ml metHb, or 2 mg/ml myoglobin was coated onto an Immunlon-2, 96-well plate in a carbonate coating buffer  overnight at 4°C. The plate was then washed; TMB was added and allowed to incubate for 1 hour. The oxidized TMB was transferred to a fresh well containing either water or 2 mM PIC1. The reaction was stopped after 1 min with 2.5 M H2SO4. Absorbance was read at 450 nm.RBC lysate (2.5 × 10μl of ascorbic acid (100 mM) was added to each sample and allowed to incubate for 5 minutes. Then 50 μl of ammonium acetate (16%) and 50 μl of ferrozine (16 mM) were added to the samples and mixed. After incubating for 5 minutes, absorbance was measured at 562 nm. A standard curve was prepared using ammonium Fe(III) sulfate and a linear regression was used to calculate the free iron concentration.Iron release from the RBC lysate, metHb, or myoglobin was measured utilizing a ferrozine assay . After t7 cells/ml RBC lysate, 0.2 mg/ml metHb, or 0.2 mg/ml myoglobin followed by various concentrations of PIC1, ABAH, and NaOCl , in a nontreated 96-well plate. As each component was added to the RBC lysate, metHb, or myoglobin, it was allowed to incubate for 5 minutes. Absorbance values were recorded from 300 to 550 nm [Absorbance spectra experiments were performed with 1.25 × 10o 550 nm .t-test using Excel . P values ≤ 0.05 were considered statistically significant.Quantitative data were analyzed determining means, standard error (SEM), and Student's P < 0.001). In order to further evaluate PIC1 inhibition of peroxidase activity over a range of RBC lysate concentrations, a more extensive dose-response experiment was conducted with RBC lysates from one donor. Concentration-dependent PIC1 inhibition of RBC lysate peroxidase activity was demonstrated for all RBC lysate concentrations .In an attempt to ascertain whether PIC1 would have any effect on the peroxidase activity of extracellular Hb, RBC lysates were prepared from four donors and incubated with increasing amounts of PIC1 followed by TMB as the target for oxidation. Addition of PIC1 led to a dose-dependent inhibition of the TMB oxidation product 3,3′,5,5′-tetramethylbenzidine diimine  demonstrtrations . PIC1 at7 RBCs  improvement in metHb inhibition compared with ABAH. As expected, the PIC1 inhibition of metHb peroxidase activity was very similar to that of the RBC lysate tested in parallel which demonstrated superior activity to ABAH (P = 0.009) improvement in Hb inhibition compared with ABAH. In order to determine if the peroxidase inhibitory activity of PIC1 was active against another toxic heme-bearing enzyme, the TMB assay was performed with pure myoglobin. PIC1 dose-dependently inhibited the peroxidase activity of myoglobin by 14.3-fold (P = 0.006) at 5 mM compared with no PIC1  was utilized. ABAH is an inhibitor that specifically and irreversibly inhibits the peroxidase activity of MPO, a heme containing enzyme produced by neutrophils and macrophages , 16. On  to ABAH . At 1.25 no PIC1 . PIC1 in no PIC1 .To rule out that PIC1 inhibition of extracellular Hb-mediated TMB oxidation in this assay was specific to the substrate used, the reagents ABTS and O-dianisidine were utilized in place of TMB. Both PIC1 and ABAH showed dose-dependent inhibition of free Hb peroxidase activity for ABTS  and O-diPIC1 contains vicinal cysteine residues at positions 9 and 10 of the peptide. It is well established that the thiol group of cysteine residues are efficient peroxidase inhibitors with L-cysteine demonstrating significant antioxidant activity . In ordeP ≤ 0.003) reduction in oxidized TMB  for each of the proteins incubated with PIC1 compared to the water only control  the following experiment was conducted. RBC lysate, metHb, or myoglobin was coated onto a 96-well plate and incubated overnight. The plate was then washed and TMB added for 1 hour to allow the substrate to oxidize. Each solution was then transferred to a fresh well which contained either water or PIC1. PIC1 was able to reverse the oxidation state of TMB for the RBC lysate, metHb, and myoglobin as measured by absorbance at 450 nm with a 7.2–8.4-fold ( control .P ≤ 0.003 for 2.5 mM PIC1 versus no PIC1) (Hypochlorous acid (HOCl) produced by MPO present in phagocyte granules has been shown to mediate heme destruction and free iron release , 4. HOClno PIC1)  consisteGiven the similar levels of peroxidase inhibition by ABAH and PIC1 shown in the TMB assay Figures , we nextHeme is an essential component of proteins such as Hb and myoglobin where it functions in life-sustaining oxygen transport and storage, respectively. However, severe hemolysis leading to release of free Hb plays a significant role in pathological conditions such as sickle cell disease, ischemia-reperfusion injury, malaria, hemolytic anemia, blood transfusion, and renal failure , 6. RhabThe experiments shown here demonstrate that PIC1 can dose-dependently inhibit the peroxidase activity of heme from RBC lysates as well as pure metHb and myoglobin for three different substrates: TMB, ABTS, and O-dianisidine. The level of inhibition was comparable to or better than that of the MPO inhibitor ABAH. Furthermore, PIC1 was able to reverse the oxidation of TMB for RBC lysates, metHb, and myoglobin. PIC1 was found to dose-dependently inhibit NaOCl-mediated destruction of heme and prevent the release of free iron as shown by preservation of the Soret peak in the spectral assay and the ferrozine assay, respectively. Interestingly, while ABAH was shown to inhibit Soret peak flattening, the inhibition was not dose-dependent and did not prevent free iron release, suggesting a different mechanism of Hb protection from NaOCl by PIC1 versus ABAH. The inhibition of Hb and myoglobin peroxidase activity are consistent with our recently published results demonstrating that PIC1 inhibits peroxidase activity of MPO . TogethePIC1 derivative PA-dPEG24 contains two vicinal cysteine residues at positions 9 and 10. It is possible that the observed inhibition of peroxidase activity for metHb and myoglobin is mediated by the reduction of higher oxidation states of these proteins by these cysteine residues. Oxidation of the cysteine residues would be predicted to either result in intermolecular disulphide bonds between the cysteine residues of two PIC1 molecules or possibly intramolecular cystine formation. PIC1 was found to dose-dependently inhibit peroxidase activity of RBC lysates in a similar manner to L-cysteine  suggestiFree heme is exquisitely toxic to the kidney where it can lead to renal failure through a number of mechanisms including oxidative damage and heme deposition in the renal tubules . We haveFree Hb from lysed RBCs and myoglobin released from damaged muscle cells are inherently toxic molecules in the blood stream due to their intrinsic peroxidase activity and have been demonstrated to contribute to pathogenesis in hemolytic diseases and rhabdomyolysis, respectively. Currently there are no inhibitors of peroxidase activity in the marketplace to address these unmet medical needs. Peptide Inhibitor of Complement C1 (PIC1) significantly inhibits peroxidase activity of RBC lysates and purified Hb as well as myoglobin. Based on these findings, future experiments to test PIC1-mediated inhibition of peroxidase activity in preclinical animal models are currently planned.


Potyviridae comprise the largest and most important family of RNA plant viruses. An essential overlapping ORF, termed pipo, resides in an internal region of the main polyprotein ORF. Recently, expression of pipo was shown to depend on programmed transcriptional slippage at a conserved GAAAAAA sequence, resulting in the insertion of an extra A into a proportion of viral transcripts, fusing the pipo ORF in frame with the 5′ third of the polyprotein ORF. However, the sequence features that mediate slippage have not been characterized. Using a duplicate copy of the pipo slip site region fused into a different genomic location where it can be freely mutated, we investigated the sequence requirements for transcriptional slippage. We find that the leading G is not strictly required, but increased flanking sequence GC content correlates with higher insertion rates. A homopolymeric hexamer is optimal for producing mainly single-nucleotide insertions. We also identify an overabundance of G to A substitutions immediately 3′-adjacent to GAAAAAA in insertion-free transcripts, which we infer to result from a ‘to-fro’ form of slippage during positive-strand synthesis. Analysis of wild-type and reverse complement sequences suggests that slippage occurs preferentially during synthesis of poly(A) and therefore occurs mainly during positive-strand synthesis.The  Paramyxoviridae (e.g. measles and mumps viruses) and Filoviridae (e.g. Ebola virus) use this mechanism , limited number of transcript species, limited access to host splicing machinery and frequent structural differences between virus transcripts and host mRNAs, RNA viruses have evolved a plethora of unusual mechanisms to express their genes. Well-known examples include internal ribosome entry site driven initiation, programmed ribosomal frameshifting and programmed stop-codon readthrough –2. Anoth–32 kb, lechanism . In addiechanism –12. Rece viruses , raisingPotyviridae have single-stranded positive-sense RNA genomes approximately 10 kb in length  subgroup contain yet another ORF, termed pispo, that overlaps the P1-encoding region in the −1/+2 frame  accessible for translation. P3N-PIPO has been demonstrated to be required for virus cell-to-cell movement and P1N-PISPO is a suppressor of RNA silencing  cannot be ruled out without further investigation. Bioinformatic analysis has shown that, throughout family Potyviridae, the GAAAAAA sequence is highly conserved at the pipo (and pispo) slip sites . Howeveip sites ,14,20. Aip sites . This supipo slip site region fused into a different genomic location where it can be freely mutated, we investigated the role of sequence surrounding the GAAAAAA slip site in modulating the efficiency of transcriptional slippage during potyviral infection. We reveal that the leading G is not strictly required for slippage and that slippage efficiency is modulated by the flanking nucleotides. Increased GC content upstream and/or downstream of the homopolymeric run correlates with higher insertion rates. Further, the data suggest that a homopolymeric run of 6 nt is optimal for producing mostly single-nucleotide insertions  during slippage. We also identify an overabundance of G to A mutations immediately 3′-adjacent to the GAAAAAA sequence in insertion-free transcripts, which we infer to result from a ‘to-fro’ slippage movement during positive-strand synthesis. Analysis of wild-type (WT) and reverse complement sequences suggests that slippage occurs preferentially during synthesis of poly(A), rather than poly(U) and therefore, at the native pipo slip site occurs mainly during positive-strand synthesis.Using a duplicate copy of the SmaI and NheI restriction endonuclease sites , based on isolate UK1, has GenBank accession EF028235. Reported sequence coordinates correspond to this accession. For agroinfiltration, TuMV-GFP was ligated into pGreenII as described previously . MutagenNicotiana benthamiana plants were grown under a 16 h photoperiod at 22°C  and 60% humidity. About 3- to 4-week old plants were inoculated by agroinfiltration as described previously . Leaf disks were frozen in liquid nitrogen, homogenized and total RNA was extracted as described in ref. (eviously . Briefly in ref. .Reverse transcription was carried out on 2 μg of RNA using SuperScript III (LifeTechnologies) at 46°C for 15 min. Two primers containing the high-throughput sequencing adapter sequence and viral target sequences for sequencing the 2nd-GA6 and WT-GA6 sites, TuMV-GFP nt 1940–1963 and 3843–3859 respectively, were used together in reverse transcription. Next, excess primers were removed by 10 U of exonuclease I (NEB) at 37°C for 30 min followed by inactivation at 70°C for 15 min. PCR was performed with Q5 High-Fidelity DNA Polymerase (NEB) for 17 cycles together with high-throughput sequencing adapter primers with upstream TuMV binding sites, TuMV-GFP nt 1922–1939 and 3809–3824 for 2nd-GA6 and WT-GA6, and indexing adaptor primers matching to the first strand synthesis primers. Each biological sample was indexed separately and the PCR produced two amplicons with high-throughput adapter sites flanking viral target 2nd-GA6 and WT-GA6 sites, containing TuMV-GFP nt 1922–1963 and 3809–3859, respectively. After amplification, libraries were separated by 1× Tris-Borate-EDTA 10% polyacrylamide gel electrophoresis, target fragments were cut together from the gel and extracted. The purified libraries were quantified fluorometrically using the Qbit dsDNA HS kit (Life Technologies), normalized and sequenced using the NextSeq500 platform (Illumina). Reads were checked for quality, clipped for adapter sequence and demultiplexed based on site (2nd-GA6 and WT-GA6) using the FASTX Toolkit (Hannon lab). Reads containing Ns, too short reads, obvious contaminating reads from other libraries (errors in indexing), and reads less abundant than 1/10 000 of the most abundant read (i.e. below 0.01%) were not included in the analysis. Reads were subsequently analysed for insertions, deletions and substitutions using custom scripts mostly utilizing BioPython . Data prpipo slip site and flanking sequence (WT-GA6) was inserted between the C-terminus of GFP and the NIa-protease cleavage site that defines the N-terminus of HC-Pro to make clone TuMV-GA6  RNA with slippage at 2nd-GA6 (or both 2nd-GA6 and WT-GA6) resulting in translation termination shortly after the 2nd-GA6 slippage site. As the RNAs edited at either site do not produce NIb replicase in cis, it is our belief that they probably have similar stability and are possibly restricted from accumulation by similar replication restriction and/or decay mechanisms  (More than 30 clones with differently mutated 2nd-GA6 sequences were constructed (Supplementary Table S1). Each clone was introduced into three  separate plants by agroinfiltration. RNA was extracted from systemically infected leaves, the 2nd-GA6 and WT-GA6 regions were reverse transcribed, amplified and subjected to high-throughput sequencing, and the levels of insertions and/or deletions at each site for each mutant were quantified (Supplementary Table S2). Slippage rates at the unchanged WT-GA6 site were analysed for all infected plants (e n = 2) . The meae n = 2) .Next we analysed slippage rates at the various mutated slip sites introduced at the 2nd-GA6 site. Initially, considering that the proportion of edited transcripts might change slightly over the course of infection  or between different plants, we considered normalizing slippage rates at the mutated 2nd-GA6 site by slippage rates at the unchanged WT-GA6 site (for the same infected plant), as a means of reducing inter-biological repeat variability. However, a comparison of variability between the two sites revealed considerable scatter , making such normalization futile.t-test values for different comparisons; P-values quoted in text are from two-tailed t-tests). Nonetheless, the slippage rates for the TuMV WT and TuMV WT-L sequences inserted at the 2nd-GA6 site differ (P = 0.005), and both are less than the slippage rate for the same sequence in its natural context at the WT-GA6 site (P = 0.002), indicating that more distant sequences likely also play a role in modulating slippage efficiency.Slippage rates at the 2nd-GA6 site of viruses containing the TuMV slip site (TuMV WT), a slightly longer insert  or the SPFMV PISPO slip site (PISPO WT) are shown in Figure P = 0.147; not statistically significant) and 3.41 ± 0.32% (P = 0.050), respectively. Further, the single-nucleotide deletion rates increased to 1.22 ± 0.05% and 1.50 ± 0.00% and, for TuMV +AA, 0.97% of transcripts had a 2-nt deletion. Note, however, that it is possible that the increased deletion (and insertion) rates on these longer poly(A) tracts partly reflect increased slippage during reverse transcription, PCR and sequencing. As expected, no insertional slippage was detected (<0.01%) on a run of five As (TuMV delA), or when AAAAAA was replaced with UUAAAA, AAAAUU or AUAUAU ) .When the length of the homopolymeric sequence was increased by inserting an additional A (TuMV +A) or AA (TuMV +AA) (with downstream deletions to maintain reading frame) Figure , the sinP = 0.177; not statistically significant), while changing the nucleotide to U (TuMV dnGtoU) had little apparent effect (1.00 ± 0.1%). On the other hand, changing the downstream C in the PISPO context to either G (PISPO dnCtoG) or U (PISPO dnCtoU) resulted in insertion rates  similar to the PISPO WT sequence. Thus, single nucleotide changes next to the slip site can influence the insertion rate but effects vary depending on the surrounding nucleotide context.Clones with the TuMV WT or PISPO WT sequence inserted at the 2nd-GA6 site  were used as templates for further analysis of the impact of mutations flanking the slip site. The impact of the nucleotide immediately downstream of the AAAAAA  was assessed by mutating it to either of the two other possible non-A nucleotides Figure . MutatinP = 0.011). Thus, the leading G of the highly conserved GAAAAAA Potyviridae slip site sequence is not essential for slippage per se. Similarly, when the upstream GG was replaced with CG in the PISPO context (PISPO upGtoC) the insertion rate was 2.45 ± 0.37%, similar to PISPO WT.Impacts of the two nucleotides immediately upstream of the AAAAAA were also assessed Figure . ChanginP = 0.005), which is even higher than PISPO WT. Conversely, a PISPO upstream, TuMV downstream hybrid (PISPO-TuMV) had an insertion rate of 1.27 ± 0.08%, only slightly higher than TuMV WT.  These results suggest that the higher insertion rate of PISPO WT in comparison to TuMV WT is caused by differences in downstream sequence.The role of more distant sequence elements was assessed using hybrid sequences Figure . The AAAP = 0.042), while PISPO 6Ato6U had a lower insertion rate . The reverse complement sequences had insertion rates of 0.53 ± 0.04% (TuMV RC) and 2.05 ± 0.34% (PISPO RC), both lower than the respective WT sequences (P = 0.004 and P = 0.152), though the reduction was not statistically significant for PISPO RC. In another set of mutants, only the upstream or downstream sequences were replaced with their respective reverse complements, making the sequence identical in positive- and negative-sense RNA apart from the homopolymeric hexamer   or where the whole inserted motif was replaced with its reverse complement (TuMV RC and PISPO RC) Figure . In comp) Figure . A TuMV P = 0.002) and 1.66 ± 0.17% (P = 0.008) respectively (cf. 0.92 ± 0.08% for TuMV WT). A cumulative effect was seen when the pairing was strengthened on both sides . The same trend was seen when the PISPO WT sequence was altered, with insertion rates of 3.69 ± 0.05% (P = 0.057), 4.02 ± 0.73% (P = 0.061) and 5.47 ± 0.51% (P = 0.002), for mutants PISPO 5′str, PISPO 3′str and PISPO 5′&3′str, respectively (cf. 2.65 ± 0.46% for PISPO WT). Strengthening the duplex downstream of the slip site seems to elevate the insertion rate slightly more than strengthening the duplex upstream.Based on these results, we hypothesized that stretches of stronger-pairing nucleotides (i.e. G or C) upstream and/or downstream of the slip site favour higher insertion rates. This was tested with more-specific mutants Figure . StrengtP = 0.005). Placing one strong-pairing nucleotide directly before and after the slip site (SPS str1) raised the insertion rate to 1.24 ± 0.17%, while placing two strong-pairing nucleotides on either side (SPS str2) further raised the insertion rate to 2.20 ± 0.2%, similar to SPS WT. A combination of two strong-pairing nucleotides upstream and two weak-pairing nucleotides downstream (SPS str-wk) had an insertion rate of 1.15 ± 0.09% while the inverse combination (SPS wk-str) had an insertion rate of 1.30 ± 0.14%. In summary, the slip site surrounded by weak-pairing nucleotides still directs mainly the introduction of insertions, but at a considerably reduced rate. The insertion rate increases when the duplex is strengthened on either side and the increase correlates with the number of strong-pairing nucleotides. The identity of individual nucleotides at each position also modulates the insertion rate.The preceding mutations were made in the context where the WT strong-pairing GG immediately upstream of the AAAAAA was left unchanged. To test weaker-pairing nucleotides upstream and downstream of the slip site, a slightly different setup was used Figure . In orden = 3) than at the WT-GA6 site . This may partly be related to the stochastic nature of substitutions (substitutions occurring early in infection may be inherited by a larger proportion of the sequenced population) and small sample size for the WT sequence at the 2nd-GA6 site, but may also reflect purifying selection against substitutions at the WT-GA6 site , whereas the 2nd-GA6 site is largely free from coding constraints. Analysis of DNA and ΔGDD controls revealed that even higher levels of substitutions can be introduced during reverse transcription, library amplification and sequencing, or potentially at other stages, but not consistently between samples nor specifically at positions −1 and +7  had a several-fold-higher substitution rate than the surrounding nucleotides Figure . Moreove7 Figure  and D.We hypothesize that the elevated substitution rate at position +7 results from ‘to-fro’ slippage events where the nascent RNA slips back 1 nt, allowing the polymerase to insert an extra A templated by the last U of the 3′-UUUUUU-5′ slip site antigenome sequence (as with slippage during a 1-nt insertion), but then, after translocation, the nascent RNA slips forward again and polymerization continues in the original register from position +8, thus replacing the G at +7 with an A templated from position +6  was observed at the −1 position and, this time, 94% of the substitutions where C to U, consistent with the C at −1 being replacing by a U templated from position +1 resulting from to-fro slippage during negative-strand synthesis Figure . AlthougExtending the analysis to the various mutants, we observed considerable variation in substitution rates and patterns between different sites and different mutants . To summarize the data, we focused on mutant/position combinations with mean substitution rates greater than 0.5% Figure  and B. THigh substitution rates at position −1 were observed for only two mutants, TuMV RC  and SPS wk . The latter could not be explained by a to-fro slippage mechanism as most mutations were U to G rather than the expected U to A. Within the analysed region , we decided to investigate the role of flanking nucleotides in modulating the efficiency of viral transcriptional slippage on GAAAAAA sequences. Our results indicate that slippage efficiency can be modulated by nucleotides immediately adjacent to the slip site, nucleotides within a 21-nt window encompassing the slip site, and also by more distant sequences not tested in the current analysis. Nonetheless, only mutations within the AAAAAA sequence itself completely inhibited slippage. In other words, it would appear that potyvirid species have the potential to evolve slippage rates at least in the range 0.6–6%, and, notwithstanding competing selection pressures from amino acid coding constraints, the actual slippage rates may have evolved to optimal levels for P3N-PIPO expression (low) and P1N-PISPO expression (high), while minimizing production of ‘defective’ viral transcripts.Our study comes with a few caveats. First, we have not ruled out the possibility that the two RNA populations with an insertion either at 2nd-GA6 or WT-GA6 might have different turnover rates. Second, although we hypothesize that transcripts with insertions or deletions should not be replicated, there is some evidence to suggest that they are but at a low level  so that the proportion of slippage transcripts increases slowly over the course of infection. These factors could partly explain differences between our 2nd-GA6 site TuMV and PISPO slippage rates and previously reported native-site insertion rates derived from later infection stages ,14. Thusper se but, as its presence is highly conserved throughout family Potyviridae, it presumably still plays some relevant role in the process. Unfortunately, with our current approach we are not able to measure polymerase drop-off.Our results generally support the hypothesis that increased GC content upstream and/or downstream of the slip site elevates the insertion rate, although questions remain unanswered . Conversely, A/U nucleotides at these positions may result in decreased slippage. Intriguingly, these two nucleotide positions are highly conserved as U (at position +9) and to a lesser extent A (at position +10) at the pipo site across most potyvirus species -specificity like the ‘to-fro’ form of slippage (see below). It is possible that increased GC content downstream slows the replicase at the slip site by impeding strand displacement, leaving more time for slippage to occur. Cellular RNA and T7 polymerases are reported to melt only 1 bp downstream of the active site . On the s Figure ). While Unexpectedly, we observed an increased rate of nucleotide substitutions at sites immediately adjacent to the slip site. The identity of these substitutions [predominantly N to A adjacent to poly(A) sites and N to U adjacent to poly(U) sites], indicated that the substitutions were largely templated by the homopolymeric sequence and occurred as a result of a ‘to-fro’ slippage. Unlike insertional or deletional slippage, the direction of synthesis is readily apparent in ‘to-fro’ slippage. High levels (>0.5%) of to-fro slippage occurred at position +7 for positive-sense AAAAAA sites and at position −1 for positive-sense UUUUUU sites, indicating that to-fro slippage was occurring during positive-strand and negative-strand synthesis, respectively. In both cases, however, high levels of slippage occurred during poly(A) synthesis, with substantially lower levels of to-fro slippage observed during poly(U) synthesis. Many RNA viruses utilize polymerase stuttering on a poly(U) template as a mechanism for maintenance of the positive-sense viral RNA 3′ poly(A) tail ,35, and To our knowledge, to-fro slippage has not been reported for other cases of transcriptional slippage and this may be related to the probable requirement for nucleotide bulging in positive-sense RNA virus transcriptional slippage, in contrast to slippage during ribonucleoprotein-templated negative-sense RNA virus transcription and cellular DNA-templated transcription, where slippage may involve complete dissociation and realignment of the nascent RNA:template strands. The unpaired bulge nucleotide may have a tendency to re-pair, driving realignment of the duplex/replicase complex back into the original register.Potyviridae slip sites) and only at a substantially lower level during synthesis of poly(U). This may be functionally beneficial as slippage during negative-strand synthesis would produce a viral replication complex only capable of making PIPO- or PISPO-encoding transcripts and, further, promote higher levels of additional slippage , where slippage may occur predominantly during negative-strand synthesis, low insertional slippage was observed for TuMV RC only . Much smaller amounts of to-fro slippage occur during synthesis of poly(U). Further work is required to verify whether insertional slippage—the form functionally utilized to express the P3N-PIPO and P1N-PISPO proteins—has the same poly(A) preference as to-fro slippage. Both to-fro and insertional slippage are affected in different ways by the identity of nucleotides flanking the shift site, with a pyrimidine 3′ adjacent to the GAAAAAA, and increased GC content upstream and/or downstream of the slip site, generally leading to higher levels of insertional slippage. These results are likely to be relevant to understanding the infidelity of the polymerases of all positive-sense RNA viruses.


Using an atomistic graph with energy-weighted covalent and weak bonds, we define a bond-to-bond propensity quantifying the non-local effect of instantaneous bond fluctuations propagating through the protein. Significant interactions are then identified using quantile regression. We exemplify our method with three biologically important proteins: caspase-1, CheY, and h-Ras, correctly predicting key allosteric interactions, whose significance is additionally confirmed against a reference set of 100 proteins. The almost-linear scaling of our method renders it suitable for high-throughput searches for candidate allosteric sites.Allostery is a fundamental mechanism of biological regulation, in which binding of a molecule at a distant location affects the active site of a protein. Allosteric sites provide targets to fine-tune protein activity, yet we lack computational methodologies to predict them. Here we present an efficient graph-theoretical framework to reveal allosteric interactions (atoms and communication pathways strongly coupled to the active site) without  Allostery is a key molecular mechanism underpinning control and modulation in a variety of cellular processes. Here, the authors present a method that can be used to predict allosteric sites and the mediating interactions that connect them to the active site of the protein. Allostery is a key molecular mechanism underpinning control and modulation in a variety of cellular processes1The realization that all proteins exhibit innate dynamic behaviour410Classic thermodynamic models of allostery, such as the Monod–Wyman–Changeux91820222324211126The connection between diffusion processes  on a network and the vibrational dynamics of the network is well established283436M, a discrete Green's function in the edge space of the graph recently introduced in ref. M, we show that it can be used to calculate the effect that the fluctuations of an edge have on any other edge of the graph. The resulting propensity score for each bond, Πb, measures how strongly bond b is coupled to the active site through the graph. This bond-to-bond formalism provides a natural way of uncovering how long-range correlations between bonds contribute to allosteric signalling. The computation time scales almost linearly in the number of edges41We start by building an atomistic graph model of the protein: nodes are atoms, and weighted edges represent both covalent bonds as well as non-covalent bonds , with weights derived from interatomic potentials We first analyse in detail three important allosteric proteins: caspase-1, CheY and h-Ras. In each case, given the location of the known active site, we correctly predict the location of the allosteric site and uncover communication pathways between the two sites. Each example highlights a particular aspect of the method. In caspase-1, comparison of our results with those obtained using RRINs shows that atomistic physico-chemical detail can be necessary for the reliable identification of the allosteric site. With CheY, we illustrate how information can be gained from ensembles of nuclear magnetic resonance (NMR) structures: the variance of the propensity across the NMR ensemble reveals residues involved in allosteric signalling that cannot be identified from the static X-ray structure alone. In h-Ras, we show that signal propagation between the active and allosteric sites is crucially dependent on the interaction between the protein and specific structural water molecules. Finally, we evaluate our approach against a further test set of 17 allosteric proteins. We find that the bond-to-bond propensity is a good predictor of allosteric potential, suggesting it could be used to guide efforts in structure-based allosteric drug discovery.b for all bonds in the protein . Residues E390, S332 and R286, which have been found to belong to a hydrogen bond network between the active and allosteric sitesOur method finds a ‘hotspot' of residues with high quantile scores in a cavity at the dimer–dimer interface , left. Tpb≥0.99 (pb=0.999), S332:S339 (pb=0.996), R286:N337 (pb=0.992) and A284:S332 (pb=0.990). Bonds in this pathway have previously been identified by Datta et al.Making use of the physico-chemical detail afforded by our atomistic description, we find the high propensity bonds that lie on communication pathways connecting the allosteric site to the active-site ligand. Concentrating on the top quantile pb≥0.99 , the twopb≥0.99 , includiP=0.5399). The results obtained with RRINs are in general dependent on the cutoff radius used. For caspase-1, the allosteric site is not detected in RRINs with cutoff radii of 6, 7 and 8 Å. The allosteric site is found to be significant with cutoff radius 10 Å, but the signal is considerably weaker than for the atomistic network , it causes a change in the rotation direction of the flagellar motor, thus regulating the tumbling rate of Escherichia coli. This regulation is achieved through a post-translational modification; phosphorylation of CheY at the distant residue D57 increases its affinity for FliM, making this an interesting example of a single-domain allosteric protein.2+, BeF3 and FliM. We identify a number of hotspot surface residues with high quantile scores (pR=0.96). Residues in the allosteric site (<3.5 Å from phosphorylation site) have higher average quantile score than non-allosteric residues  in fully activated CheY (PDB ID: 1F4V) bound to Mge scores , includipR ≥ 0.9 . In addipR ≥ 0.9 , which cIn contrast to caspase-1 above, using a RRIN with cutoff radius of 6 Å, we identify the phosphorylation site of CheY as a hotspot: the average quantile score of allosteric residues is much higher than for the rest of the residues . Considering high quantile scores (pb≥0.97), we find several bonds connecting the allosteric phosphorylation site to the key binding site residue Y106 (pb=0.991) and E89:Y106 (pb=0.977); a second pathway is formed by K109, which has high quantile score bonds with D12 (pb=1) and D57 (pb=0.993). These residues have been discussed extensively in the biochemical literature as crucial for allosteric signalling (see Discussion).due Y106 . One patr=0.94). Using Cook's distance, a well-known method to detect influential points in linear regressionIn addition to fully activated CheY, we studied four conformations of CheY across a range of activation stages  and 27 NMR structures of the fully activated CheY bound to the phosphate mimic BeF3 (PDB: 1DJM). We calculated the average 〈ΠR〉NMR and the standard deviation SD(ΠR)NMR of the propensity of each residue over the ensemble of NMR structures, and compared them against the obtained from the X-ray structure.To explore the effect of small structural changes on the propensities of CheY, we analysed 20 NMR structures of the inactive conformation r2=0.96) with its X-ray propensity, r2=0.84), as seen in et al.The results of comparing NMR ensemble versus X-ray structures differ between inactive and active conformations, suggesting that dynamical reconfigurations have a consistent effect in the calculated propensities. For inactive CheY, the average ensemble NMR propensity of each residue, NMR, we find W58, T87, E89 and K109, which were also found to have high propensities in the active X-ray structure. These residues are known to be functionally relevant, and recent NMR relaxation–dispersion experiments have suggested that they form part of an allosteric network undergoing asynchronous local switchingWe computed the variability of the propensity of each residue across the active NMR ensemble . Among tSDSDα4—β4 loop: T87, A88 and E89. When we examine the individual NMR structure in which M17 has the highest propensity, M17 bonds directly with A88 and is indirectly connected to T87 through a hydrogen bond with K109  with and without inclusion of structural water molecules in the graph. In the absence of water , left, wThis water-mediated link between Q99 and S65 connects the allosteric binding pocket on helix 3 with the helical structure known as the switch 2 region, at the bottom of which lies the key catalytic residue Q61 ref. . Our respb in the previous sections identify bonds with high propensities compared with bonds at a similar geometric distance from the active site within the same protein. To assess the absolute significance of bond propensities, we assembled a reference set of 100 protein structures from the SCOP databased, and the total number of weak interactions in the protein, E, we apply QR against both d and E, as given by The QR scores We obtained the absolute quantiles ed above . Reassured above . Visualiic sites . These ret al.To test our methodology, we computed the bond propensities of 17 additional proteins known to exhibit allostery. Ten of these proteins were taken from a benchmark set collected by Daily pb, pR) and absolute  of all its bonds and residues, both intrinsic , focussing on the contribution of high propensity bonds to pathways (or networks) of weak bonds linking the active and allosteric sites. The weak bond network found in caspase-1 (E390/R286/S332/S339/N337) has previously been tested experimentally and shown to be functionally important4749Determination of protein structures from NMR solution experiments results in multiple models, each consistent with experimentally derived distance restraints. The ensemble of structures is not a true thermodynamic ensemble, since variation could be due to actual flexibility and thermal motion during the experiment, or to inadequate (or under-constrained) interatomic distance restraints. Our analysis suggests that the variation within NMR structures can reveal functionally relevant information. For CheY, residues with highly variable propensities across the NMR ensemble (E89/W58/T87/E89/K109) form an asynchronously switching allosteric circuit after phosphorylation, as revealed by NMR relaxation–dispersion experimentsComparison across conformations indicates that propensities are fairly robust to local dynamic fluctuations, as shown by the strong correlation between active and inactive conformations and across NMR structures . Additioet al.The role of structural water molecules in mediating allosteric communication has so far received limited attention. In a recent study of a PDZ domain, Buchli To what extent does the identification of the allosteric site require an atomistic, chemically detailed graph construction? To answer this question, we applied our propensity measure to RRINs, the coarse-grained residue-level models used in almost all previous network analyses of proteins. For caspase-1, we found that allosteric residues are not significant in RRINs , whereas, on the other hand, the allosteric site of CheY was consistently detected by both atomistic and residue-level descriptions. This indicates that both coarse topological features and detailed chemical communication pathways can be relevant for allostery, depending on the protein. Hence the atomistic graph with detailed physico-chemical information can in some cases be important to capture the communication features of the protein, for example, in caspase-1, the binding of the allosteric ligand perturbs a network of strong hydrogen bonds and salt bridges as identified in our analysis. The analysis of RRINs for all 20 proteins in our allosteric test set  confirmsFinally, it is important to remark that our method is computationally efficient. To obtain the bond-to-bond propensities, we solve a sparse linear system  involvin41Fluctuations and the edge-to-edge transfer matrix of a graph. The edge-to-edge transfer matrix M was introduced in ref. Mji reflects the effect that an injected flux on edge i has on the flux along edge j after the fluxes are redistributed over the whole graph when at equilibrium. Alternatively, M can be understood as a discrete Green's function in the edge space of the graph. See ref. M, which can be understood as describing how fluctuations of edge weights propagate through the graph. This reinterpretation underpins the work in this paper, linking M to the analysis of bond fluctuations in biomolecules.Here, we derive a complementary interpretation of the edge-to-edge transfer matrix As a starting point, consider the well-known Langevin equation, also denoted the heat kernel equation60L28L. In contrast to coarse-grained methodsx here is associated with atomic fluctuations, that is, our graph model reflects an atomic description that incorporates physico-chemical interactions derived from the three-dimensional structure of the protein in the PDB file. The resulting graph contains energy-weighted interactions representing bonds in the protein, including both covalent bonds and weak interactions such as hydrogen bonds, salt bridges, hydrophobic tethers and electrostatic interactions. For details of the graph construction see ‘Construction of the atomistic graph' and Formally, L is the graph LaplacianThe matrix wij is the weight of the edge between nodes (atoms) i,j. In this case, wij is the energy of the bond between both atoms. Thermal background fluctuations are modelled by ϵ, a zero mean white Gaussian noise input vector, that is, a simple heat bath acting independently on all atomic sites with covariance matrixwhere δ stands for the Dirac delta function.where x, we wish to study the coupling between bonds, and thus concentrate on the bond (edge) variables of the graph:Instead of focusing on the atomic (node) variables yb describes the difference of the node variables at the endpoints of the associated bond b, that is, a fluctuation associated with the bond between two atoms. The vector of bond fluctuations can be compactly represented in vector notation asClearly, B is the incidence matrix of the graph relating each edge variable to its corresponding node variables, that is, Bbi=1 if node i is the head of bond b; Bbi=−1 if node i is the tail of bond b; and Bbi=0 otherwise.where We can now calculate the cross-correlations between edge fluctuations asL† is the (Moore–Penrose) pseudo-inverse of the Laplacian matrix. Each entry b2 is correlated with a fluctuation at bond b1 at time τ. See where G=diag(wb):Biophysically, we are ultimately interested in the energy fluctuations induced by bonds on other bonds. Therefore, we multiply the correlation matrix τ. Our measure of bond-to-bond propensity is obtained from the instantaneous correlations  leading to the edge-to-edge transfer matrix:to obtain the matrix of bond-to-bond energy correlations with delay M are indeed related to the average energy stored in the bond fluctuations: Mb1b2 reflect how a perturbation at bond b2 affects another bond b1 weighted by the strength of bond b1. Hence the influence on a stronger bond is considered to be more important. Although we have not considered here time-delayed correlations , this is an interesting direction for future research.Note that the diagonal entries of Definition of the bond-to-bond propensity. To construct our measure of propensity, we only assume knowledge of the active site and proceed as follows. Let us consider all the ligand–protein interactions formed at the active site and compute their combined effect on each bond b outside of the active site:This raw propensity reflects how closely the active-site is coupled to each individual bond. Note that the computations include all the bonds in the protein . However, in the paper we only report the effect on weak bonds, since it is changes in weak-bonding patterns that usually drive allosteric response in proteins. Since different proteins have different numbers of bonds, we make the measure consistent by normalizing the score:b is referred to as the propensity of bond b; a measure of how much edge b is affected by the interactions at the active site. The propensity of a residue is defined as the sum of the  propensities of its bonds:Throughout the manuscript, the quantity ΠComputational cost of bond-to-bond propensity. The computation of the propensities is efficient. Note that M matrix corresponding to protein–ligand interactions. Crucially, we do not need to compute the full pseudo-inverse L† in 41E is the number of bonds (edges) and Na is the number of atoms (nodes). Our method therefore is scalable to large systems. Using the Combinatorial Multigrid toolbox written by Koutishttp://www.cs.cmu.edu/jkoutis/cmg.html) propensities for all the bonds in proteins with ∼100,000 atoms can be run in minutes on a standard desktop computer.pb is a measure of how high the propensity Πb is relative to other bonds in the sample which are at a similar distance from the active site.To identify bonds (and residues) with high propensities relative to others at a similar distance from the active site, we use quantile regressionpth quantile, Qp, of a distribution is given by the solution of the following optimization problem: given a sample {yi}i=1n parametrically dependent on m variables β, the estimate of the conditional pth quantile of the sample distribution is obtained by solvingAlthough QR goes back more than 200 years, it has only become widely used recently, due to the availability of computational resources. The mathematical basis of the method stems from the fact the ρp(·) is the tilted absolute value functionwhere I(·) is the indicator function. If the dependence is assumed to be linear, Q=β0+βTxi, the optimization can be formulated as a linear program and solved efficiently through the simplex method to obtain and b of bonds within each protein so as to take into account their dependence with respect to db, the minimum distance between bond b and any bond in the active site:In the sections ‘Allosteric site and functional residues in caspase-1', ‘Uncovering allosteric communication pathways in CheY' and ‘Structural water is crucial to allosteric pathways in h-Ras', we have applied QR to the propensities Πvb contains the coordinates of the midpoint of bond b. On the basis of the observed exponential decay of Π with d, we adopt a linear model for the logarithm of the propensities and estimate the conditional quantile functions by solving the minimization problemwhere the vector b at distance db from the active site and with propensity Πb, by finding the quantile pb, such thatwhere the sum runs over the weak bonds of the corresponding protein. From the estimated model for the protein, we then calculate the quantile score of bond d, and the number of non-covalent bonds in the protein, E. Since the mean propensity scales as E−1, we also assume a power-law dependency of the quantiles. Hence, we solveSimilarly, in ‘Absolute bond propensities against a SCOP reference set', we use QR to obtain absolute quantile scores of bonds and residues with respect to a reference set of 100 proteins from the SCOP database. In this case, the propensities are regressed against both the distance to the active site p, the model is defined by the equation of a plane b at a distance db from the active site in a protein with Eb non-covalent bonds is found by solvingwhere the sum runs over all the weak bonds of all the proteins in the SCOP reference set. For each quantile a plane  . The gloR.Quantile scores for residues are obtained by applying the same process to the propensities Πquantreg (http://cran.r-project.org/web/packages/quantreg/index.html) developed by KoenkerThe QR computations have been carried out using the R toolbox α, β, α/β, α+β, and multi-domain, covering all the major fold-types for cytosolic proteins. To obtain a representative set of proteins from the database, we randomly selected 20 proteins from each of the five classes. Note that we only include proteins for which there is a structure with a ligand bound to the active site. Our reference set thus covers a broad region of protein structure space. Details of the 100 proteins selected can be found in The SCOP database is a manually curated database which uses a hierarchical classification scheme collecting protein domains into structurally similar groupsdb, and we calculate the propensity, Πb, for all its E weak bonds. Across the 100 proteins, we obtain a total of d and E, are now referred to the whole SCOP reference set and are not specific to any one particular protein. We then use the quantiles of the reference set to compare the bond propensities of any protein of interest and compute the ‘absolute' quantile score pbref for each bond, as given by For each protein in the data set, we compute the distance from the active site, ence set . We thenTo validate our findings on the allosteric protein test set, we evaluated the significance of the computed quantile scores according to four statistical measures, based on the following metrics:(i) The average bond quantile score:Nb,site is the number of bonds in the site.where (ii) The average residue quantile score:NR,site is the number of bonds in the site.where pb > 0.95, denoted P. Since the quantile scores are uniformly distributed, 0.05 is the expected proportion of bonds with quantile scores above 0.95.(iii) The proportion of allosteric bonds with (iv) The average reference bond quantile score:Nb,site is the number of bonds in the site.where These four measures are introduced to check robustly for the significance of the bonds in the allosteric site from distinct perspectives. If the functional coupling between active and allosteric sites is due to a cumulative effect of the entire allosteric site, then average quantile scores over all bonds in the allosteric site should be an accurate measure of its allosteric propensity. Measures (i), (ii) and (iv) capture this property at the level of bonds and residues for both intrinsic and absolute propensities. It is also possible that functional coupling to the active site is concentrated on a small number of high quantile score bonds, with most others only being involved in structural or energetic aspects of binding to the allosteric ligand and having low quantile scores. Our metric (iii), which measures the number of high quantile score bonds in the site, can capture this behaviour based on the tail of the distribution. Reassuringly, the four measures provide complementary, yet largely consistent outcomes.Structural bootstrapping. To establish the significance of the average quantile scores ric site . A bootsValidation on the allosteric test set. pR scores; yet when bonds are considered individually through their pb scores, the high quantile scores are averaged out over the whole allosteric site. score,  . The propb,allo>0.95) in the allosteric site, as compared with the expected proportion (0.05) above this quantile. The proportion of high quantile score bonds in the allosteric site is greater than expected in 17 of the 20 proteins  are available at figshare with DOI: 10.6084/m9.figshare.3413605.v1.How to cite this article: Amor, B. R. C. et al. Prediction of allosteric sites and mediating interactions through bond-to-bond propensities. Nat. Commun. 7:12477 doi: 10.1038/ncomms12477 (2016).Supplementary Figures 1-5, Supplementary Tables 1-6, Supplementary Notes 1-3, Supplementary Methods and Supplementary References.