Heliconius butterfly wing patterns. Although this work demonstrates a number of cases where homologous loci underlie both convergent and divergent wing pattern change among different Heliconius species, it is still unclear exactly how many loci underlie pattern variation across the genus. To address this question for Heliconius erato, we created fifteen independent crosses utilizing the four most distinct color pattern races and analyzed color pattern segregation across a total of 1271 F2 and backcross offspring. Additionally, we used the most variable brood, an F2 cross between H. himera and the east Ecuadorian H. erato notabilis, to perform a quantitative genetic analysis of color pattern variation and produce a detailed map of the loci likely involved in the H. erato color pattern radiation. Using AFLP and gene based markers, we show that fewer major genes than previously envisioned control the color pattern variation in H. erato. We describe for the first time the genetic architecture of H. erato wing color pattern by assessing quantitative variation in addition to traditional linkage mapping. In particular, our data suggest three genomic intervals modulate the bulk of the observed variation in color. Furthermore, we also identify several modifier loci of moderate effect size that contribute to the quantitative wing pattern variation. Our results are consistent with the two-step model for the evolution of mimetic wing patterns in Heliconius and support a growing body of empirical data demonstrating the importance of major effect loci in adaptive change.Recent studies indicate that relatively few genomic regions are repeatedly involved in the evolution of  Heliconius butterflies in particular have become a useful model for understanding the evolution, genetic basis, and development of complex adaptive variation Heliconius is incredibly diverse and characterized by repeated cases of extreme divergence and convergence in wing patterns. While the first can occur between closely related species or races within species, the latter is the product of Müllerian mimicry between distantly related species. As an example, the two unpalatable co-mimetic species H. erato and H. melpomene diverged from each other more than 8 million years ago H. erato and H. melpomene represent one of the best example of a parallel radiation H. erato and H. melpomene provides a vivid example of the importance of natural selection in shaping phenotypic variation Butterflies are among the most charismatic insects and their kaleidoscopic color variation has fascinated scientists for centuries. The wing pattern radiations of et al.Heliconius erato and Heliconius melpomene. Together these studies described 22 distinct color pattern genes in H. erato and 17 in H. melpomene based on observations of how color pattern variation segregated across a series of opportunistic crosses. The relationship among some of the genes described by Sheppard and collaborators Heliconius wing pattern, the work of Sheppard et al.Heliconius wing color pattern variation focused on mapping in order to describe the action of single genes action in stand-alone crosses The work of Turner and Crane H. erato wing patterns. Our strategy allows us to dissect the genetic control of wing color pattern variation in H. erato in order to easily understand the homology of major color pattern genes between distinct morphs of H. erato and to characterize the contribution of alternative loci to the total quantitative variation of major patterns variants. We generated a comprehensive description of the genetic architecture of H. erato wing color pattern diversity in three major steps. First, we created large replicate mapping families of four distinctive races of H. erato by crossing each race to the inter-fertile sister species H. himeraH. erato color pattern radiation. Third, we conducted an initial quantitative analysis of color pattern variation segregating in one of our crosses to more deeply explore the underlying architecture of pattern variation. Our high definition linkage mapping and a quantitative trait locus (QTL) analysis allows us 1) to explore the phenotypic effects of major patterning loci, 2) to gain a more detailed appreciation for the genetic architecture of pattern variation across the H. erato radiation, and 3) to identify other loci that underlie color variation and determine their overall contribution to the quantitative variation observed in our crosses.To fill these gaps we used a combination of genetic mapping and quantitative genetic analysis to uncover the genetic architecture of inter-racial variation in H. erato. Finally, our findings reinforce past models about the evolution of new color patterns within species and add to the growing body of empirical work demonstrating the importance of major effect loci in adaptive evolution.Overall, our results paradoxically show that while the genetic basis of major wing pattern elements variation between races is simpler than previously envisioned, these elements are affected by quantitative variation that is more complex than ever depicted. Several color pattern genes previously assigned to distinct chromosomes are actually located in the same genomic region, suggesting that they are most likely allelic variants of a single locus. Nonetheless, we uncovered new loci that modify these color patterns and explain a substantial component of phenotypic variation segregating in our crosses. Thus, with our experimental design it was possible to test hypotheses regarding the number and effect size of major versus minor loci underlying the adaptive wing pattern diversity in H. erato crossed to H. himera, were generated for this study  in the forewing and hindwing and the other affecting the size and shape of the forewing band. The gene controlling the broken band phenotype of H. e. erato was previously called Ly by Sheppard et al.H. erato basal forewing and hindwing rays and the red H. himera hindwing bar resembled exactly the segregation observed in the H. himera×H. e. etylus cross for both F1 parents and F2 offspring. Variation in the size and shape of the forewing band again suggested the interaction of alternative complementary melanin alleles. The parental F1 had an intermediate phenotype with a reduced yellow pattern. More precisely there were five yellow spots specific to the parental F1s with their position defined by wing veins: 1) the discal spot, 2) a spot between Cu1b and Cu1a wing veins, 3) a spot between Cu1a and M3, 4) a spot between Sc and R1 wing veins, and 5) a spot between R1 and R2 wing veins .ng veins  and 2. AH. himera×H. erato cyrbia: Most of the phenotypic variation in our H. himera×H. e. cyrbia crosses could be explained by two major color pattern genes, Cr and D, D locus and F1 individuals had red on both the fore and hindwing. In addition a second locus, Cr, was responsible for the yellow ventral line and white fringes of the H. e. cyrbia hindwing and interacted with D to determine the presence/absence of the forewing band. Similar to Sd above, the Cr locus largely acted by controlling melanic patches on both the forewing and the hindwing, with H. e. cyrbia Cr alleles positioning a large melanic patch across the middle of the forewing and portions of the hindwing; whereas the Cr allele in H. himera put melanin across the entire hindwing. The epistatic interaction between the hiCr and cyrD alleles created some scoring difficulties for Cr. When individuals were homozygous for the H. himera D allele, the forewing band was either completely absent (cyrCrcyrCr), nearly completely covered with melanic scales (cyrCrhiCr), or fully yellow (hiCrhiCr). In contrast individuals heterozygous for D had a red forewing band H. himera×H. e. notabilis: Qualitative variation in individuals from H. himera×H. e. notabilis mapping families followed a simple segregation pattern consistent with bi-allelic variation at two major effect loci . However, the amount of red present in the forewing band varied extensively in the F2, from nearly fully red to red restricted to the margins of the forewing bands. In addition, yellow pigments were always present in F2 individuals that had the H. himera red hindwing bar, whereas, individuals with red on both the forewing and hindwing, or on the forewing only, had white scale cells in the forewing bands. The geometric shape of the forewing band in our H. himera and H. e. notabilis crosses was complicated as well. Previous crossing experiments implicated three loci, St, Sd and Round (Ro), as responsible for the distinctive split forewing band of H. e. notabilisH. himera and H. e. notabilis, with black melanin scales phenotypically masking yellow or white scales. However, there were typically some remnants of the upper forewing band in F1 individuals, represented by diffuse pigmentation around the edge of the middle forewing band near the intersection of Cu1a – M3 and M3 – M2 wing veins  in several of our H. erato reference crosses. Using this strategy we identified a number of AFLP markers tightly linked (1–3 cM) to the main color pattern genes  segregating in our crosses, and used them as landmarks for cross- comparison of linkage between H. erato races and for BAC library screening Cr color pattern gene we utilized a gene-based marker linked to Cr color pattern gene  F2-Not9We examined 23 AFLP primer combinations . In the F2-Not9 cross alone, which was used as the backbone reference linkage map  20 AFLP loci (5 MI and 16 BI) were discarded and did not enter the final dataset to construct the F2-Not9 linkage map To generate a high resolution map of wing color pattern loci across these kage map , we scorkage map . In addikage map . RoughlyH. erato , and thuH. erato . UtiliziH. e. notabalis and H. himera Z chromosomes in this study. Finally, our final mapped dataset consisted of 446 loci, 197 censored BI loci D and Sd loci to be around 3 cM and 5 cM respectively. If we assume a one-to-one correspondence between physical and recombination size D and Sd color pattern genes respectively.Taking advantage of the absence of crossing over in female Lepidoptera F2-Not9 cross, tightly linked to D , unlinked to the D locus, controlled the presence of white scales in the forewing of H. e. notabilis. However, in our H. himera by H. e. notabilis crosses white and yellow color variation perfectly co-segregated in the two homozygote types: white pigment was never present in individuals that were homozygous for the H. himera D allele, whereas, all the individuals homozygous for the H. e. notabilis D allele showed white scales on their forewings  distance between LAAA145 and ACCA167 was 3 cM in F2-Et2, 2.2 cM in F2-He11, and 2.9 cM in F2-Not9; while between ACCA167 and LAAA182 the relative distance was 8.8 cM in F2-Et2, 7.5 cM in F2-He11, and 7.1 cM in F2-Not9. The most likely placement of the three color pattern genes in each case was between ACCA167 and LAAA182 – an interval of ∼2 Mb assuming 276 kb/cM Sd, St, and Ly, which were previously thought to map to independent chromosomes or to very distant portion of the same linkage group, into a single relatively small region of the same chromosome. Although this region could easily contain hundreds of genes Sd for continuity with previous studies. Sd controls all wing color pattern variation in the middle of the forewing.A number of distinct loci have been described to explain some aspect of forewing band shape variation in homology  and thesG 10 see , suggestF2-Not9 (H. himera×H. e. notabilis) was largely explained by the Sd locus. QTL analysis of H. notabilis band size variation where measured in the large central area of the forewing band characterized proximally by a Big-Spot (BS) and distally by a Not-Spot (NS)  Quantitative analysis of the forewing band variation in our reference cross (NS) see . These mforewing . HoweverCr locus, which segregates in our H. himera×H. e. cyrbia crosses, was unlinked to Sd and falls on LG 15 on our color pattern linkage map  the most quantitative wing pattern variation, b) the segregation of variation previously ascribed to at least five color pattern genes , and c) the availability of a very high resolution genetic map. Two aspects to the wing color pattern variation in the F2-Not9 cross emerged from our analysis. First, there was ample variation in the amount of red in the forewing band (p = 0.002) and explained 88.3% of the variation  on LG 10 and another QTL on the same chromosome. The two QTL peaks were close together , which we refer to as Sd  improve our understanding of the phenotypic control of major wing color pattern loci across races in order to test homology among these loci and (2) asses the quantitative genetic control of the remaining continuous variation. By combining linkage analyses in multiple broods with a single cross QTL study, we have developed a comprehensive foundation of the genetic architecture controlling le locus . Similarto as Sd . HoweverHeliconius are colored by pigments derived from the tryptophan-ommochrome biosynthetic pathway H. erato a major QTL centered on the D locus, is largely responsible for white, yellow, orange and red wing pattern variation  and white (no pigment) scales cells in at least two distinct ways.Our study, however, cannot rule out that the large effect of chromosome 18 may fractionate into several QTLs controlling quantitative variation in pattern color composition independently of r yellow . In spitD/optix locus on qualitative and quantitative color variation, other regions of the genome play an important role in defining H, erato wing color pattern. Of particular significance is the presence of three QTLs on three different chromosomes. These loci explained nearly 30% of residual forewing variation in the amount of red, which varied from nearly all red to nearly all white/yellow under the epistatic model  with a spatial distribution that prefigure the red/white upper and lower forewing bands. Thus, while optix acts as a major determinant of non-black wing scale identity, additional modifiers interact with the pathway regulated by optix. This implies that there are additional loci that repress pigment synthesis in order to temper the final color fate. We speculate these color-repressing modifiers may modulate the expression of transporters or enzymes required for ommochrome pigment synthesis.Despite the major control of the ic model . Two of H. erato. Sheppard and colleagues H. erato to a number of distinct loci  there is a peak in LOD scores at Sd followed by a curious step rise in values towards the end of the same chromosome , first deduced by Schwanwitsch Heliconius and Charaxes can be shown to come about by the confluence of smaller black pattern elements that expand from organizing centers that are homologous to those that organize eyespot patterns Heliconius wings are labeled c, d, f and h in H. erato and H. melpomene have been shown to affect the degree of expansion of these black areas WntA/Sd gene  or two (d and f) of the serially homologous organizing centers in either the anterior compartment of the wing  nearby WntA that might explain the two distinctive QTL peaks (Sd/WntA and possibly a color pattern gene named Ro) that we observe in our analysis on such chromosome. These loci become interesting candidates for future developmental analysis.The bold black regions on the wings of /Sd gene . Each ofSd/WntA locus is homologous to the Ac locus in H. cydnoAc controls the presence/absence of melanic scales on central portions of the forewing and hindwing in H. cydnoH. erato, several loci in H. melpomene and H. cydno have been proposed to be responsible for variation in melanin patterns across the forewing and hindwing of different color pattern races of each species. For example, distinct studies list several different loci that have some effect on the shape of the forewing banding patterns in H. melpomeneH. cydnoH. cydno and H. melpomene is that the number of unique loci involved in black patterning within each adaptive radiation will collapse to a much smaller set of loci, each containing large effect alleles, similar to what we observe in H. erato.Comparative mapping and expression experiments suggest that the H. erato morphs, our study describes the genetic architecture underlying quantitative wing color pattern variation in H. erato at greater detail than has been previously possible. Our work expands upon two previous studies Heliconius butterflies in order to increase the overall knowledge on the genetic control of this diversity. Although the QTL analysis from in H. melpomeneH. numataHeliconius butterflies. For example, Baxter and collaborators H. melpomene  and LG 10 (Sd) and sex Z chromosome  P locus, contribute to the overall variation observed in the H. numata wing patterns . Each of these rings can contain a large number of distantly related species but only a handful of distinctive rings coexist in any given location Heliconius that show strong selection against oddly patterned individuals In a broader context, the overall genetic architecture of As a final remark, our study provides additional data and insights into the genetic mechanisms driving mimicry evolution and more in general the genetic architecture of adaptive variation. The central debate on mimicry evolution has always focused on the number of steps (few versus many) and their effect size  needed in order to resemble the model. Overall, our data are consistent with recent theoretical work modeling the distribution of effect sizes over the course of an adaptive walk towards a local optimum 2 and backcross mapping families by crossing four different geographic races of H. erato to the same stock of H. himera (H. erato notabilis (north-eastern Ecuador), 2) H. erato etylus (South-Eastern Ecuador), 3) H. erato cyrbia (western Ecuador), and 4) H. erato erato (French Guiana). This design allowed us to score H. erato color patterns against the common phenotypic background of H. himera, which has a fully developed middle forewing yellow band and a red hindwing bar. All crosses were carried out in the Heliconius Insectaries at the University of Puerto Rico from stocks originally collected in the wild. Individuals used to established our stocks were collected from the following locations: H. himera, Vilcabamba, Ecuador ; H. e. notabalis, Puyo, Ecuador ; H. e. cyrbia, Guayquichuma Glen, Ecuador ; H. e. etylus, Zamora River, Ecuador ; H. e. erato, Maripasoula, French Guiana . Butterflies were cared for as outlined in th shutter speed, 20.2 mm focal length, constant distance from subject and a neutral gray background overlayed with a with 5 mm grid, in sRGB color space) for further morphological analysis.We generated F. himera . Races iSd, St, Ly, D, Cr, and Wh) that segregated in our collection of crosses : This locus controls red color patterns in H. erato and was originally proposed by Sheppard et al.D(ennis), R(ays), and Y(ellow band). D controls the red/orange at the base of the forewing; R organizes the red/orange rays in the hindwings; and Y controls the color of the forewing band, which can be yellow or red. Recently, we presented strong genetic and gene expression evidence that the three loci are cis-regulatory elements of the transcription factor optixDRY locus as D.Wh (White): This locus controls the abundance of white scales on the forewing band present in the east Ecuadorian race H. e. notabilis. Sheppard et al.Wh locus with another color pattern locus, but they were unable to assign the Wh locus to a linkage group.Sd (Short band) and St (Split band): Sheppard et al.Sd and St, in crosses that involved the east Ecuadorian race H. e. notabilis. Sd is responsible for shortening the forewing band relative to other H. erato races while the St gene controls the splitting of the forewing band into two portions : The major phenotypic action of this gene is to alter the forewing band shape, causing it to be fractured relative to the solid forewing band of other H. erato races. The locus was described from crosses between Suriname hybrids (H. e. amazona×H. e. hydara) and population from Trinidad (H. e. hydara) and was subsequently confirmed by crossing populations of H. e. amalfreda from Manaus, Brazil and H. e. phyllis from East Brazil Cr (Cream rectangles): This locus controls a variety of characteristics. It was originally described for its effect of weakening the expression of the forewing yellow line of H. e. phyllis and the yellow hindwing bar of H. e. favorinusCr also controls a series of white marks along the edge of forewings and hindwings (i.e. H. e. cyrbia) and has a pleiotropic effect on the amount of red pigment in the forewing band H. erato genome. Genomic DNA was digested with EcoRI and MseI endonucleases and a total of 23 AFLP primer combinations (EcoCN/MseCNN) were analyzed across several crosses using previously described methods H. himera×H. e. etylus, H. himera×H. e. notabilis, and H. himera×H. e. erato - were screened for co-segregating AFLP loci  on each linkage group containing a color pattern gene. Co-segregating AFLPs linked to pattern genes were excised from gels, cloned size-verified, and sequenced  window from each targeted color pattern gene were identified  equenced . In addientified . To suppB303 See et al. methods et al.et al.et al.p<0.007 or lower of being unlinked under the hypothesis of no scoring error , to quantify variation in 1) the amount of red across the central forewing patch, 2) the amount of white versus yellow scales  using the computer program DIGIT (courtesy of H. F. Nijhout). DIGIT generates positional  coordinates from points on the image. In total, we scored 58 positions across the wing, including 14 “landmark” coordinates chosen to reflect the positions of the major venation patterns and 44 coordinates that reflected the size and position of the proximal and distal forewing bands  within the QTL. For each phenotype, the selected markers were analyzed individually and then together assuming additivity between markers in a linear regression model. The amount of phenotypic variance explained by each marker and each model was estimated as the r-squared value of the linear regression. We then tested if an additive model better explained the phenotypic variation than a model with just one marker (one-way vs. multi-way ANOVA).optix is such a strong candidate gene for the D locus D and other makers were significant, and if the epistatic model better explained the data than a purely additive model (additive vs. interaction ANOVA). Similarly, for variation in forewing band size we compared a purely additive and a more complex epistatic model using one marker per QTL and building our model around the marker with the highest explanatory power.The simplest model of genetic architecture assumes that contributions of individual markers to the phenotype are additive (Fisherian-type genetic architecture) Figure S1Size analysis. A and B represent the location of landmarks used for quantitative measure of band size variation in the forewing Big-Spot (BS) and Not-Spot (NS) in a simplified butterfly's wing cartoon and real sample respectively. Functions used for calculations of areas were: WingSpot = abs((x3*y10−x10*y3+x10*y11−x11*y10+x11*y12−x12*y11+x12*y13−x13*y12+x13*y14−x14*y13+x14*y15−x15*y14+x15*y16−x16*y15+x16*y3−x3*y16)/2). Big-Spot = abs((x25*y7−x7*y25+x7*y6−x6*y7+x6*y31−x31*y6+x31*y32−x32*y31+x32*y33−x33*y32+x33*y34−x34*y33+x34*y35−x35*y34+x35*y36−x36*y35+x36*y37−x37*y36+x37*y38−x38*y37+x38*y22−x22*y38+x22*y21−x21*y22+x21*y20−x20*y21+x20*y19−x19*y20+x19*y18−x18*y19+x18*y17−x17*y18+x17*y25−x25*y17)/2). Not-Spot = abs((x50*y51−x51*y50+x51*y52−x52*y51+x52*y53−x53*y52+x53*y54−x54*y53+x54*y55−x55*y54+x55*y56−x56*y55+x56*y57−x57*y56+x57*y58−x58*y57+x58*y49−x49*y58+x49*y48−x48*y49+x48*y47−x47*y48+x47*y46−x46*y47+x46*y45−x45*y46+x45*y44−x44*y45+x44*y43−x43*y44+x43*y42−x42*y43+x42*y41−x41*y42+x41*y50−x50*y41)/2). In the above functions x and y correspond to X and Y coordinates and numbers correspond to landmark numbers. The size of the Big-Spot and the Not-Spot was standardized by the size of the WingSpot.(TIF)Click here for additional data file.Figure S2Color spectrum and absorbance. A) Area of the wing measured by a spectrophotometer to distinguish between the amount of white and yellow pigment. B) From right to left the typical absorbance spectrums for individuals that were fully white, yellow, or red is presented.(TIF)Click here for additional data file.Table S1D and Cr interval. Recombinant information for eight loci in the D interval and one in the Cr interval screened in our collection of crosses are reported.Co-dominant markers in the (JPG)Click here for additional data file.Table S2D color pattern gene interval. Primers sequence and reference article in which the loci have been developed is reported together with locus ID and linkage information.Co-dominant and gene based markers. Information of co-dominant and gene based markers used respectively for linkage analysis and fine scale mapping of the (JPG)Click here for additional data file.Table S3Anchors AFLP loci. Anchors AFLP loci. AFLP markers isolated and sequenced across our collection of crosses and their relative nucleotide composition are shown. Provenance of allele and characteristic  is reported.(JPG)Click here for additional data file.Table S4D, notSd, etySd, Ly), are reported. Some of these markers, identified with a star (*) did not enter the final linkage analysis given the very stringent parameters used to create the reference map.Tightly linked AFLP loci. AFLP markers tightly linked (≤3 recombinants) to four different color pattern genes ((JPG)Click here for additional data file.Table S5Overall QTL analysis for amount of red. QTL analysis for amount of red scales showing chromosomes on which QTLs were found. Only those QTLs with probabilities smaller than 0.01 of occurring by chance were considered significant, and were included in additional analyses. LG = linkage group; marker = marker name; Redness.F.value = LOD score; Redness.Pr.F = probability of observing the LOD score by chance; Redness.c950 = expected LOD score with a P = 0.05 generated via non-parameteric bootstrap; Redness.c990 = expected LOD score with a P = 0.01 generated via non-parameteric bootstrap; Redness.c999 = expected LOD score with a P = 0.001 generated via non-parameteric bootstrap; last three columns = visual representation of marker significance at the P = 0.05 (*), P = 0.01 (**), and P = 0.001 (***) levels.(JPG)Click here for additional data file.Table S6Overall QTL analysis for whiteness. Overall QTL analysis for whitness showing chromosomes on which QTLs were found. Only those QTLs with probabilities smaller than 0.01 of occurring by chance were considered significant, and were included in additional analyses. LG = linkage group; marker = marker name; Whiteness.F.value = LOD score; Whiteness.Pr.F = probability of observing the LOD score by chance; Whiteness.c950 = expected LOD score with a P = 0.05 generated via non-parameteric bootstrap; Whiteness.c990 = expected LOD score with a P = 0.01 generated via non-parameteric bootstrap; Whiteness.c999 = expected LOD score with a P = 0.001 generated via non-parameteric bootstrap; last three columns = visual representation of marker significance at the P = 0.05 (*), P = 0.01 (**), and P = 0.001 (***) levels.(JPG)Click here for additional data file.Table S7Overall QTL analysis for the forewing Big-Spot (BS) size variation. Overall QTL analysis for shape in the forewing Big-Spot (BS) showing chromosomes on which QTLs were found. Only those QTLs with probabilities smaller than 0.01 of occurring by chance were considered significant, and were included in additional analyses. LG = linkage group; marker = marker name; BS Spot,F.value = LOD score; BS Spot.Pr.F = probability of observing the LOD score by chance; BS Spot.c950 = expected LOD score with a P = 0.05 generated via non-parameteric bootstrap; BS Spot.c990 = expected LOD score with a P = 0.01 generated via non-parameteric bootstrap; BS Spot.c999 = expected LOD score with a P = 0.001 generated via non-parameteric bootstrap; last three columns = visual representation of marker significance at the P = 0.05 (*), P = 0.01 (**), and P = 0.001 (***) levels.(JPG)Click here for additional data file.Table S8Overall QTL analysis for the forewing Notabilis Spot (NS) size variation. Overall QTL analysis for size in the forewing Not-Spot (NS) showing chromosomes on which QTLs were found. Only those QTLs with probabilities smaller than 0.01 of occurring by chance were considered significant, and were included in additional analyses. LG = linkage group; marker = marker name; NS Spot,F.value = LOD score; NS Spot.Pr.F = probability of observing the LOD score by chance; NS Spot.c950 = expected LOD score with a P = 0.05 generated via non-parameteric bootstrap; NS Spot.c990 = expected LOD score with a P = 0.01 generated via non-parameteric bootstrap; NS Spot.c999 = expected LOD score with a P = 0.001 generated via non-parameteric bootstrap; last three columns = visual representation of marker significance at the P = 0.05 (*), P = 0.01 (**), and P = 0.001 (***) levels.(JPG)Click here for additional data file.


Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established.To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI) Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors. Recent advances in neuroimaging research have increased our understanding of the development and decline of cognitive ability across the human lifespan. Despite such progress, the neural underpinnings of transition periods within the lifespan are less well-established. Adolescence represents a neurobiological transition period sometimes marked by impulsivity, lack of foresight, poor decision-making, elevated emotional reactivity, and sensation-seeking behavior Recent accounts of cognitive control suggest it can be differentiated into multiple component processes. In the “Cascade-of-Control Model,” we recently distinguished between processes related to the implementation and maintenance of a top-down attentional set from those related to later stages of selection through response selection and evaluation Of relevance to lifespan development, older adults appear to shift from a proactive strategy to a reactive strategy, likely mediated by declining dopamine availability and compromised lateral prefrontal function A growing number of studies suggest that adolescents exhibit different patterns of functional activation than adults during tasks requiring cognitive control The present study aimed to extend previous adolescent neuroimaging findings by examining the developmental trajectories of both proactive and reactive control and by exploring the neural predictors of individual differences in real-world measures of cognitive function We predicted that functional maturation of sustained, proactive aspects of control would continue throughout adolescence, and that adolescents might compensate with increased reliance on late-stage reactive mechanisms, particularly for difficult trials requiring response-related interference resolution. As suggested by prior models, immature proactive control might manifest as decreased sustained blood-oxygenated level-dependent (BOLD) activity in lateral prefrontal regions including pDLPFC, whereas reactive response-related compensatory mechanisms might manifest as elevated transient activity in regions such as the ACC and the supplementary motor area (SMA) Participants under the age of 18 were recruited from after-school programs, community centers, and through fliers on local bus routes in metropolitan Denver. Adult participants were recruited via flyers in communities similar to those where youth were recruited, including bulletin boards for custodial and maintenance staff at hospitals and at community colleges, grocery stores, churches, and local bus routes. Advertisements were also placed in local newspapers and email lists. Before study enrollment, participants were screened to exclude those who were left-handed, those who learned English as a non-native language, those who were pregnant or trying to become pregnant, and those with a history of psychiatric or neurological disorders, drug abuse, head trauma, claustrophobia, metallic implants or other MRI contraindications. Informed consent was obtained in writing from all participants of age to provide consent (≥18), and from the legal guardians or parents of all participants below the age of consent (<18). Additionally, all minors signed an additional “assent” form explaining the procedures of the study. All procedures were approved by the Colorado Multiple Institutional Review Board and participants were reimbursed with cash.A total of 41 adolescent individuals (ages 14–17) and 43 adult individuals (ages 18–25) met the above criteria and participated in the study. However, 7 adolescent and 7 adult participants either failed to complete the study or produced unusable imaging data due to scanner artifacts and/or excessive movement (>2 mm linear displacement). Additionally, 1 adolescent and 2 adults were excluded for failing to respond on a considerable number of trials, while 1 adolescent and 1 adult qualified as outliers for their respective groups (>2.5 SD from the group mean) on overall accuracy and were excluded from subsequent analyses. No participants were considered outliers on overall response time (RT).t = 2.33, p = 0.022).After eliminating participants based on the above criteria, 32 adolescents  and 33 adults  yielded useable imaging and behavioral data and were analyzed in the present manuscript. Demographic information is outlined in Participants completed a hybrid blocked/event-related version of the Stroop Color-Word task c) to allow for comparisons between trial types within blocks. Similarly, within incongruent blocks, six incongruent trials (i) were mixed with six block-general neutral trials (ni). Neutral blocks consisted of 12 block-general neutral trials (nn) and 12 neutral trials that were specific to the neutral block (n). In total, participants completed 324 task trials, with 54 trials corresponding to each trial type.Participants completed three Stroop task runs while scanned with fMRI. Each run comprised four, 24 s fixation (F) blocks interleaved with nine, 24 s task blocks. Three task blocks were grouped into triads and each triad consisted of a congruent (C), incongruent (I), and neutral (N) block, first-order counterbalanced across triads and participants. For example, the order of one run might be F-CIN-F-INC-F-NCI-F. Half of the trials in each block consisted of stimuli that were specific to that block (i.e. incongruent (i), congruent (c), neutral (n)) and the remaining half of the trials consisted of neutral stimuli that appeared across all blocks. The trial types within blocks were pseudo-randomly ordered such that no more than two trials of the same type could appear in a row. The inclusion of block-general neutral words within each block allows one to examine transient changes in attentional control . In addition, the presence of these neutral words minimizes any potential habituation effects that might occur in the incongruent and congruent blocks and ensures that within the congruent block participants do not “cheat” and adopt a strategy of reading the word. Hence, within the congruent blocks, six congruent trials (c) were mixed with six block-general neutral trials , Anticipation of Future Consequences , and Time Perspective (e.g. “Some people would rather be happy today than take their chances on what the future may bring” BUT “Other people will give up their happiness now so that they can get what they want in the future”). Scores ranged from 1–4, where 1 = least future orientation and 4 = most future orientation.Participants also completed the Resistance to Peer Influence questionnaire Future Orientation questionnaire, participants selected one of two opposing statements that they believe best fit their own personality (e.g. “Some people would do something that they knew was wrong just to stay on their friends' good side” BUT “Other people would not do something they knew was wrong just to say on their friends' good side”). Next, participants quantified whether the chosen statement was “very true” or “sort of true.” Scores ranged from 1–4 where 1 = least resistance to peer influence and 4 = most resistance to peer influence.The 10-item Sensation Seeking ScaleImpulsivity and Lack of Foresight subscale. The 12 remaining items, which were not of interest for the present study, generally assessed the degree to which participants engage in thrill-seeking behavior (e.g. “I like doing things just for the thrill of it”). Participants indicated whether each item was “true” or “false.” Higher scores indicate greater impulsivity/lack of foresight and thrill-seeking behavior.Finally, participants completed the 19-item Because planning and foresight (which are often negatively correlated with impulsivity) Weinberger Adjustment Inventory Impulse Control subscale, the Planning Ahead subscale of the Future Orientation questionnaire, the Impulsivity/Lack of Foresight subscale of the Sensation Seeking Scale, and the Resistance to Peer Influence questionnaire. Principal Components Factor Analyses on these measures (after controlling for the effect of age) resulted in a single significant factor explaining 52% of the variance in participants' scores . However, while the measures relating to impulse control and planning ability loaded heavily on the single factor , “Resistance to Peer Influence” loaded on this factor to a smaller degree (factor loading = 0.30). These results suggest that the control of social behavior may be governed somewhat by other factors.To examine these possibilities, we selected scores from the Impulsivity/Lack of Foresight measure, and averaged the z-scores across measures to create a cognitive/social control composite. Higher scores are indicative of greater cognitive/social control. Then, for purposes of comparing scores across adolescent and adult groups, we computed z-scores using the mean of the entire group.Based on the extraction of a single significant factor, we converted each self-report measure to z-scores separately for each group , reverse-scored the Scanning was performed on a 3T GE Signa scanner , with a standard 4-channel head coil. Three-dimensional, high resolution, T1-weighted IR-SPGR anatomical images were acquired using the following parameters: repetition time (TR) = 9.61 ms, echo time (TE) = 2.0 ms, inversion time (TI) = 500 ms, field-of-view (FOV) = 220 mm, matrix size = 256×256, in-plane resolution = 0.87 mm×0.87 mm, slice thickness = 1.7 mm, 124 coronal slices. Additionally, T2*-weighted gradient echo, echo-planar functional images (with ramp sampling) were acquired using the following parameters: TR = 2000 ms, TE = 32 ms, flip angle = 77°, FOV = 220 mm, matrix size = 64×64, in-plane resolution = 3.44 mm×3.44 mm, slice thickness = 4 mm, 29 interleaved slices acquired parallel to the AC-PC line, 163 volumes.Stimuli were programmed using E-Prime software  and were viewed through MRI-compatible goggles. Participants were given earplugs to dampen scanner noise and an air pillow was inflated around each participant's head to minimize head movement. Participants held a four-button fiber-optic button box in each hand and responded to each trial with one of two buttons per hand colored in either red, green, blue, or yellow ink.www.fmrib.ox.ac.uk). The first 7 volumes were discarded to ensure scanner intensity stabilization, followed by motion correction using a rigid-body translation and rotation algorithm (MCFLIRT) and extraction of brain tissue (BET). Next, the three functional runs were concatenated. Within FMRIB Easy Analysis Tool (FEAT), the 4D concatenated images were corrected for differences in slice timing, were spatially smoothed using an 8 mm full-width half-maximum (FWHM) Gaussian kernel, and were pre-whitened with FMRIB's Improved Linear Model (FILM). Run constants and linear trends were modeled within each GLM to account for differences in overall intensity between runs and to remove low-frequency scanner drift.To prepare the data for statistical analyses, a series of image preprocessing steps were performed using FSL tools , incongruent (I), neutral (N)) were modeled by convolving a double-gamma response function with the onsets of each initial correct trial in a string of correct trials as an epoch. Additionally, three separate regressors were modeled to account for incorrect (error) trials within each block type. In order to ensure that blocked effects were independent of these error trials, each blocked regressor was orthogonalized with respect to the corresponding error regressor. As described in more detail in the i), congruent trials (c), neutral trials within congruent blocks (nc), neutral trials (n), neutral trials within neutral blocks (nn), and incorrect trials (e). For each regressor, a double-gamma response function was convolved with the onset of each trial. The contrast of i - ni trials  was explored for the purposes of the present study since this contrast reflects the greatest difference in transient demand on executive control.To explore event-related effects, seven regressors corresponding to separate trial types were modeled in a single GLM: incongruent trials (i), neutral trials within incongruent blocks (nt-tests.FMRIB's Improved Linear Model (FILM) was used to separately compute the blocked and event-related GLMs for individual participants. Each participant's data was registered to the Montreal Neurological Institute (MNI) stereotaxic template using FMRIB's Linear Image Registration Tool (FLIRT) two-step process. Higher-level, group analyses for each contrast of interest (i.e. I block parameter estimate – N block parameter estimate) were computed using FMRIB's Local Analysis of Mixed Effects (FLAME), which models the within-subject variance using fixed-effects analyses and the between-subject variances using random-effects analyses. Within these higher-level GLMs, covariates of non-interest included each participant's fullscale IQ, overall error rate, and overall non-response rate. Thus, one can be reasonably confident that observed effects of age are not driven by individual differences in IQ or error rate. Within FLAME, group difference analyses  for each contrast of interest were computed using two-sample i trials) and composite scores reflecting self-report measures of cognitive/social control were performed using FSL's robust regression to minimize the impact of outliers Higher-level whole-brain correlation analyses between fMRI Stroop interference estimates (parameter estimates for the contrasts of I-N blocks and i-np<0.005 (two-tailed) and a cluster size of 103 contiguous voxels. The peak x,y,z coordinate in MNI space was extracted from each significant cluster and listed in fMRI tables, as well as the number of voxels comprising each cluster and the z-statistic corresponding to the adolescent, adult, and group difference maps separately. In some cases, a significant cluster comprised a large number of voxels and spanned distant brain regions. In such cases, the larger cluster was subjected to increasingly stricter voxel-wise thresholds and increasingly smaller cluster-wise thresholds (in accordance with AlphaSim) until it partitioned into smaller clusters. The peak coordinates from these smaller clusters are listed in the table. The anatomical description of each significant cluster was classified primarily using the Harvard-Oxford Probabilistic Structural Atlas. If a smaller cluster spanned multiple regions, all regions are listed (e.g. IFG/MFG). We omit the reporting of Brodmann areas in statistical tables since Brodmann areas can vary substantially across atlases and are often determined from a single individual as opposed to probabilistic brain template To determine appropriate voxel-wise and cluster-wise statistical thresholds for functional images, Monte Carlo simulations were performed using the AlphaSim algorithm t-tests, independent samples t-tests, correlation analyses) were calculated using two-tails.Behavioral data reported in the Note that our design is slightly different from “state-item” designs, which ensure that blocked and event-related effects are statistically independent of each other because both effects are modeled within a single GLM In our current hybrid design, it is important to note that event-related contrasts are pure measures of “transient” differences in activity since different event types are compared within blocks. Therefore, differences between events are independent of the level of sustained activation across the block. However, blocked effects are affected by both sustained and transient activation p<0.05. For this reason, we consider blocked group differences to be driven primarily by group differences in “sustained” activity and event group differences to be driven by group differences in “transient” activity.To further examine the possibility that blocked effects were confounded by transient effects, we treated areas that yielded significant adult>adolescent group differences in blocked activity as ROIs and tested whether they yielded significant differences in transient activation. None of these regions exhibited a significant group difference in event-related BOLD activity, even at a low threshold of RT Effects: paired t-test, Adolescents: t(31) = 7.44, p<0.001, Adults: t(32) = 9.82, p<0.001; Accuracy Effects: paired t-test, Adolescents: t(30) = −2.91, p<0.01, Adults: t(31) = −4.12, p<0.001). Additionally, participants were significantly slower and less accurate when responding to incongruent trials (i) than to neutral trials within incongruent blocks (ni)  = 9.59, p<0.001, Adults: t(32) = 9.31, p<0.001; Accuracy Effects: paired t-test, Adolescents: t(29) = −4.85, p<0.001, Adults: t(31) = −4.88, p<0.001).Consistent with prior studies examining the Stroop color-word task r(62) = 0.066, p = 0.61; Trial RT interference: r(62) = 0.046, p = 0.72; Blocked accuracy interference: r(62) = 0.15, p = 0.24; Trial accuracy interference: r(60) = 0.095, p = 0.46) or a quadratic effect on behavior (Blocked RT interference: F(61) = 0.66, p = 0.52; Trial RT interference: F(61) = 1.98, p = 0.15; Blocked accuracy interference: F(61) = 0.77, p = 0.47; Trial accuracy interference: F(59) = 1.61, p = 0.21). Additionally, age did not exhibit a significant linear effect on behavior within the adolescent group alone (Blocked RT interference: r(30) = 0.13, p = 0.47; Trial RT interference: r(30) = 0.27, p = 0.13; Blocked accuracy interference: r(29) = 0.28, p = 0.13; Trial accuracy interference: r(29) = 0.22, p = 0.23).No group differences in accuracy or RT were observed Table 2.r(61) = 0.26, p = 0.039; FSIQ×Trial RT interference: r(61) = 0.43, p = 0.001). As mentioned in the However, significant relationships between RT interference (but not accuracy interference) and full-scale IQ were observed such that, across the two groups, individuals with higher FSIQ exhibited larger RT interference effects  and away from the more automatic task-irrelevant processes (word reading). Even in the neutral condition where control demands are lessened compared to incongruent and congruent trials, the presence of a word compared to non-word strings interferes with the process of color naming Figure S1; Table S1). The superior parietal lobule, a region implicated in selective attention To examine the age-related neural underpinnings associated with proactive maintenance of task goals, we first performed a contrast of task blocks versus fixation baseline blocks separately for each block type  and group . For each of the three contrasts, both groups activated several frontal and parietal brain regions implicated in top-down control, including regions at or near the middle and inferior frontal gyrus, ACC, anterior inferior parietal cortex, superior parietal cortex, and precuneus. Importantly, consistent with the possibility that adolescents may be less effective at maintaining a proactive attentional set, whole-brain analyses performed at the group level revealed increased activity in adults compared to adolescents during all task blocks, notably in clusters corresponding approximately to mid and posterior dLPFC, extending into inferior frontal junction (IFJ) (Differences between Conditions: Our next objective was to examine group differences in activation between blocks. The contrast between I and N blocks isolates a number of control processes: a) increased demands to bias attention towards task-relevant processes (color information) and away from task-irrelevant processes (word reading), b) the requirement to distinguish between two sources of color information that compete for attentional priority (color information extracted from the ink color and color information extracted from word reading) as compared to just one , and c) the requirement to distinguish between conflicting semantic and stimulus-response mappings (color information extracted from word reading has a different meaning and leads to a different response than color information extracted from the ink color).p<0.005) within an 8 mm radius sphere from each cluster's peak. Post-hoc analyses revealed that though activity in many of the regions exhibited significant linear relationships with age, the relationship tended to be best fit by a quadratic function, with activation slowly increasing until approximately age 21 and decreasing thereafter . As demonstrated in Finally, Stroop facilitation effects were explored with the contrast of C versus N blocks. However, no significant group differences were observed.i trial types within I blocks. Brain regions demonstrating trial-related Stroop interference may represent the neural underpinnings of biasing attention towards task-relevant dimensions of the particular stimulus (i.e. the specific ink color), detection and resolution of interference, selection of task-relevant responses, inhibition of irrelevant prepotent responses, and/or evaluation of one's response following the appearance of the stimulus and are thus appropriately described as “reactive control” mechanisms according to the Dual Mechanisms of Control model Thus far, these results suggest that compared to adults, adolescents under-recruit a number of lateral prefrontal regions commonly implicated in proactive, sustained top-down control. Our next objective was to explore whether adolescents instead mainly rely on reactive aspects of control by comparing activity between i and ni trials within I blocks. Furthermore, only two small clusters in the right inferior frontal gyrus/frontal pole  and the right parietal operculum  exhibited linear relationships with age. However, post-hoc exploration revealed that the BOLD pattern elicited by the adolescent group was strongly dependent upon age within the adolescent group. When the adolescent group was subdivided into a group of 15 participants between the ages of 14–15 and 17 participants between the ages of 16–17 to examine if reactive control processes develop earlier in adolescence, significant differences between the two adolescent age groups were observed. Whereas the 16–17 year olds robustly activated a number of prefrontal and parietal regions typically involved in cognitive control and response inhibition or monitoring of attention  = 1.26, p = 0.21).To our surprise, we did not observe any significant differences between adults and adolescents for the event-related contrast of i vs. nention ; Table 4p<0.005 and a cluster-wise threshold of >103 contiguous voxels) or when restricting our analyses to the IFJ/pDLPFC ROI . However, within this ROI, both adolescent groups exhibited significantly reduced blocked activity compared to adults .One question that arises is whether the two adolescent groups exhibited trade-offs in their use of reactive and proactive control mechanisms, consistent with an “either-or” approach to increasing task demands. In order to explore this possibility and to mirror analyses done with respect to trial-related activity, we directly compared the two adolescent groups with respect to their blocked activity. Whereas the older adolescents exhibited more trial-related (i.e. reactive) activity in late-stage response-related regions than younger adolescents, the two adolescent groups did not differ significantly in terms of their blocked-related activity (I-N blocks), both when analyzed in a whole-brain manner (voxel-wise threshold of t-test: t(28) = −1.90; p = 0.068).These results are consistent with the idea that proactive and reactive control exhibit different developmental trajectories. In particular, our results suggest that compared to adults, the 14–15 year olds seem to show reduced BOLD activity associated with both proactive and reactive control. However, the 16–17 year olds only exhibit reductions in BOLD activity linked to proactive control. In these older adolescents, activity linked to reactive control is elevated, consistent with a compensatory reliance on reactive response-related mechanisms. Consistent with this observation, the younger adolescents, who did not employ this potential compensatory reactive mechanism, trended towards more errors than the older adolescents  and is a key component of our Cascade-of-Control model r = −0.22, p = 0.25; I-N percent signal change×Accuracy measures: partial r = 0.23, p = 0.22). In other words, the adolescents who activated their IFJ/pDLPFC more on I compared to N blocks were faster to respond and made fewer errors on I blocks than those who didn't activate this region as strongly. Considering structural studies indicating the dLPFC and inferior frontal sulcus develop last among the frontal regions For each participant, we extracted fMRI activity  in the IFJ/pDLPFC since this region exhibited consistent group differences across the blocked contrasts . Moreover, these relationships were significantly different than those observed in adolescents . These results are consistent with the idea that in adults, decreased pDLPFC/IFJ activity might reflect improved efficiency of proactive control, possibly due to more developed prefrontal mechanisms and more experience utilizing proactive control.In contrast, adults exhibited the opposite relationship with Stroop behavior, such that adults who activated their IFJ/pDLPFC to a lesser degree trended toward more successful Stroop behavior  = 1.71, p = 0.093).If blocked recruitment of IFJ/pDLPFC indexes proactive maintenance of task goals, then increased neural engagement of these regions in adolescents might be beneficial for adolescents' everyday cognitive and social behavior including planning, control of one's impulses, and resistance to peer pressure. To explore this possibility, we created a composite measure of cognitive/social control by averaging the z-scores from participants' self-report questionnaires pertaining to impulse control, planning ahead, and resistance to peer influence  = 0.44, p = 0.020), even when controlling for the effect of age on both variables  = 0.45, p = 0.020). In other words, adolescents who activate their pDLPFC to a greater degree report being better able to control their behaviors and plan ahead. A much weaker, non-significant relationship between the two variables was observed in adults (r(27) = 0.14, p = 0.47; partial r(26) = 0.13, p = 0.50). However, the correlations were not significantly different between the two age groups after Fisher's z transformation .Both Table S2). In adults, greater activity in a single cluster in the left medial PFC near BA8/9 predicted better cognitive/social control .To examine whether blocked Stroop activity (I-N activity) in regions other than the IFJ/pDLPFC exhibit significant relationships with cognitive/social control, we performed whole-brain regressions separately for adolescents and adults. The cognitive/social control composite score for each individual, along with his/her demeaned age, were included in the GLM. In addition to pDLPFC, activity in the frontal pole and bilateral inferior frontal gyrus extending into the orbitofrontal cortex predicted better cognitive/social control in adolescents  compared to fixation, B. Congruent blocks (C) compared to fixation, and C. Neutral blocks (N) compared to fixation. Red voxels indicate greater activity in adults compared to adolescents; blue voxels indicate greater activity in adolescents compared to adults. For all three contrasts, adults activated left lateral prefrontal regions  to a greater degree than adolescents. Note: A voxel-wise threshold of p<0.005 and a cluster-wise threshold of >103 contiguous voxels have been applied to the statistical maps using Monte Carlo permutation simulations (AlphaSim). Results are projected onto a surface template (Caret Software).Between-group blocked differences in the three Stroop task conditions compared to fixation. Clusters of BOLD activity demonstrating significant between-group differences are displayed for the contrasts: (TIF)Click here for additional data file.Figure S2A. In adolescents, activity in several regions was significantly positively correlated with self-report measures of cognitive/social control: a) a cluster at or near left posterior dLPFC (pDLPFC) overlaping with the a priori region (see b) bilateral clusters near inferior frontal gyrus extending into the orbitofrontal cortex and frontal pole. B. In adults, activity in a left medial PFC region near BA8/9 positively correlated with self-report measures of cognitive/social control. Note: A voxel-wise threshold of p<0.005 and a cluster-wise threshold of >103 contiguous voxels were applied to the statistical maps as calculated by Monte Carlo permutation simulations (AlphaSim). Results are projected onto a surface template (Caret Software).Whole-brain exploratory relationships between fMRI blocked Stroop activity and self-report measures of cognitive/social control. Whole-brain correlation analyses between the contrast of I-N blocks and individual self-report measures of cognitive/social control were performed separately for adolescents and adults, including and age as a covariate of non-interest. gion see Fig. 5, (TIF)Click here for additional data file.Table S1fMRI group differences for task blocks compared to fixation.(DOC)Click here for additional data file.Table S2Relationships between blocked fMRI stroop activity (I-N) and self-report measures of cognitive/social control.(DOC)Click here for additional data file.


Glycine max (soybean) ATP sulfurylase isoform 1)]. For the forward reaction (APS synthesis), initial velocity methods indicate a single-displacement mechanism. Dead-end inhibition studies with chlorate showed competitive inhibition versus sulfate and non-competitive inhibition versus APS. Initial velocity studies of the reverse reaction (ATP synthesis) demonstrate a sequential mechanism with global fitting analysis suggesting an ordered binding of substrates. ITC  showed tight binding of APS to GmATPS1. In contrast, binding of PPi (pyrophosphate) to GmATPS1 was not detected, although titration of the E•APS complex with PPi in the absence of magnesium displayed ternary complex formation. These results suggest a kinetic mechanism in which ATP and APS are the first substrates bound in the forward and reverse reactions, respectively.In plants, sulfur must be obtained from the environment and assimilated into usable forms for metabolism. ATP sulfurylase catalyses the thermodynamically unfavourable formation of a mixed phosphosulfate anhydride in APS (adenosine 5′-phosphosulfate) from ATP and sulfate as the first committed step of sulfur assimilation in plants. In contrast to the multi-functional, allosterically regulated ATP sulfurylases from bacteria, fungi and mammals, the plant enzyme functions as a mono-functional, non-allosteric homodimer. Owing to these differences, here we examine the kinetic mechanism of soybean ATP sulfurylase [GmATPS1 ( Gen pathway . Downstrate) Keq~0−7–10−9  pathway ,12. Alte pathway –15. In a in vivo .Arabidopsis thaliana , Brassica juncea (Indian mustard), Solanum tuberosum (potato) and Glycine max (soybean) and share >60% amino acid sequence identity  showed that the enzyme functions as a homodimer  [B])/(KAKB+KB [A]+[A] [B]), and random sequential, v=(Vmax [A] [B])/(α KAKB+KB [A]+KA [B]+[A] [B]), kinetic mechanisms, where v is the initial velocity, Vmax is the maximum velocity, KA and KB are the KM values for substrates A and B, respectively, and α is the interaction factor if the binding of one substrate changes the dissociation constant for the other ) (1+[I]/Ki))) or uncompetitive, v=Vmax/(1+[I]/Ki+Km/[S]) inhibition [For the inhibition studies, initial velocities were determined in reactions containing chlorate (0–0.3 mM) and varied concentrations of either ATP or Nahibition .i were prepared in the same buffer. Protein and ligand solutions were degassed at room temperature (22°C) prior to use. For each titration, 20–30 injections of 10 μl of ligand were added into sample solutions containing protein (18.5–50 μM) in the presence or absence of ligands. GmATPS1 complexed with APS was formed by incubating protein and 0.1 mM APS 30 min (4°C) followed by equilibration at 25°C before titration. Data were analysed using a one-site  binding model, as follows:iQtot is the total heat after the ith injection, V0 is the calorimetric cell volume, iMtot is the concentration of protein in the cell after the ith injection, ΔH is the corresponding enthalpy change, n is the number of nucleotide-binding sites per monomer, and K is the equilibrium-binding constant. Fitting of data was performed with Origin software.ITC  experiments were performed using a VP-ITC calorimeter . GmATPS1 was dialysed at 4°C in 25 mM HEPES, pH 7.5, 200 mM KCl, 5% glycerol. Stock solutions of APS and PPE. coli as an N-terminally hexa-histidine tagged protein and was purified by nickel-affinity and size-exclusion chromatographies [The kinetic mechanism of the physiological APS synthesis reaction of GmATPS1 was first examined using a bi substrate variation experiment. For these experiments, GmATPS1 was expressed in graphies . Three pgraphies .2SO4 was used to distinguish between the three possible mechanisms (r2=0.989) slightly better than the fit to an ordered mechanism (r2=0.980), but the interaction factor (α) for the random mechanism was 9.53, which suggests that binding of one substrate is a negative interaction that decreases the affinity for the second substrate. Also, as discussed below, dead-end inhibition studies were consistent with an ordered mechanism. Thus, Initial velocity data generated by co-varying the concentrations of ATP and Nachanisms . The pri3−) was used as a dead-end inhibitor. Chlorate has also been demonstrated in tissue culture to reduce sulfur assimilation pathway activity [P. chrysogenum [To further examine the order of substrate addition in the forward reaction, chlorate (ClOactivity  and is aysogenum . This apysogenum .3−) and carbonate (CO3−). Nitrate and carbonate were chosen because they are naturally present in the chloroplast and may regulate ATP sulfurylase activity [Ki=132±4 μM) versus ATP (Ki=103±19 μM) GmATPS1 with respect to sulfate  compared with a random sequential mechanism (r2=0.909). The fitted kinetic parameters for the reverse reaction are summarized in Using the bi substrate variation approach described for the forward reaction, the ATP synthesis or reverse reaction catalysed by GmATPS1 was examined. Initial velocity patterns for the energetically favourable reverse reaction showed intersecting lines consistent with a sequential type of mechanism . Global To determine the order of binding, and thus the kinetic mechanism, in the reverse reaction, ITC was used. Product inhibition by ATP was complicated by the use of a coupling system and the binding of oxyanions  was too weak to compete with the energetically favourable ATP synthesis reaction. Ligand-binding analysis provides a direct method for distinguishing between random versus sequential order of addition.i yielded clearly distinct results  do not accumulate [Sulfur, like nitrogen, phosphorus and potassium, is a macronutrient essential for plant growth, crop yields and resistance to pathogens –5,39. Incumulate . In contcumulate ,24–28. Scumulate ,39–42. Ti from ATP and sulfate (i because of how tightly the products inhibit the enzyme and the effect of APS on the coupling enzymes in the assay [Physiologically, ATP sulfurylase catalyses the formation of APS and PP sulfate . Kinetic sulfate . The inthe assay ,36. The i binding to GmATPS1 (i (i to the E•APS complex, titration to the complex was performed in the absence of magnesium to prevent catalysis [i titration to free enzyme, a strong heat signature was detected for formation of the binary complex (For the reverse or ATP synthesis reaction catalysed by GmATPS1, the bi substrate variation experiment agrees with the single-displacement mechanism observed in the forward reaction . In this GmATPS1 . This ap GmATPS1 . In contATPS1 (i , even thatalysis . Unlike  complex . These ri.An overall kinetic mechanism for GmATPS1, and the related plant enzymes, is now proposed . In the P. chrysogenum were consistent with an ordered addition of substrates in each reaction direction [To date, kinetic studies of the ATP sulfurylases from mammals, fungi and plants have led to two different models for binding of substrates ,36,43. Iirection . The kinirection  differ iirection . Later, irection –32. As n


Remifentanil, an ultra-short-acting opioid, is widely used for pain control during surgery. However, regular dose (RD) remifentanil exacerbates postoperative pain in a dose-dependent manner. Recent studies suggest that high-dose (HD) remifentanil offers sustained analgesia in experimental studies. We thus hypothesized that intraoperative administration of high-dose remifentanil may attenuate postoperative pain.−1min−1 (HD group) remifentanil during thyroidectomy. A visual analogue scale (VAS) was used to measure pain intensity. Mechanical pain threshold on the forearm was assessed using von Frey filaments before surgery (baseline), 2 h postoperatively and 18–24 h postoperatively. The primary outcome was to compare the difference of VAS score at different time points after operation and morphine consumption 24 h postoperatively between RD and HD groups. The second outcome was to compare the difference of mechanical pain thresholds in the forearm postoperatively between RD and the HD groups.In this prospective, randomized, double blind, controlled clinical study, sixty patients undergoing thyroidectomy (18–60 years-of-age) received an intraoperative infusion of 0.2 (RD group) or 1.2 μg kgVAS scores were lower 30 min postoperatively in the HD group  compared with the RD group  . Postoperative morphine consumption was much lower in the HD group compared with the RD group . In both groups, mechanical pain threshold was decreased 18–24 h postoperatively (2.93±0.209 Ln(g) vs. 3.454±2.072 Ln(g), p = 0.032 in RD group; 2.910±0.196 Ln(g) vs. 3.621±0.198 Ln(g), p = 0.006 in HD group, 18–24 h postoperatively vs baseline).Intraoperative administration of high-dose remifentanil decreased VAS scores and morphine consumption postoperatively. Thus, modulation of intraoperative opiates may be a simple and effective method of postoperative pain management.ClinicalTrials.gov, with the Name: Effect of Higher Doses of Remifentanil on Postoperative Pain in Patients Undergoing Thyroidectomy, and ID number: NCT01761149.This trial is registered in  However, remifentanil, like other μ-opioid receptor agonists, exacerbates postoperative pain as indicated by enhanced pain sensitivity, increased postoperative pain scores, and opioid consumption during remifentanil withdrawal −1h−1) prevented the LFS-evoked spinal LTP of C-fiber evoked field potential. However, relatively low-dose remifentanil (225 μg kg−1h−1) infusion could not reverse the activated spinal LTP of C-fiber evoked field potentials. Furthermore, a 1-h infusion of HD remifentanil inhibited capsaicin-induced mechanical allodynia for more than 6 h −1min−1 remifentanil for 20 min inhibited thermal hyperalgesia in a neuropathic pain model Interestingly, an experimental study by Drdla-Schutting and colleagues revealed that brief application of HD remifentanil reversed various forms of activity-dependent long-term potentiation (LTP) at C-fiber synapses, a synaptic model of hyperalgesia The present study hypothesized that intraoperative infusion of HD remifentanil attenuated postoperative pain and aimed to test this hypothesis in the patients undergoing thyroidectomy. Here we showed that HD remifentanil decreased VAS scores and reduced morphine consumption postoperatively. However, HD remifentanil still reduced mechanical pain thresholds in the forearm, a site remote from the surgical site. Thus, the present study suggested that different mechanisms may regulate the effect of remifentanil on pain sensation in intact sites relative to injured sites; and intraoperative modulation of high dose opioids may offer a convenient and effective technique for postoperative pain control.The protocol for this trial and supporting CONSORT checklist are available as supporting information; see 2 in the left central volar forearm and the visual analogue scale  was shown to the patients. A set of 10 hand-held Von Frey monofilaments calibrated to deliver an increasing force on the skin from 4 to 300 g was used for this purpose  as described previously On the day before surgery, baseline mechanical pain thresholds were measured in a delimited area of 3×3 cm−1) based on the BIS value. Once the BIS value was below 60, 0.15 mg kg−1 cisatracurium was administered to facilitate tracheal intubation. After tracheal intubation, patients were ventilated with 40% oxygen without any inhaled anesthetics. Maintenance of anesthesia was achieved with propofol only by target controlled infusion to maintain BIS values between 40 and 60.Anesthesia was induced with continuous propofol infusion and remifentanil by target-controlled infusion . Before the study began, a random-number table was constructed. For each patient, an envelope containing the group assignment was prepared, sealed, and sequentially numbered. On the day of surgery and before induction of anesthesia, an anesthesiologist not involved in the evaluation of the patient opened the patient’s envelope and prepared remifentanil syringes. The patients and the anesthesiologists involved in assessing postoperative pain, morphine consumption, VAS data collection and data analyses were unaware of any group assignment. In case of emergency, the attending anesthesiologist was allowed to break the code.The two treatment groups were as follows:−1min−1 starting from 1 minute prior to skin incision to the completion of surgery.Clinical-regular-dose remifentanil (RD): patients were administered with remifentanil at a rate of 0.2 μg kg−1min−1.High-dose remifentanil (HD): The procedure was similar to the HD group except that the dose was 1.2 μg kgIn the case of hypotension, defined by a systolic arterial pressure less than 80 mm Hg or a mean arterial pressure less than 60 mm Hg, ephedrine  was given and additional intravenous fluids were administered as needed by the anesthesiologist. If the patient’s heart rate was less than 50 beats per min, then atropine (0.3 mg) was administered to maintain a heart rate greater than 50 beats per min.All patients were transferred to the post-anesthesia care unit and extubated there. An anesthesiologist, blinded to the patients’ group assignment, evaluated the VAS score in the post-anesthesia care unit at 30, 45, 60, 75, 90, 120 min and 18–24 h postoperatively. When the patients described VAS ≥4, morphine was infused  until the VAS<4. Patient-controlled analgesia was not used because of its rarity in this clinical situation. Postoperative nausea and vomiting were prevented with ondansetron  after surgery was concluded.Postoperative mechanical pain thresholds were measured at 2 h and 18–24 h after surgery in a manner identical to that described previously. To avoid inter-rater variations, all pain measurements were performed by one trained investigator.The primary outcome was to compare the difference of VAS score at 30 min, 45 min, 60 min, 90 min, 2 h and 18–24 h after operation and cumulative morphine consumption 24 h postoperatively between the RD group and the HD group. The second outcome was to compare the difference of mechanical pain thresholds in the forearm at 2 h and 18–24 h postoperatively between the RD group and the HD group.In the preliminary study (unpublished data), VAS scores were 2.32±0.33 for the RD group and 1.44±0.45 for the HD group 30 min postoperatively. The estimated sample size was 4 patients per group with a β-risk of 80% at an α-level of 0.05 for detecting a difference in postoperative VAS at least of 0.8 at 30 min postoperatively. In contrast, baseline mechanical pain thresholds for the forearm were 3.6±0.8 Ln(g), and 3.0±0.8 Ln (g) at 24 h after surgery in the HD group  t test. Hemodynamic parameters including systolic (SBP), diastolic (DBP) and mean arterial pressure (MAP), heart rate (HR), the BIS scale, mechanical pain thresholds and VAS scores were analyzed by two-way ANOVA for inter-group comparisons. In cases of statistical significance, post hoc tests were conducted with a Bonferroni’s adjustment. The χ2 test was used to compare gender, ASA levels, number of patients requiring ephedrine or morphine, and postoperative complications . All analyses were performed using SPSS 13.0 and GraphPad Prism 5.Age, weight, height, BMI, duration of surgery, intraoperative propofol consumption and postoperative morphine consumption were analyzed with an unpaired  = 2.356, p = 0.0236). At 30 min postoperatively, VAS scores were significantly higher in the RD group  compared to the HD group  . At later time points, no significant differences were observed between VAS scores in these two groups.As shown in In the RD group, ten of 29 patients required morphine treatment, but only two of 28 patients in the HD group required morphine treatment  = 15.40, p<0.0001). Compared with baseline, mechanical pain thresholds were significantly lower at 18–24 h postoperatively in both groups (2.93±0.209 Ln (g) vs. 3.454±0.207 Ln (g), p = 0.032 in the RD group; 2.910±0.196 Ln (g) vs. 3.621±0.198 Ln (g), p = 0.006 in the HD group, 18–24 h postoperatively vs baseline, two-way ANOVA followed by Bonferroni’s test). In the HD group, mechanical pain thresholds at baseline and 2 h postoperatively did not differ significantly . However, in the RD group, mechanical pain thresholds were significantly lower 2 h postoperatively (2.910±0.203 Ln (g)) compared with baseline (3.454±0.2072 Ln (g)) . Post hoc Bonferroni’s test also showed that no significant difference in the mechanical pain threshold in the post-operative 2 h  and 18–24 h  between the RD and the HD groups.No significant difference was observed in propofol consumption, waking time or extubation time in these two groups. Patients requiring ephedrine treatment did not differ significantly between the RD and the HD group. Two patients in the HD group and one patient in the RD group reported postoperative nausea or vomiting .The present study has made two important findings. First, HD remifentanil attenuated early postoperative VAS scores and reduced morphine consumption after mildly painful surgery (thyroidectomy), indicating that intraoperative HD remifentanil exerts a persistent analgesic effect, which may be a novel strategy for postoperative pain control. Second, HD remifentanil also reduced mechanical pain thresholds of the forearm, a site remote from the surgical site, implying that different mechanisms regulate the effect of remifentanil on pain sensation in intact sites relative to injured sites.−1h−1 remifentanil was continuously infused in rats to block capsaicin-evoked hyperalgesia −1h−1 as used in this study) −1min−1) remifentanil on postoperative pain, previous studies indicated that patients receiving an intraoperative infusion of 0.05 μg kg−1min−1 remifentanil during thyroidectomy had high VAS scores 6 h postoperatively and that 70% of those patients required analgesics This HD dosage was based on a recent experimental study in which 450 μg kg−1min−1 remifentanil decreased the mechanical sensory threshold in the forearm after septorhinoplasty, suggesting increased pain sensitivity −1min−1) increased sensitivity of painful pressure stimulation In present study, both RD and HD remifentanil reduced the mechanical pain threshold in the forearm, a site distant to the surgical site. Echevarria and colleagues recently reported that 0.2 μg kg2+ concentrations −1min−1) induced mechanical hypersensitivity in the forearm are consistent with animal studies. NMDA receptor antagonists, ketamine, magnesium sulfate and propofol reduced remifentanil-induced hypersensitivity, further suggesting the involvement of the NMDA receptor in opioid-induced mechanical pain hypersensitivity Nevertheless, recent studies of spinal LTP induction by C-fiber-evoked field potential as a synaptic model of hyperalgesia to investigate the effect of HD remifentanil on hyperalgesia indicated that HD remifentanil may have opposing effects on spinal LTP induction in naïve or noxious-stimulated rats The persistent analgesic effect of HD remifentanil suggests that intraoperative infusion of HD opioids may be a novel strategy for postoperative pain control. This method may be simpler to undertake during surgery and postoperative treatment may not be required. In addition, reducing the use of other analgesics may also reduce side effects such as respiratory depression and itching. However, the development of hyperalgesia and the cost of remifentanil may limit potential clinical applications of remifentanil. Further studies are required to examine the effect of HD remifentanil on postoperative pain in other more painful surgeries such as coronary artery bypass graft. In the latter case, the potential attenuation of postoperative pain by HD remifentanil would greatly improve the quality of postoperative recovery and this has obvious clinical significance.Of note, in the present study, we used propofol for maintenance of anesthesia. Numerous studies suggest that propofol, as a NMDA receptor antagonist, may attenuate the development of acute postoperative hyperalgesia by remifentanil withdrawal, and thereby reduce postoperative morphine consumption In conclusion, the present study showed that compared to RD remifentanil, HD remifentanil attenuated postoperative pain by reducing postoperative VAS score and morphine consumption. Thus, intraoperative modulation of high dose opioids may offer a convenient and effective technique for postoperative pain control.Checklist S1Supporting CONSORT checklist.(DOC)Click here for additional data file.Protocol S1Trial Protocol in Chinese submitted to the hospital ethical committee.(DOCX)Click here for additional data file.Protocol S2English copy of trial protocol.(DOCX)Click here for additional data file.


Emerging evidence showed that common functional −31G>C polymorphism (rs9904341 G>C) in the promoter region of the survivin gene is involved in the regulation of survivin expression, thus increasing an individual’s susceptibility to gastrointestinal tract (GIT) cancer; but individually published results are inconclusive. The aim of this systematic review and meta-analysis was to derive a more precise estimation of the association between survivin −31G>C polymorphism and GIT cancer risk.A literature search of PubMed, Embase, Web of Science and CBM databases was conducted from inception through July 1st, 2012. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association.Nine case-control studies were included with a total of 2,231 GIT cancer cases and 2,287 healthy controls. The results indicated that survivin −31G>C polymorphism was associated with increased risk of GIT cancer. In the stratified analysis by cancer types, significant associations were observed between survivin −31G>C polymorphism and increased risk of colorectal and gastric cancers. However, the lack of association of survivin −31G>C polymorphism with esophageal cancer risk may be due to a lack of a sufficient number of eligible studies and the influence of different genetic and environmental factors.Results from the current meta-analysis suggests that survivin −31G>C polymorphism might increase the risk of GIT cancer, especially among gastric and colorectal cancers. Gastrointestinal tract (GIT) cancer, especially gastric, esophageal and colorectal cancers, are a global epidemiological health concern Survivin, an inhibitor of apoptosis protein (IAP), is involved in the regulation of apoptosis and in cell cycle control More than 10 common single nucleotide polymorphisms (SNPs) in the promoter region of the survivin gene have been reported, but the −31G>C polymorphism (rs9904341 G>C) is one of the most common variants. The survivin −31G>C polymorphism is a transversion mutation of G to C substitution at position −31 in the promoter region Relevant papers published before July 1st, 2012 were identified through a search in PubMed, Embase, Web of Science and CBM databases using the following terms: (“genetic polymorphism” or “polymorphism” or “SNP” or “gene mutation” or “genetic variants”) and  and . The references used in eligible articles or textbooks were also reviewed to find other potentially sources. Disagreements were resolved through discussion between the authors.Studies included in our meta-analysis have to meet the following criteria: (a) case-control study or cohort study focused on associations between survivin −31G>C polymorphism and GIT cancer susceptibility; (b) all patients diagnosed with GIT cancers should be confirmed by pathological or histological examinations; (c) published data about the size of the odds ratio (OR), and their 95% confidence interval (CI) must be sufficient. Studies were excluded when they were: (a) not a case-control study or a cohort study; (b) duplicates of previous publications; (c) based on incomplete data; (d) meta-analyses, letters, reviews or editorial articles. If more than one study by the same author using the same case series was published, either the studies with the largest sample size or the most recently published study was included. The supporting PRISMA checklist is available as supporting information; see Using a standardized form, data from published studies were extracted independently by two authors. For each study, the following characteristics and numbers were collected: the first author, year of publication, country, language, ethnicity, study design, numbers of subjects, source of cases and controls, pathological type, detecting sample, genotype method, allele and genotype frequencies, and evidence of Hardy-Weinberg equilibrium (HWE) in controls. In case of conflicting evaluations, disagreements were resolved through discussion between the authors.Two authors independently assessed the quality of papers according to modified STROBE quality score systems P-value <0.05 as statistically significant heterogeneity 2I test (ranges from 0 to 100%), which represents the proportion of inter-study variability that can be contributed to heterogeneity rather than by chance P<0.05) or 2I>50% indicated that heterogeneity among studies existed, the random effects model (DerSimonian Laird method) was conducted for meta-analysis. Otherwise, the fixed effects model (Mantel-Haenszel method) was used. To establish the effect of heterogeneity based on the results from the meta-analyses, we also performed subgroup analysis by cancer types, ethnicity, country, source of controls and genotype methods. We tested whether genotype frequencies of controls were in HWE using the 2χ test. Sensitivity was performed by omitting each study in turn to assess the quality and consistency of the results. Begger’s funnel plots were used to detect publication biases. In addition, Egger’s linear regression test which measures funnel plot asymmetry using a natural logarithm scale of OR was also used to evaluate the publication biases P values were two-sided. All analyses were calculated using STATA Version 12.0 software .The strength of the association between survivin −31G>C polymorphism and GIT cancer susceptibility was measured by ORs with 95%CIs under five genetic models, including allele model (C vs. G), dominant model (CC+GC vs. GG), recessive model (CC vs. GG+GC), homozygous model (CC vs. GG), and heterozygous model (CC vs. GC). The statistical significance of the pooled OR was examined by Z test. Between-study variations and heterogeneities were estimated using Cochran’s Q-statistic with a P>0.05). All quality scores of included studies were higher than 20 . The characteristics of the included studies are summarized in According to the inclusion criteria, 9 studies P<0.05), which might be a result of the difference in cancer types, ethnicity, country, source of controls and genotype methods, so random effects model was used. The meta-analysis results showed that survivin −31G>C polymorphism was associated with increased risk of GIT cancers under all genetic models   .P<0.001; dominant model: OR = 1.51, 95%CI: 1.22–1.88, P<0.001; recessive model: OR = 1.58, 95%CI: 1.08–2.32, P = 0.020; homozygous model: OR = 1.84, 95%CI: 1.20–2.82, P = 0.006). Furthermore, we also found significant connections between the CC genotype of survivin −31G>C polymorphism and increased risk of gastric cancer under the recessive and heterozygous genetic models   . HoweverP = 0.022; recessive model: OR = 1.57, 95%CI: 1.12–2.20, P = 0.009; homozygous model: OR = 1.66, 95%CI: 1.09–2.52, P = 0.018; heterozygous model: OR = 1.50, 95%CI: 1.11–2.02, P = 0.008). Also, we found significant associations between the C carrier (CC+GC) of survivin −31G>C polymorphism and increased risk of GIT cancer among Caucasian populations under the dominant model   . Subgrou= 0.044) .Sensitivity analysis was performed to assess the influence of each individual study on the pooled ORs by omission of individual studies. The analysis results suggested that no individual studies significantly affected the pooled ORs under any genetic models of survivin −31G>C polymorphism , indicatP = 0.966; dominant model: t = 0.01, P = 0.997; recessive model: t = 0.07, P = 0.948; homozygous model: t = 0.03, P = 0.974; heterozygous model: t = −0.04, P = 0.971).Publication biases within available research results might not be representative of all research results. Begger’s funnel plot and Egger’s linear regression test were performed to assess the publication biases of included studies. The shapes of the funnel plots did not reveal any evidence of obvious asymmetry under the dominant model . Egger’sSurvivin, a novel identified member in inhibitor of IAP family, is usually described as an apoptosis inhibitors and plays a key role in anti-apoptosis mechanism of cancer metamorphosis Due to the complex functional mechanism and regulatory roles of survivin in tumorigenesis, the relationships between survivin −31G>C polymorphism and GIT cancer susceptibility have been widely studied, however, these results were inconsistent. A clinical and genetic study suggested that the CC genotype of survivin −31G>C polymorphism might increase colorectal cancer risk among Caucasians In interpreting our results of the current meta-analysis, some limitations need to be addressed. Firstly, the sample size is still relatively small and may not provide sufficient statistical power to estimate the correlation between survivin −31G>C polymorphism and GIT cancer risk. Secondly, the selection bias may exist because of the differences in source of controls or detection samples. Thirdly, our meta-analysis was based on unadjusted ORs estimates because not all published presented adjusted ORs and if they did, the ORs were not adjusted by the same potential confounders, such as ethnicity, age, gender, geographic distribution, etc. Nevertheless, it is well acknowledged that many other factors, such as gene-gene or gene-environment interaction may affect the risk of GIT cancer. Furthermore, the present meta-analysis also includes most of the studies from Asian populations, which may not provide strong evidence of heterogeneity by ethnicity. Finally, although all cases and controls of each study were well defined with similar inclusion criteria, there may be other potential factors that were not taken into account that may have influenced our results.In spite of these limitations, our meta-analysis still has some advantages. The specific aim of this study is to update the previous meta-analyses and focus on the correlation between survivin −31G>C polymorphism and GIT cancer risk. Unlike previous meta-analyses, we find that survivin 31G>C polymorphism is associated with increased risk of gastric and colorectal cancers. It is worthwhile to mention that we have established an effective and efficient searching strategy based on computer-assisted program and manual search to find all possible and eligible studies. Through this search strategy, the quality of studies included in this meta-analysis satisfied our selection criteria. Furthermore, explicit methods for study selection, data extraction, and data analysis were well designed before initiating the calculations. Last but not least, there were no evidences of publication bias in this meta-analysis and the sensitivity analysis indicated that the results are statistically robust.In summary, this meta-analysis suggests that survivin 31G>C polymorphism may be a risk factor for developing GIT cancer, especially among gastric and colorectal cancers. However, further studies are necessary in order to warrant and validate the associations between survivin gene polymorphisms, other gene polymorphisms and GIT cancer risk.Supplement S1PRISMA Checklist.(DOC)Click here for additional data file.Supplement S2Modified STROBE quality score systems.(DOC)Click here for additional data file.


Punica granatum L., cultivars  was investigated. Total phenolic compounds were determined with the Folin–Ciocalteu colorimetric method. The results showed that the levels of total phenolic compounds changed depending on cultivars and fruit parts. In all cultivars, the highest levels of total phenolic content were obtained from the peel extracts. The total phenolic content ranged from 1775.4 to 3547.8 mg gallic acid equivalent (GAE)/L among the cultivars. However, the total phenolic content of pomegranate juice and seed extract ranged from 784.4 to 1551.5 mg GAE/L and 117.0 to 177.4 mg GAE/L, respectively. “Lefan” displayed the highest amount of the total phenolic content among the four popular cultivars tested.The total phenolic distribution of juice, peel, and seed extracts of four Turkish pomegranate,  Punica granatum L.) is an ancient fruit with exceptionally rich ethnomedical applications. The peel (pericarp) is well regarded for its astringent properties; the seeds for conferring invulnerability in combat and stimulating beauty and fertility.[Pomegranate  and frozen at –20°C. Three replicates were maintained for each analysis, each replicate indicating four pomegranate fruits. Pomegranate juice was obtained from pomegranate arils by a hand press. The peels and seeds were manually removed, sun-dried, and homogenized for phenolic extractions.*, a*L, and *b values. The chromameter describes color in three coordinates: *L, lightness, from 0 (black) to 100 (white); *a, from –60 (green) to 60 (red); and b*, from –60 (blue) to 60 (yellow). Chroma values was calculated as chroma (2+ b2a)1/2.The rind color of pomegranates was determined using a Minolta portable chromameter  which provided CIE Also, three subsamples of 10 fruits were used for measurements of horticultural attributes such as fruit weight (g), fruit length (mm), fruit diameter (mm), shape index, aril weight (g), seed weight, fruit juice yield (%), and rind thickness (mm). Total soluble solid (TSS) content was determined with a digital refractometer . The total acidity (TA) was measured by titration with 0.1 N NaOH.Five gram of each sample was homogenized in 25 mL of the 50% (v/v) ethanol/water solution. The extract was filtered through the Whatman no. 41 filter paper for the removal of peel and seed particles. Only the peel extracts were diluted 10 times using distilled water. After filtration, the concentrations of total phenolics (TP) in extracts were determined by the Folin-Ciocalteu colorimetric method. Samples P<0.05 using Duncan’s multiple range test and the system program SAS software package.[The estimation of the total phenolic content in the extracts was carried out in triplicates and differences among the means were determined for significance at  package.The results indicated that there were significant differences among the cultivars for the variables tested. The soluble solid content of analyzed cultivars varied from 13.90% to 14.97%. “Cekirdeksiz-IV” and “Katirbasi” displayed the highest TSS . In other studies, the TSS content varied from 14.4% to 16.2%, 14.1% to*,” “a*,” “b*,”“L and “C” values were recorded [L value) was obtained from “Cekirdeksiz-IV” (94.26) and “Lefan” (91.76) cultivars. The fruit color of all the cultivars ranged from greenish yellow to pinkish red. Especially the color of “Lefan” cultivar was darker red than all the other cultivars. That’s why redness indication of a* values was the highest in “Lefan” (40.06), followed by “Katirbasi” (30.39). “Lefan” had the lowest (22.37) b* values indicating the color change from yellow to blue. “Asinar” had the lowest chroma values among the other cultivars.The peel colors were measured using a colorimeter and the recorded . The higet al.[et al.[et al.[in vitro models compared to seed extracts. It is known that these activities related to phenolic compounds. Most of the phenolic materials that are present in the pomegranate peel are passed onto the juice during pressing.[The total phenolic contents of the peel, juice, and seed from pomegranate cultivars are presented in et al. found thl.[et al. who founl.[et al. identifipressing.et al.[et al.[The highest total phenolic content 3547 mg/L was found in the peel of “Lefan” followed by the peels of “Katirbasi” (3127 mg/L), “Cekirdeksiz-IV” (2537 mg/L), and “Asinar” 1775 mg/L) cultivars. The highest total phenolic content value for fruit juices (1551.5 mg/L) was found in “Lefan” followed by “Asinar” (1307.3 mg/L) and “Katirbasi” 1229.5 mg/L). There were significant statistical differences between the cultivars. Ozgen 75 mg/L c9.5 mg/L.l.[et al. It is obThe results indicated that the total phenolic contents of pomegranate vary considerably from one cultivar to another. In addition, the concentration of phenolics varied depending on different parts of the fruit. Aril color and peel of fruits were responsible for the majority of the total phenolic content of pomegranate. Cultivars with a dark red color peel and aril displayed a higher phenolic content.