BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations.Although BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods.We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component , reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs.The most parsimonious model included the effects of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.The resulting model can be used to obtain the risk of developing OvC on the basis of  It is well-established that OvC has a significant genetic component, with the risk to first-degree relatives of patients with OvC estimated to be approximately three times greater than the risk to women in the general population.BRCA1 and BRCA2 account for ∼27% of these familial cancersRAD51C, RAD51D and BRIP1 (http://www.nature.com/icogs/primer/common-variation-and-heritability-estimates-for-breast-ovarian-and-prostate-cancers/).Ovarian cancer (OvC) is the third most common gynaecological cancer  or BRCAPROBRCA1 and BRCA2 mutations but this does not reflect our understanding of OvC genetic susceptibility. As a consequence, these models may underestimate OvC risks in women without mutations in these genes. Therefore, how to counsel women with family history of OvC but without BRCA1 or BRCA2 mutations has remained a major unresolved question in clinical cancer genetics.Risk models that incorporate both BRCA1 and BRCA2 mutations and model the residual familial aggregation to OvC. The explicit effects of 17 common OvC susceptibility alleles, identified through genome-wide association studies (GWAS), were then incorporated into the algorithm. We finally considered the implications of our risk prediction model for OvC risk stratification in the general population and its use in OvC prevention.We have used data from a large, population-based series of cases diagnosed with OvC, the Studies of Epidemiology and Risk factors in Cancer Heredity (SEARCH), and segregation analysis methods to develop genetic models for OvC that incorporate the effects of We used data on 1548 OvC cases (probands) recruited between 1999 and 2010, along with information on their first-degree and second-degree relatives ascertained through an epidemiological questionnaire. The probands were drawn from SEARCH, a large population-based study with cases ascertained through the Eastern Cancer Registration and Information Centre.BRCA1 and BRCA2 mutations as part of a separate project to evaluate the contribution of rare, high-risk and moderate-risk variants to overall OvC risk in the general population.BRCA1 and BRCA2 mutation status information was available on all 1548 probands. The following alterations were considered pathogenic: protein-truncating insertion/deletion variants, nonsense mutations, consensus splice-site variants and missense variants with reported damaging effect on protein function. For the purpose of our analysis, BRCA1 and BRCA2 mutation status were both recorded simply as mutation-positive or negative, with no distinction between different mutation types by location or functional effect.SEARCH OvC probands were screened for BRCA1 and BRCA2 mutations on OvC risk. Female family members were followed from birth until the first of OvC diagnosis age, age at questionnaire, death age or age 80. We also considered breast cancer occurrence, but individuals were continued to be followed up for OvC after a breast cancer diagnosis in the analysis. Data on risk-reducing surgeries were not available in relatives of probands, and we were therefore unable to censor at these events. However, since this is a population-based study in which women with OvC diagnosis were recruited soon after diagnosis, and participants were not aware of their mutation status at the time of recruitment, we do not expect a high prevalence of risk-reducing surgeries at the time of pedigree collection.Complex segregation analysis was used to fit genetic models to the occurrence of OvC in families, incorporating the explicit effects of BRCA1 and BRCA2 mutations and to take account of changes in cancer incidences over time, the OvC incidence for a female i was assumed to depend on the underlying genetic effects through a model of the formBRCA1/BRCA2 mutation status g, for age t and cohort k; Mi is the logarithm of the relative risk associated with a third hypothetical major gene and Pi is the polygenic component. Pi is assumed to have a normal distribution with variance σ2 and mean zero and is approximated by the hypergeometric distribution to make it amenable to ‘peeling’.BRCA1 and BRCA2 mutation carriers in the SEARCH dataset was too small to obtain reliable cancer risk estimates for mutation carriers, we also used external estimates of the OvC and breast cancer relative risks for BRCA1 and BRCA2 carriers relative to population incidences, based on some of the largest studies available.BRCA1 and BRCA2 OvC and breast cancer incidences over all possible genetic effects in the model were fixed. The cohort-specific baseline incidences for BRCA1 and BRCA2 carriers were obtained for each cohort separately by constraining the average incidences over all possible genetic effects to agree with the external estimates.BRCA1, BRCA2, other major gene and polygenic effects to agree with the population incidences .To incorporate the effects of BRCA1 and BRCA2 mutations are also associated with increased breast cancer risks,BRCA1 and BRCA2 mutations.Since BRCA1 and BRCA2 effects, and models that additionally included a dominant, recessive or co-dominant hypothetical major gene and/or a polygenic component.In our analyses, we considered models with just the i is the phenotype of the proband. A sensitivity parameter was introduced, giving the probability of detecting a mutation if one existed, to take account of the fact that mutation screening methods used cannot detect large rearrangements in BRCA1 and BRCA2.BRCA1 and BRCA2 was used in all models, but additional sensitivity analyses were performed.All the families used in the analysis consisted of women ascertained on the basis of OvC. Thus, to adjust for ascertainment bias,BRCA1 and BRCA2 effects using likelihood ratio tests (LRTs). Further LRTs were used to test for differences between the fit of nested models and the Akaike information criterion (AIC) equal to −2(log-likelihood – no. of parameters) was used to compare non-nested models.Maximum-likelihood estimates of the gene frequencies, polygenic standard deviation and the log relative risk for the hypothetical major gene were calculated using pedigree analysis software MENDEL.BRCA1 and BRCA2 mutation carrier frequencies and the risk of developing OvC in the future using the methods previously described in ref [2 goodness-of-fit tests that compared the observed and expected number of mutations and used these as an indicator of the model fit to the data. The expected number of mutation carriers was computed as the sum of the predicted BRCA1 and BRCA2 carrier probabilities across all SEARCH families.We used each of the models fitted to predict d in ref . The preWe used the most parsimonious model to estimate risk of developing OvC for a 50-year-old woman to demonstrate the possible clinical implications for different scenarios of BRCA1 and BRCA2 carrier status and extent of family history. The results were compared with the corresponding predictions from the current BOADICEA model.i for each individual was divided into two parts for this purpose: a known-variant polygenic component Pk,i reflecting the polygenic risk score (PRS) due to 17 SNPs known to be associated with OvCU,i. The two components were assumed to be independent and normally distributed with mean 0 and variance We extended the most parsimonious model to also incorporate the explicit effects of the known common OvC susceptibility alleles following the methodology already published in the context of prostate cancer.BRCA1. However, as there are thought to be many as yet undiscovered common variants and their effects are assumed to be additive on the logarithmic scale a woman with a high polygenic load is likely to have a greatly increased risk of OvC compared with someone with a low polygenic load. Being able to distinguish between high-risk and low-risk individuals in the population could be a valuable tool in clinical practice. Therefore, we considered the potential for risk prediction based both on known common variants and the total hypothesised polygenotype. We followed a similar approach to the methods described in ref. [The OvC risk associated with any individual common genetic variant is very small compared with rare variants like  in ref.   provided significantly better fit than the model that included only BRCA1 and BRCA2 (p <2.8×10−5). The worst-fitting model for the residual familial aggregation of OvC, other than BRCA1 and BRCA2, was the major recessive and the most parsimonious was the polygenic model, with AICs of 5772.244 and 5764.372, respectively. Although the mixed models of inheritance all had slightly larger log-likelihoods, they did not improve the fit significantly over the model with only a polygenic component in addition to the BRCA1 and BRCA2 effects . In all models that included a hypothetical third major gene, the relative risk for the susceptible women was very high (ranging between ∼54 and ∼122). The estimated population allele frequency for BRCA1 and BRCA2 under the polygenic model were 0.08% (95% CI 0.06% to 0.11%) and 0.26% (95% CI 0.002% to 0.33%), respectively, with a SD of the polygenic component of 1.43 (95% CI 1.1 to 1.86).The results for the seven models that incorporate the explicit effects of BRCA1 and BRCA2 mutation carriers computed under each of the models are displayed in BRCA2 mutation carriers and there was a slight improvement in accuracy of BRCA1 number under the mixed models. In comparison, under the current implementation of BOADICEA, the predicted BRCA1 numbers were very close to the observed values but the number of BRCA2 carriers was substantially underpredicted .The expected numbers of Similarly, when computing the expected number of families with a mother, a sister or mother and sister diagnosed with OvC, the predicted numbers were closer to that observed for the polygenic and mixed models of inheritance (see online supplementary table S3).We estimated the probabilities of developing OvC for a 50-year-old woman born in 1940, with the following family histories: (i) no information on relatives; (ii) having a mother and sister cancer free at ages 65 and 50; (iii) mother and sister diagnosed with OvC at ages 65 and 50; and (iv) and (v) with both combinations of one diagnosed and one cancer free at these same ages. We compared these estimates with the risk estimates from the current version of BOADICEA.BRCA1 or BRCA2 mutation. Under the best-fitting model, the risk of OvC increases with increasing number of relatives diagnosed with OvC. In contrast, the corresponding predictions under BOADICEA remain the same under all assumptions about family history. Similar patterns are observed when the index female is assumed to carry a BRCA1, or a BRCA2 mutation, where the risks in mutation carriers also depend on the exact family history information (see online supplementary figures S1 and S2). Under BOADICEA, the risks in mutation carriers are not modified by family history and are all very close to the corresponding risks predicted by our polygenic model algorithm for a women with no family history information.BRCA1 carriers, BRCA2 carriers and non-carriers, each observed SNP profile was translated into a PRS. This PRS was assumed to have a centralised normal distribution with a variance of 0.0915, explaining about 4.5% of the total polygenic variance in our model.The loci, minor allele frequencies and ORs for the 17 SNPs considered are displayed in online supplementary table S2. Under the assumptions that the effects of the SNPs on OvC are all mutually independent and the same for -BRCA1/2 mutation carrier, conditional on known PRS and family history of OvC, are shown in The lifetime risks of OvC to a 20-year-old nonExamples of age-specific risks for a 50-year-old woman at the 5th and 95th percentiles of the PRS and by different family history assumptions are shown in online supplementary figures S3–S5For a polygenic log-risk with the SD of 1.434, estimated under the best-fitting segregation analysis model, and assuming a baseline population OvC risk of 0.02 by age 80, the half of the population at higher risk accounts for 92% of all OvCs. In BRCA1 and BRCA2 mutations, family history and several newly established common OvC susceptibility alleles using data from a population-based study of OvC cases in the UK. Our model accounts for the familial aggregation of OvC and helps inform a major unresolved clinical question on how to counsel women with family history of OvC but without BRCA1 or BRCA2 mutations.We used complex segregation analysis to develop a risk-prediction model for familial OvC that incorporates the effects of BRCA1 and BRCA2 mutations together with a polygenic component. This suggests that most of the familial aggregation not accounted for by BRCA1 and BRCA2 consists of the effects of a large number of genetic variants, each having small contributions to the OvC familial risk. This is in line with results from recent OvC GWASThe most parsimonious model included the effects of BRCA1 and BRCA2 mutations were based on 10-fold smaller sample set of high-risk OvC families and did not investigate polygenic models for the residual familial aggregation of OvC. In contrast to the present study, those studies found no significant evidence of a third high-penetrance gene in addition to BRCA1 and BRCA2. The difference could be explained primarily by the much lower power of those analyses due the smaller sample size but also due to the fact the ascertainment adjustment involved conditioning on all family phenotypes that imposed a much greater penalty in comparison to the present analysis that used families selected only on the OvC status of the index case.Previous OvC segregation analysesBRCA1 and BRCA2 mutation frequencies were estimated to be 0.00079 and 0.0026, respectively, corresponding to a carrier frequency of 1 in 630 population for BRCA1 and 1 in 195 population for BRCA2. These were higher than the BOADICEA estimates of 0.0006 for BRCA1 and 0.001 for BRCA2,BRCA2 . This was also reflected in the significant underprediction of BRCA2 mutations under the BOADICEA model in the current dataset. This difference between the studies is probably partly due to the data sources and differences in the mutation screening techniques. The 2785 families used to fit the BOADICEA algorithm were ascertained primarily through population-based patients with breast cancer. This source of difference would be in line with the fact that BOADICEA was found to predict BRCA1 and BRCA2 mutations and breast cancer risk well in independent datasets of families with breast cancer.BRCA2. An alternative explanation could be a differential response rate for participating in the present study between mutation carriers and non-carriers. BRCA1 and BRCA2 mutations have both been associated with improved short-term OvC survival. In particular, BRCA2 mutation carriers have been reported to have a better prognosis.Under the best-fitting model, the In the long term, we expect that these differences will be resolved by fitting a single algorithm to all available data that models comprehensively both the genetic susceptibility to breast cancer and OvC. However, at this stage this is not feasible based on current technologies due to computational complexities . The current approach aims to develop separate algorithms for the susceptibility to breast cancer and OvC  that individually incorporate the explicit effects of all observed and unobserved genetic variants such that we obtain accurate risks of each cancer. Validation studies in independent datasets will determine the most appropriate model for use in each context. As technologies evolve over time, in the long term we expect to synthesise the models into a single algorithm.BRCA1 mutation frequency was estimated to be 0.083% and BRCA2 mutation frequency was 0.27% with a polygenic SD of 1.46). Therefore, our results were not sensitive to these assumptions.In our analyses, we took account of OvCs occurring after a breast cancer diagnosis, assuming the OvC incidence remains the same before and after the breast cancer diagnosis. Repeating the analysis but censoring at the first cancer yielded similar results .BRCA1 and BRCA2 mutations to model genetic susceptibility to OvC. As a result, under BOADICEA and BRCAPRO, OvC risks are determined only by the BRCA1 and BRCA2 mutation status, no matter what their family history is. Three quarters of OvC familial relative risk is not accounted for by BRCA1 and BRCA2 mutations;Under our models, the probabilities of developing OvC increase with the number of OvCs in relatives, while under BOADICEABRCA1 or BRCA2 mutation and non-carriers. This assumption is supported by recent studiesBRCA1 and BRCA2 carriers and non-carriers.BRCA1-specific or BRCA2-specific modifiers of risk, it should be possible to extend the present model to allow for this level of complexity.In all models incorporating a polygenic component or known SNPs, the effects were assumed to be the same for carriers of a RAD51C, RAD51D and BRIP1.Although we have incorporated the explicit effects of the common low-risk alleles, future efforts should focus on incorporating the explicit effects of other intermediate risk OvC susceptibility variants such as We also used our models to investigate the possible implications for OvC risk stratification in the population. Using the parameters from the polygenic model, we estimate that 50% of OvCs occur within 7.7% of the population at highest risk. Meanwhile, half of the population at lower risk is forecast to contain only 1 in 13 cancer cases. Targeting the 10% at highest polygenic risk for preventative measures or excluding the low-risk half could therefore lead to a much more efficient distribution of resources. However, to achieve this will require that we identify all the genetic factors that contribute to polygenic inheritance. The almost flat curve in BRCA1 or BRCA2 mutations. This would be helpful to both BRCA1 and BRCA2 carriers and non-carriers while making decisions regarding clinical interventions following counselling. Probabilities of developing OvC based on family history, BRCA1, BRCA2 mutation status and/or polygenic risk could be used to assess the risk to an individual and to discriminate between high-risk and low-risk individuals, which may in time prove useful for targeting appropriate interventions.Our model can be used in the genetic counselling process of women with family history of OvC as well as for counselling women both with and without RAD51C, RAD51D and BRIP1 that are known to be associated with OvC risk,Although the mutation carrier probability algorithms produced very accurate estimates of the number of carriers in the SEARCH data, an external validation is needed to establish the performance of the model in independent datasets and to assess the model performance in predicting OvC risk in prospective studies. Future plans to extend the models include the addition of lifestyle and reproductive factors such as parity, breast feeding and oral contraceptive use,


Petunia hybrida transports strigolactones (SLs) from the root tip to the plant shoot but also outside to the rhizosphere, where SLs are the main attractants to mycorrhizal fungi. Last but not least, ABCG36 and ABCG37 possibly play a dual role in coumarine and IBA transport.Plant hormones (phytohormones) integrate endogenous and exogenous signals thus synchronizing plant growth with environmental and developmental changes. Similar to animals, phytohormones have distinct source and target tissues, hence controlled transport and focused targeting are required for their functions. Many evidences accumulated in the last years about the regulation of long-distance and directional transport of phytohormones. ATP-binding cassette (ABC) transporters turned out to play major roles in routing phytohormones not only in the plant body but also towards the outer environment. The ABCG-type proteins ABCG25 and ABCG40 are high affinity abscisic acid (ABA) transporters. ABCG14 is highly co-expressed with cytokinin biosynthesis and is the major root-to-shoot cytokinin transporter. Pleiotropic drug resistance1 (PDR1) from  Plant hormones (phytohormones) orchestrate plant growth and development in response to endogenous cues and environmental changes. For most phytohormones the importance of transport has been presented or suggested, compatibly with their role of message carriers throughout the plant body. As their animal counterparts, most phytohormones need to be delivered to target tissues far from their biosynthetic sources. The first evidence for a long-distance and directional transport was presented for auxin by Went in 1928 . Physiolde novo in the vasculature can be released rapidly to trigger stomatal closure [+-ATPase and a concomitant increase in the apoplastic pH and hence shifting the ratio of the protonated towards the unprotonated, non-diffusible ABA [ABA is known as the hormone playing a main role in biotic and abiotic stress response such as pathogens, salinity, temperature, wounding and water shortage . During  closure . Free AB closure ,18. Furt closure . Althougible ABA . Such a atabcg25 mutant seeds was inhibited by externally applied ABA stronger than the corresponding wild-type. AtABCG25 localizes to the plasma membrane and is mainly expressed in the vasculature of roots and fruits, but only very weakly in leaves. Additionally it is induced by exogenous ABA treatment in the shoot vasculature. Therefore, AtABCG25 expression strongly overlaps with ABA biosynthetic enzymes. Indeed, transport experiments using vesicles isolated from insect cells expressing AtABCG25 revealed that AtABCG25 is a high affinity ABA exporter exhibiting a strong substrate preference for the natural (+)-ABA. Further investigations showed that overexpressing AtABCG25 caused an increase in leaf temperature, indicating a decreased transpiration, which is in line with the role of AtABCG25 as ABA efflux pump and supply of de novo synthesized ABA.Two reports on ABA transporters were published simultaneously ,22. Bothatabcg40 was impaired in stomatal movement after treatment with exogenous ABA. Using a thermographic camera, the authors could show that leaves of atabcg40 plants heated up much slower when exposed to ABA, indicating a higher transpiration rate in response to ABA and osmotic stress. Expression of AtABCG40 in yeast and tobacco Bright Yellow-2 (BY-2) cells revealed that AtABCG40 exhibits a high-affinity and stereospecific ABA uptake activity. This was confirmed in planta by the observation that ABA responsive genes were up-regulated much slower in atabcg40 plants than in the corresponding wild-type, when exposed to exogenous ABA.The other ABA transporter was discovered using a different approach. Based on the observation that full-size ABCGs can transport terpenoids, the authors postulated that this class of ABC transporters may also be involved in ABA transport. A screen of a large number of ABCG knockout plants revealed that AtABCG22 is expressed in the aerial part of Arabidopsis mainly in guard cells and localizes to the plasma membrane. Interestingly, additive phenotypes were observed for the double knockouts of atabcg22 with the open stomata mutant1/snf1-related protein kinase 2 (ost1/srk2) as well as with mutants for 9-cis epoxycarotenoid dioxygenase3 (nced3) involved in ABA synthesis. These results suggest that AtABCG22 has an ABA-related phenotype, but does not act directly as an ABA transporter. In line with this hypothesis, the authors could not see any AtABCG22-mediated ABA transport activity when expressed heterologously in various systems.Based on their initial finding that AtABCG25 acts as an ABA transporter but that the corresponding mutant exhibits only a partial ABA-related phenotype, Kuromori et al.  searched2 or exposure to ABA, Merilo et al. [atabcg22 mutant when plants were transferred to dry air. Although after 60 min stomata were as closed as in the wild-type, the closure kinetics in the mutant was strongly delayed. No significant difference was observed when plants were exposed to ABA, although a slight change in stomatal kinetics was also observed in atabcg40. The lack of clear mutant phenotypes under these conditions can be explained with the fact that (i) AtABCG25 is an ABA exporter and therefore spraying ABA will not significantly change the AtABCG25-dependent apoplastic ABA content in the mutant; (ii) spraying will very probably not affect apoplastic pH and hence mainly result in increasing ABA diffusion; (iii) the function of AtABCG22 remains elusive and cannot be deduced from the experiments performed in this study. Furthermore, it should be mentioned that it is likely that other ABC proteins act as ABA transporters and that members of other transporter families, such as ABA-importing transporter1 (AIT1)/nitrate transporters (NRTs) and multidrug and toxin efflux transporters (MATEs) have also been reported to act as ABA transporters [To investigate the role of the different ABA transporters and additional ABA related genes in respect to a sudden reduction in high humidity, increase in COo et al.  performesporters .Arabidopsis, it has been well-documented that germination is inhibited by a continuous delivery of de novo biosynthesized ABA from the endosperm towards the embryo and by uptake into the embryo [Besides stomatal control, ABA plays an important role also in germination and plant tolerance to pathogens. The ABA transporter mutants reported so far are all partially impaired in germination. For e embryo . HoweverPseudomonas syringae DC3000 (Pst DC3000) secretes coronatine, a virulence factor that forces stomata to reopen when plants are attacked by this pathogen. Furthermore, Pst DC3000 leads to an up-regulation of the ABA signalling pathway, which in turn suppresses the salicylic acid-mediated resistance. Using a genome wide association mapping, Ji et al. [atabcg16 knockouts were also more susceptible to Pst DC3000. The data indicate that AtABCG16 could be an ABA exporter; however, this has to be proven as well as the exact mechanism how the increased sensitivity to pathogens is linked to the increased sensitivity to exogenously applied ABA. Furthermore, it has to be mentioned that ABCG16 has also been reported to be required for the synthesis of an intact pollen cell wall [i et al.  identifiCytokinins are phytohormones involved in regulating a myriad of physiological and developmental processes, including shoot apical meristem formation, cambial growth, cell differentiation and nodulation to mention just a few .Arabidopsis [Lotus japonicas roots [tZ-type cytokinins promote shoot growth [Until recently it was assumed that cytokinins are synthesized in the root and transported to the shoot via the xylem ,33. Howebidopsis , as wellas roots , whereast growth .in silico analysis on ABCG transporters of unknown function. In one case, the authors first focused on mutants of ABC transporters exhibiting a strong phenotype possibly related to a specific substrate; in the other the authors investigated whether an ABC transporter was strongly co-expressed with the cytokinin synthesizing genes adenylate isopentenyltransferase3 (IPT3) and cytochrome p450 monooxygenase (CYP735A2) in the root. Consequently, in the first case the authors deduced from the mutant phenotype that AtABCG14 could be involved in cytokinin transport whereas in the second they identified AtABCG14 as a gene highly co-expressed with cytokinin biosynthesis and hence putatively involved in cytokinin transport. Mutants in AtABCG14 had much smaller rosette leaves and shorter and thinner flower stems than in the wild-type. Furthermore, the number as well as the size of xylem and phloem cells were strongly reduced in the mutant. Spraying with tZ rescued the phenotype in atabcg14 knockout plants, supporting the idea that AtABCG14 could mediate root to shoot translocation of tZ-type cytokinins. Determination of cytokinin contents in roots and shoots revealed that tZ-type cytokinins levels were strongly reduced in shoots but increased in roots of the atabcg14 mutant plants. In contrast, iP-type cytokinin levels were increased both in atabcg14 roots and shoots. Since former studies showed that cytokinins are transported within the xylem, Ko et al. [tZ-type cytokinins are transported and that the concentration in the xylem is reduced by ∼90% in atabcg14 plants. Finally, both laboratories performed loading and transport studies at the whole plant level by applying labelled tZ cytokinins to the roots. Both reports showed that abcg14 mutants transported much less of this cytokinin to the shoot, which is in line with the other results and underlines the role of AtABCG14 as a root localized transporter responsible for the allocation of tZ cytokinin to the shoot.Recently, two publications reported highly similar results about the discovery of a cytokinin transporter involved in the transfer of cytokinin from the root to the shoot ,39. In batabcg14 mutant root exhibited growth retardation. This result shows that the allocation of tZ-type cytokinins by AtABCG14 to the shoot is a prerequisite for the correct development of this organ. The discrepancies between the two observations is very probably due to the fact that if a wild-type shoot is grafted on biosynthesis mutant, iP-type cytokinins are transported via the phloem to the root, where they are converted into tZ-type cytokinins and reallocated to the shoots via AtABCG14.Former studies using grafting experiments of multiple biosynthesis mutants concluded that transport of cytokinins from the root to the shoot is not required for shoot development. In contrast, the studies on AtABCG14 show that the wild-type shoot grafted on the Several questions are still open regarding long-distance transport of cytokinins. It is likely that AtABCG14 is the major transporter for the transfer of cytokinins from the root to the shoot, but it cannot be excluded that one or several other transporters participate in this process. A more striking question is how cytokinins are loaded to the phloem to be transferred to the root. This transport process has not only been shown to be important for the development of roots but also for the correct establishment of nodules in nitrogen fixing plants .Arabidopsis thaliana indicated that SLs are synthesized both in shoots and roots by shared players: two carotenoid cleavage dioxygenases  and a plant-dependent number of cytochrome P450 mono oxygenases (more axillary growth1 (MAX1) and MAX1-like). However, when wild-type roots were grafted on a SL biosynthesis mutant shoots, the root-stock could supply SL to the scion and complement for the mutant phenotype. From these studies it was deduced that SLs have to follow two transport routes: on one side they have to be exuded from the roots to induce hyphal branching, on the other they are transported shoot wards to regulate the shoot architecture. Based on the observations that ABCG transporters were shown to transport terpenoids [Petunia hybrida as a model system they screened for root-expressed ABCGs that are up-regulated by phosphate starvation, a condition known to induce SL excretion [PDR1, fulfilled these requirements and was further analysed.SLs have been originally characterized as germination stimulants of parasitic weeds  and for rpenoids  and thatrpenoids , Kretzscrpenoids  hypothesxcretion  and by Gdefective Transposable element petunia hybrida 1 (dTph1) [PDR1 and showed that this mutant released less orobanchol, the most abundant SL synthetized in petunia, whereas the content within the root was not altered. Furthermore, root exudates collected from pdr1 mutant plants were much less efficient in inducing hyphal branching and germination of the parasitic weed orobanchae. In order to test whether PDR1 could be indeed a SL exporter, the authors expressed petunia PDR1 in the far-related species A. thaliana, a plant exhibiting a low endogenous SL-exudation activity [Arabidopsis plants overexpressing PDR1 were more tolerant to supplements of the synthetic SL GR24 and when pre-loaded with radioactively-labelled GR24 excreted by far more of this SL than the corresponding wild -type. These results confirmed that petunia PDR1 was a SL transporter. Above ground, pdr1 petunia mutants exhibited a stronger shoot lateral branching than the corresponding wild-type, although the phenotype was clearly weaker than what observed for biosynthesis mutants. Similar observations as shown in Kretzschmar et al. [NtPDR6 resulted in plants with a bushy phenotype.Kretzschmar et al.  showed t (dTph1) , the autactivity  and wherr et al.  were recr et al.  for tobaPDR1 was co-expressed with the SL biosynthetic gene decreased apical dominance1 (DAD1/CCD8) and it was localized at the apical membrane of root hypodermal cells, presumably mediating the shootward transport of SL. Above the root tip in HPCs, PDR1 was present in the outer-lateral plasma membrane, compatible with its postulated function as SL exporter from root to soil. Transport studies using radiolabelled GR24 supported the localization studies, since the pdr1 mutant displayed impaired transport of SLs out of the root tip to the shoot as well as into the rhizosphere. Interestingly, the biosynthesis gene DAD1 was strongly down-regulated in the root tips of pdr1 mutant plants, indicating a feedback regulation and cross-talk between SL biosynthesis and transport. Furthermore, in plants overexpressing PDR1, the protein levels of the auxin carrier PIN1 were strongly down-regulated in the stele of the root tip whereas the auxin carrier PIN2 was not affected or in the contrary even induced. These results showed how tightly SL and auxin transport are interlinked, as already suggested by previous works, where SLs were exogenously applied to root and shoots of Arabidopsis [A recent study aimed to investigate in more detail how SLs are transported from their site of synthesis in the root tip shootwards and into the soil. To answer this question Sasse et al.  producedbidopsis –57.polar auxin transport inhibitor sensitive1 (pis-1) mutation and found that the associated gene was AtABCG37. The authors showed that the pis-1 mutant is also sensitive towards natural auxinic compounds such as indole 3-butyric acid (IBA) and that this ABC transporter is localized in the outer-lateral plasma membrane of root epidermal cells. Since a similar localization had been reported for the close homologue AtABCG36 [Saccharomyces cerevisae or HeLa cells expressing this ABC transporter retained more.In order to identify components of auxin homoeostasis Fujita and Syono  performeAtABCG36 , the autAtABCG37 provided convincing evidence that this ABC transporter is also important for iron nutrition [AtABCG37 may be involved in the excretion of phenolic compounds. Indeed, Fourcroy et al. [AtABCG37 was required for the secretion of coumarine compounds that can complex iron and hence supply iron to plants under conditions of scarce iron availability.Two later studies with utrition ,62. Basey et al.  could shIt should be considered that a great variety of microorganisms is associated with the plant root surface and that auxinic compounds may act as signal- or precursors of signal molecules for these organisms too . TherefoA targeted analysis of IBA in root exudates will reveal whether this compound is found in root exudates.In this review we concentrated on transport processes mediated by ABCG proteins. However, it should be mentioned that, for ABA as well as for cytokinin, transporters of other protein families have been also shown to mediate their transport. In the case of ABA, NRTs have been identified as ABA importers. NRT1.2 knockout plants exhibit a higher sensitive towards ABA in germination and inflorescence stomata closure . A MATE Arabidopsis harbours 21 and 8 members of PUPs and ENTs respectively, an extended study of these groups of transporters might reveal other cytokinin transporters.In the case of cytokinins, purine permeases (PUPs) and equilibrative nucleoside transporters (ENTs) have been shown to mediate cytokinin uptake into cells. Since AtPUP1 is highly expressed in the epithelium of hydathodes and stigma surface of siliques, AtPUP1 was suggested to be involved in the import of cytokinins from xylem sap into the cells . In contWe predict that there will be many more discoveries of plant hormone transporters belonging to ABC families and other transporter families. Furthermore, we predict that scientists will find interactions between hormone transporters influencing each other's activity and expression, thus forming a complex network of cross-talks.


We studied the evolution of information-seeking networks over a 2-year period during which an organization-wide intervention was implemented to promote evidence-informed decision-making (EIDM) in three public health units in Ontario, Canada. We tested whether engagement of staff in the intervention and their EIDM behavior were associated with being chosen as information source and how the trend of inter-divisional communications and the dominance of experts evolved over time.Local managers at each health unit selected a group of staff to get engage in Knowledge Broker-led workshops and development of evidence summaries to address local public health problems. The staff were invited to answer three online surveys  including name generator questions eliciting the list of the staff they would turn to for help integrating research evidence into practice. We used stochastic actor-oriented modeling to study the evolution of networks. We tested the effect of engagement in the intervention, EIDM behavior scores, organizational divisions, and structural dynamics of social networks on the tendency of staff to select information sources, and the change in its trend between year 1 and year 2 of follow-up.In all the three health units, and especially in the two units with higher levels of engagement in the intervention, the network evolved towards a more centralized structure, with an increasing significance of already central staff. The staff showed greater tendencies to seek information from peers with higher EIDM behavior scores. In the public health unit that had highest engagement and stronger leadership support, the engaged staff became more central. In all public health units, the engaged staff showed an increasing tendency towards forming clusters. The staff in the three public health units showed a tendency towards limiting their connections within their divisions.The longitudinal analysis provided us with a means to study the microstructural changes in public health units, clues to the sustainability of the implementation. The hierarchical transformation of networks towards experts and formation of clusters among staff who were engaged in the intervention show how implementing organizational interventions to promote EIDM may affect the knowledge flow and distribution in health care communities, which may lead to unanticipated consequences.The online version of this article (doi:10.1186/s13012-015-0355-5) contains supplementary material, which is available to authorized users. Organizational innovations can affect social relations within networks. Implementation of innovations is a complex and dynamic process by which the people who are bound within relationships with each other in a social context make adjustments to achieve desired outcomes , 2. ChanInterventions to promote evidence-informed decision-making (EIDM), like other organizational behavior change interventions, may have social consequences and affect how individuals interact with each other . While iIn a recent 2-year study, we examined the information-seeking relationships of staff of three public health units in Ontario, Canada, before and after implementing a multi-faceted and site-tailored EIDM intervention. Our goal was to understand how information-seeking networks evolved over time and how engagement in the intervention and evidence-informed behavior of staff associated with their evolving network positions and relational tendencies.Three public health units in Ontario, Canada, participated in a 22-month multi-faceted and site-tailored intervention to promote EIDM among public health professionals . Senior In each public health unit, a group of staff was recruited by local managers to get engaged in the development of summary evidence reviews to address local public health problems, while the majority of their peers had very limited contact with the intervention. Local managers chose these individuals because their roles were already (or were planning to be) associated with EIDM. The KB interacted with this “highly engaged” staff either one-on-one (through consultations) or as members of project-specific teams to develop summary evidence reviews. During and after the intervention, “highly engaged” staff continually communicated with their peers, through which they had the opportunity to share their experience and accomplishment and get recognized by the staff as EIDM experts.Little is known about the effect of organizational interventions on the dynamics of social relations among the members of the organization. Garcia  proposedWe expected that the intervention would affect the tendency of staff to seek information from experts in EIDM, the distribution of knowledge in the organization, the tendency of highly engaged staff to communicate with each other, and inter-divisional communication. Figure In organizational interventions, to promote EIDM, it is often more justifiable to train a selected group of health practitioners, rather than educating all the organization. “Highly engaged” staff were mainly chosen based on the relevance of their formal roles to EIDM. Especially in health units A and C, some of them had managerial roles with the power to promote the implementation of the intervention. And, especially in unit A, many of them were already at the center of information-seeking networks and were recognized by many in the health unit as experts in EIDM , 13. ThiSo, we hypothesized that the highly engaged subgroup would gradually take a more central position in the social network over time, as noted below:Hypothesis H1: The tendency of staff to seek information from highly engaged peers would increase over timeIn addition to her interactions with highly engaged group, the KB also communicated with the senior management of each health unit in the development of organizational policies related to EIDM. As a result of this, and potential social influence effect of highly engaged members, we expected that the intervention would affect the attitude of the staff towards the relevance of EIDM to their practice and their engagement in EIDM activities. So, they would be more likely to consider the expertise in EIDM when choosing the information sources:Hypothesis H2: The tendency of staff to seek information from experts in EIDM would increase over timeConsequently, selective training of certain staff in the health unit might result in wider recognition of highly engaged staff as experts, thereby leading to an ever-increasing centrality of experts.The highly engaged group communicated with each other frequently through the development of summary evidence reviews. We anticipated this might result in stronger social ties and bonds of trust among the highly engaged group . So we hHypothesis H3: The tendency of highly engaged staff to seek information from each other would increase over time.As highly engaged staff feel more confident to engage in conversations regarding to EIDM, we expected to see more reciprocated interaction among a growing number of highly engaged peers  , 11. So Hypothesis H4: The tendency of highly engaged staff to reciprocate information-seeking connections would increase over time.Accessibility of the information source is another contributing factor in shaping information-seeking behaviors, which is represented in the geographical distance, timeliness of the advice, and the level of engagement the advice seeker expects .Peers in the same organizational divisions represent more of these characteristics. People prefer to turn to socially close peers who are geographically accessible and have more common interests and assignments. Empirical evidence in various contexts showed that health practitioners tend to form small local circles based on overlap in professional duties –19.We expected the intervention in this study would promote inter-divisional communications. Co-participation of staff from various divisions in training programs would facilitate inter-divisional communication , 11. In Hypothesis H5: The tendency of staff to seek information from peers in other organizational divisions would increase over time.The three public health units enrolled in the study differed in terms of demographics, organizational structure, and their capacities and policies to promote EIDM. Unit A served a large urban population (>1.5 million). The leaders of the organization were strong advocates of EIDM. At the time the study commenced, unit A had in place many trained project specialists assigned to practice-based teams, with responsibility for conducting literature reviews to address practice issues. Also, more than 100 staff members, mainly managers and project specialists, had attended a weeklong workshop on EIDM. The “highly engaged” staff frequently met at progress meetings and critical appraisal clubs to share their problems and progress with other review teams. At the end of the project, completed reviews were presented in department-wide research events and other local meetings. Based on the KB’s journals, attendance lists of educational workshops and summary evidence review teams, and data exported from the online survey, we classified 51 (8 %) of 620 staff at unit A as highly engaged in the intervention.Unit B was the largest health unit in the study, serving a large urban population area (>1.5 million). They attached health promotion consultants to specific teams to conduct literature reviews to address practice issues. The adoption of the EIDM intervention was more localized to specific organizational divisions in this unit, within which, managers identified important public health questions and assigned relevant health promotion consultants and other staff to conduct summary evidence reviews. Thirteen staff members (1.2 % of 1068) were highly engaged in the intervention.Unit C served a smaller mixed urban-rural community . At unit C, public health nurses had the responsibility for searching and applying evidence to practice, along with carrying out their daily public health duties under the supervision of program managers. Much similar to unit B, a few divisions of unit C participated in the intervention, and nurses were assigned to small groups to conduct summary evidence reviews. There were 18 highly engaged staff members (9 % of 202).The staff of three health units were invited by senior management to participate in an online survey at baseline and two follow-up assessments with yearly intervals . Participation in the study however was voluntary and confidential. The study was approved by the Hamilton Integrated Review Board (HiREB) and corresponding bodies of three health units.The staff who consented to participate in the survey answered name generator questions about their information-seeking relationships in the health unit . RespondWe used the evidence-based practice (EBP) implementation scale of Mazurek Melnyk and colleagues to assess the extent to which respondents implemented EIDM in their practice . This scIn the subgroup of participants who provided data at all three time points, we calculated basic aggregate network structural indicators at baseline and the two follow-up assessments, using UCINET 6 . We calcWe used stochastic actor-oriented modeling (SAOM) to assess the dynamics of social relations . These mThe included variables, their definitions, and their corresponding hypotheses are provided in Table We included variables corresponding to being highly engaged in the intervention (yes/no), EBP implementation scores at baseline, and the difference of EBP implementation score at follow-up 2 from baseline assessment. We tested hypothesis H1 by analyzing the tendency towards connecting to highly engaged peers and hypothesis H2, the tendency towards connecting to people based on their EIDM behavior. We used the tendency of highly engaged staff to make or maintain ties with each other to test the hypothesis H3. We tested hypothesis H4 about an increased tendency towards reciprocation of information-seeking ties among highly engaged staff by assessing the trend of changes in the reciprocity in the highly engaged group. The tendency towards forming ties with people in other divisions was used to test hypothesis H5.The value of coefficients for each independent variable in the model is the log odds ratio of the likelihood of actor A making or maintaining connections with actor B vs. actor C who have one unit difference in the independent variable, keeping all other values the same. We included a dummy variable for time, corresponding to the evolution from baseline to follow-up 1 (period 1), and the difference in the parameters between period 2 (follow-up 1 to follow-up 2) and period 1. We used the parameters at period 1 and the changes in parameters from period 1 to period 2 to test the study hypotheses.Due to basic differences in the structure of the health units in this study and the way the intervention was implemented at each site, we ran SAOM models for each health unit separately. The models were developed in Siena software (version 4.0) in R environment .At unit A, 119 staff  provided the information-seeking network data at all three rounds of assessment Table . The mosAt unit B, 133 staff  provided information-seeking data at all three rounds. The most frequent job titles held among the respondents in unit B were consultants (28 %), public health nurses (21 %), and managers (23 %). Of these respondents, 10 (8 %) staff members were highly engaged, of whom 70 % had Masters + degrees, and 70 % were health promotion consultants. Their average baseline EBP score did not differ from the rest of the respondents (10 vs. 10).At unit C, 49 staff  included in information-seeking networks at three rounds. The frequently reported job titles were nurse (49 %), public health inspector (14 %), and manager (20 %). In this unit, 15 (31 %) staff members were highly engaged, of whom 25 % had Masters + degrees, 6 (40 %) were nurses, and 4 (27 %) were managers. Their average baseline EBP score (9.6) was higher than the rest of respondents (7.7), but the difference was not statistically significant.As shown in Table Table At baseline, 37 % of non-engaged staff of unit A, 11 % of unit B, and 44 % of unit C turned to highly engaged staff for information , with a small decrease at period 2, compared to a large decrease in tendency at period 2 in the two other units (most prominent in unit C with 1.11 decrease from period 1). These findings are consistent with a rising trend of group centrality of highly engaged staff (Table Only in unit A did the staff show a significant positive tendency to make or maintain ties with highly engaged staff at period 1 (source-baseline EBP score), which was statistically significant in units A and B. At period 2, the change in the trend was larger in units C (0.05 increase) and A (0.02 increase), implying an increasing tendency to seek information from staff with higher EBP scores over time. We also observed a positive tendency in staff to make or maintain ties with others with higher improvement in their EBP implementation scores (source-EBP score change) at period 1, which was significant in all the three health units. The change at period 2 was small and positive in three units. The positive tendency towards staff with higher EBP scores at period 1 could be explained either by the effect of intervention on the social selection of staff or the already central position of those staff at baseline .At period 1, the staff had a positive tendency to make or maintain ties with others with higher baseline EBP implementation scores  and higher changes in scores (seeker-EBP score change) were more active at unit B compared to the other two units. At period 2, the staff with higher changes in EBP scores significantly improved their tendency towards being more active in unit C (0.07) and A (0.03).In all three health units, highly engaged staff showed a positive tendency to make or maintain ties with each other at period 1 (seeker x source-highly engaged). This tendency increased in period 2, especially in units A and C. These findings imply that the highly engaged staff showed an increasing tendency towards forming clusters with themselves over time.Among the highly engaged staff, the tendency towards reciprocation did not differ significantly from the rest of the network at period 1. However, in unit A, the already negative tendency in period 1 (seeker x source-highly engaged reciprocity-period 1 = −0.83) decreased even more in period 2 (seeker x source-highly engaged reciprocity-period 2 change = −1.25), but showed some increase in two other units. None of these effects were statistically significant, not supporting hypothesis H4.The staff in the three health units did not tend to make or maintain ties with other divisions, as indicated by the significant and negative values for inter-divisional connections at period 1. At period 2, the tendency showed a larger decrease in unit B (−0.39), which was consistent with a decrease in E-I index at follow-up 2 in this unit. The tendency only increased in unit C with a 0.80 increase in the coefficient, supporting hypothesis H5 about an increase in inter-divisional connections only in unit C . These findings were consistent with a larger increase in E-I index from follow-up 1 to follow-up 2 in unit C, compared to the other two units.reciprocity-period 1: 1.82). The tendency towards reciprocation decreased at period 2 in all three health units, with a larger decrease in unit C (reciprocity-period 2 change: −0.98). The positive reciprocity effect at period 1 shows an internal tendency towards reciprocation in networks, as well as the possibility of a transient increase in tendency towards reciprocation, which was followed by a decrease in tendency. It was also consistent with the observed decrease in reciprocity in all three units from follow-up 1 to follow-up 2 . The tendency increased at period 2 in all the three health units.The preferential in-degree centrality effect, that shows the tendency of actors to make or maintain ties with the staff who are already central, was significant and positive in all networks at period 1, with a larger value in unit C . These findings in presence of a decreased tendency towards making inter-divisional connections and a decrease in E-I index from follow-up 1 to follow-up 2 imply that the staff tended to maintain and strengthen the intra-divisional ties rather than turning to external experts. In unit C, at period 1, the transitivity effect was highest and 3-cycle effect was lowest among all units . At period 2, both effects decreased. This finding along with a decreased reciprocity and an increase in tendency of inter-divisional connections implies a trend in staff to make new connections  rather than closing triangles. It is also consistent with the observed pattern between follow-up 1 and follow-up 2, which shows more connections from practice-based divisions towards the supervisory/administrative division.In all three health units, the staff showed larger tendencies to seek information from peers whose behavior was more in line with EIDM principles over time. Only in unit A did the highly engaged staff become more central, supporting hypothesis H1. In all three health units, and especially in units A and C, the network evolved towards a more centralized structure, with a preferential centrality of already central staff.If the intervention to empower health practitioners through the EIDM process is adopted successfully, we expect that those engaged in the intervention will become increasingly recognized by their peers as experts in EIDM. Analyzing the evolution of networks showed that only in unit A did the highly engaged staff  become even more central over time.transactive memory principles explaining how group members retrieve and distribute knowledge to effectively improve their collective productivity [Seeking information from peers in finding and applying research evidence is a routine behavior in public health organizations  and amonuctivity . Group muctivity . ConsequThe tendency towards centralization may show that the EIDM intervention was more effective in empowering and popularizing the already known experts, rather than distributing the knowledge more evenly among staff. Bunger et al. observed a similar increase in centralization of clinicians’ advice networks around faculty experts, with a decline of local private connections among clinicians after a learning collaborative intervention, which was the result of a wider recognition of experts by the clinicians . This inThe implementers of EIDM training programs should be prepared for such social changes and plan accordingly during the design stage. The implementers should consider whether they aim to train a group of staff as experts in EIDM who will act as information sources and experts or as champions who will enhance the process of organizational change. The former objective would be achieved by choosing the highly engaged group according to their baseline expertise, the relevance of their jobs to EIDM, and also their personal interest in the training. The latter would be fulfilled by choosing individuals at the center of formal and informal social networks and in position of power within the organization.In all units, the highly engaged staff showed an increasing tendency towards forming clusters. This could be the result of the communicative nature of the intervention. The intervention provided the highly engaged staff the opportunity to meet with each other and share their experiences through progress meetings and critical appraisal clubs. Co-participation in those events might have resulted in the formation of sustainable ties.Formation of ties among staff who are comparable in terms of social and organizational status  but areFormation of strong cohesive ties among members nurtures the atmosphere of trust, support, and feedback, and is a powerful step in overcoming the social resistance and enhancing the commitment in adopting new innovations . This teThese findings imply that the implementers of EIDM interventions should actively consider developing and sustaining social networks as part of the intervention process. This could be achieved by maintaining a communicative training strategy through formation of small work groups and regular progress meetings among the participants, along with promoting informal communications.The staff in the three units showed a tendency towards limiting their connections within their divisions over time. These findings did not support hypothesis H5 about the effect of the intervention in increasing the communication between divisions. The tendency of health practitioners to seek information from socially and geographically proximate peers has been shown in different studies , 39. In This finding highlights the need for organizational strategies to maintain and reconnect newly shaped ties by facilitating continuous communication through regular meetings and conventions, developing common programs and tasks, and reducing the cost of maintaining connections by facilitating the formation of third-party linkages or common partners for pairs who bridge clusters . The reaLow response rate is the main threat to the generalizability of findings of this study. According to our analysis of the characteristics of the non-respondents, our field knowledge, and the results of qualitative interviews with the participants (in progress), many of the non-respondents were the staff who did not consider EIDM relevant to their practice (such as administrative staff). This difference biases the results of the current study to the staff of health units who deal with research evidence more frequently and are more supportive of the EIDM in general, so the conclusions about the effects of the intervention are probably optimistic.In addition, due to the lack of a parallel control group, our findings on the changes in social networks through the implementation of the intervention could be simply the result of natural tendencies in social networks through time and not the effect of the intervention per se. However, comparison of trends between two periods provides clues for the causality.In summary, we found a significant association between engagement in the intervention and improved EIDM behavior with becoming more central in information-seeking networks. The networks became more centralized around a few already central experts, leading to a more hierarchical information-seeking structure. Highly engaged staff formed clusters among themselves. The information could not promote inter-divisional communications. However, where formal EIDM professionals were not available locally (such as in unit C), the staff also turned to known external experts.An indicator of sustainable organizational change is the extent to which the desired behavior is observed in organizational routines after the innovation was introduced . Our finOur findings also confirmed that trajectories of implementing capacity building interventions are complex and far from being a linear domino reaction . Therefo


The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems. While transcriptional regulation is one of the most highly studied areas in biology, the ability to quantitatively predict gene expression from DNA sequence remains inadequate10. Knowledge of transcription factors and their cognate binding sites continues to grow and has enhanced our ability to make qualitative predictions about gene expression. For example, a number of transcription factors are now well known to be involved in differentiation of stem cells into specific cell types, leading to potentially clinically useful applications such as induced pluripotent stem cells11. Inspite of this progress, only limited quantitative predictions of gene expression are possible13. Knowledge that promoter sequences of genes encode both qualitative (e.g. when to switch a gene on and off) and quantitative properties (e.g. precise levels and noise) of gene expression is implied by the heritable nature of these attributes14. It is becoming increasingly clear that while transcription factors are critical in gene regulation, regulatory outputs are ultimately determined by co-operation between regulators in complex circuits17 and with chromatin states21. In particular, transcription factors compete for DNA binding sites with nucleosomes23. The information for nucleosome binding is largely encoded in the DNA sequence27, even thoughin vivo nucleosome occupancy is highly dynamic29. Quantitative models of gene expression, therefore, benefit from the integration of nucleosome and transcription factor binding data30.Transcription is a fundamental step in the decoding of information encoded in DNA into phenotypes. Therefore, knowledge of transcriptional regulation is crucial for understanding the natural variation of gene expression31 and sequence dependent read-depth coverage32, respectively. To overcome these limitations, approaches based on promoters fused to fluorescent reporters have been developed to generate direct, real-time measurement of promoter activity with high accuracy33. This has been applied in large libraries of synthetic bacterial promoters thereby generating new insights on combinatorial cis-regulation8. It was not until recently that the first large-scale library of naturally occurring promoters of any eukaryote fused to yellow fluorescent protein (YFP) became available30. 110 yeast ribosomal protein (RP) promoters were fused to YFP and integrated into a different strain at a fixed genomic location, hence alleviating both post-translational and genomic context related effects30. Consequently, this data set is very well poised for the computational modeling of the relationship between promoter sequence and transcription activity of a eukaryotic promoter.A key barrier to quantitative modeling of gene expression using promoter sequence has been the lack of experimental methods for accurately measuring transcript levels. DNA microarrays and RNA-seq are the most widely-used systems for measuring transcript abundance, but this measurement can reflect many effects including promoter sequence, genomic position of a gene and post-transcriptional regulation of mRNA levels by processes like mRNA degradation. In addition, microarray and RNA-seq can be affected by systematic biases arising from sequence dependent hybridization kinetics30. Participants were provided with the activities of 90 promoters and their corresponding promoter sequences and challenged to predict the activity of 53 promoters whose activities were known only to the organizers of the challenge (P = 0.002). Our approach was built upon three key propositions: i) transcription factor binding and nucleosome binding, as well as other regulatory signals are encoded in DNA27, ii) if i) is true, then explicit prior knowledge of transcription factor and nucleosome binding is not a mandatory prerequisite for prediction of promoter activity if training data is available. That is, an unbiased approach that explores the associations between DNA sequence patterns and promoter activity should be able to rediscover patterns that relate to the observed activity. To do this, we used machine learning methods to iteratively explore the association between promoter activity and DNA sequence patterns in 100 bp windows of promoter sequence. We considered sequence patterns such as k-mers (k = 1 to k = 5), homopolymer stretches, nucleosome binding and three mechanical properties of DNA . Based on iterative exploration of different machine learning models, we established that a support vector machine (SVM) was the most predictive of promoter activity based on specific sequence patterns in the 100 bp upstream of the translation start site (TrSS). Our model outperformed those which applied transcription factor binding sites of known RP promoters34, implying that other sequence patterns besides transcription factor binding sites can help in fine-tuning gene expression. Indeed, among the predictive features employed by our model were poly(dT-dA) tracts that occlude nucleosomes; these have since been applied to fine-tune gene expression beyond resolutions attainable by transcription factor site mutations38. Our study expands the understanding of sequence patterns that could potentially be useful in engineering fine-tuned gene expression.To provide a fair assessment of the relationship between promoter sequence and quantitative transcript levels, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) organized an open community challenge in 2011 . Details of promoter construction are available from Zeeviet al. 201130 and the DREAM website. Briefly, the organizers considered the promoter region as the sequence 1200 bp upstream of a gene or until the nearest gene. Each promoter was linked to a URA3 selection marker and inserted into the same fixed genomic location of a master yeast strain containing theYFP gene. In total, 110 natural RP promoter strains and 33 strains with synthetically mutated RP promoters were constructed. As a control for experimental variation, all these strains contained a control promoter (TEF2) driving the expression of red fluorescent protein (mCherry). ThemCherry,TEF2,URA3,RP promoter andYFP were all a single contiguous DNA sequence arranged in that order. Measurements of themCherry expression levels and replicates of promoters had very low variation, enabling the distinction between any two promoters with activities differing by as little as ~ 8%. The promoter activity was determined as the amount of YFP fluorescence produced during the exponential growth phase, divided by the integral of the OD during the same period. The promoter activity measures the average amount of YFP produced from each promoter, per cell, per second during the exponential phase.The training data composed of DNA sequence for 90 yeast RP promoters with known activities and a test data set of 53 promoters was downloaded from the DREAM challenge website (53 and BioMoby web-services (accessed in August 2011) imported from the Molecular Modeling and Bioinformatics Group, Barcelona, Spain54. Bendability was estimated based on trinucleotide parameters obtained from DNase I digestion and nucleosome binding data35. Deformability was based on parameters from the analysis of protein-DNA crystallography structures36. Bending stiffness was based on bending free energy using the near-neighbor model37. Nucleosome binding was based on trinucleotide preferences55.Each promoter sequence was divided into 100 bp non-overlapping windows. The full promoter sequence was considered as another window. To extract information from each of the windows, we considered the frequencies of specific sequences in k-mers (k = 1 to 5), length of homopolymeric stretches DNA, mechanical properties  and nucleosome binding. K-mer counts were performed using custom scripts. DNA mechanical properties were computed using workflows constructed in the Taverna Workbench version 2.2.056 to select feature combinations that are most predictive of promoter activity. Performance of feature combinations was tested using 5- and 10-fold cross validation.For each window, feature selection was performed using a linear regression wrapper in the WEKA machine learning toolkit version 3.456 were considered: SVM regression using sequential minimal optimization (SMO), linear regression and regression trees. Models were trained using 66% of the data and tested using 34%, and included only the features that were selected as important by the linear regression wrapper. Performance was determined using Pearson correlation between model predictions and actual promoter activities computed in R version 2.11.1. The SVM model was selected for refinement based on high performance compared to the other models.Three models implemented in the WEKA toolkitPromoter activities were not available to the participants of the challenge. We applied the ensemble of 501 SVMs built from 500 different training/test sets in which 80% of the data was used in training and 20% in testing and a single SVM validated by 66% training set and 34% testing sets. Each SVM model utilized the 24 features selected by a linear regression wrapper as most predictive of promoter activity. To predict activities of the DREAM6 test set, the 24 features were extracted from the upstream 100 bp sequence for each promoter. Predictions were then made using each of the SVM models and averaged to obtain the final predictions.P-values to evaluate the performance on the test set34. Details of the equations used for these statistics have been published separately by the DREAM6 Promoter Prediction Consortium34.Predictions from the SVM ensemble were submitted through the DREAM website to the organizers for a blinded evaluation on the test set. The DREAM organizers used four statistics and corresponding1.P-value for observing a Pearson correlation coefficient of the same magnitude or smaller than that of a given participant, a null distribution was generated by randomly sampling predictions from other teams and repeating this 10,000 times34.Pearson correlation between predicted and observed activities for each model submitted: To generate a2.P-value was then generated using a null distribution obtained from randomly sampling the predictions made by the other participants. The process was repeated 10,000 times34.Spearman correlation for participant between ranks of the predicted and actual ranks of promoter activities: A3.P-value for observing a chi-square distance metric of the same magnitude or smaller than that of a given model submission, a null distribution was generated by randomly sampling predictions from other teams and repeating this 10,000 times34.Chi-square distance metric measuring the distance between predicted and actual promoter activities: To generate a4.P-value was generated from a null distribution obtained by randomly sampling predicted ranks from other teams, repeating this 10,000 times.A rank distance metric measuring the difference in ranks between predicted ranks and actual ranks of promoter activities. AP-values34. All these scores were computed using R version 2.11.1.The overall score was defined as the product of the fouret al. 2016Raw data for 'Prediction of fine-tuned promoter activity from DNA sequence’, SiwoREADME.txt contains a description of the files.Click here for additional data file.Copyright: © 2016 Siwo G et al.2016Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).30. We first partitioned the promoter sequences into 100 bp non-overlapping windows, extracted specific DNA features from each window and considered the full promoter sequence as its own window  and another set of 53 promoters whose activity was withheld from participants (test set) window . The fea window . The per window  selectedhttps://www.synapse.org//#!Synapse:syn2820426/wiki/71013). Predictions from our team had a Spearman correlation of 0.65 , chi-squared (χ2) distance metric of 52.62 (P = 0.508) andR2 statistic measuring the difference in ranks between predicted and actual promoter activities of 35.85 (P = 0.004). TheP-values were generated from the probability of obtaining a comparable or lower performance using a null distribution in which predictions were made by randomly choosing an activity for each promoter amongst all the 21 participating teams. A combined score based on the negative logarithm (base 10) of the geometric mean of theP-values for all the 4 scores ranked our team first34  and poly(dT-dA) tracts which influence the rigidity of DNA26, thereby directly impacting nucleosome binding. Furthermore, insertion of poly(dT-dA) sequences into promoters can be used to regulate gene expression to a finer degree and at more gradual intervals than could be attained by transcription factor binding site mutations38. Some transcription factors are also highly dependent on the ability of DNA to bend43. In particular, TATA binding protein (TBP), which binds to the TATA box, is important for regulating the activity of RP promoters45. Another directly biologically relevant feature identified by the SVM was the deformability of DNA46. Promoters of low activity had more deformable DNA than those of high activity (P = 0.008). This was particularly evident at 40 to 60 bp from the TrSS when comparing the top 20 promoters with the highest versus those with the lowest activity  and poly(dA-dT) tracts . The relctivity .Finally, some of the features may affect mRNA stability, especially given their potential location downstream of the transcription start sites (TSS). Besides sequence features in the 5’UTR that are close to the TSS could affect transcription, translation and mRNA stability.Rap1, Fhl andSfp1, as well as introduction of nucleosome disfavoring sequences and random mutations. At the time of the challenge, participants were not aware of these mutations. The performance of our model on the set of natural promoters was much higher  compared to that for the mutated promoters . The prediction error was significantly less for natural promoters versus the mutated promoters  so the correlation between activity and prediction error may be a consequence of the low predictability of synthetic promoters. Thus, future models may benefit from data on activities of mutated promoters, which could enable a more accurate modeling of the impact of mutation on specific transcription factor binding sites.Understanding the biases in prediction accuracy could provide biological insights into promoter classes and allow for refinement of models. Therefore, we investigated relationships between the nature of the test promoters and the magnitude of prediction error made by our model. Among the 53 test promoters provided by the DREAM challenge, 20 were natural yeast RP promoters while 33 were variants of these promoters with specific synthetic mutations introduced. These mutations included changes in the binding sites of the TBP,30, the challenge posed was more targeted towards DNA sequence patterns that fine-tune gene expression rather than simply determine the ‘on/off’ expression status. RP transcription regulation occurs in a highly coordinated manner and is critical for growth, allowing cells to adjust their protein synthesis capacity to physiological needs48. This is especially crucial as RP gene expression accounts for 50% of transcripts produced by RNA polymerase II49 and their dysregulation leads to reduced fitness48. The yeast genome contains 137 RP genes, of which 19 encode a unique RP and 59 are duplicated. The proper functioning of ribosomes requires that all the ribosome components be expressed in equimolar concentrations50 while simultaneously remaining responsive to physiological needs52. This is potentially challenging given the copy-number differences between the RP genes because high copy number genes generally show increased expression. The regulatory mechanisms underlying this fine-tuned regulation are not known. By accurately predicting the activity of the RP genes using the promoter sequences, we demonstrate that a considerable amount of this information is encoded in the DNA sequence.The quantitative modeling of gene expression has the potential to enhance our understanding of how gene regulation is fine-tuned in natural populations and has implications for the design of predictable gene expression systems. The DREAM6 challenge data set for promoter activity prediction was a unique opportunity to evaluate the predictability of gene expression from its promoter sequence. Given that all promoters were derived from natural yeast RP promoters that are expressed in the exponential phase38. Cooperation between transcription factors or competition among them17, and with nucleosomes23, provides an additional mechanism for fine-tuned gene expression. RP promoters with high activity have not only more nucleosome disfavoring sequences but also characteristic spatial organization of the binding sites forRap1,Sfp1 andFhl130. The low performance of our model on synthetic promoters containing targeted mutations in transcription factor binding sites and nucleosome disfavoring sequences reinforces the importance of these factors. Consistent with this, the combination of our model and the mechanistically driven model involving transcription factors and nucleosome binding30 was more predictive of promoter activity34. Our findings have implications for understanding the fine-tuned regulation of RP genes and engineering desirable activity in synthetic promoters.It is intriguing that our model did not explicitly use transcription factor binding site information and focused only on the 100 bp upstream region. Some of the features identified by our model may influence transcription factor binding or nucleosomes indirectly, and could even affect mRNA translation. Transcription factors are critical for gene regulation. Their empirically identified binding sites are 6 to 8 bp, theoretically putting an upper bound on the level of regulatory flexibility that can be attained by mutating positions at these sitesThe data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Siwo G et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).F1000Research: Dataset 1. Raw data for ‘Prediction of fine-tuned promoter activity from DNA sequence’, Siwoet al. 2016,10.5256/f1000research.7485.d11351657 The authors present FIrST, an approach for predicting promoter activity from sequence, which won one of the DREAM6 challenges. FIrST is using only simple sequence features in a limited range (100 bp) upstream of the translation start site for making its predictions, which distinguishes it from several other approaches in this field. Prediction results are convincing and the method appears to be sound. However, currently the method is not described detailed enough. In addition, I have a few further major and minor concerns regarding the current version of the manuscript:In the list of features described in section "Feature extraction", some seem redundant to me. For instance, the trinucleotide parameters for bendability are just computed from the k-mers for k=3. Also nucleosome binding prediction was based on trinucleotide preference. Please explain why it may be useful to also include those 3-mer-derived features in addition to the 3-mers themselves.The description of methods in section "Machine learning model exploration" is too coarse. Please provide more detail on the SVMs, linear regression, and regression trees employed. It also remains unclear if the scales of features are normalized somehow, before their values are provided to the SVM.No details are given on the selected 3-mers and 4-mers (Table 1). Please provide a list of the specific k-mers selected by FIrST. It may also be reasonable to discuss potential biological reasons for their importance (as partly covered for TATA-boxes on page 6).Considering Fig. 3, I wondered if the difference in deformability may be related to transcription initiation. Or, stated differently, might we observe an ever clearer signal if all sequences (and their deformability profiles) would be aligned by the transcription start site (TSS) instead of the TrSS? One idea in the same direction, which could contribute to the novelty of the manuscript, would be to evaluate similar profiles  for all features found to be informative by FIrST. For instance, one could expect to see something like general fluctuations of G/C content, or the TATA-box in 4-mer profiles as a spike approx. 35 bp before the TSS. From my perspective, this might improve the novelty of the manuscripts and the interpretation of features. Major comments:The data from the DREAM6 challenge only consider a special subset of genes  and only in yeast. It is unclear if the features derived by the authors' method would also be informative for higher eukaryotes. I understand that this question cannot be finally answered from the DREAM6 data, but the authors might comment on this issue.Figure 1B remains a bit unclear. In the caption and the main text, the authors explain that they use non-overlapping 100bp sub-sequences. However, from the figure it rather seems that they consider upstream sequences of 300 bp, 200 bp and 100 bp (and the full promoter sequence) relative to the translation start site. Please clarify.In section "DREAM6 challenge data" of "Methods", the authors refer to "the sequence 1200 bp upstream of a gene", where "upstream of the translation start site" (as in the remainder of the text) would be more specific.In section "Feature extraction", the authors explain that "each promoter sequence was divided into 100 bp non-overlapping windows", while in the previous section they explain that the full 1200 bp sequences do not extend over the nearest gene. From my understanding, this may result in some of the sequences being shorter than 1200 bp, and their length might not be dividable by 100. Please explain how such cases are handled.10 of the geometric mean of the p-values was used as the overall measure. Although bot definition are equivalent with respect to the resulting ranking, I would suggest to provide one consistent definition of the overall score.At the end of section "Validation of model by DREAM6 consortium", the authors explain that "the overall score was defined as the product of the four P-values", whereas later they explain that -logFrom the manuscript it did not become fully clear if the TA-tracts  tracts in some parts of the manuscript) are tracts of poly "A or T" or tracts of poly "AT"-dinucleotides.In section "Error profile of SVM promoter activity model", the authors explain that natural promoters had (slightly) lower activity than synthetic promoters and that the prediction error of the SVM is lower for natural promoters. However, I did not get the idea, why this should explain that low activity genes had larger prediction errors.In section "Error profile of SVM promoter activity model", the authors explain that one reason why FIrST did not perform well for synthetic promoters is that most mutations had been introduced outside the 100 bp range considered by FIrST. However, this reasoning partly contradicts the claim of the authors that most of the transcriptional activity may be explained from the sequence in that 100 bp window. If this would truly be the case, mutations outside this range should have only minor effects.In the Discussion, the authors mention that TF binding motifs are 6 to 8 bp in length. While this may be true for several yeast TFs, it is not correct for eukaryotes in general and motifs may be wider than 10 bp. Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. et al., 2013), this article provides more details of the winning method and some additional analysis of the predictive model, which may lead to better understanding of the predictability of gene transcription. While its contribution is undoubtable, this article should be revised to address several issues:This article describes the winning method of the DREAM6 promoter activity prediction challenge. While a meta analysis of the competing methods participated in the challenge has already been published (MeyerThe 23 features utilized by the SVM model (as well as their coefficients in the model) is not provided explicitly in the main text nor in the supplement file. Table 1 in the main text shows that 6 trinucleotides and 12 tetranucleotides are important features, but it is nowhere to be found which tri- and tetra-nucleotides they are. For lengths of T or TA-tracts, the supplement file shows several different values, including mean, median and stdev. It is unclear which one is actually used by the SVM model. Similarly, supplement file shows 79 values for deformability and it is unknown which one is used. In the case of ranking the features by their SVM coefficients, the authors need to clarify if the feature values were normalized prior to model building, as these features are on very different scales and if not normalized the ranking of the coefficients are not very meaningful.The main conclusion in the subsection "Error profile of SVM promoter activity model" do not seem to make sense. First, promoters of low activity had larger prediction error. Then the authors stated that natural promoters had lower activity. This seems to contradict with their observation that the prediction error was significantly less for natural promoters than for mutated promoters.Major issues:Authors only mentioned that feature selection was done in WEKA with wrapper. More details need to be given. For example, what was the selection strategy used by the wrapper, e.g., exhaustive search, greedy forward search, backward search, or other types of heuristics? What is the purpose of first training 1000 SVM classifiers using 66% of data as training and 34% as testing, and then another 500 SVM classifiers using 80% as training and 20% as testing?Minor issues:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. et al. give a detailed report of their entry to the DREAM promoter activity prediction assessment, conducted in 2011. The paper describing the results of the assessment appeared in 2013 , and the entry from Siwoet al. (“FiRST”) was the top-performer overall. Meyeret al. gives few details about the specific methods, mentioning only that the FiRST entry used an SVM and did not use TF binding site motif information. Here it is clarified that FiRST is a simple method that uses only part of the sequence and the most prominent features were about nucleotide content.Siwoet al. but it could be fleshed out further to get at what is going on. My suggestions for revisions are to give more detail about the properties of the sequences used and the relationship to performance.Because it is perhaps a little eye-opening  that the best method in the assessment is so simple, this paper is an important footnote to Meyeret al. in the methods section), and that their predictions were dominated by the effect of a simple measure of G content. Siwoet al. report that they did worse at predicting the synthetically mutated promoters . In Meyeret al., adding tf binding information to FiRST improved performance.FiRST predicts from only the 100 bases of sequence upstream of the translation start  are not in the 100 bp window used. That is, because in most cases these synthetic sequences were (as I understand it) identical in features to other examples while having different activities, for the purposes of FiRST, they could only introduce prediction errors. In light of this fact the rest of the speculation about why performance varied in this way seems extraneous.The authors mention this, but the most important reason that FiRST does poorly at predicting the synthetic mutations seems to be that most of the mutations , and the prediction error in each case. How well does one predict using G content alone? This might all be reconstructed from the data supplement helpfully provided, but the authors should consider providing the analysis. It also seems reasonable to ask for more details about the performance of other sequence windows.The main other missing piece from this paper is any discussion or evidence that the method works beyond the narrow confines of the DREAM setup. Even for the RP genes, does it make a useful prediction, that increasing the G content of RP promoters in that 100 bp window will decrease promoter activity? I am fine with leaving this as “future work” but it would be worth mentioning.et al., except FiRST is not marked . The authors should clearly cite Meyeret al. in the figure caption as the source of the data for this figure, or simply point the readers to Meyeret al., or else explain where the data came from if not from Meyeret al.Figure 2B is apparently the same as part of Figure 1E from MeyerI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.