To identify differentially expressed genes, it is standard practice to test a two-sample hypothesis for each gene with a proper adjustment for multiple testing. Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals.By building on an earlier proposed multivariate test statistic, we propose a new algorithm for identifying differentially expressed gene combinations. The algorithm includes an improved random search procedure designed to generate candidate gene combinations of a given size. Cross-validation is used to provide replication stability of the search procedure. A permutation two-sample test is used for significance testing. We design a multiple testing procedure to control the family-wise error rate (FWER) when selecting significant combinations of genes that result from a successive selection procedure. A target set of genes is composed of all significant combinations selected via random search.A new algorithm has been developed to identify differentially expressed gene combinations. The performance of the proposed search-and-testing procedure has been evaluated by computer simulations and analysis of replicated Affymetrix gene array data on age-related changes in gene expression in the inner ear of CBA mice. The dimension of X is typically very high relative to the number of observations (replicates of experiment). The standard practice is to test the hypothesis of no differential expression for each gene. Formulated in terms of the marginal distributions of all components of X, this hypothesis means that the expression levels of a particular gene are identically distributed under two (or more) experimental conditions. It is commonly believed that the only challenging problem here is that of multiple statistical tests, because the corresponding test statistics computed for different genes are stochastically dependent. This problem is discussed in [The set of microarray expression data on ussed in  in the cussed in ,4 providIn a recent paper, Szabo et al.  proposedNo matter what specific statistical techniques are chosen to approach the problem of identifying differentially expressed gene combinations rather than individual genes, the hypothesis that the expression levels of a given set of genes are identically distributed across the conditions under study is the most meaningful hypothesis to be tested. However, this hypothesis is now formulated in terms of the joint distribution of expression levels. The issue of multiple testing is dramatically magnified with multivariate methodology, because the total number of tests to be carried out at all steps of multivariate selection may be many orders of magnitude larger than with univariate methods. A constructive idea is to design a random search procedure for identifying differentially expressed sets of genes followed by testing significance of a final set. Szabo et al. ,6 proposTo compare gene expression signals in two different experimental conditions (states) one needs a pertinent distance between two random vectors. Such a distance is expected to satisfy the following requirements: (1) it should have a clear probabilistic meaning; (2) it should accommodate both continuous and categorical data; (3) its estimate should be stable to random fluctuations and numerical errors; (4) its computation should not be too time consuming. A distance that meets all the above requirements was proposed in .X = X1,..., Xd and Y = Y1,..., Yd, d ≤ p, be two random sub-vectors with probability measures μ and ν, respectively, defined on the Euclidean space Rd. Let K(x, y) be a strictly negative definite kernel, that is  for any x1,..., xs from Rd and any real numbers h1,..., hs, , with equality if and only if all hi = 0. Introduce the following expressionLet N(μ, ν) can be shown [Rd, so that the null hypothesis in two-sample comparisons can be formulated as H0 : N(μ, ν) = 0. A normalized version of N can be derived as , whereThe quantity be shown  to be a K(x, y) = Ψ(x - y) and Ψ(·) is homogeneous of any order, then Nnorm is both location and scale invariant.If n1and n2 observations respectively, represented by the d-dimensional vectors  and , and introduce an empirical counterpart (nonparametric estimate) of N(μ, ν) as followsConsider two independent samples, consisting of  of the distance N is that it does not involve numerically unstable high-dimensional components (such as covariance matrix or its inverse), thus it is expected to be numerically stable even for small sample sizes. This was corroborated by a computer simulation study [A very important advantage of the empirical counterpart on study , in whic is a wide selection of negative definite kernels that are sensitive to various departures from the hypothesis: μ = ν. Let x and y denote observations in two samples on a particular set  of variables. We consider either of these observations to be points in Euclidean space Rd. One natural choice is the Euclidean distance between points representing experimental measurements:Another distinct advantage of the approach based on  is scale invariant. Another possible choice is a bounded kernel exemplified byWhen this kernel is applied to logarithms of gene expression signals the corresponding distance Yet another kernel based on the correlation coefficient tends to pick up sets of genes with separated means and differences in correlation in the two samples under comparison . One cand-th power of the total number of genes. The issue of computational complexity can be resolved by applying random search methodology. Random search can be designed in a number of various ways. One simple algorithm was described in [Once a multivariate distance between expression signals has been selected, it can be employed in a search for differentially expressed genes with the target subset of genes being defined as a subset for which the distance between the two groups under comparison attains its maximum. Unlike univariate testing, an exhaustive multivariate search is computationally prohibitive because the number of possible subsets increases as the ribed in ,8. We usribed in .To reduce the selection bias associated with choosing a small number of variables from a large set , Szabo eu1 samples from one group of arrays and u2 samples from the other group.1. Randomly draw (without replacement) 2. Leave out the selected arrays and find the optimal (in accordance with the chosen criterion) subset of genes using only the data from the remaining arrays.v times to obtain v "optimal" sets of genes.3. Repeat steps 1 and 2 in succession  averaged over all cross-validation samples. With this approach, the correlation structure is better preserved when combining the results of cross-validation. The foregoing description of the second-stage search may be summarized in the following algorithm:The main problem here is that the algorithm results in many overlapping sub-optimal sets, and one needs to somehow combine them to report a single final set. Szabo et al. resorted to a somewhat unnatural way of forming a final set by selecting single genes with the highest frequencies of occurrence in sub-optimal sets. In our new algorithm, this is accomplished through designing a second-stage cross-validated search limited to the union of the previously selected sets. In the second-stage search procedure, cross-validation is carried out at each step of random search with the distance 1. Form the union of all sets resulted from Algorithm A1 to represent an initial target set. Drop the data on all other genes from the data set.2. Initiate a random search algorithm.l1 samples from one group of arrays and l2 samples from the other group. Leave out the selected arrays and compute the N-statistic using only the data from the remaining arrays. Perform this computation r times.3. At each step of the search algorithm, randomly draw (without replacement) N-statistics resulted from step 3. Denote this average by .4. Compute the average (arithmetic mean) of the  as a pertinent objective function to be maximized.5. Move to the next step of random search using the statistic ) gene combination of a given size.In the application discussed in the present paper, we used Algorithm A2 with 200 cross-validated samples in the second stage of the search algorithm. The two-stage search algorithm runs with multiple random starts and returns the most differentially expressed . For each of the m permutation samples, run (without testing) the successive selection algorithm to find a preset number I of disjoint sets. At each step of successive selection, an optimal k-element set is identified by the two-stage cross-validated search algorithm and the corresponding m sequences of -values are stored.1. Form I optimal sets of the same size k and compute the respective test statistics  for the selected sets.2. Returning to the original two-sample setting, find a sequence of N-statistics resulting from Steps 1 and 2. If the number of rejected hypotheses is less than I then stop and declare all the rejected sets of genes differentially expressed, otherwise return to Step 1 and continue successively selecting sets of genes. A faster version of Step 3 uses the single-step resampling adjustment [3. Apply the step-down multiple testing resampling algorithm by Westfall and Young  to the Njustment .p-values. The algorithm is computationally more expensive than its prototype presented in [The above algorithm can be reformulated in terms of ented in . We used is not distribution free. For this statistic, however, it can be proven that permutations produce samples from a distribution that is, in some sense, the least favorable for rejecting an underlying composite null hypothesis. In other words, permutations provide an optimal choice of a null distribution. More precisely, this theoretical result is valid for the resampling (with replacement) analog of permutations, but regular (without replacement) permutations may be a good approximation to this resampling procedure if both samples under comparison are not too small. This concept and its mathematical framework is discussed at length in our previous report [With the above approach, no distributional assumptions are needed although the test statistic s report .p-values associated with sets of genes resulting from random search, it is also desirable that the test statistic be scale invariant for any sample size. A statistic that meets this requirement is an empirical counterpart of the normalized distance Nnorm with a properly chosen kernel function, see formula (2) and the succeeding explanation. Yet another possibility is to use the kernel K1 with log-intensities of gene expressions. We employed the latter pivoting structure of the N-statistic in the analysis of simulated and biological data presented in the subsequent sections.For efficient nonparametric estimation of adjusted We first tested our methodology by computer simulations. To this end, we designed a simulation study as follows.σ = 0.5 or σ = 1 for all individual genes. The mean log-expression values τ for the genes assigned to the target set of genes were specified as follows: τ = 5 for the first 4 genes (Subset 1), τ = 4 for the second group of 4 genes (Subset 2), τ = 3 for the third group of 4 genes (Subset 3). The remainder of the genes  were simulated as log-normally distributed random variables with τ = 1 and the same standard deviation (either σ = 0.5 or σ = 1) and correlation coefficient. The 1,000 genes in the control group were simulated just like those that were not differentially expressed in the treatment group.Two sets of data on 1,000 genes were simulated. For convenience we will label them as "control" and "treatment" samples, respectively. The size of each sample was equal to 10. In the treatment group, the first 12 genes were set to be differentially expressed. To simulate these genes, logarithms of gene expression signals were generated from a multivariate normal distribution with an exchangeable correlation structure. The algorithm designed to simulate such data is presented in the Appendix. The correlation coefficient for all pairs of gene log-intensities was set equal to 0.6, while the standard deviation was chosen to be either I in Algorithm A3 was set equal to 5. Since the sole purpose of our simulations was to check how well a given algorithm finds a maximum of the N-statistic over gene sets, no recourse to cross-validation was made in this study. The number of permutations was set at 200. Because such simulations are very time consuming the experiment was repeated only 100 times. Two samples (control and treatment) were generated in each of the 100 experiments.Our search-and-testing procedure was applied to the data sets thus generated in order to see whether (and how frequently) it can find all subsets, as well as all individual genes, included in the target set of differentially expressed genes. In each experiment, the SRS algorithm was run with multiple random starts. At each step of the successive selection of genes, the algorithm sought for a subset of 4 genes. The parameter σ = 0.5 for the treatment group the algorithm was able to correctly recover Subset 1 in 82%, Subset 2 in 72%, and Subset 3 in 76% of simulation runs. The proportion of cases where all 12 genes were correctly recovered (irrespective of the order they entered the selected subsets) was 61%. The false discovery rate, defined as the mean proportion of falsely discovered genes among the true differentially expressed genes, was equal to 0.02.First we tested the SRS algorithm with 8 random starts and 2,500 search steps. When σ = 1 the SRS algorithm recovers Subset 1 in 76%, Subset 2 in 56%, and Subset 3 in 39% of the simulation runs. The proportion of cases where all 12 genes were correctly recovered was 53%. The false discovery rate was equal to 0.04.When σ = 0.5, the rate of correct discovery becomes 100% for all three sets. For σ = 1 the algorithm correctly recovers Subset 1 in 81%, Subset 2 in 65%, and Subset 3 in 48% of simulation runs. The proportion of cases where all 12 genes are correctly recovered is 62%. However, the false discovery rate remains essentially the same as when running the SRS algorithm with 8 starts and 2,500 search steps. The results on individual simulated genes are presented in Table As one would expect, the SRS algorithm performed better with 16 random starts and 3,600 search steps. For N at the same level of FWER. While the results for σ = 0.5 were identical (100% correct recovery), the univariate method recovered less genes (45%) in the target set when we set σ = 1. In the latter case, the univariate algorithm had a uniformly lower correct discovery rate for genes #9 through #12 (69%, 71%, 70%, 71%, respectively) in comparison to the multivariate method  with one random start and the same parameters of the simulation model. Although computationally expensive, the SAO algorithm is easier to handle when tuning its parameters in simulation experiments. Proceeding from the less favorable case of N-distance is known. We randomly selected 2000 genes from the data set discussed in the next section. All possible pairs were formed from the 2000 genes and the corresponding N-statistic between the two samples (young versus old mice) was computed for each pair. The data were normalized before the analysis (see Section "Results and Discussion"). Having determined a maximum value of the N-statistic over all pairs, we ran the SRS and SAO algorithms (with parameters suggested by our simulation experiments) to see whether they could find the actual maximum. Both algorithms hit the target.Another way of testing the two algorithms is to apply them in a situation where the true global maximum of the The biological purpose of our experimental study was to better understand age-related changes in gene expression that occur in mouse inner ear (including the organ of Corti and stria vascularis). Since we do not expect numerous genes to be involved in the process of aging of the auditory system, this experimental system seems to be especially promising for the use of multivariate methods.Hearing loss or deafness affects about 10% of the U.S. population, or about 30 million people, most of them over age 60. Presbycusis – age-related hearing loss – is a primary sensory problem in the elderly population, the number one communicative disorder, and one of the top three chronic medical conditions affecting the aged. It is often described as difficulty in understanding speech, especially in conditions of high ambient background noise. Most elderly persons have a reduction in hearing acuity. For example, cross-sectional and longitudinal studies have consistently demonstrated gradually decreasing pure tone thresholds by cohort groups of elderly ,14. The Much progress had been made in the field of auditory aging research regarding sensitivity deficits and metabolic problems of the cochlea. As humans and animals age, they lose sensory hair cells, 8th cranial nerve (i.e., vestibulocochlear) fibers, and develop stria vascularis/potassium recycling metabolic problems that degrade audibility and spectral tuning -21.In addition, the differing roles of the ear and brain in presbycusis, and aging deficits in speech understanding in background noise, and their respective neural bases are beginning to be understood. Age effects in these areas are distinguishable and age-related problems in the brain can be influenced by the peripheral etiologies of presbycusis -24. ConsThe goal of the present study is to explore the underlying cochlear gene expression changes that may predispose or cause presbycusis. Common neurodegenerative diseases such as presbycusis are likely to be caused by several fundamental problems that interact with each other and with environmental factors, including genetic pre-dispositions to environmental insults, noise and ototoxic medications . AlthougI = 5, u1 = 4 (out of 9 arrays), u2 = 6 (out of 12 arrays), v = 10, l1 = 4 (out of 9 arrays), l2 = 6 (out of 12 arrays), r = 200.The two groups of arrays under comparison included 9 and 12 arrays, respectively (see the next section). The data were normalized using the quantile normalization method ,12 carrip-value of less than 0.05. For comparison, we applied the Wesfall and Young step-down multiple testing procedure with a univariate counterpart of  as the test statistic. This method selects only 6 genes at the same FWER; all of them appear among those genes that have been selected by the multivariate search-and-testing procedure. The final list of 10 genes was evaluated further for consistency with the existing biological knowledge.Although the lists of genes produced by both algorithms are quite similar, there are still some discrepancies between them which may be attributed to the choice of parameters for each method. Since the SAO algorithm is less sensitive to the choice of the initial gene combination, we present only the results obtained with this algorithm. In the "young" versus "old" comparison, the procedure selected two sets of 5 genes with an adjusted Of the 10 identified genes (from 2 sets) exhibiting major expression changes with age, there are 6 differentially expressed genes having to do with immune system function. This is important from an aging point of view for two reasons. First, immunoprecipitations or immunoproducts can be damaging to nerve cells, and have been implicated as being responsible for age-related neurodegeneration in the brain in general, and in Alzheimer's disease specifically, but this is a new finding for the cochlea and age-related hearing loss – presbycusis. Second, autoimmune problems, where the immune system starts attacking its own nerve cells, is another leading candidate for a causative factor in neurodegenerative aging conditions. These immune products are likely to come from the vascular supply to the cochlea, yet may be a causative component for age-related hearing loss due to the resultant damage to the cochlea sensory cells.There are 3 genes having to do with post-translational protein changes, including protein binding properties, with two of these genes involved in carbohydrate metabolism (sugar/glucose binding in mitochondria for cellular respiration). These genes are related to the production of reactive oxygen species (ROS), which damage nerve cells, and have been implicated in age-related neurodegenerative disorders, and in cases of cochlear sensorineural hearing loss. For example, problems in cellular respiration can lead to accumulation of toxic intracellular substances, causing damage to sensory cell structures and abnormal metabolic processing along with increased levels of ROS -33.The last gene, involved in mammary gland functioning, showed a significant increase with age. A closer inspection of the expression levels for this gene have shown that the observed effect cannot be attributed to the presence of outliers in the data. Although not directly involved in sensory functioning, this gene may change its espression as part of general degenerative processes in inner ear. An error in this gene annotation cannot be ruled out as well. This observation is definitely worth another look.The above-described initial observations are quite provocative, in that we have several groupings of genes that have important functional significance for aging and hearing, including important aspects of cochlear, inner ear functioning. These animal-model gene-array investigations are quite useful for guiding human genetics experiments aimed at identifying candidate genes involved in the susceptibility and progression of human age-related hearing loss and other age-dependent neurosensory disorders.Regarding methodological aspects of this paper, we would like to note that a pertinent multivariate method for selection of differentially expressed genes should include two components: finding subsets of candidate genes that jointly separate the classes (states) under comparison and testing statistical significance of this separation; the latter does not necessarily refer to characteristics of a classification  rule such as classification error rates. We also would like to stress that the problem of significance testing in the multivariate formulation is not equivalent to the problem of statistical classification (supervised learning). While closely related, these problems are fundamentally different. For example, the use of the classification error rate as a criterion for selection of important variables is appropriate where the aim is to form a discriminant rule for the subsequent outright allocation of unclassified samples to one of the known classes. A very good separation between classes can sometimes be provided by looking at a single feature variable (gene) so that the classification error rate is difficult to reduce further by including other (probably quite significant) variables in the rule. However, one would like to keep the chance of missing other interesting variables to a minimum. The problem dealt with in this paper is not that of classification or prediction. Our method is designed to find gene combinations that change in concert (as a set) their expression due to some biological factors. The problem thus formulated reduces to that of significance testing.It must be emphasized that our method is designed not only to identify sets of genes whose interrelationships differ but also those genes with marginal effects. More importantly, the method seeks to provide an alternative way of making a specific FWER-based multiple testing procedure less conservative and, to some extent, less dependent on the subset pivotality requirement  and old (N = 12, 24–33 months). All animal procedures were approved the University of Rochester Committee on Animal Resources.. All samples were stored at -80°C for microarray gene expression processing.Subject groups of the present report had extensive behavioral and neurophysiological hearing testing prior to sacrifice, verifying that the old mice had age-related hearing loss. Mice were sacrificed by cervical dislocation. Then both cochleae for each mouse were immediately dissected using a Zeiss stereomicroscope. The cochleae were placed in cold saline for micro dissection of the cochlear partition , and were then placed in cold Trizol. A detailed protocol for Trizol can be found at . Affymetrix M430A High density oligonucleotide array set (A) which queried 20,000 murine probe sets was used. Each gene on the subarray is represented by 11 pairs of 25 mer oligonucleotides that span the coding region for the 20,000 genes and ESTs represented (clear overlapping of genes is evident). Each probe pair consists of a perfect match (PM) sequence that is complementary to the cDNA target, and a miss-match (MM) sequence that has a single base pair mutation in a region critical for target hybridization; this sequence serves as a control for non-specific hybridization. Staining and washing of all arrays was performed in the Affymetrix fluidics module per manufacturer's protocol. Streptavidin phycroerythrin stain (SAPE, Molecular Probes) was the fluorescent conjugate used to detect hybridized target sequences. All arrays in this study were assessed for "array performance" prior to data analysis.The RNA quality was assessed by electrophoresis using the Agilent Bioanalyzer 2100. Between 200 ng and 2 ug of total RNA from each sample was used to generate a high fidelity cDNA, which was modified at the 3' end to contain an initiation site for T7 RNA polymerase, while 1 ug of cDNA was used in an in vitro transcription (IVT). 20 ug of full-length cRNA, from each mouse (age groups as described above), was fragmented. After fragmentation, the cDNA, full-length cRNA, and fragmented cRNA were analyzed by electrophoresis using the Agilent Bioanalyzer 2100 to assess the appropriate size distribution prior to microarray hybridization. Detailed protocols for sample preparation using the Ambion MessageAmp protocol can be found at The methodology of data analysis and design of computer simulations have been described at length in the preceding sections. The relevant software for data analysis and simulations is included in the X in Rd with mean vector M ∈ Rd and covariance matrix Σ whose entries are σ2 and ρσ2 on and off diagonal, respectively. It is well-known that X can be represented in the formSuppose we want to generate a normal random vector X = M + CZ,Z is the standard normal vector with mean 0 in Rd and C is a d × d matrix with CCT = Σ. (Here CT denotes the transpose of C.) The matrix C may be chosen symmetric and can be computed using well-known algebraic procedures. However, our matrix Σ has a special structure:where ρ)σ2Id + ρσ21d × d,Σ = , 2αβ = ρσ2, so thatFrom the relations YX is responsible for the computational component of this study. He also participated in the methodology development. LK, AG, and AY have equally contributed to various methodological aspects of the proposed multivariate analysis. RF provided experimental data and biological interpretation of the net results of data analysis.The additional folder "MultivariateSearch" includes the following three sub-folders: 1. SAO _Simulation 2. SRS_Simulation 3. TSSearch Each subfolder contains a Unix executable file. The executable file "SASearch" implements the algorithm based on simulated annealing optimization. The executable file "SRSearch" implement the version based on simple random search. The exectuable file "TSSearch" for the two-stage search is is located in the sub-folder "TSSearch". Each sub-folder also contains two input files. The file "simulation04_UI.txt" is an input file for data analysis. Suppose the data file is named xxxx.marr, then the input file should be named as xxxx_UI.txt. To analyze the data from the file xxxx.marr, type: [Executable file] xxxx or [Executable file] 0 xxxx. The input file "simulation04_ui.txt" is designed for simulation experiments. To conduct simulations, one has to prepare an input file with the name: XXX_simu_ui.txt, where XXX is a string that follows the naming convention of computer files. An input file for data analysis with the name XXX_ui.txt is also needed. To run simulations, type: [executable file] 1 xxxx.Click here for file


We also examined the relationship between chromatin composition and replication timing of artificial chromosomes.Human centromere regions are characterized by the presence of alpha-satellite DNA, replication late in S phase and a heterochromatic appearance. Recent models propose that the centromere is organized into conserved chromatin domains in which chromatin containing CenH3 (centromere-specific H3 variant) at the functional centromere (kinetochore) forms within regions of heterochromatin. To address these models, we assayed formation of heterochromatin and euchromatin on Heterochromatin factors (histone H3 lysine 9 methylation and HP1α) were enriched on artificial chromosomes estimated to be larger than 3 Mb in size but depleted on those smaller than 3 Mb. All artificial chromosomes assembled markers of euchromatin (histone H3 lysine 4 methylation), which may partly reflect marker-gene expression. Replication timing studies revealed that the replication timing of artificial chromosomes was heterogeneous. Heterochromatin-depleted artificial chromosomes replicated in early S phase whereas heterochromatin-enriched artificial chromosomes replicated in mid to late S phase.Centromere regions on human artificial chromosomes and host chromosomes have similar amounts of CenH3 but exhibit highly varying degrees of heterochromatin, suggesting that only a small amount of heterochromatin may be required for centromere function. The formation of euchromatin on all artificial chromosomes demonstrates that they can provide a chromosome context suitable for gene expression. The earlier replication of the heterochromatin-depleted artificial chromosomes suggests that replication late in S phase is not a requirement for centromere function. In the post-sequencing phase of genome characterization, it is important to understand the contribution of non-coding sequences to higher-order genome structure and stability. Maintenance of genome integrity and the faithful transmission of genetic information in mitosis and meiosis are essential to organism survival and are critically dependent on two repetitive chromosomal elements. Telomeres protect against chromosomal truncation or fusion events , while cde novo centromeres on human artificial chromosomes following transfection of some types of alpha-satellite sequences into human cells [Defining the minimal DNA sequences required for centromere function on a normal human chromosome has proved challenging, owing to the complex nature of inter- and intra-chromosomal homology and variability in genomic DNA content near the primary constriction. Common to all normal human centromeres are large amounts of alpha-satellite DNA, which is comprised of a family of diverged 'monomers' of around 171 base-pairs (bp) that have been amplified in multimeric groups (higher-order repeats) on different chromosomes to form chromosome-specific arrays typically megabases in length -17. In aan cells -14.Paradoxically, despite conservation of the functional role of the centromere in every eukaryotic cell, DNA sequences at eukaryotic centromeres are quite divergent in sequence even between closely related species ,25. AlthRecent evidence suggests that a complex system of epigenetic modifications based on histone variants and histone tail modifications is important for centromere activity (reviewed in ,31), in Schizosaccharomyces pombe. Detailed analyses of a 40-kilobase (kb) S. pombe centromere revealed that it encompasses both the kinetochore, as defined by the exclusive association of Cnp1, the fission yeast CenH3, with the central core element [The most completely studied complex eukaryotic centromere at the molecular level is that of the fission yeast  element  and adja element  that are element -40. With element .per se.There is substantial evidence that HP1 is involved in setting up and/or maintaining a repressed chromatin state in several epigenetic systems (reviewed in ). HP1 prtrans-acting chromatin factors in human centromere function is through the construction of detailed genomic maps of human centromeric regions and evaluation of their associated proteins [de novo centromeres on human artificial chromosomes without large amounts of adjacent heterochromatin demonstrate the same chromatin characteristics as either normal human centromeres or human artificial chromosomes with large amounts of heterochromatin.One way to address the interacting and complementary role(s) of DNA sequence and proteins ,19,56,57proteins  as toolsproteins ,13,58. Hde novo centromere formation and function is likely to be generally conducive to gene expression, as will probably be required for either gene-transfer experiments and/or functional genomic applications of the artificial chromosome technology. Further, the data raise the possibility that functional centromeres may adopt a novel chromatin state that is, contrary to what has been long assumed, quite distinctive from that of conventional heterochromatin.In the present study, we have characterized the nature of heterochromatin and euchromatin formed on a series of human artificial chromosomes derived from higher-order repeat alpha satellite from chromosomes X or 17 ,14. Whilde novo centromere assembled from the transfected DNA, as well as at least one copy of a functioning gene used as a selectable marker. Together, this panel of artificial chromosomes provides an opportunity to examine the nature of heterochromatin and euchromatin assembled on the transfected DNA sequences. The high mitotic stability and de novo composition of artificial chromosomes generated from D17Z1 (17-E29, 17-D34 and 17-B12) or DXZ1 (X-4 and X-5) have been described [in situ hybridization (FISH), artificial and host chromosome segregation products can be measured and nondisjunction or anaphase lag defects recorded.To examine the chromatin composition of human artificial chromosomes, we used a panel of artificial chromosomes formed after transfection with vectors containing either synthetic chromosome 17 (D17Z1) or cloned X chromosome (DXZ1) alpha-satellite sequences ,14. EachIn X-4 and X-5, artificial chromosomes mis-segregated in 1.8% and 2.4% of cells, respectively  was applied to metaphase spreads. Methylation of lysines at these sites has been associated with formation of repressive chromatin, including pericentric heterochromatin in mouse cells -53,59,60per se. Such a model [In a parallel approach, we examined the distribution of HP1α in four lines containing D17Z1-based artificial chromosomes. Each line was stably transfected with a Myc-epitope tagged form of HP1α  to permit detection of HP1α using an anti-Myc antibody. The smaller artificial chromosomes stained very weakly (at a level similar to that of the staining on the euchromatic chromosome arms), well below the levels of HP1α detected at the centromeric region of the endogenous chromosome 17s Figure . As seen a model ,3 is alsFor their potential use as gene-transfer vectors or as general vehicles suitable for interrogation of genome function, human artificial chromosomes must also be capable of forming euchromatin to support gene expression. Indeed, one would hypothesize that at least small amounts of transcriptionally active chromatin must form during artificial chromosome formation to permit expression of the selectable marker gene(s) contained on the transfected constructs ,12,14. Iper se.As a test for formation of permissive chromatin on artificial chromosomes, we stained metaphase spreads with an antibody that recognizes histone H3 dimethylated at lysine 4 (H3DimK4). All artificial chromosomes tested stained positively for H3DimK4 modifications Figure ; Table 2Previous structural analyses of artificial chromosomes indicate that they consist of input DNA multimers arranged as blocks of alpha-satellite DNA interspersed with vector sequences ,11,12. TWhile the genomic determinants of potential origins of DNA replication in the human genome, as well as of their timing of replication during S phase, are still not well understood, the generally accepted paradigm is that expressed sequences replicate in the first half of S phase, while non-expressed sequences replicate in the second half . ConsistWhile there was overlap between artificial chromosome replication timing patterns and those of the host 17 centromere regions during mid S phase Table , we founcis- and trans-acting factors necessary for chromosome segregation and offer potential for both functional genomics and gene-transfer applications. The artificial chromosomes we used contain defined alpha-satellite DNA sequences [de novo centromere on artificial chromosomes may reveal the critically important components and may help distinguish between those features that are characteristic of the functional centromere itself and those that are markers of the surrounding heterochromatin. Such a distinction is extremely difficult in normal human chromosomes but should be enhanced by the ability to generate a variety of different artificial chromosomes made with different input sequences.Human artificial chromosomes provide a novel system for analyzing equences ,14. StudRecent detailed molecular studies in the fission yeast have revealed that such epigenetic factors are critical for centromere function. The fission yeast CenH3, Cnp1, is deposited only at the central core domain, while heterochromatin (marked by methylation of histone H3 at lysine 9 and by binding of the HP1 homolog, Swi6) forms on the surrounding inverted repeats ,36,41. TDrosophila and human chromosomes [While the data presented here are largely consistent with these models, they permit two important refinements. First, large amounts of heterochromatin  are not required for effective chromosome segregation during mitosis; indeed, the small artificial chromosomes examined here do not contain detectable amounts of H3TrimK9/K27  replicated in the first 2 hours of S phase  these fCytological data indicate that the amount of CENP-A modified chromatin  is similar on endogenous human chromosomes and on all artificial chromosomes regardless of the amount of total alpha satellite present. This suggests that the amount of CENP-A chromatin and/or the size of the kinetochore is regulated and/or limited in some manner -14,58,61To evaluate their potential for characterization of genome sequences and, eventually, for gene transfer or gene therapy applications, we sought to determine the extent of transcriptionally competent chromatin formation in artificial chromosomes. Epigenetic modification of histone H3 by dimethylation at lysine 4 (H3DimK4), a marker of transcriptionally competent chromatin, was present on all artificial chromosomes tested. This contrasts with the staining pattern associated with the centromere regions on human metaphase spreads, where this modification is largely undetectable, probably reflecting the general absence of genes mapping to centromere regions  detected in the anaphase assay . All artificial chromosomes were formed in human HT1080 cells. Cell lines were grown as described [Characterization of cell lines containing mitotically stable human artificial chromosomes formed after transfection with either synthetic D17Z1 arrays (PAC17HT1.E29 (17-E29), PAC17HT1.D34 (17-D34), BAC17HT4.B12 (17-B12) or cloned DXZ1 sequences (X-4, X-5) have been described previously . The artAnaphase assays used to directly measure chromosome segregation defects in 17-B12 and 17-C20  and subcloned using standard techniques [bsr marker gene. Clonal lines (17-C20-1.B22 and 17-B12-1.B10) resistant to 4 μg/ml Blasticidin S HCl (ICN) were selected and expanded. Confirmation of Myc-tagged HP1α expression was by immunofluorescence using a mouse monoclonal anti-Myc antibody (Invitrogen).The nucleotide sequence of human HP1α (NCBI Nucleotide database: S62077) was used in BLAST searches against entries in the human expressed sequence tag (EST) database using the NIH BLAST server . A reprechniques  into pcDchniques  that carMetaphase spreads were prepared for immunofluorescence using previously described protocols . Primary2 flasks for 2 h intervals. Following three PBS washes, medium supplemented with 50 μM thymidine (Sigma) was added. Cells were left in thymidine-containing medium until chromosome harvest. At appropriate intervals, colcemid was added to block cells in mitosis. Cells were harvested and fixed in 3:1 methanol/acetic acid. Metaphase spreads on microscope slides were baked for 1 h at 60°C. Primary anti-BrdU antibody (Roche) was added for 1 h at room temperature. Rhodamine-donkey-anti-mouse secondary antibodies (Jackson ImmunoResearch) were used to visualize sites of BrdU incorporation. Typically, 25 metaphase spreads were captured following BrdU detection and their coordinates on the slide recorded. Subsequent analyses with a D17Z1 probe were used to confirm identity of artificial or endogenous chromosomes.Cells were pulsed with 10 μM BrdU (Roche) in T25 cm


TP53 gene. Isolated and multiplexed PCR products, 108 and 92 bp long, from exons 7 and 8, respectively, were obtained from 24 different samples. Single-stranded target DNA was then prepared from isolated or multiplexed PCR products, through cyclic DNA synthesis. Independent ssDNA's were annealed with the corresponding pairs of labeled stacking oligonucleotides to create partially duplex DNA having a 7-nt gap, which contains the sequence that will be interrogated by the capture probes forming double tandem hybridization. In the case of multiplexed ssPCR products, only two stacking oligonucleotides were added per target, therefore the gap for the PCR products having two consecutive codons to be interrogated in exon 7 was 12 nt long, so only single tandem hybridization was produced with these respective probes.We have developed an oligonucleotide microarray (genosensor) utilizing a double tandem hybridization technique to search for 9 point mutations located in the most frequently altered codons of the 18 codon substitutions were found by DNA sequencing. In 13 of them a perfect correlation with the pattern of hybridization was seen . In 3 other cases a mutation was falsely suggested by the combination of the absence of the wild type signal along with a false signal in the other probe. In the other 2 cases the presence of the mutation was not detected due to the production of a false hybridization signal with the wild type probe. In both cases  relatively stable mismatched target/probe duplexes should be formed. These problems could be avoided by the addition of probes to improve the performance of the array.TP53 microarray employing short (7-mer) probes, used in combination with single or double tandem hybridization approach and a simple or multiplex target preparation method, can identify common TP53 missense mutations from a variety of DNA sources with good specificity.Our results demonstrate that a simple  Cancer is a group of diseases characterized by uncontrolled cell growth. According to The World Cancer Report, cancer rates could further increase by 50% to 15 million new cases in the year 2020 [TP53 (MIM # 191170) which encodes a transcription factor (p53 protein) with cancer inhibiting properties. This gene is the most frequently mutated gene in human cancer. Between 30% and 70% of cancers of almost every organ and histological subtype have a point mutation in one of the two TP53 gene copies together with loss of the other copy [TP53 gene is tumor-specific, clustered between exons 5, 7 and 8. This region is highly conserved throughout evolution [TP53 gene is different between cancers, for example, codons 157, 158, 248, 249, and 273 have been designated as TP53 mutational hotspots in lung cancer [The progression of mammalian cells towards malignancy is an evolutionary process that involves an accumulation of mutations at the molecular and chromosomal level. A candidate for involvement in this process is the tumor suppressor gene her copy -4. In mag cancer , while iTP53 gene are so common in human tumors and have been widely reported in the literature, extensive mutational databases exist, such as that maintained by IARC [TP53 related to changes in protein structure and function. Martin et al. [TP53 mutations [TP53 gene.Because mutations in the  by IARC . Walker  by IARC , have utn et al.  performen et al. . A growiutations -15. In mutations . Major autations ,18 and gutations ,20. Thesutations ,22. Althutations ,24. In tPCR products from exon 7 (codons 248 and 249) and exon 8 (codon 273) were obtained from each type of DNA sources listed in Materials and Methods. Figure The reference hybridization patterns produced using synthetic DNA targets are shown in Figures. Hybridization of individual or multiplexed ssDNA targets prepared from the 24 DNA samples (that correspond to 72 codons in 24 samples) was performed with similar results. Base substitutions were seen in 11 out of the 24 samples tested Table . One, twTP53 tumor suppressor gene has proven to be one of the cellular genes most often mutated in human neoplasias. Analysis of the mutational events that target the TP53 gene has revealed evidence for both exogenous and endogenous mutational mechanisms. This gene mutational spectrum suggests evidence for a direct causal effect of tobacco smoke in lung cancer [TP53 gene may play an important role in the diagnosis, staging, and management of the cancer patients.The g cancer ,27, aflag cancer . TherefoTP53 gene, more specifically on exons 5 through 8 which encode the DNA binding domain [TP53 mutations on all clinical parameters. One approach is the p53 GeneChip of Affymetrix used to identify mutations in this gene [More than 70% of the molecular studies have focused on the central region of the his gene ,14. Howehis gene ,30.TP53 gene. TP53 somatic mutations are found in most types of sporadic human cancers at various frequencies (from 20% to 60%) [The updated IARC TP53 mutation database  shows th to 60%) ,14. The Most SNP diagnostic arrays use probes varying from 15 to 20 nt in length with similar Tm values . HoweverThe tandem hybridization method described here offers several advantages over the traditional oligonucleotide array configuration in which each interrogated target sequence is represented by a single surface-tethered probe. Because the long stacking probe targets the analysis to a single unique site within a nucleic acid, direct hybridization analysis of nucleic acid samples of high genetic complexity using short capture probes may be enabled .Recently, we successfully used a double tandem hybridization strategy in the genesensor chip system to discriminate point mutations in codon 634 of RET oncogene in individuals affected by medullary thyroid carcinoma . This DNTP53 gene.The goal of the present work was to develop a simple format array able to perform a primary screening of the mutations occurring in the most frequently affected codons of the It has been recently reported that detection of mutations yields variable results depending on the type of DNA sample ,36. Is cIn the experiments employing synthetic DNA targets hybridized with arrays of mutant and WT probes the expected hybridization signals were seen both in homozygous and heterozygous conditions Figure. . This reThe hybridization of natural targets was done both with isolated and multiplexed PCR products with similar results. Multiplexing was applied in all  the steps. So these results are suggesting that multiplexing can be confidently employed for this analysis.2→T) was seen, which could be relatively stable under the conditions tested. Therefore an additional probe, searching by the AGA sequence, should be added in the future array to discriminate between the two alternative silent (AGA) and missense (AGT) genotypes. In the other two codons affected (L1 codon 248 and L4 codon 248) the presence of signal with the wild type probe was seen even when the sequencing was showing a base change (wtCGG→mutCGT in sample L1 or 248 wtCGG→mutCGK in sample L4). In both cases a G:N mismatch is formed which, according to SantaLucia [Base changes were found in 11 DNA samples. Two were from plasmid, seven from cell lines and two from blood samples. In 5 of these samples a single codon was affected, in other 5 samples two codons were altered, and in the other sample three codons were affected. Therefore a total of 18 codon alterations were detected. In five of the 18 codon alterations (Samples L2 codon 273, L7 codon 273, L8 codon 273, B3 codon 273 and B6 codon 273) both the absence of hybridization signal with the wild type probe and the production of signal with the appropriated probe was seen. In eight cases (Samples P1 codon 248, P6 codon 273, L8 codons 248 and 249, L9 L8 codons 248 and 249 and L8 codons 248 and 249 and, L10 codons 248 and 249) no hybridization signal was seen even with the wild type sequence. This was due to the absence of the corresponding mutant probe in the array. In three cases (P1 codon 249, L4 codon 249 and L7 codon 249) having either homozygous (sample P1) or heterozygous (samples L4 and L7) genotypes (wtAGG→mutAGA) no hybridization signal with the wild type probe and, an additional false hybridization signal with the probe corresponding to the substitution of guanine for thymine in codon 249  observed an Arginine was involved, which can be expected because it is coded by 6 codons and it frequently produces non conservative aminoacid substitutions.The array is able to confidently detect most of the mutations searched, however, additional probes should be added to improve its performance.TP53 microarray using short (7-mer) probes, used in combination with a double tandem hybridization approach and a simple multiplex fragment preparation method, can identify common TP53 point mutations from different DNA sources. All the previous data confirms that the tandem hybridization technology, with the appropriated set of probes, is highly specific and can be confidently employed for diagnostic purposes. This system could provide rapid, economical and accurate mutational screening in the TP53 gene. We are presently improving our system by adding probes for these and other TP53 mutational hot spots.Our results demonstrate that a simple TP53 gene associated with all cancers war retrieved from the IARC TP53 database [TP53 most frequently altered in cancers were obtained. Both wild type (accession # U94788) and mutated TP53 sequences were used to design the probes, stacking oligonucleotides, PCR primers and synthetic targets.The distribution of nucleotidic changes in the database . Then, tTP53 gene. Six stacking oligonucleotides of 17–21 nt length were annealed with their corresponding target DNA's, in three pairs, to produce partially duplex DNA's having a 7-nt gap which is the sequence interrogated by the capture probes , two obtained from healthy subjects and six obtained from lung cancer affected patients, and ten established cancer cell lines or recombinant DNA's acquired from the ATCC (L). Two of the cell lines were a generous gift from Dr Mary A Salazar (IIB, UNAM) and one was a gift from Dr José Sullivan (INER, SS, Mexico). The other eight cell lines were provided by the DNA bank from Genomic Oncology Laboratory (CMN S XXI). Finally, six recombinant plasmids (P) harboring TP53 (wild type or different point mutations), provided by MSc. Irene Correa (CMN S XXI) were employed to standardize the hybridization arrays. The names, identification numbers, source and known genotypes in the 24 samples studied are described in Table 2Twenty-four DNA samples from different sources were probed, some having known wild type or mutant ® dsDNA Quantitation Kit (Molecular Probes).Genomic DNA was isolated and purified using the Genomic DNA Extraction Kit (Life Technologies Inc., Gaithersburg, MD). DNA concentration was determined by absorbance at 260 nm in a spectrophotometer MBA 2000 (Perkin-Elmer) and by the PicoGreenPlasmid DNA was prepared from bacterial cultures using the alkaline lysis/SDS minipreparation according to the manufacturer's instructions (Life Technologies).2, 0.5 μM each primer, 2.5 U of Taq DNA pol (Promega) and purified DNA (50–100 ng) in a final volume of 100 μL. The PCR profile consisted of an initial heating at 94°C for 5 min, followed by 30 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 30 s, with a final extension step at 72°C for 7 min. The PCR product was assessed by electrophoresis in 1 % agarose gel stained with ethidium bromide.The quality of the DNA preparations was assessed by gel electrophoresis in 1% agarose. All the reagents for PCR reactions were purchased from Promega (Madison WI. USA). The PCR primers are described in Table r cutoff), and resuspended in the same volume of HPLC-grade water (OMNI SOLV®, EM Science). A 5-μL aliquot of this solution was added to a 100-μL PCR reaction, along with the primer corresponding to the target strand, then incubated for 27 temperature cycles using the same temperature profiles described above. The formation of each amplicon was assessed by mobility shift in agarose gel electrophoresis.Single-stranded target DNA was prepared by cycle synthesis as follows. A 30-μL aliquot of PCR product was processed using Ultrafree (Millipore, Bedford, MA) spin filters (30,000 M32P]ATP (10 μCi/μl, 3000 Ci/mmol) 2.5 μl (NEN, Boston MA), specific activity 7000 Ci/mmol, diluted in sterile water to 7 μCi/μl, and autoclaved water HPLC-grade to 25 μl. The mix was incubated 10 min at 37°C and the reaction was stopped by adding 5 mM EDTA (2.5 μl).Each one of the six stacking oligonucleotides was 5'-labeled by reaction with T4 polynucleotide kinase (Invitrogen, San Diego, CA) as follows. 5 pmol of each dephosphorylated stacking oligonucleotide; 5X Forward Reaction Buffer (5 μl); T4 Polynucleotide Kinase (10 unit); [γ32P-labeled stacking oligonucleotides was preannealed with the respective synthetic DNA target, single-stranded PCR fragments or multiplexed, single-stranded PCR products to form a 7-nt gap in each target. The annealing mixture contained 50 μL 20X SSC, 10 μL 1 M Tris-HCl (pH 8.0), 3 μL 0.5 M EDTA, 0.2 pmol of each labeled stacking oligonucleotide, 10 μL of target DNA, and HPLC-grade H2O to a final volume of 90 μL. The mixture was incubated at 95°C for 5 min, 45°C for 5 min, then 6°C for 5 min. Excess [γ32P]ATP was removed by filtration through an Ultrafree spin filter (3,000 Mr cutoff) (Millipore. Bedford, MA), and the retained DNA was dissolved in 20 μL of 1X SSC.To prepare labeled partially duplex (gapped) target DNA's, each pair of γIn the case of multiplexed targets, only 4 stacking oligonucleotides (SO-248-R, SO-249-L, SO-273R and SO-273-L) were added, one pair for each exon in the study, forming gap sizes of 12 and 7 nt for exons 7 and 8, respectively. Thus, with exon 7 a single tandem hybridization was conducted with the glass-tethered 7-mer capture probes, whereas with exon 8 double tandem hybridization was conducted.TP53 targets under study, two negative controls consisting of 7-mers targeted to the wild type sequences from codons 157 and 158 of TP53 gene, plus one blank spot without probe. As depicted in Fig. TP53 capture probe was arrayed in triplicate (to assess the reproducibility of the results) in an upward direction starting from position 1 and the printing was divided into three groups. Positions 1–14 were printed first, then the pattern was repeated twice more across the slide. Position 15 was left empty  and served as a negative glass control. After spotting, each slide was washed with water to remove unbound oligonucleotides, air-dried, then stored in a dessicator at room temperature. This attachment procedure results in a surface density of 1010-1011 molecules mm2, which corresponds to intermolecular spacing of about 30–100 Å across the surface [Glass microscope slides were prepared by soaking in hexane 20 min, then 20 min in absolute ethanol followed by drying 5 h at 90°C. Oligonucleotide probes containing 3'-terminal aminopropanol modification were dissolved in sterile water to a final concentration of 20 μM, and 200 μl droplets of each probe were applied to the glass slides using a model 417 Affymetrix (Santa Clara, CA) arrayer to place submicroliter droplets onto glass slides. Each array contained 14 probes: three wild type sequences and nine mutant sequences corresponding to the  surface .32P-labeled target molecules bound across the hybridization array was performed with a scanner (HP Scanjet 4400 c), and analysis of densitometric images was performed using ScanAnalyze 2 software (Stanford University).Just prior to hybridization the slides were soaked for 1 h at room temperature with blocking agent (10 mM tripolyphosphate) and then rinsed twice with water and air-dried. The hybridization cocktail contained: 118 μL 5 M tetramethyl-ammonium chloride (TMAC) (Life Technologies), 9 μL 1 M Tris-HCl (pH 8.0) (Life Technologies), 0.72 μL 0.5 M EDTA, 1.8 μL 10% (w/v) sodium dodecyl sulfate, 14.4 μL 40% (w/v) polyethylene glycol, 20 μL labeled target DNA in 1X SSC, and 35.3 μL HPLC-grade water. A 40-μL aliquot of this cocktail was placed onto each array and a cover slip was applied. Slides were incubated in a humid chamber for 3 h at 25°C. After hybridization, the slides were washed by dipping several times in the same hybridization solution without polyethylene glycol and DNA, air-dried, and then wrapped in plastic film and placed against X-ray film (Kodak BioMax) for autoradiography. Detection and quantitation of To provide confidence in the interpretation of the signals produced by human samples, reference hybridization patterns were prepared using a full set of synthetic targets. For this purpose, thirteen synthetic targets (44–50 nt), representing the wild type and mutant sequences, were used independently and in combination, to represent homozygous and heterozygous conditions.The PCR products obtained were purified using the QIAEX II kit (QIAGEN Inc. USA). For each PCR product the presence or absence of a mutation in exons 7 and 8 was demonstrated via sequencing using the Big Dye Terminator kit (Perkin-Elmer) and a model 373 automated DNA sequencer .ARL performed all molecular assays, data collection and analysis, and drafted the manuscript. RMR conceived and designed the study, coordinated and managed the study, performed data analysis and participated in drafting the manuscript. MSV conceived and designed the study, coordinated and managed the study, performed data analysis and participated in drafting the manuscript. JMEL participated in the design and coordination of the study. AMT designed the oligonucleotide probes, performed bioinformatic analysis of targets, and participated in drafting the manuscript. KLB assisted in coordination of the study, data analysis, and drafting the manuscript. All authors read and approved the final manuscript.


Recombination is a nearly ubituitous feature of genomes; where and when it occurs can provide insights about its evolution and can affect our ability to identify genes that cause disease Consider a piece of text, either this one that you are now reading or any other. Surely they are all pretty much alike, in so far as they are all run-on strings of characters. In this same sense, we can envision that all DNA strands are alike because all are monotonous polymers with the same general chemical makeup. Indeed, this is how we think of DNA when considering its basic function of inheritance, in which all parts of all chromosomes must be duplicated and then passed from one cell generation to the next. The capacity for inheritance is fundamentally a consequence of DNA's general molecular structure, and not of its sequence per se, as But sequence does matter when DNA fulfills its other, more directly functional role. When the DNA that makes up a gene is exposed and expressed, when a gene is serving its individual function, then the detailed sequence means all.So where does recombination  fit in? Recombination hotspots are of strong interest to at least two quite different groups of biologists. For geneticists and cell biologists who study meiosis, the existence of recombination hotspots offers a way to learn what other processes are associated with recombination. This is partly how we know that homologous crossovers in yeast and other eukaryotes are initiated by the cleavage of single chromosomes, called “double-strand breaks” . It turnFor population geneticists, much of the interest in recombination hotspots comes from their possible effect on the patterns of DNA sequence variation along human chromosomes and from the possibility that these patterns could be used to map the position of alleles that cause disease. When multiple copies of the DNA sequence of a gene, or of a larger region of a chromosome, are aligned, they reveal the location and distribution of variation at individual nucleotide positions—single nucleotide polymorphisms (SNPs). Each particular sequence, or haplotype, will carry a configuration for the SNPs for that region . Investihttp://www.hapmap.org/), which has the goal of identifying a subset of SNPs that capture most of the relevant linkage information in the human genome  to fluctuate in location and intensity, in ways that would be hard to precisely predict without knowing what genes have been under selection and what patterns of linkage disequilibria there may have been.PLoS Biology is especially interesting. They report that chimpanzees do not have a recombination hotspot in the TAP2 region where humans have a fairly well characterized recombination hotspot (In this light, the paper by  hotspot . Ptak etCaenorhabditis elegans and the fruitfly Drosophila melanogaster) have not shown evidence of recombination hotspots. If we compare these organisms with yeast and mammals, which do show hotspots, we gain some more insight into the factors affecting the evolution of hotspots.As appealing as the recombination modifier theory of recombination hotspots may be, there is circumstantial evidence that argues against it and that suggests that recombination hotspots are not directly the byproduct of selection on alleles in linkage disequilibrium. Particularly important in this regard is that some wellstudied organisms   and thus elegans . Double-Recombination during meiosis seems to be required for proper chromosome segregation; however, in those organisms where recombination and double-strand-break hotspots occur, these phenomena are also required for proper formation of the SC. It is as if the recombination machinery has been partly co-opted for chromosome alignment in some eukaryotes more so than in others. The implication of these findings is that recombination hotspots are byproducts of other functional constraints associated with the recombination process. This does not rule out the evolutionary theory of recombination modifiers, or that the location and intensity of recombination hotspots may evolve rapidly, but it does suggest that we may not need to invoke the evolutionary modifier theory to explain the existence of recombination hotspots.Recombination hotspots co-occur with double-strand-break hotspots in some eukaryotes, and together these phenomena appear to play an important role in the formation of the SC in those organisms. Given the limited phylogenetic occurrence of recombination hotspots , general theories for the evolution of recombination may not be very helpful for understanding the existence of recombination hotspots. However, in those species where they do occur, it is quite possible that recombination hotspots do evolve in location and intensity. Furthermore, the presence of recombination hotspots in humans may have large effects on the length of local patterns of linkage disequilibrium (haplotype blocks) and thus on our ability to map disease alleles by their association with other markers.Double-strand break: A break in both strands of a DNA molecule, as distinguished from a break in just one strand.Linkage disequilibrium: A pattern of association between two SNPs or two loci that each have multiple alleles, such that pairs of particular SNPs or alleles, one from each locus, tend to co-occur within individuals or genomes more often than would be expected if the loci are sorting independently of each other.Recombination: The process of one double-stranded DNA molecule joining with another; specifically in the context of meiosis, the process of two homologous chromosomes exchanging large portions of their DNA .


Several studies have investigated the association between male pattern baldness and disease such as prostate cancer and cardiovascular disease. Limitations in the lack of standardized instruments to measure male pattern baldness have resulted in researchers measuring balding patterns in a variety of ways. This paper examines the accuracy and reliability of assessment of balding patterns by both trained observers and men themselves, using the Hamilton-Norwood classification system.An observational study was carried out in Western Australia with 105 male volunteers aged between 30 and 70 years. Participants completed a short questionnaire and selected a picture that best represented their balding pattern. Two trained data collectors also independently assessed the participant's balding pattern using the same system and the men's self assessment was compared with the trained observer's assessment. In a substudy, observers assessed the balding pattern in a photo of the man aged 35 years while the man independently rated his balding at that age.Observers were very reliable in their assessment of balding pattern (85% exact agreement, κ = 0.83). Compared to trained observers, men were moderately accurate in their self-assessment of their balding status (48–55% exact agreement, κ = 0.39–0.46). For the substudy the exact agreement between the men and the observers was 67% and the agreement within balding groups was 87%.We recommend that male balding patterns be assessed by trained personnel using the Hamilton-Norwood classification system. Where the use of trained personnel is not feasible, men's self assessment both currently and retrospectively has been shown to be adequate. Male pattern baldness is the most common form of baldness observed in human beings. The twoSeveral studies have been conducted investigating the association between male pattern baldness (as a proxy for testosterone levels) and various health issues such as prostate cancer -7 and caSubjects were male volunteers between the ages of 30–70 years recruited from sporting clubs, shopping centres, universities, the central business district and other public areas throughout the metropolitan area of Perth, the capital city of the state of Western Australia, during March 2003 to August 2003. Of the 105 volunteers recruited, two were less than 30 years of age, and therefore were ineligible for the study.Participants completed a questionnaire providing minimal information regarding demographic characteristics, including age, ethnicity, and level of education. Included in the questionnaire was an unlabelled Hamilton-Norwood classification scale Figure , which pWhilst the subjects completed the questionnaire, two data collectors with some training in recognising different balding patterns independently assessed the participant's balding pattern using the same classification system. On recruitment, participants were told the study was to help with medical research, however the role of the data collectors with regard to their independent assessment of the participant's balding pattern was not revealed until after the questionnaire was complete.Where possible, (sporting clubs and universities) volunteers were asked to bring a photograph of themselves at age 35 on the day on which the study would be done. The photographs were used by the observers to independently assess the men's balding patterns at age 35 while the participants completed the questionnaire. These participants were also asked to select the picture from the Hamilton-Norwood scale which best depicted their balding pattern at age 35, and were not permitted to look at the photographs while completing the questionnaire.For data analysis, we compared the assessments of the two observers with each other, and the assessment of the participants with the assessments of the observers. Initially, each of the 12 reference pictures included in the Hamilton-Norwood classification system[There were 69 participants (67%) between the ages 30–49 years, and 34 (33%) were between the ages 50–70 years. Whilst 70% of subjects considered themselves of "Australian" ethnicity , the remainder was made up of representatives from Europe (20.4%), Asia (4.9%), New Zealand (3.9%) and South America (1%). A third of subjects had trade education or equivalent (30.1%); 22.3% had completed high school but not gone onto further education; 12.6% had completed junior high school only; and, because of recruitment through universities, 19.4% of subjects had an undergraduate degree and a further 15.5% had completed post-graduate study.Overall, trained observers were found to be highly reliable at analysing balding patterns with an exact agreement of 85.4% and an agreement within balding pattern groups of 90.3% Table . CompareIn regards to how different demographic characteristics affect men's ability to predict their balding pattern Table , the chaThere were 15 subjects who provided photos of themselves aged approximately 35 years and both observers examined 13 of these. The inter-observer reliability for exact match was 81.8% (κ = 0.766, p < 0.001) and agreement within balding pattern groups increased to 100% (κ = 1, p < 0.001). Observer 2 only examined 13 subject, so for the 15 subjects examined by Observer 1, agreement between the men and the observer was 66.7% for exact match and 86.7% for agreement within the balding pattern groups.In this study we have shown that trained observers are very reliable in assessing men's balding patterns. Our data also show that, when compared to the trained observers, men themselves can assess their balding patterns quite well. In particular, men are accurate in assessing which balding pattern group they have. This result is important due to previous research suggesting that it is the overall pattern of hair loss rather than extent of balding that determines whether men are at an increased risk of developing negative health outcomes including prostate cancer,4,7.et al[There have been several studies investigating the link between male pattern baldness and prostate cancer-5,7,10, et al assessmeOther methods for assessing baldness have been used in clinical situations including reference grids used with standardized photographs of the scalp or in vivo; and vidThe majority of studies,7,10,15 The strengths of our study included the recruitment of volunteers from a broad cross-section of the population thus allowing for extrapolation of the results back to the wider community. As our research was performed as an observational study, care was taken to avoid the data collectors influencing the results. This included not informing participants that the observers would be assessing their balding pattern until after the questionnaire was complete. The data collectors also refrained from giving advice to participants when asked to help assess their balding pattern.Older subjects appeared to be better at assessing their balding group than men less than 50 years. This may be due to greater hair loss resulting in a more straightforward distinction between balding patterns, or possibly just a greater self-awareness of degree of balding amongst those in the older age group. The results with regard to education were confusing with men who had only senior high school education seeming to be best at assessing their balding patterns. This may have just been due to small numbers in the groups. Other demographic characteristics not included in our questionnaire may have been of interest in determining factors that influence accuracy of men's self assessment of their balding pattern. These include factors such as marital status, occupation, and personality sub-types, and we would encourage any further research into the area to investigate the possible relationship between these aspects and the accuracy of men's self-assessment.Any extrapolation of our data needs to take into account the differing methods of data collection between our study and other studies in which the man may obtain advice from partners or friends in the home environment, as well as have access to mirrors and photographs to assist in their assessment of current and past balding patterns. These factors do not negatively affect our results however, as the use of such help would ultimately increase accuracy of balding assessment from the already acceptable level shown in our results, rather than detract from it.The study of the accuracy of previous balding was limited by small sample size. Participants had to be approached prior to the study to provide a photograph of them at age 35 years. This limited numbers of eligible participants, and also meant that the men may have viewed the photograph of themselves before completing the questionnaire, which may have increased their accuracy of retrospective assessment. Difficulties in assessing vertex balding from the photographs by the observer also became apparent, as often photographs did not provide an adequate view of the top and back section of a participant's head. It is unlikely that these limitations can be overcome, as it would be difficult to devise another method for the observer to retrospectively assess the participant's balding pattern at age 35.Previous research has demonstrated an intra-observer rate of consistency in assessment of 98–99% using the Hamilton-Norwood scale,10. HamiFrom this study, we suggest that any further work requiring assessment of male balding patterns consider the use of trained observers as the gold standard of assessment. Where this is unattainable, we have shown that men's self evaluation is accurate enough to ensure reliability and validity of results. In addition, we believe this study demonstrates that if links are found between male balding patterns and health effects, that men can reliably determine their own balding pattern and assess their own risk.The author(s) declare that they have no competing interests.RT and JM planned the study, collected the data, analysed the data under supervision, and wrote the study report for a student project.JL assisted in planning the study and drafted the manuscriptLF assisted in planning the study, supervised data collection and analysis and edited the manuscript.All authors read and approved the final manuscriptThe pre-publication history for this paper can be accessed here:


When gene therapy was introduced nearly fifteen years ago, it was widely hailed as a panacea. Since many diseases have a genetic component, the hope was that gene therapy could replace compromised genes with healthy versions to treat everything from inherited disorders like cystic fibrosis to cancer and HIV. That great promise was quashed when a teenager suffering from a rare hereditary liver disorder died after participating in an experimental gene therapy trial in 1999: four days after being injected with millions of viruses engineered to deliver healthy genes to his liver, Jesse Gelsinger died. It seems the virus, derived from an adenovirus, targeted his immune cells rather than his liver cells, which triggered an immune response against the virus, resulting in massive organ failure.In another case, two young boys who received gene therapy for the severe immunodeficiency disorder known as “bubble boy disease” developed leukemia-like symptoms 30 months after treatment. In this case, the viral vector inserted itself near a promoter region—a site that initiates gene transcription—of a proto-oncogene, a gene that can initiate cancer. Since viral vectors can integrate at various genomic locations, the safety and effectiveness of gene therapy ultimately depends on being able to predict a virus's particular bias. Comparing retroviral vectors derived from three viruses, including two common gene therapy vectors, Rick Mitchell et al. report 3,127 sites where these viruses typically integrate into the human genome. The different vectors, they found, show different target preferences.Retroviruses use viral enzymes to copy their own genome, which is stored in an RNA transcript, into DNA. Now recognizable by the host's genome, the virus can integrate into one of the host's chromosomes. In this study, Mitchell et al. studied vectors derived from the human immunodeficiency virus (HIV), avian sarcoma-leukosis virus (ASLV), and murine leukemia virus (MLV). Introducing the viral vectors into human cells, the authors analyzed the gene expression profiles of the cells to determine where vectors integrate into human chromosomes and which, if any, genes they activate. Mitchell et al. then compared the integration sites with the transcription profiles.Each retrovirus, they discovered, showed distinct preferences for genome integration. HIV vectors tend to integrate into sites of active transcription, favoring chromosomal regions rich in expressed genes. MLV vectors tend to integrate near transcription initiation sites, confirming the results of a previous study, with a weak bias toward active genes. In contrast, the authors report, the ASLV vector “does not favor integration near transcription sites, nor does it strongly favor active genes.”Early efforts to understand how chromosomes may influence where viruses insinuate themselves into a chromosome focused on factors governing accessibility. Viruses are more likely to be integrated into chromosomal regions that are more accessible, which tend to be transcriptionally active sites. But since each of the three viruses studied here routinely targeted different sequences, the authors note, accessibility is probably just one factor. Specific chromosomal proteins, for example, might interact with the viral integration machinery and facilitate integration at nearby sites. Another possibility, the authors propose, is that DNA-binding proteins that bind to specific DNA sequences assist integration of one virus while impeding another.This could explain why ASLV behaved as it did in the human cells studied here. The virus might have more refined integration preferences during normal infection of chicken cells, the authors note, but its integration machinery can't interact properly with human cells. The leukemia-like effects of the bubble boy gene therapy stemmed from integration of a mammalian retrovirus—the MLV vector—near an oncogene promoter region. Since ASLV tends to avoid both transcription initiation sites and active gene sites, it could be a more promising candidate for human gene therapy. With the draft chicken genome sequence now complete, researchers can investigate whether that proves true. But for now, Mitchell et al. make the case that scientists can gain more control over where viral vectors integrate into the human genome by selecting different retroviral integration systems. Only time will tell whether more control translates into safer gene therapy protocols.


Only the latter reduced (by greater than 95%) constitutive and inducible nicotine. With D4-nicotinic acid (NA), we demonstrate that silencing pmt inhibits nicotine production, while the excess NA dimerizes to form anatabine. Larvae of the nicotine-adapted herbivore Manduca sexta (tobacco hornworm) grew faster and, like the beetle Diabrotica undecimpunctata, preferred IRpmt plants in choice tests. When planted in their native habitat, IRpmt plants were attacked more frequently and, compared to wild-type plants, lost 3-fold more leaf area from a variety of native herbivores, of which the beet armyworm, Spodoptera exigua, and Trimerotropis spp. grasshoppers caused the most damage. These results provide strong evidence that nicotine functions as an efficient defense in nature and highlights the value of transgenic techniques for ecological research.Plants produce metabolites that directly decrease herbivore performance, and as a consequence, herbivores are selected for resistance to these metabolites. To determine whether these metabolites actually function as defenses requires measuring the performance of plants that are altered only in the production of a certain metabolite. To date, the defensive value of most plant resistance traits has not been demonstrated in nature. We transformed native tobacco Transgenic plants confirm the role that nicotine has in protecting tobacco plants from predators in nature and demonstrates the power of transgenic tools in studying ecological interactions in the field Plants produce many secondary metabolites, of which some are thought to function as direct defenses against pathogens and herbivores by reducing their performance, survival, and reproduction. Numerous plant allelochemicals with antiherbivore properties are classified according to their mode of action (e.g., toxins, antifeedants, antidigestive proteins, etc.)  and haveArabidopsis. In this study, we use transgenic silencing to alter a single putative resistance trait—the production of nicotine—and thereby establish its contribution to plant resistance in the field.Pharmacological studies demonstrating a resistance effect of metabolites applied to plants or artificial diets , and stuManduca sexta (tobacco hornworm) tolerates doses of nicotine that are fatal to unadapted herbivores but grows more slowly on high-nicotine diets  in its native habitat increases nicotine content, which is correlated with enhanced plant fitness when plants are attacked (In the native tobacco species attacked . HoweverM. sexta larvae were established using transgenic N. sylvestris plants silenced in their nicotine biosynthesis by antisense expression of putrescine N-methyl transferase (PMT). Plant consumption and the performance of M. sexta larvae were negatively correlated with constitutive nicotine levels in laboratory feeding trials (N. attenuata with inverted-repeat pmt (IRpmt) and antisense pmt constructs and found that only IRpmt plants had strongly reduced nicotine content. We characterized the defense and growth phenotypes of two independently transformed homozygous IRpmt lines and found that measured direct and indirect defenses did not differ from those of the wild-type (WT) plants, except for a dramatic reduction (greater than 95%) of MeJA-elicited and constitutive nicotine production and an increase in anatabine content. In pulse-chase experiments with D4-nicotinic acid (NA) ethyl ester, we demonstrated that the increased anatabine likely results from a dimerization of the NA that would normally have been used in nicotine biosynthesis. In feeding trials, M. sexta larvae preferred and grew faster on IRpmt than WT leaves. We transplanted WT and IRpmt plants into N. attenuata's native habitat in southwestern Utah and elicited a subset with MeJA. Several naturally occurring herbivore species attacked and damaged unelicited IRpmt plants more than unelicited or elicited WT and elicited IRpmt plants. These results demonstrate that nicotine functions as an effective resistance trait under natural conditions.In laboratory trials, resistance benefits of nicotine production against g trials ; whetherpmt constructs (25 lines of pNATPMT1 and six lines of pCAMPMT1) compared to WT  with reduced nicotine levels were further characterized. Southern blot analysis using a probe hybridizing to the selective marker in the IRpmt construct demonstrated that both lines contained a single insertion  contained the same flanking sequence  alkaloid contents of the two IRpmt lines were about one-half and one-third of the WT levels, respectively, of which anatabine comprised 30% and 23% . The differences were comparable to those observed for M. sexta larvae reared on nicotine-enriched artificial diets  plants, preferred to initiate feeding on the latter . Such behavior suggests that nicotine plays an important role in determining feeding sites of M. sexta larvae, as has been suggested in a study with cultivated tobacco  verified the results described above for the same experiment conducted with glasshouse-grown plants. Thus, the phenotype of glasshouse-grown IRpmt plants was not altered by growth under field conditions. In addition, choice tests with field-collected D. undecimpunctata, which was observed colonizing only IRpmt plants in the field plantation, revealed that 77% of these beetles preferred the nicotine-deficient IRpmt leaf material over WT . Another beetle species observed occasionally on WT plants, Trichobarus mucorea, does not distinguish between WT and IRpmt leaf material in choice tests .Since secondary metabolism is known to be sensitive to environmental parameters that differ between glasshouse and field conditions  for the introduction of organisms altered through genetic engineering, flowers were removed as they matured, and therefore we could not directly measure the fitness consequences of this greater herbivore load. However, in other experiments with N. attenuata plants grown in natural populations, leaf area damage is negatively correlated with capsule number .IR feeding C. One-thpmt plants to levels found on WT plants, suggesting that MeJA treatment elicits defense traits that are as efficient as the constitutive levels of nicotine in protecting plants. MeJA elicitation of N. attenuata plants is known to induce a diverse suite of transcriptional responses and secondary metabolites including TPIs, phenolics, flavonoids, phenolic putrescine conjugates, diterpene sugar esters, and volatile organic compounds  in agricultural systems  grown from field-collected seeds (Agrobacterium tumifaciens (strain LBA 4404)–mediated transformation procedure are described in N. attenuata pmt genes, plants were transformed with pCAMPMT1 and pNATPMT1 vectors, which contain a gene fragment of pmt1 (which has 95% identity to pmt2) in an antisense orientation, and pRESC5PMT, which contains the pmt gene fragment twice in an inverted orientation separated by intron 3 of the Flaveria trinervia gene pyruvate orthophosphate dikinase (pdk) (for vector construction and plasmids see 1 plants were screened for hygromycin resistance (hygromycin phosphotransferase II gene of the vector pCAMBIA-1301) and constitutive and induced nicotine accumulation; homozygosity was determined by resistance screening of the T2 plants. Two independently transformed homozygous IRpmt lines (108 and 145) were further characterized by Southern blot analysis and by the rescuing of the transformation vector from genomic DNA into Escherichia coli to identify copy number and insertion site of the T-DNA (see ed seeds  and inbr-DNA see .n = 3/line/treatment). PMT transcript accumulation was analyzed by real-time PCR . cDNA was generated from 20 ng of RNA with MultiScribe reverse transcriptase (Applied Biosystems), and amplified using the qPCR core reagent kit (Eurogentec, Searing, Belgium) and a probe and primers that were gene-specific (for sequences see n = 8–10/line/treatment). Samples were analyzed by HPLC for alkaloids, caffeoylputrescine, chlorogenic acid, and rutin  and WT plants were grown in 1-l hydroponic vessels in a climate chamber as described in nd rutin . A peak 4-NA ethyl ester (1 mM) in their hydroponic solution 24 h after MeJA elicitation (n = 4/line/treatment). After 4 d, the treated leaf was harvested and extracted as above, but analyzed by LC/MS/MS to detect incorporation of the deuterium into nicotine and anatabine  enclosed in open-top volatile collection chambers were collected for an 8 h period starting 24 h after MeJA elicitation of the first two source leaves, and analyzed by GC/MS (n = 5/line/treatment) by radial diffusion activity assay  were placed on the first-stem leaf of 8-wk-old WT and IRpmt (108 and 145) plants and allowed to feed for 14 d. Larval mass was recorded at 8, 10, 12, and 14 d.In the glasshouse, 2-wk-old seedlings were planted individually into 2-l pots with potting soil  at 26–28 °C under 16-h supplemental light from Philips Sun-T Agro 400- or 600-W Na lights. For analysis of performance, newly eclosed M. sexta was determined by placing newly eclosed larvae in the center of a 3-cm–diameter cup containing, on opposite sides, 1.5-cm2 WT and IRpmt (108) leaf pieces and recording the leaf on which larvae started feeding (n = 44).The first feeding choice of N. attenuata's natural habitat in southwest Utah, transformed IRpmt (108) and WT plants were exposed to naturally occurring herbivores dispersing from adjacent populations. To allow for spatial heterogeneity, plants were transplanted in a paired design (with 0.3 m and 1.5 m between plants of a pair and between pairs, respectively) in which plants were matched for equal rosette diameters. Plants were grown in soil  for 5 wk after germination (pmt plants were elicited with 150 μg of MeJA per plant applied in 20 μl of lanolin paste to the two youngest rosette leaves. Starting 4 d after transplantation, each plant was examined for damage and insects (including predators and eggs) every other day for 14 d. Damage amount was estimated as a percentage of the total leaf area, and the characteristic damage caused by caterpillars, beetles, grasshoppers, myrids, and leafhoppers was noted separately. The most abundant herbivores observed in the field plantation during the release were S. exigua, Trimerotropis spp., E. hirtipennis, and D. undecimpunctata. M. sexta and M. quinquemaculata occurred in the season only rarely, and no eggs were laid in the plantation during the 14 d. As plants began to elongate and produce flowers, they were examined daily, and all flowers were removed before opening and anthesis to meet the performance standards in the Code of Federal Regulations (7CFR340.3c). Consequently, direct fitness measures were unobtainable in this experiment.In a field plantation  in mination , and werpmt plants in the plot (n = 6) were taken 7 wk after transplantation and frozen (dry ice). To determine if the herbivore phenotype of IRpmt plants observed in glasshouse-grown plants was retained in plants grown under natural light conditions, the M. sexta choice experiment was repeated. The first feeding choices of freshly eclosed M. sexta larvae (North Carolina State University) and of adults of field-collected D. undecimpunctata and Trichobarus mucorea (Chrysomelidae and Curculionidae) found on N. attenuata were determined as described above.For analysis of alkaloids and TPIs under field conditions, leaf samples of WT and IRFigure S1N. attenuata IRpmt lines (108 and 145) and WT plants. Genomic DNA (15 μg) from individual plants of the three genotypes and the plasmid used for transformation pRESC5PMT (4 ng) were digested with EcoRV and blotted onto nylon membranes (hygromycin phosphotransferase II gene from pCAMBIA-1301, which is specific for the selective marker on the T-DNA and signifies one insertion in each of the two lines.(A) Southern blot analysis of two independently transformed embranes . The blopmt lines and therefore loading of the WT was controlled by stripping and rehybridization with a PMT probe, which clearly revealed the endogenous pmt1 and pmt 2 genes described ((B) Ethidium bromide staining of the DNA revealed an overload of the DNA of the IRescribed  (unpubli(6.3 MB TIF).Click here for additional data file.Figure S2pmt did not change the levels of (A) anabasine, (B) caffeoylputrescine, (C) chlorogenic acid, and (D) rutin (mean ± standard error [SE]) in two independently transformed N. attenuata lines (108 and 145) compared to WT plants. Plants were harvested 4 d after receiving one of four treatments: untreated control (Con), wounding (W), wounding and regurgitate application (W+R), and application of 150 μg of MeJA per plant applied in a lanolin paste. Plants were treated at the first two fully expanded (source) leaves and wounding was performed by generating three rows of puncture wounds on each leaf side using a pattern wheel. Subsequently, 10 μl per leaf of either water or M. sexta regurgitate diluted 1:1 (v:v) was dispersed over the puncture wounds (n = 8–10).Inverted-repeat silencing of (179 KB PPT).Click here for additional data file.Figure S3cis-α-bergamotene emission (mean ± SE) in two independently transformed N. attenuata IRpmt lines (108 and 145) did not differ from WT plants 4 d (for TPI) and 10 h (for cis-α-bergamotene) after receiving one of four treatments (as described for S2): untreated control (Con), wounding (W), wounding with additional regurgitate application (W+R), and MeJA elicitation. IS, internal standard.Levels of (A) TPI and (B) (73 KB PPT).Click here for additional data file.Figure S4N. attenuata plants transformed with an IRpmt construct (108 or 145) did not differ in (A) stalk length [nPMT = 43, nWT = 57, nPMT* and nWT* = 28] and (B) rosette diameter [n = 8] from WT grown under either field (A) or glasshouse (B) conditions. Plants in (A) were untreated or elicited (*) with MeJA 7 d after plants were transplanted into a field plot in a native habitat.(98 KB PPT).Click here for additional data file.Figure S5N. attenuata lines with reduced levels of two PMTs due to posttranscriptional gene silencing. Both (A) pCAMPMT1 (10.7 kb) and (B) pNATPMT1 (9.7 kb) allow the synthesis of pmt antisense RNA. (C) pRESC5PMT (12.4 kb) was used for the synthesis of pmt RNA capable of forming an inverted repeat. Functional elements: bla, beta-lactamase gene from plasmid pUC19; hptII, gene for hygromycin resistance from pCAMBIA-1301; LB and RB, left and right border of T-DNA; nptIII, aminoglycoside phosphotransferase of type III from Streptococcus faecalis; ori ColE1, origin of replication from pUC19; ori pVS1, origin of replication from plasmid pVS1; PCaMV and TCaMV, 35S promoter and terminator of cauliflower mosaic virus; pdk i3, intron 3 of pdk; pmt1, gene fragment of pmt1 ; PNOS and TNOS, promoter and terminator of the nopaline synthase gene; repA pVS1, replication protein gene from pVS1; sat-1, nourseothricin resistance gene; staA pVS1, partitioning protein gene from pVS1. Displayed restriction sites mark the borders of functional elements, which are displayed in gray if on the T-DNA and in black if outside the T-DNA.This figure shows plasmids used for the generation of (56 KB PPT).Click here for additional data file.Figure S6N. attenuata pmt1 and pmt2 mRNA .Click here for additional data file.Protocol S1(58 KB DOC).Click here for additional data file.bla from puc19 (L09137), hygromycin phosphotransferase II from pCAMBIA-1301 (AF234297), pdk (X79095), pmt1 (AF280402), and pmt2 (AF280403).GenBank accession numbers for the genes discussed in this paper are