P=0.0012). Analysis of a number of other genes that are strongly and specifically expressed in tumour over normal lung, including SERPINB5, TERT and PRAME, showed marked allelic expression imbalances in the tumour tissue in the context of balanced or only marginally imbalanced relative allelic copy numbers. Our data support a model of early carcinogenesis wherein defects in the process of inactivation of lung stem-cell associated genes during differentiation, contributes to the development of carcinogenesis.Aurora kinases are key regulators of chromosome segregation during mitosis. We have previously shown by microarray analysis of primary lung carcinomas and matched normal tissue that AURKB (22 out of 37) and AURKA (15 out of 37) transcripts are frequently over-represented in these tumours. We now confirm these observations in a second series of 44 carcinomas and also show that aurora B kinase protein levels are raised in the tumours compared to normal tissue. Elevated levels of expression in tumours are not a consequence of high-level amplification of the AURKB gene. Using a coding sequence polymorphism we show that in most cases (seven out of nine) tumour expression is predominantly driven from one AURKB allele. Given the function of aurora B kinase, we examined whether there was an association between expression levels and genetic instability. We defined two groups of high and low AURKB expression. Using a panel of 10 microsatellite markers, we found that the group showing the higher level of expression had a higher frequency of allelic imbalance ( Lung cancer is the leading cause of cancer-related death . Conversely, where alleles are balanced in genomic DNA, the identification of allelic expression imbalance (AEI) in tumours, but not matched normal tissue  sequences are likely to make the most effective drug targets. If we further assume that such upregulation is often associated with localised genetic or epigenetic damage, then one approach to identify potentially targetable genes that are pathologically miss-regulated is to compare the allele-specific levels of gene expression in tumour and normal tissues  as differentially expressed in a high frequency  of primary NSCLC , is thou7) were split into two flasks and after further growth, were subsequently harvested in either an exponential or confluent, growth arrested state (each flask after different lengths of time in subculture). Total RNA was extracted from these parallel cultures using a standard Trizol protocol and cDNA synthesised as for primary tissue as described in RNA and DNA was extracted from tumour and matched normal lung (NSCLC) tissue samples from 48 Swiss patients  and a smaller number  of Liverpool patients  as part μg ml−1 ethidium bromide, in 0.5 × TBE and visualised by UV illumination. Relative expression was quantified on an Agilent 2100 Bioanalyser using DNA 1000 LabChips® (Agilent Biotechnologies). The peak height of the test gene was divided by that of the control gene within each sample. The comparative value in the patient tumour tissue was then divided by that from the matched normal to calculate the fold difference in expression of the test gene in each patient. For the purpose of this analysis, differential expression of the test gene of ⩾2-fold in the tumour was arbitrarily considered to indicate a relative over-representation of that gene in the tumour. Primers used for APPBP2 were 5′ gaactgtgtgcactcctatttg and 5′ ccgtgccaaatacactgcatgt and for AURKB, 5′ cagagagatcgaaatccaggc and 5′ ccttgagccctaagagcagat. Primers were confirmed to be cDNA specific by test amplifications of genomic DNA.Comparative multiplex RT–PCR was used to compare levels of test gene transcripts between normal lung and tumour samples, as described previously (Allelic (im)balance relates to the genomic balance of the two parental alleles in a DNA samples; allelic expression (im)balance refers to the relative levels of expression of each of those alleles in a cDNA sample. A tissue may therefore show allelic balance but AEI if one parental allele is expressed more strongly than the other. Measurements of AI and AEI were carried out using single-nucleotide polymorphisms (SNPs) localised with the mRNA coding region of each specific gene (cSNPs) in simple PCR (AI) and RT–PCR (AEI) restriction fragment length polymorphism (RFLP) assays. Single-nucleotide polymorphisms were selected from public domain databases and validated in PCR-RFLP assays of DNA extracted from normal lung tissue. Exon-spanning primers were designed around the SNP and confirmed to be cDNA-specific in test amplifications. Subsequently, the patient population  was scanned to identify informative (heterozygote) individuals. Allelic balance in normal and tumour DNA, and in normal (where a product was amplifiable) and tumour cDNA was measured for each informative patient.NcoI RFLP, with the T (CCATGG) but not the C (CCACGG) allele cleaved by the restriction enzyme. DNA specific (5′ cagaatgggtagtcaaggaag and 5′ gcgacagattgaagggcagag) or cDNA specific (5′ caacgagacctatcgccgc and 5′ gcaccctccgagagttggccc) primer pairs were used in 25 μl PCR (or RT–PCR) reactions containing 1 μl (0.1 μg) of genomic DNA (or 1 μl of cDNA), 0.25 μl of each primer (0.5 μg μl−1) and 12.5 μl of Qiagen HotStarTaq Master Mix and 11 μl of dH2O. Reactions were cycled at 95°C for 15 min, 30  cycles of  followed by 30 min at 60°C in a Biometra T3 Thermocycler. Products were visualised on 2.5% agarose gels.A nonsynonymous C/T (Thr/Met) polymorphism in exon 9 (nt 950 in NM_004217) creates a natural RFLP-PCR and RFLP-RT–PCR analyses: A 3 μl aliquot of each PCR product (∼0.5 μg) was digested with 3 U of restriction endonuclease in 20 μl reactions at 37°C for 5 h. Digests were visualised by gel electrophoresis and relative allelic expression quantified by running 1 μl aliquots of the digest on an Agilent 2100 bioanalyser (Agilent Technologies) across a DNA 1000 LabChip®. Ratios of the peak height of the smaller cut band  to the larger uncut band  for each of the heterozygous genomic DNA and cDNA samples were calculated. The mean and the standard deviation of the values for the normal genomic DNA samples and the normal cDNA samples were determined, and tumour genomic DNA and the tumour cDNA samples that deviated by more than three standard deviations (s.d.) from the mean of the corresponding normal samples were scored as showing AI or AEI.Hpy8I RFLP with the G (GTCCAC) but not the A (ATCCAC) allele cleaved by the restriction enzyme. DNA (5′ gtggactaatcccagcaccat and 5′ ctatggaatccccaccatcttc) and cDNA specific 5′ cagtgcagatgatgaacatgg and 5′ ctatggaatccccaccatcttc primers were used in PCR/RT–PCR reactions with the conditions described for AURKB. Reaction products were digested as described but using only 0.5 U of enzyme/reaction for 5 h. Allelic ratios were determined as for AURKB but as so few normal lung cDNAs gave an analyzable product, both AI and AEI were scored with reference to the mean and s.d. values of normal DNA digests.A nonsynonymous A/G  polymorphism in exon 7 (nt 1022 in NM_002639) creates a natural FauI RFLP with the G (CCCGC) but not the A (CCCAC) allele cleaved by the restriction enzyme. DNA (5′ caggaggccgttgcttcgta and 5′ cgtctactgtgagggacctc) and cDNA specific (5′ cagtgcagatgatgaacatgg and 5′ ctgccagctccacaagtctc) primers were used in PCR/RT–PCR reactions with the conditions described and digests carried out using 0.6 enzyme units/reaction at 55°C for 5 h. AI and AEI were scored as for SERPINB5.A nonsynonymous A/G (Trp/Arg) polymorphism in exon 3 (nt 305 of NM_206956) generates a natural Psp0M1 RFLP with the G (GGGCCC) but not the A (AGGCCC) allele cleaved by the restriction enzyme. DNA primers (5′ cgaagaagccacctctttggag and 5′ cagggagatgaacttcttggtgt) were used in standard PCR reactions and products digested with 3 enzyme units/reaction at 37°C for 3 h. Tumour AI was scored by visual comparison with normal DNA products. Given the apparently low levels of telomerase mRNA in primary tumour tissue, a nested protocol was used for RT–PCR. Initial products (38 cycles with 5′ cgaagaagccacctctttggag and 5′ tacacactcatcagccagtgca) were diluted 1/50 and a 1 μl aliquot reamplified with internal primers (5′ tttggagggtgcgctctctg 5′ caggatctcctcacgcagac). Products were digested as for DNA. Only one allelic band was visible in tumour RT–PCR products and so relative quantitation was not attempted.A synonymous (A/G) polymorphism in exon 2 (nucleotide 970 of AF015950) creates a natural Western analysis of protein extracts of tumour and normal lung tissue were carried out essentially as described .Gene expression relative to the nondifferentially expressed control gene, APPBP2 was measured using Applied Biosystems (AB) predesigned TaqMan cDNA-specific assays: for APPBP2 (Hs00197271_m1), AURKB (Hs00177782_m1), AURKA (Hs00269212_m1) and SERPINB5 (Hs00184728_m1). It should be noted that the real time assays measured expression at a different point within the cDNA to the comparative multiplex assays. Triplicate 20 CT of test and control genes in amplifications of normal and tumour tissue derived DNA. Control loci which in our experience do undergo gene amplification or homozygous deletion in primary lung carcinomas were selected. For the initial analysis, the MGMT locus was used (10q26.3 – defined by NM_002412). Taqman PCR assays were designed for this locus, a second control region  and for the test gene: AURKB, using Applied Biosystems Primer Express v2.0 software and standard parameters. Triplicate PCR reactions for each sample containing 20 μl of AB TaqMan Universal PCR Master Mix, ∼200ng of genomic DNA, PCR primers (1 mM) and a TaqMan probe (250 nm) in a total of 40 μl were cycled under the standard AB conditions detailed above. The ΔCT between the AURKB and MGMT amplifications were calculated for each tumour and compared to a mean ΔCT derived from 20 normal lung DNA samples. Tumour samples with ΔCT values greater than the normal mean±3 standard deviations were considered to show significant shifts in relative gene copy number from normal DNA. Subsequently, for a small number of samples a ΔΔCT value was calculated by comparing tumour DNA to its matched normal tissue derived DNA. Primer sequences used were, for MGMT, 5′ gcagggtaacgcatagccttac and 5′ tcgcagcacactgttttgaga with a TaqMan probe of 5′ 6-FAM cacaaccgcaaccacgagtccgt TAMRA 3′; AURKB, 5′ tccctgttcgcattcaacct and 5′ gtcccactgctattctccatcac with a TaqMan probe of 5′ 6-FAM ctgttctctccttagctgcccctggc TAMRA 3′; HBG 5′ gcccttgtggatgcctaaga and 5′ gtctctcccttgaaatgctgtga with a TaqMan probe of 5′ 6-FAM cccatgccctcaagtgtgcagattg TAMRA.Relative gene copy number was quantified using the ΔΔμl) containing 2 μl (0.2 μg) of genomic DNA were cycled on a Biometra T3 Thermocycler. In addition to target DNA, each reaction contained following primers: D3S1263 (2.5 pmol), D3S1300 (2 pmol), D3S1566 (1 pmol), D5S644 (2.2 pmol), D9S157 (5 pmol), D9S161 (1.7 pmol), D13S153 (2 pmol), D13S171 (2 pmol), D13S263 (2 pmol), D17S938 (4 pmol); 5 μl of Qiagen Multiplex PCR Kit Master Mix and 2 μl dH2O. Cycling conditions were 95°C for 15 min, 30 cycles  followed by 30 min at 60°C.Proprietary PCR primers for the microsatellite loci D3S1263, D3S1300, D3S1566, D5S644, D9S157, D9S161, D13S153, D13S171, D13S263, D17S938 were selected from Applied Biosystems linkage sets. These markers have previously been shown to show consistent allelic imbalance in lung carcinoma samples . Samples were denatured at 95°C for 5 min, chilled on ice and 1 μl was loaded on a 5% denaturing polyacrylamide gel on a 377 ABI sequencer. The gel image was analysed using the ABI Genescan™ and Genotyper™ software. Allelic imbalance was scored according to previously established performance criteria if the allelic ratio (A1/A2 tumour)/ was outside a range of 1.25–0.75 (PCR products were purified using 2 Comparisons were carried out using a 2 × 2 contingency table running two-tailed Fisher's exact tests (GraphPad). For associations with survival, patients were further placed into two groups according to their AURKB expression levels . Survival differences were analysed using Kaplan–Meier survivor function estimators and log-rank test .In addition to the noted over-representation of AURKB transcripts in NSCLC, further analysis of the original gene expression array data set . ExpressCT quantitative RT–PCR (qRT–PCR) was used to examine the expression of AURKB relative to APPBP2 in the same 44 samples (P=0.028) (P=0.153) (data not shown).In a confirmation of the cmRT–PCR data, Taqman real-time ΔΔ samples . This an samples . Surviva samples . A thresP=0.028) , whereasCT of 12 cycles). AURKA expression was also reduced in confluent relative to exponentially growing cells but to a lesser extent . In each case APPBP2 was used as the qRT–PCR control gene. This raised the possibility that the strong expression of AURKB seen in tumours was merely a consequence of the higher proliferative state of tumour over normal tissue. If this were the case, then it might be expected that both parental alleles would be expressed equally in tumour tissue. We therefore examined whether expression of the gene in both normal and tumour tissue was allelically balanced.In order to determine whether the elevated expression of aurora B kinase was a normal characteristic of proliferating bronchial cells, we firstly examined AURKB expression levels in a primary cell culture model under different conditions. The early-passage primary culture was split into two flasks and the cells were allowed to grow for different lengths of time. From the first flask, RNA was isolated when the population was subconfluent and the cells in exponential growth. In the second parallel flask, the cells were grown to confluence and held for 2 days in this state before RNA was isolated. Using comparative multiplex  and realNine patients  were heterozygous for a nonsynonymous cSNP in exon 9 of the gene. Using PCR-RFLP and RT–PCR-RFLP analyses, we were able to show that relative to the patient matched normal cDNA, seven out of nine of these samples showed a marked AEI favouring one or other AURKB allele . In six out of seven of these cases, this AEI was associated with a phase-matched allelic imbalance in the tumour DNA . The allCT (AURKB-MGMT) for 24 normal lung DNA samples was 0.4 with a range of 0.6 cycles. The mean ΔCT for the 48 tumour DNA samples was significantly different at −0.3  and the range  was wider, suggesting that a number of the tumour samples had marginal increases in AURKB copy number. The ΔCT of 17 out of 48 tumour DNA samples were outside the normal sample ΔCT mean−3s.d. .Elevated gene expression for AURKA is associated in a number of tumours with high-level amplification of the gene  in this analysis also acted therefore as a control for MGMT stability. In both cases, the qPCR data suggested a low-level over-representation of the AURKB genomic locus in the tumour DNA relative to control levels :patient 224 : 1.9 and 1.7-fold and patient 230 : 2.9 and 2.5-fold . These results therefore confirm that the MGMT locus is not affected by copy number differences in these tumours. The data also thereby confirm an increase in AURKB copy number has occurred in these tumours and further suggest that the allelic imbalance in case 224 was a consequence of additional copies of one allele and not deletion of the other in a subpopulation of tumour cells.Taken together, these data suggest firstly that no tumour samples show high level amplification of the AURKB gene and, secondly, that a large number of tumours show apparent low level increases in gene copy number. Although we have no data suggesting that the MGMT locus undergoes homozygous deletion in NSCLC cells , we repeated the analysis on two samples showing apparently low levels of AURKB amplification using a second locus on a different chromosome as a control. Two samples  were analysed with their matched normal tissue and a formal ΔΔIn vitro data have indicated that the forced expression of aurora B kinase in Chinese hamster embryo cells is associated with increased expression of histone H3 and subsequent anueploidy  over those with low-level  expression (P=0.0012). To confirm this conclusion, these 30 cases were reranked using the Taqman qRT–PCR data .ueploidy . TumoursPCR data . While dvs 66, 52 vs 69, P=0.8).Our earlier microarray analysis had indicated that AURKA was also frequently over-represented in primary NSCLC. Mindful of the suggestion of Our previous analyses of the expression of two genes that are strongly expressed in tumours have suggested that these sequences are normally expressed in multipotent components of the bronchial airway . Given ovs normal tissue by microarray analysis  progenitor. In terms of the utility of a particular protein as a diagnostic or drug target, the reason why a candidate gene is strongly expressed in the tumour cell may be of secondary importance compared to how specifically that gene is expressed by the target cell and how vital that product is to host cell function. However, if our aim is to identify genes that are causally driving aspects of the neoplastic phenotype, then we need to identify those sequences which are expressed inappropriately as a consequence of induced DNA damage or erroneous epigenetic marking. Such information may increase our understanding of the disease process, guide future drug discovery or provide a rationale for the use of particular drugs in particular diseases.A central question when examining such gene expression data, especially when as in most cases the expression pattern or even the identity of the cell of origin of the tumour is not known, is whether strong differential tumour expression in bulk tumour in vitro bronchial cell preparation used in our analysis was uninformative for the cSNP, we could not confirm that the high level of gene expression seen in actively growing normal cells was bi-allelic. However, this conclusion is suggested by the analysis of AURKB expression in normal lung tissue from informative patients, where both alleles were expressed equally in each case. There are a number of reasons why the high-level expression of a gene in a tumour might occur predominantly from one of two genomically balanced alleles. These include cis-located mutational or epimutational differences, chromosomal translocation and polymorphism in tumour-utilised promoters or transcription factor binding sites. Further investigations are required to determine the cause(s) of the AEIs seen in AURKB and other genes implicated in NSCLC.Following such arguments, this study was an attempt to examine whether deregulation of aurora kinase genes, in particular AURKB, was implicated in lung cancer development. We have shown that AURKB is strongly expressed in exponentially proliferating bronchial epithelial cells in culture and that this expression is markedly reduced in confluent cells. We have also shown that almost all tumours show higher levels of AURKB (and AURKA) expression than their matched normal lung tissues, which could therefore simply be a consequence of a higher proliferative index, or be typical of the progenitor cell and atypical of the bulk of normal lung cells. But crucially, we have provided evidence that this expression is likely to be inappropriate given that in tumours but not matched normal tissue, it is generally strongly biased towards one parental allele. In many cases, this was associated with a marginal allelic imbalance at the genomic locus and a marginal increase in certain samples in locus copy number. As the Further to the allelic expression data, we were able to show that patients with tumours expressing AURKB at the highest levels had a shorter progression-free survival. This observation supports the hypothesis that excessive AURKB expression is a marker for aggressive tumour biology. However, it must be stressed that this patient series is small and therefore this hypothesis must be viewed with some caution. Additional studies on larger patient series will be needed to more fully explore the role of AURKB expression as a potential prognostic marker for NSCLC patients.The aurora family  are serine threonine kinases. Considerable attention is currently being given to the question of whether these proteins might be effective anticancer drug targets  attachments  as a specific example, we reasoned that the rare expression of such genes in histologically normal differentiated cells and more commonly in preneoplastic lesions (Elegant experimental evidence exists that a stem cell chromatin strand is retained in the stem cell progeny of asymmetric divisions (We believe that this work represents the first comprehensive analysis of the expression of AURKB in primary NSCLC. We further believe that the study provides some measure of rational justification for the testing of aurora B kinase inhibitors in the treatment of invasive lung cancer.


Examination of an exhaustive database of uncomplexed proteins reveals that exposed BHBs invariably occur within such sub-nanoscale cavities in native folds, while this level of local ruggedness is absent in other regions. By contrast, BHB exposure in misfolded proteins occurs with larger local curvature promoting backbone hydration and consequently, structure disruption. These findings unravel physical constraints fitting a spatially dependent least-action for water coordination, introduce a molecular design concept, and herald the advent of water-tight peptide-based materials with sufficient backbone exposure to remain flexible.Soluble proteins must maintain backbone hydrogen bonds (BHBs) water-tight to ensure structural integrity. This protection is often achieved by burying the BHBs or wrapping them through intermolecular associations. On the other hand, water has low coordination resilience, with loss of hydrogen-bonding partnerships carrying significant thermodynamic cost. Thus, a core problem in structural biology is whether natural design actually exploits the water coordination stiffness to seal the backbone in regions that are exposed to the solvent. This work explores the molecular design features that make this type of seal operative, focusing on the side-chain arrangements that shield the protein backbone. We show that an efficient sealing is achieved by adapting the sub-nanoscale surface topography to the stringency of water coordination:  The loss of hydrogen-bonded partners implies a significant thermodynamic cost for a water molecule dehydronsTo maintain structural integrity, soluble proteins often protect backbone amide-carbonyl hydrogen bonds BHBs,  by “wrapAn exhaustive analysis of uncomplexed proteins reveals that under-wrapped backbone regions present cavities fine tuned (∼1 to 3.5 Å-curvature radius) to exclude water by curtailing its hydrogen-bonding capabilities. This exclusion may be relaxed if water is capable of forming more than one hydrogen bond with the protein, as in water-mediated intramolecular interactions de facto exposed to the solvent. This introduces a conundrum as solvent exposure can only be reconciled with structural stability if we assume that water cannot penetrate available interfacial space. This is indeed the case as shown in The exposed backbone hydrogen bonds (EBHBs) represent a class of structural vulnerability whereby the bond is insufficiently wrapped or shielded from solvent by side-chain nonpolar groups in lieu of the carbonyl oxygen are statistically indistinguishable. Strikingly, water gets closer to a well-wrapped BHB, with most probable distance ∼3Å, than to an exposed bond, where the distance distribution peaks at ∼5Å.To determine the location of the closest water molecule around a BHB, we compute the minimum distance between the oxygens of carbonyl and water. This convention is adopted for computational simplicity, given the proximity of the water oxygen to the baricenter of the molecule. The results obtained when adopting the amide nitrogen The most probable distance for well-wrapped BHBs is consistent with the typical arrangement for simple hydrophobic surfaces like a flat graphene sheet, arranged mainly within a three-fold HB coordination with other water molecules (lacking a fourth neighbor in the direction of the hydrophobic surface) with HBs internal geometries improved with respect to typical bulk values The conclusion that transpires from min = 3Å to the BHB for curvature range θ≥4.5Å. Thus, hydration of an EBHB is practically forbidden since the fraction of EBHBs with θ≥4.5Å is less than 1%  and hence are unlikely to prevail due to competing backbone hydration . Then, the action A[g] becomesrj indicate an individual water molecule and its respective position, W is the set of water molecules, n(j) denotes a neighboring water molecule coordinated to the jth molecule, and M(j) is the collection of neighboring molecules around the jth molecule. Thus, A[g] is minimized by a discrete scalar field g = {gj}j∈W that reflects the tendency of interfacial water to retain its g = 3 coordination . Thus, the cavities around exposed backbone hydrogen bonds are too small to enable penetration of a g = 3 water molecule. The structural seal thus works by adapting the sub-nanoscale ruggedness of the protein surface to a spatially dependent least-action of water. This geometric adaptation tells apart native from misfolded structures, and hence introduces a physical constraint guiding the design of loose peptide-based materials that must remain water-tight to maintain structural integrity. Thus, the design concept introduced in this work will likely stimulate further endeavors in biomolecular engineering.HB between N-H and O = C bonds satisfying 120°≤aHB≤180°. A special kind of structural deficiency, the exposed backbone hydrogen bond (EBHB) is identified in soluble proteins with PDB-reported structure n, n = 0, 1, 2, 3) contained within a desolvation domain that represents the hydrogen-bond local environment. This domain is defined as the reunion of two intersecting spheres of fixed radius (∼thickness of three water layers) centered at the α-carbons of the residues paired by the hydrogen bond. In structures of PDB-reported soluble proteins, backbone hydrogen bonds are protected on average by ρ = 26.6±7.5 side-chain nonpolar groups for a desolvation sphere of radius 6Å. Thus, structural deficiencies lie in the tail of the protection distribution, i.e. their microenvironment contains 19 or fewer nonpolar groups, so their ρ-value is below the mean ( = 26.6) minus one standard deviation ( = 7.5). While the statistics on ρ-values for backbone hydrogen bonds vary with the radius, the tails of the distribution remain invariant, thus enabling a robust identification of structural deficiencies A backbone hydrogen bond (BHB) is geometricaly defined r) that assigns to each position vector r a scalar value indicating the expected hydrogen-bond coordination of a water molecule situated within a sphere centered at position r with radius 2.5Å (the thickness of single water layer). The coordination indicates the number of hydrogen-bonding neighboring water molecules. The expected g(r) value was computed as a time average over solvent configurations determined by molecular dynamics over a 100ns-period after the initial PDB-reported structure was equilibrated with the solvent (see below). A water-water hydrogen bond was defined by the geometric constraints: O-O distance <3.2Å and O-H-O angle aHB satisfying 120°≤aHB≤180°. Local hydration patterns were described by the equilibrium parameters dmin, gmin, representing respectively the minimum distance between the oxygens of backbone carbonyl and solvating water molecule, and the coordination of the water molecule that realizes dmin. The equilibrium parameters dmin, gmin were obtained from classical MD trajectories generated using the GROMACS 3.0 package 3 nm3 and periodic boundary conditions Botulinum neurotoxin , with maximum cell dimension b = 123.12Å. The long-range electrostatics were treated using the Particle Mesh Ewald (PME) summation method min, gmin. Across the exhaustive database of monomeric free proteins with sustainable autonomous structure, the RMSD between the solvent-equilibrated structure and the starting PDB structure was found to be <1.0Å for backbone atoms and <1.5Å when side chains are included.The solvent structure was described by the scalar field g = g(Table S1Exhaustive nonredundant dataset of 2661 monomeric uncomplexed PDB-reported proteins lacking prosthetic groups or ion coordination.(4.10 MB DOC)Click here for additional data file.


Newly-evolved multiplex sequencing technology has been bringing transcriptome sequencing into an unprecedented depth. Millions of transcript tags now can be acquired in a single experiment through parallelization. The significant increase in throughput and reduction in cost required us to address some fundamental questions, such as how many transcript tags do we have to sequence for a given transcriptome? How could we estimate the total number of unique transcripts for different cell types (transcriptome diversity) and the distribution of their copy numbers (transcriptome dynamics)? What is the probability that a transcript with a given expression level to be detected at a certain sampling depth?We developed a statistical model to evaluate these parameters based on transcriptome-sampling data. Three mixture models were exploited for their potentials to model the sampling frequencies. We demonstrated that relative abundances of all transcripts in a transcriptome follow the generalized inverse Gaussian distribution. The widely known beta and gamma distributions failed to fulfill the singular characteristics of relative abundance distribution, i.e., highly skewed toward zero and with a long tail. An estimator of transcriptome diversity and an analytical form of sampling growth curve were proposed in a coherent framework. Experimental data fitted this model very well and Monte Carlo simulations based on this model replicated sampling experiments in a remarkable precision.Taking human embryonic stem cell as a prototype, we demonstrated that sequencing tens of thousands of transcript tags in an ordinary EST/SAGE experiment was far from sufficient. In order to fully characterize a human transcriptome, millions of transcript tags had to be sequenced. This model lays a statistical basis for transcriptome-sampling experiments and in essence can be used in all sampling-based data. In recent years, sequencing technology is undergoing a revolution where highly-multiplexed sequencing instruments allow effective acquisition of sequence reads by millions in a single experiment de novo sequencing of large and complex genomes, it is sufficient for transcript tag sequencing. As their throughputs and protocols are being improved constantly, sequencing-based methods are expected to gain a great momentum in the years to come Transcriptomes vary significantly according to specialization of cell types as well as their life cycle or dynamic status, such as growth and apoptosis under various physiological and pathological conditions. This extremely dynamic nature of transcriptomes requires thorough and unbiased profiling experiments to identify as many transcripts as possible, including alternative spliced variants and non-coding RNAs There have been several attempts to model transcriptome-sampling data in recent years. Stern and colleagues empirically estimated the relative abundance of a transcript as the ratio of its sampling frequency over the sample size and transcriptome diversity by a simple correction of sampling errors In this study, we proposed an effective statistical model for systematically analyzing transcriptome-sampling data. We used continuous probability distribution to model relative abundances of all transcripts in a transcriptome, and then mixed it with a binomial or Poisson model to derive the distribution of sampling frequencies. The resulted distribution was explicitly distinguished from the underlying relative abundance distribution since it has taken sampling errors into account. We exploited the beta-binomial, gamma-Poisson, and generalized inverse Gaussian-Poisson models, and concluded that the relative abundances of a transcriptome followed the generalized inverse Gaussian distribution. We proposed an effective estimator for transcriptome diversity and provided an analytical form of sampling growth curve. Our results were derived from a coherent statistical model thus superior to other empirical curve-fitting methods. Monte Carlo simulations of transcriptome-sampling process were also carried out, and both the experimental and simulated data fitted our model fairly well. Through extensive simulations, we could determine the probability of detecting transcripts with a certain expression level at a given sampling stage, which provides important implications for future experimental design. Our method can be readily programmed with a moderate demand for computing time.For illustration of our model, we used a selected dataset from SAGE Genie website We eliminated erroneous tags by two criteria. First, we matched all tags to human genomic sequences (UCSC Golden Path hg18) N transcripts (or transcript tags) are sequenced from a transcriptome of a given cell type, let xf be the number of unique transcripts that are detected x times. {f1, f2,…} is termed as the frequencies of frequencies (FOF), as it is irrelevant to the identity of transcripts. The sample size N = Σx⋅xf and s = Σ xf is the total number of unique transcripts detected in the library (x≥1). Assuming that there are S (unknown) unique transcripts expressed in the RNA preparation (transcriptome diversity), f0 = S−s stands for those undetected transcripts.We model transcriptome-sampling data as follows. When xf as x/N and used FOF to formulate the distribution of relative abundances directly φ(π), 0<π<1 to model relative abundance distribution (RAD). That is, any transcript has a probability φ(π)dπ to be expressed at relative abundance π. RAD was then mixed with a basic sampling model, binomial or Poisson distribution, to give sampling frequency distribution (SFD) P(x|N), x = 0,…, N, which gives the probability for any transcript of being detected x times when N transcripts are sequenced. That is, a proportion P(x|N) of total unique transcripts are expected to occur x times in a sample of size N. Since FOF is generated from SFD, we used FOF to fit SFD rather than empirically formulate RAD.Previous studies estimated the relative abundances of all transcripts in N transcripts are sequenced, there are s(N) = S[1−P(0|N)] unique transcripts expected to be detected. If we actually detect s unique transcripts, the total number of unique transcripts can be estimated as φ(π) has expectation S is large. Using the estimated transcriptome diversity When the mixture model is fitted, one can deduce the estimator of transcriptome diversity and sampling growth curve in a unified statistical framework. When x = 1,…, N and N in our study made BB and GP mixtures behave in the same way, consistent with their theoretical behaviors. From a practical point of view, there was no difference found between these two mixtures; both fitted the FOF data poorly , gamma-Poisson (GP), and generalized inverse Gaussian-Poisson (GIGP) distribution. We first used Lib843 to demonstrate their performances in fitting the experimental data. The error-filtered SAGE data were first formulated as FOF data, and SFD were fitted by using maximum likelihood method. Once fitted, the expected FOF can be written as rvations . The maga poorly . The fitγ = −0.6439, b = 0.0518 and c = 0.0008 predicted FOF data fairly well for Lib843 , from 100,000 to 5,000,000. Based on the fitted RAD and transcriptome diversity S, the copy numbers were clustered into different expression level bins and the number of unique transcripts in each bin was formulated in In order to make the concept of relative abundance more biologically relevant, the copy number of a transcript in a given cell can be calculated by multiplying its relative abundance with the estimated total number of transcripts in that cell. A lower bound of this estimated total was based on the RNA-DNA hybridization experiment; it was about 300,000 mRNA molecules in HeLa cell Based on repeated Monte Carlo simulations and assuming there are 1,000,000 total transcripts per cell, we calculated the mean and median copy numbers of detected transcripts  and the s(N) can be recorded. We did so and plotted the expected growth curve against the simulated one . In general, sampling histories are not available for SAGE data archived in public databases. Since tags are assumed to be randomly sampled, one can approximate the sampling history by drawing tags from the library without replacement, and at each sampling point, the observed number of unique transcript tags ated one , showingN from 0 up to 3,000,000 with step length 120,000, the simulated growth curve and that predicted by equation (12) were plotted in We further used Monte Carlo simulation to evaluate the overall behavior of transcriptome sampling. For sampling effort Although we used SAGE data for illustration in this manuscript, our method is certainly applicable to other types of transcriptome-sampling experiments such as EST and MPSS as well as other large-scale sampling-based methodologies. For example, our method may still be useful for analyzing chromatin immunoprecipitation data (ChIP-tag) All tag-based methods essentially depend on the assumption that tags contain sufficient information to establish one-to-one correspondence between tags and transcripts. However, this assumption may collapse to some extents due to many factors. First, sequencing and PCR amplification errors often contribute a large fraction to unmatched tags π, the sampling frequency when N transcripts are sampled can be modeled by Binomial. The binomial distribution is often approximated by Poisson distribution Poisson(λ) with λ = π⋅N when N is large, π is small, and π⋅N is moderate, which is precisely the case even for the most abundant transcript. Assuming there are S (unknown) unique transcripts expressed in a given cell type, and each of them has relative abundance π1, π2,…, πS respectively. For mathematical convenience, we assume that iπ s are distributed as a continuous probability density (RAD) φ(π), 0<π<1 under the constraint x = 0,…, N. As φ(π) is necessarily highly skewed toward zero in our context, the binomial in (1) can be approximated by the Poisson distribution. Writing λ = π⋅N and ψ(λ) is a re-parameterized form of RAD φ(π). Extending the upper integration limit N to infinity is justifiable as the integration between N and infinity is negligibly small. Using different RAD leads to different SFD; the justification for one or another depends on its ability to characterize the transcriptome.Binomial distribution is a fundamental statistical assumption about sampling process. For a given transcript with relative abundance x = 0,…, N; α,β>0 are two parameters and B is the beta function. Under the Poisson assumption, let the parameter λ follow the gamma distribution ψ(λ). According to (2), GP mixture can be written asx = 0,…, N; α,β>0 are two N-dependent parameters and Γ(α)is the gamma function. Note that ψ(λ) depends on the sample size N, and so do its parameters. RAD can be obtained simply as φ(π) = N⋅ψ(πN). It is worth noting that gamma distribution can be obtained from beta distribution analogous to that Poisson approximates to binomial. GP mixture thus is an approximate form of BB mixture when N is large.Beta distribution is straightforward for modeling how proportions vary. Mixing it with binomial distribution according to (1) leads to the widely known BB mixtureb>0 and c>0 are three parameters. γK(α) is the second kind of modified Bessel function of order γ. Under the Poisson approximation, according to (2) the GIGP mixture can be written asTo capture the singular characteristics of RAD, i.e., highly skewed toward zero and having a long tail, some sophisticated distributions are to be applied. Generalized inverse Gaussian distribution is such a flexible distribution N is sequenced, any transcript has probability P(0|N) to be missed. That is, there ares unique transcripts when totally N transcript tags are sequenced, the total number of unique transcripts can be estimated asBased on SFD, one can deduce the estimator of transcriptome diversity and sampling growth curve in a systematical manner. According to the probability (1) or (2), when a sample of size φ(π) has expectation In addition, it is worth noting that RAD Equation (7), (8) and (9) can be applied to any probability mixture. As BB and GP mixtures fit experimental data rather poorly, we only present results of GIGP mixture; yet that of BB and GP mixture can be written out in a similar way. Under GIGP mixture, according to (8) it is straightforward to obtainS can be estimated alternatively asAs distribution (5) has mean S, one can write out an analytical form of the sampling growth curve under GIGP mixture according to (7) asIn our study, estimator (10) and (11) give identical estimate to the second decimal. Using (11) as an estimator of f0 representing the number of undetected transcripts is unknown, the FOF {f1, f2,…} is actually drawn from the zero-truncated SFD given byThe parameters of SFD (3), (4) and (6) can be fitted using experimentally observed FOF data. Since the zero frequency bin f1, f2,…} can be written asθ represents the general model parameter. We highly recommend to evaluate the probability involved in (14) using recurrence formula under each mixture, as in our experiences, directly evaluating high order Bessel function through easy mathematical routine often leads to computational overflow. The maximum likelihood estimation of model parameters can be computed by maximizing (14) numerically. Burrell and Fenton In this study, we used maximum likelihood method to fit the parameters. The log-likelihood of {S are determined simultaneously under the sampling model. Based on these parameters, one can carry out Monte Carlo simulation to ab initio imitate experimental sampling processes. At first, a virtual transcriptome with S transcripts indexed by 1,…,S is created. Relative abundances π1,…, Sπ are randomly generated from fitted RAD and normalized to fulfill the constraint r is then chosen for each tag and its identity is determined by looking up r in a table of the cumulative sum of the simulated relative abundances. This ensures that the ith transcript has probability iπ to be detected. Repeatedly choosing N random numbers generates a virtual library of size N.Once SFD is fitted based on FOF data, RAD and transcriptome diversity


Circulating TGFβ1 levels, procollagen type III levels, microalbuminuria, and left ventricular geometry and function were evaluated in all the hypertensives with LVH subgrouped according to T29C TGFβ1 gene polymorphism. Circulating TGFβ1 was evaluated by ELISA technique, procollagen type III by a specific radioimmunoassay, microalbuminuria by radioimmunoassay, and left ventricular geometry and function by echocardiography. All groups were comparable for gender, age, and sex. Regarding T29C TGFβ1 gene polymorphism, prevalence of TC or CC genotypes was significantly (P < .05) higher in hypertensives with LVH than hypertensives without LVH TC and CC LVH hypertensives were characterized by a higher prevalence of subjects with microalbuminuria (P < .05 TC and CC versus TT), by increased levels of TGFβ1, procollagen type III, urinary albumin excretion, LVM, LVM/h2.7, and lower values of left ventricular ejection fraction (P < .05 TC and CC versus TT). Our data suggest that T29C TGFβ1 gene polymorphism was associated with clinical characteristics adequate to recognize a subset of LVH hypertensives with a higher severity of hypertension.The distribution of the T29C TGF Hypertension represents the most common powerful risk factor for cardiovascular morbidity and mortality . High bl The disproportional accumulation of fibrous tissue is the major characteristic of the adverse structural remodelling of cardiac tissue in hypertensives promoting systolic and diastolic dysfunction –5.β1 (TGFβ1) is a multifunctional cytokine and its gene has been found in position 19q13.2, 19q13.1 on chromosome 19 and it has 7 exons and the length of the whole gene is 17.52 kb. Transforming growth factor β1 overproduction acts on cardiomyocytes as well as on cardiac fibroblasts inducing cardiac fibrosis and hypertrophy [β1 through a block of the renin-angiotensin system (RAS) was reported to be associated with an improvement of renal function and a reversion in LVH [β, through a novel orally specific inhibitor of the TGFβ receptor 1, results in significant improvement of deleterious cardiac remodelling after infarction [TGFertrophy , 7. In an in LVH , 9. Morefarction . β1 is in part under genetic control [s) have been described in TGFβ1 gene and related to its production and to hypertension and cardiovascular disease [s are located at positions 29 and 74 of the translated sequence of TGFβ1 and give rise to amino acid substitutions at positions 10 (Leu10→Pro) and 25 (Arg25→  Pro) in the signal peptide of TGFβ1, respectively [β1 mRNA in peripheral blood mononuclear cells and serum levels of TGFβ1, and the G74C was found to be related to TGFβ1 production in peripheral blood leukocytes [25 allele was associated with risk of hypertension in the normotensives [25 allele. On the contrary, no studies have been addressed the evaluation of the role of T29 → C polymorphism of TGFβ1 gene on left ventricular geometry and function in hypertensive patients [Production of TGF control . Eight s disease , 12. Twoectively . The T29ukocytes , 14. In tensives  and withtensives  comparedpatients .β1 gene polymorphism (rs1800470), LVH and clinical severity of hypertension. The aim of this study was to investigate the relationship between T29C TGFβ1, procollagen type III levels, microalbuminuria, left ventricular geometry and function were evaluated in hypertensive patients and related to genotype profile.In particular, circulating TGFSubjects eligible for the study were screened at the antihypertensive center of the Department of Internal Medicine, University of Palermo . The study population consisted of hypertensive subjects with age ≤65 years. Each patient gave a written consent after received a detailed description of study procedure. The study was approved by Ethics Committee of our Institution. Subjects under antihypertensive treatment or with a casual blood pressure (SBP) ≥140 mmHg and/or with casual diastolic blood pressure (DBP) ≥90 mmHg obtained with a standard sphygmomanometer after 5 minute of rest at three independent occasions with patients sitting were considered hypertensives. Exclusion criteria included secondary hypertension, endocrinal disease and diabetes mellitus, cardiovascular diseases , severe chronic renal failure, alcoholism and psychiatric problems.2.7≥50 g/m2.7for men and ≥47 g/m2.7for women were considered to have LVH [433 hypertensive subjects fulfilled the inclusion criteria and they were grouped according to the presence or absence of LVH, following standard echocardiographic criteria. In particular all the hypertensives with LVM/hhave LVH . AccordiGenotyping was performed by investigators blinded to clinical status.Peripheral venous blood was collected in EDTA from all the patients and stored at −70°C.β1. The PCR approach was used to analyze SNP in the coding regions of TGFβ1 polymorphism was done by analyzing the sequence. The polymorphism is on exon 1 at +869 from the beginning of the transcription and generate an amino acid substitution in position 10 (Leu → Pro). This study analyzed the genotypes derived from this substitutions. The study of the TGB PCR was performed on purified DNA obtained using the GenElute Blood Genomic Dna Kit by Sigma: which provided sequence-specific oligonucleotide primers T869C: Forward 5′-TTCCCTCGAGGCCCTCCTA -3′Reverse 5′-GCCGCAGCTTGGACAGGAT-3′μL containing approximately 5 μL of genomic DNA(0.1 μg/μL), 2 μL of forward and reverse primers (100 ng/μL), 5 μL of 10 × reaction buffer (160 mM (NH4)2SO4, 670 mM Tris-HCl , 15 mM MgCl2, 0, 1% Tween 20), 4 μL of 2 mM dNTPs (invitrogen), 4 μL of DMSO, 0, 1u of Taq Polymerase.Briefly, PCR reactions were carried out in a total volume of 50-Amplification was carried out in a Robocycler using cycle parameters of 3 minutes and 30 seconds at 95°C , 35 cycles of 95°C for 45 seconds (denaturation), 62°C for 30 seconds  and a final extension for 10 minutes at 72°C.T869C: the PCR generated amplicons with a fragment size of 294 Bp.μL of ethidium bromide  and from sequence electropherograms was analysed the presence of single-nucleotide polymorphisms T869C in all the subjects , cholesterol by routine laboratory methods.β1 levels were determined by using a solid-phase specific sandwich ELISA technique  as previously described [β1 were 8% and 6%, respectively. The sensitivity, hence minum level of detection of TGFβ1 by sandwich ELISA, was 5 pg/mL. Peripheral venous blood was obtained from each patient and the sera were isolated and stored at −70°C. TGF escribed . The intTo determine amino-terminal PIIIP, blood samples were taken from each patient and stored at 40°C until manipulation. PIIIP were determined by using a specific radioimmunoassay (Orion Diagnostic Finland), as previously described . The senTo eliminate the intra-individual day-to-day variability of UAE, three consecutive 24-hour urine collections were used. In addition, to assess the completeness of a 24 hours urine collection, measurements of urinary rate of clearance of creatinine were evaluated. UAE was measured by radioimmuno-assay . Microalbuminuric patients were defined as a level of UAE ≥20 and <300 mg/24 hours.All patients underwent an echocardiography examination M and B-mode, by a computerized echocardiography  for the determination of following parameters: left ventricular telediastolic internal diameter (LVIDd), interventricular septum (IVSTd), and posterior wall thickness (PWTd). The Penn convention was used to calculate left ventricular mass (LVM). LVM was normalized for height to the 2.7 power . The relLV relaxation and filling were evaluated by pulsed-wave Doppler interrogation of the LV inflow tract from the apical four-chamber view, with the sample volume placed at the tips of the mitral valve. After a stable signal of the transmitral flow velocity was obtained, the Doppler cursor was moved toward the LV outflow tract in the apical five-chamber view for recording both mitral and aortic signals, including the closing click of the aortic valve and the opening click of the mitral valve. Doppler signals were recorded at high speed (80–120 mm/s) with the subjects in held expiration. An average of five beats was used for analysis. E), peak late transmitral flow velocity (A), and the deceleration time of E velocity (DTE) were measured at the tips of mitral leaflets at the maximum amplitude of E velocity. DTE was measured as the time from peak E velocity to the time when E wave descent intercept the zero line.Isovolumic relaxation time (IVRT) was calculated as the time from the closure click of the aortic valve to the opening click of the mitral valve. When either the closing or opening click was not identified, the time from the end of the aortic flow to the onset of mitral flow from the continuous wave interrogation of the LV inflow-outflow tract was used. Peak early transmitral flow velocity , and TC plus CC (P = .0466) genotypes, was significantly higher in hypertensives with LVH than hypertensives without LVH . The distribution of T29C genotypes in both hypertensive groups has been reported in Genotype frequency distribution in the two groups of hypertensives occurred according to Hardy-Weinberg proportions.Since genotype frequency distribution was not significant different in hypertensives without LVH, only hypertensives with LVH were further subdivided into three groups, according to T/C genotypes.P < .05 TC and CC versus TT).The association between genotypes and clinical characteristics in the three groups of hypertensives with LVH have been reported in No significant difference in the prevalence and duration of antihypertensive drugs utilized was observed among the groups Tables  and 2. Iβ1, PIIIP, LVM and LVM/h2.7levels were significantly (P < .05) higher and left ventricular ejection fraction values were significantly (P < .05) lower in LVH hypertensives with TC or CC genotype than those detectable in LVH hypertensives homozygous for allele T  on left ventricular geometry and function in hypertensive patients. Our data indicate a higher prevalence of TC and CC Leu10→Pro polymorphism in hypertensives with LVH than hypertensives without LVH, associated to some unfavorable clinical characteristics of hypertension. In fact, LVH hypertensive subjects with TC or CC genotype were characterized by a higher prevalence of subjects with microalbuminuria, higher value of LVM and lower left ventricular ejection fraction. In our opinion, this association doesn't reflect unknown differences in population ancestry between the two hypertensive groups. We consider the probability of false positive inference attributable to population studied rather small because the two hypertensive groups were recruited from an ethnically homogenous population. In this context recent results from Xu et al. [β1 + 915 Arg → Pro at codon 25 polymorphism with LVH in a Chinese hypertensive population, while the codon 10 genotypes did not show any association to LVH. Even if in this study we have analyzed only TGFβ1 Leu → Pro at codon 10 polymorphism, our recent unpublished data indicated no association between TGFβ1 + 915 Arg → Pro at codon 25 polymorphism and hypertension in a Caucasian hypertensive population. These contrasting data may be explained by ethnicity of two hypertensive populations considered (Caucasian and Chinese).To our knowledge this is the first study to investigate the impact of TGFu et al.  revealedAlthough changes in the heart caused by hypertension are well known, the effective mechanisms are not entirely clarified. Despite the role of hemodynamic effects and growth factors have been largely reported, other metabolic and inflammatory factors have to be also considered .β1 in hypertensives. Moreover, hypertensives with target organ damage (TOD) have higher circulating levels of TGFβ1 than hypertensives without TOD. This finding seems to attribute an important role to TGFβ1 overproduction in the pathophysiology of essential hypertension and its sequelae [β1 overproduction in hypertension may be explained by the effects of various factors, such as elevated angiotensin II, increased fluid shear stress and a differential expression of TGFβ1 linked to DNA polymorphisms in the promoter [β1 is predominantly under genetic control (heritability estimate 0.54). Up regulation of TGFβ1 system in monocytes of hypertensive patients and association of TGFβ1 gene polymorphism with risk of hypertension suggests that quantitative difference in TGFβ1 production may determine the intensity of the process of vascular remodelling and therefore influence overall susceptibility to the development of hypertension [β1 overproduction, as evidenced by collagen formation, may represent a pathophysiological molecular mechanism in hypertension [β1 Arg25-Pro polymorphism might be associated with hypertension but does not address the issue of quantitative phenotypes related to TGFβ1 production in relation to hypertension. On the contrary no study has been addressed the evaluation of the role of Leu-to-Pro polymorphism at codon 10 to explain whether the substitution has functional and biological importance, or it could affect protein transport. In view of this we have now shown that the serum concentration of TGFβ1 was significantly different in LVH hypertensives subgrouped according to T29 → C polymorphism. It is possible that the T29 → C polymorphism of the TGFβ1 gene is linked to some other genes that are actually responsible for the development of hypertension.Literature data and the results of our previous studies indicate an overproduction of circulating TGFsequelae , 25. Thepromoter . Plasma rtension . On the rtension , 28. Thertension  suggest β1 values among LVH hypertensive groups indicated that TC or CC genotypes could be able to induce quantitative change in the production of the cytokine and it might affect also the function of signal peptide, perhaps influencing intracellular trafficking or export efficiency of preprotein [Moreover, the significant change in circulating TGFeprotein .β1 may contribute to the progression of renal and cardiovascular damage in obese and/or hypertensive subjects. In particular, the present study emphasizes the unfavourable effects of TGFβ1 overproduction on left ventricular geometry and function that might be mediated by an higher collagen production [β1 is a susceptibility locus for hypertension. A potential limit of this study was to have computed no a priori evaluation of the β error and consequently the power of our statistical analysis. However this involves considerations only about the sample size. Accordingly the negative results of our study has needed further evaluations.In addition, our data suggest some clinical considerations. First, LVH hypertensive subjects with TC or CC genotypes were characterized by a higher value of microalbuminuria, LVM and by a systolic left ventricular dysfunction. This might indicate that hypertension in these subjects has to be considered more severe. Another aspect of our study that seems to be interesting is related to the higher collagen production in LVH hypertensive subjects with TC or CC genotype than in those homozygous for allele T. This finding is further supported by analysis of logistic regression indicating that PIIIP may be considered the most important marker associated to T and C alleles. In fact, analysis of odd ratio indicates that the risk of association with T or C allele increases eight fold for each PIIIP quintile variation. In our opinion this finding might further support our previous results , 23 indioduction , 24, 29.10  → Pro polymorphism, rs 1800470) might be considered a particular subset of LVH hypertensives with a more severity of hypertension. However, the present results require verifications in other populations, since it is well known that hypertension is under the control of many genes that contribute modest individual effects, and TGFβ1 may act in concert with other hypertension susceptibility loci.In conclusion our data are attractive to indicate that LVH hypertensive subjects with TC or CC genotypes (Leu


Platelet-derived growth factor receptor β (PDGFRβ) is a tyrosine kinase receptor known to affect vascular development. The zebrafish is an excellent model to study specific regulators of vascular development, yet the role of PDGF signaling has not been determined in early zebrafish embryos. Furthermore, vascular mural cells, in which PDGFRβ functions cell autonomously in other systems, have not been identified in zebrafish embryos younger than 72 hours post fertilization.pdgfrβ is expressed in the hypochord, a developmental structure that is immediately dorsal to the dorsal aorta and potentially regulates blood vessel development in the zebrafish. Using a PDGFR tyrosine kinase inhibitor, a morpholino oligonucleotide specific to PDGFRβ, and a dominant negative PDGFRβ transgenic line, we found that PDGFRβ is necessary for angiogenesis of the intersegmental vessels.In order to investigate the role of PDGFRβ in zebrafish vascular development, we cloned the highly conserved zebrafish homolog of PDGFRβ. We found that Our data provide the first evidence that PDGFRβ signaling is required for zebrafish angiogenesis. We propose a novel mechanism for zebrafish PDGFRβ signaling that regulates vascular angiogenesis in the absence of mural cells. The development of blood vessels occurs in two distinct stages. Vasculogenesis is defined as the formation of new blood vessels resulting from angioblast aggregation followed by the lumenization of vascular endothelial cells. Angiogenesis is a secondary process to vasculogenesis and occurs via the sprouting of blood vessels from pre-existing vascular structures The regulation of vasculogenesis and angiogenesis each involves multiple cell types and signaling molecules necessary to coordinate the formation of the vascular system. Platelet-derived growth factor (PDGF) activates a specific family of receptor tyrosine kinases and is involved in the development of blood vessels in chicks and mammals de novo formation of the dorsal aorta and the posterior cardinal vein of the tail occurs via the fusion of angioblast precursor cells and is considered vasculogenesis. The subsequent sprouting and extension of the intersegmental vessels (ISVs) from the dorsal aorta is considered angiogenesis The zebrafish embryo is an excellent model to use for investigating vascular development. The transparency of zebrafish embryos allows for convenient observation during development while transgenic lines that express fluorescent tags in endothelial cells facilitate the study of developing blood vessels Here we provide the first analysis of PDGF signaling in vascular development in the zebrafish. We found that PDGFRβ is expressed adjacent to the dorsal aorta in the hypochord as early as 20 hours post fertilization (hpf). At these early stages of zebrafish development, there is no evidence of the presence of mural cells in the vasculature, although primitive mural cell markers are present around the anterior portion of the dorsal aorta beginning at 72 hpf colony stimulating factor 1 receptor on chromosome 14. Comparison of zebrafish PDGFRβ2 with human, mouse, Fugu and other species indicated that the predicted tyrosine kinase domain of zebrafish PDGFRβ2 was highly conserved across multiple species , therefore this tree is not presented. These analyses also indicated that zebrafish PDGF-B is evolutionarily distinct from PDGF-A. Both PDGF-B and PDGFRβ are thus highly conserved members of their respective protein families , a factor known to affect segmental artery formation, is similarly expressed in the hypochord Using t 20 hpf . At 24 ht 20 hpf . Compariboundary . The locA recent report showed the presence of mural cells in the anterior portion of the dorsal aorta and to a lesser extent in the posterior cardinal vein starting at 72 hpf pdgf-b expression is upregulated in the regenerating heart and may promote revascularization of the regenerating heart pdgf-b in the developing zebrafish embryo, we used in situ hybridization at 24, 48, and 72 hpf. pdgf-b expression was present throughout these stages of development and was localized to the head, dorsal aorta, posterior cardinal vein and ISVs  specific to  blocked . The spl embryos . When an embryos . Overallpdgfrβ2 MO-injected embryos were caused by loss of function in pdgfrβ2, we co-injected pdgfrβ2 mRNA with pdgfrβ2 MO. pdgfrβ2 mRNA was not subject to the MO splice blocking and should therefore be fully functional. After the addition of pdgfrβ2 mRNA, we observed a partial rescue of pdgfrβ2 MO-induced defects at 30 hpf  we replaced the intracellular tyrosine kinase domains of PDGFRβ with YFP to create a fusion protein that is inactive and has the ability to bind to endogenous PDGFRβ and prevent auto-transphosphorylation . Triple ture see . Heat shr 48 hpf .kdrl:GFP embryos with inhV from 6–24 hpf or 6–48 hpf and measured the extent of ISV recovery after drug withdrawal. Withdrawal of inhV at 24 hpf (treated from 6–24 hpf with inhV) resulted in a full recovery of complete ISV formation when analyzed at 48 hpf for embryos initially treated with 0.1 µM  of wortmannin cause defects in blood vessel formation pdgfrβ2 MO-injected embryos exhibited ISV sprouting defects at 24 hpf , Xenopus laevis (NP_001087935.1), Xenopus tropicalis (AAI60575.1), Gallus gallus (NP_989601.1), Mus musculus (NP_035187.2), Rattus norvegicus (NP_113712), Homo sapiens . PDGF-A (AAH78289) was used as an outgroup to PDGF-B. For PDGFRβ, two sets of phylogenetic analyses were performed: the first analysis utilized the full length PDGFRβ2 (HM439112) and the following sequences: two Fugu isoforms: PDGFRβ1 (P79749.1) and PDGFRβ2 (AAL50567), two H. burtoni isoforms: PDGFRβ a (ABD48800.1) and PDGFRβ b (ABD48798.1), Gallus gallus (XP_001233830.1), Mus musculus (NP_032835.1), Rattus norvegicus (NP_113713.1), Homo sapiens . The second analysis included a 64 amino acid residue fragment of PDGFRβ1 from D. rerio (AAN02896), with the sequences of other species edited to correspond to the 64 aa fragment. The sequences for each set of genes were aligned in Clustal X Sequences were selected from GenBank to represent the available major vertebrate groups . For PDGF-B, the following sequences were selected: Zebrafish were maintained on a 14 hr light/dark cycle at 28.5°C. All experiments involving zebrafish were performed according to the National Institute of Health guidelines and have been approved by the Institutional Animal Care and Use Committee of the Childrens Hospital Los Angeles. Embryos were collected and raised in E3 media according to standard protocol A GGA ACT GAA GTC ACT GAC CTT C; pdgfrβ control MO, ACA CGA ACT GAA CTC AGT GAC GTT C. The control MO had five nucleotides that differed from the experimental MO.Embryos were injected in E2 media at the one-cell stage with 3.5 ng morpholino oligonucleotides (MO). MOs were obtained from Gene Tools  with the following oligonucleotide sequences: pdgfrβ MO, ACEmbryos were fixed in 4% paraformaldehyde at 4°C for 16 hours and then transferred to methanol at −20°C. Embryos were then treated as previously described Zebrafish embryos were fixed with 2% glutaraldehyde in phosphate buffer, post-fixed with 1% osmium, and embedded in Epon. The areas of interest were discussed and confirmed with light microscope in longitudinal sections stained with toluidine blue solution. The ultra-thin sections were cut onto one-hole grids, stained with uranyl and lead, and examined with Morgagni 268.UAS:dnpdgfrβ-yfp transgenic fish lines. We then crossed the UAS:dnpdgfrβ-yfp transgenic fish to hsp70:Gal4 fish to obtain a double transgenic line  hsp70) promoter allowed for temporal control of expression of Gal4. The double transgenic UAS:dnpdgfrβ-yfp;hsp70:Gal4 line was then crossed with a second double transgenic fli1a:eGFP;hsp70:Gal4 line to generate triple transgenic UAS:dnpdgfrβ-yfp;hsp70:Gal4;fli1a:eGFP embryos to be used for the experiment. Embryos obtained from this crossing were then heat shocked for 20 minutes in a 40°C water bath at 10 hpf or 20 hpf. Embryos were then sorted for expression of YFP. 40 embryos each from the YFP-positive and the YFP-negative embryos were selected for their normal morphology at 48 hpf as heat shock alone caused some overall morphology defects in approximately 10–20% of embryos. These embryos were selected under normal light illumination to prevent biased selection under GFP fluorescence. From these 40 embryos, the GFP-positive embryos (n = 23 for each of the YFP-positive and YFP-negative groups) were analyzed for full ISV extension.To achieve temporal control over expression of the dominant negative receptor, we utilized a GAL4-UAS system


In the emerging field of community and ecosystem genetics, genetic variation and diversity in dominant plant species have been shown to play fundamental roles in maintaining biodiversity and ecosystem function. However, the importance of intraspecific genetic variation and diversity to floral abundance and pollinator visitation has received little attention.Solidago altissima, we document that genotypic diversity of a dominant plant can indirectly influence flower visitor abundance. Across two years, we found that 1) plant genotype explained 45% and 92% of the variation in flower visitor abundance in 2007 and 2008, respectively; and 2) plant genotypic diversity had a positive and non-additive effect on floral abundance and the abundance of flower visitors, as plots established with multiple genotypes produced 25% more flowers and received 45% more flower visits than would be expected under an additive model.Using an experimental common garden that manipulated genotypic diversity (the number of distinct genotypes per plot) of These results provide evidence that declines in genotypic diversity may be an important but little considered factor for understanding plant-pollinator dynamics, with implications for the global decline in pollinators due to reduced plant diversity in both agricultural and natural ecosystems. Plant genetic variation and genotypic diversity consistently affect community and ecosystem properties across systems and environments Populus can account for up to 70% of the variation in microbial community composition First, inter-specific plant diversity can have positive effects on insect pollinator diversity and flower visitation Solidago altissima genotypic diversity, we hypothesized that: In a two year study, using a common garden which manipulated S. altissima genotype explained 43–76% of the variation in floral abundance, 56–86% of the variation in flower visitor abundance, and 46–57% of the variation in flower visitor taxonomic richness  = 22.80, p<0.0001; Genotype: F = 1.09, p = 0.425), indicating that the effects of plant genotype on flower visitor richness were a consequence of plant traits that influence flower visitor abundance.We found that richness Table 1.S. altissima plants and both flower visitor abundance and taxonomic richness  = 7.68, p = 0.007, r2 = 0.11), flower visitor abundance in 2007 and 2008  = 7.07, p = 0.010, r2 = 0.10; 2008: F = 5.49, p = 0.022, r2 = 0.08), and flower visitor richness in 2007 and 2008  = 4.09, p = 0.048, r2 = 0.06; 2008: F = 11.99, p = 0.001, r2 = 0.16). Our data suggested a trend between genotypic diversity and floral abundance in 2008  = 2.90, p = 0.094, r2 = 0.05). We conducted a second analysis in which genotypic diversity and flower visitor abundance were the independent variables and flower visitor richness was the dependent variable. In this model, only flower visitor abundance was related to taxonomic richness  = 78.06, p<0.0001; Genotypic Diversity: F = 0.14, p = 0.71), indicating the higher taxonomic richness of floral visitors observed in genotypically diverse plots was a result of more abundant floral visitors, and not a direct effect of genotypic diversity.Genotypic diversity also had a significant, positive impact on floral abundance in 2007  = 11.97, p = 0.037, r2 = 0.89; Non-additive plant flowering: F = 22.98, p = 0.017; Year: F = 6.39, p = 0.086).There are at least two hypotheses that may explain non-additive responses of flower visitors to genotypic diversity: 1) Non-additive flower visitor abundance is a consequence of non-additive plant responses whereby the non-additive plant response is correlated with the non-additive flower visitor response; 2) Non-additive flower visitor abundance is a consequence of intra-guild interactions whereby the non-additive response of flower visitors is independent of the plants. Consistent with the first hypothesis that non-additive plant responses can lead to non-additive responses among flower visitors, when we excluded the single genotype plots where non-additive plant responses cannot occur, we found that there was a strong correlation between the non-additive response  of plant flowering and non-additive flower visitor abundance  have reported that bee diversity was positively related to fruit set in coffee plantations Over a two year period, our results provide evidence that both intraspecific genetic variation and genotypic diversity in S. altissima genotypic diversity was positively related to floral abundance and flower visitor abundance. S. altissima patches with high genotypic diversity produced more flowers , which made these plots more attractive to flower visitors. This suggests that floral abundance may mediate the positive effect of genotypic diversity on flower visitor abundance. Genotypically diverse plots had greater floral abundance, suggesting that positive genotype interactions occur in mixture plots. One potential explanation for greater floral abundance plots is increased productivity in genotypically diverse plots compared to monocultures, which has been previously shown in the S. altissima system Consistent with the hypothesis that flower visitors are more abundant where plant genotypic diversity is high, our results showed that increased S. altissima remain unknown.Non-additive responses occur when the total response is greater or less than the sum of the partitioned responses generated by the individual constituents. Although emerging studies have shown that genotypic interactions are important to non-additive outcomes at the community and ecosystem level, the exact mechanisms which promote non-additive responses are not well understood Because of the general importance of pollinators to associated biodiversity and ecosystem function, understanding the mechanisms of global decline in pollinators and the services they provide represents a major frontier in ecology Solidago altissima L.) is a dominant species in abandoned agricultural fields, where it can have major impacts on biodiversity and ecosystem function S. altissima is a common perennial herbaceous species which is broadly distributed across North America and readily produces clones which can persist for many years S. altissima patches can vary from 1 to 12 genotypes in less than a meter-square area creating a natural mosaic of single-genotype and mixed-genotype plant patches Tall goldenrod  form a panicle at the stem apex and buds open almost synchronously within a particular genotype; however, flowering phenology varies among clones Solidago spp. influence trophic interactions among galling herbivores and natural enemies, as well as arthropod diversity at large S. altissima an ideal species to investigate how plant genetic factors influence floral community dynamics, the results of which may be applicable to many other plant species.S. altissima was manipulated at the plot level by using 21 locally-collected genotypes. The genotypes were identified as unique by amplified fragment length polymorphism (AFLP). In 2005, sixty-three 1 m2 plots of equal stem density were established at random locations within the common garden with 12 replicated individuals of 1, 3, 6, or 12 randomly selected genotypes. Each plot was spaced 1m from its nearest neighbor plots. Bamboo posts were placed in each corner of the plots, and string was tied around the posts to prevent plants from different plots from touching each other. Single genotype plots included 2 replicate plots of each of the 21 genotypes . Genotypically diverse plots included 7 replicates of each of the 3, 6, and 12 genotype mixtures . The polyculture plots were created by a random assignment of genotypes, with the stipulation that no two mixtures could have exactly the same constituent genotypes. Heavy plastic lined the edges of each plot to a depth of 30 cm to prevent ramets from spreading to neighboring treatments, but ramets were allowed to spread within each plot over time.We examined the role of plant genetic factors on flower visitors with a common garden experiment established in 2005 in the National Environmental Research Park at Oak Ridge National Laboratory in Oak Ridge, Tennessee. Genotypic diversity of  = 1.23, p = 0.223). Similarly, when genotype identity and flower abundance were included in the model as covariates, we still found that the average number of flower visitors on neighboring plots did not influence the abundance of flower visitors in a focal plot  = 0.27, p = 0.613). In September 2007 and 2008, we surveyed each plot within the common garden to estimate the proportion of flowers in bloom. Because flower panicles vary in size, we used a representative panicle of S. altissima with known floral (capitula) abundance as a unit of measurement to estimate floral abundance. For each plot, we visually estimated floral abundance as the number of times the representative panicle would have to be replicated in order to equal the floral abundance of the plot Polistes wasps (Hymenoptera: Vespidae), Ailanthus webworm moths (Lepidoptera: Yponomeutidae), and skippers (Lepidoptera: Hesperidae). Other taxa of floral visitors were extremely rare, and were not included in our analysis. The abundance of floral visitors was calculated as the number of individuals of each taxa entering the plot during the observation period, regardless of the number of flowers each individual visited within the plot. We used the six general taxa to judge the taxonomic richness of floral visitors visiting each plot. We use the terms “taxonomic richness” and “richness” interchangeably to refer to the number of flower visitor taxonomic groups observed in a given plot. We observed significantly higher floral visitation and richness in 2007 compared to 2008 although overall patterns in the results between 2007 and 2008 were similar.To determine whether plots were impacted by their proximity to neighboring plots, we tested for spatial autocorrelation by comparing the abundance of flower visitors in each plot to the average abundance of flower visitors on neighboring plots S. altissima, we used restricted estimated maximum likelihood in SAS-JMP 5.1. The statistical model included plant genotype and row as random effects. The significance of plant genotype on floral abundance, flower visitor abundance, and flower visitor taxonomic richness was tested with a log-likelihood ratio test. Because the genotypes of S. altissima used in this study were clonally replicated we calculated the broad-sense heritability for each trait as H2B = Vg/Vt where Vg is the amount of variation in the trait that is explained by genetic variance and Vt is the total variance in the phenotype of the trait . The standard error for H2 depends on the intraclass correlation (t), the number of clones (S), and the number of individuals per clone (k). According to Becker (1985), the following formula approximates SE, assuming that t is normally distributed: S.E. = {[2 (n.−1)(1−t)2 (1+(k1−1) t)2]/[k12 (n.−S)(S−1)]}1/2To test for differences in phenotypic and extended phenotypic traits  were also used as observed values to test for non-additivity against a null model.To understand how genotypic diversity affects Non-additivity occurs if the total response of a variable is greater or less than the sum of the partitioned responses generated by the individual constituents We recognize that the genotypic diversity of the plots has likely not remained constant over time, which could affect the accuracy of our null model. However, the effects of genotypic diversity were extremely similar between the 2007 and 2008, suggesting that selection on particular genotypes was not strong during the time we were collecting data. Our data do clearly show a non-additive effect Table S1This table presents the results of null model simulations testing for non-additivity in floral abundance, floral visitor abunance, and floral visitor richness in 2007 and 2008. Expected values are mean results from null model simulations, and lower and upper confidence intervals (CI) represent 95% confidence intervals. P-values represent the number of simulations expressed as a proportion out of 1000 which fall above the observed value. Non-additivity values represent the percent increase in observed values above expected values.(0.05 MB DOC)Click here for additional data file.


Microfabrication of polydimethylsiloxane (PDMS) devices has provided a new set of tools for studying fluid dynamics of blood at the scale of real microvessels. However, we are only starting to understand the power and limitations of this technology. To determine the applicability of PDMS microchannels for blood flow analysis, we studied white blood cell (WBC) margination in channels of various geometries and blood compositions. We found that WBCs prefer to marginate downstream of sudden expansions, and that red blood cell (RBC) aggregation facilitates the process. In contrast to tubes, WBC margination was restricted to the sidewalls in our low aspect ratio, pseudo-2D rectangular channels and consequently, margination efficiencies of more than 95% were achieved in a variety of channel geometries. In these pseudo-2D channels blood rheology and cell integrity were preserved over a range of flow rates, with the upper range limited by the shear in the vertical direction. We conclude that, with certain limitations, rectangular PDMS microfluidic channels are useful tools for quantitative studies of blood rheology. In 1961, Williamson in vitro were performed in glass tubes or other simple geometries Initial pioneering studies of blood cell dynamics Advances in microfabrication technology have provided useful tools for studying blood flow and cell interactions in artificial networks with complex topology, at the scale of the microvasculature In this study, our aim was to characterize WBC margination in microchannels of various geometries. Specifically, we quantified how flow conditions, fluid composition, conduit size and channel geometry affect leukocyte margination in microchannels molded in PDMS. Our results are discussed in the context of previous experiments using real vessels or circular tubes.w) and depth (d)), 3) shear rate along the channel width (wγ) and 4) hematocrit (oH).To assess the dynamics of blood in pseudo 2D rectangular microchannels, we performed experiments in which we varied 1) the suspending media composition 2) channel size and geometry .It is well known that RBC aggregation plays an important role in margination in tubes, so we used various blood compositions that either enhanced or inhibited RBC aggregation. RBCs form aggregates when suspended in large polymer solutions such as Dextran which effectively cross-link the RBCs w) increased for the RPMI sample. Analyzing the results farther downstream revealed three general trends for margination .At oH = 20%, extensive margination was observed for all channel widths (77.8–95.6%).At oH (10%) was similar to that at high oH, with smaller channels supporting more margination (p<0.01). Aggregation was inhibited in these dilute suspensions, and the RBCs failed to organize into a substantial core to exclude the WBCs. In this setting, we expect the leukocyte motion to be dominated by Segre-Silberberg inertial forces The trend at low et. alin vivo observation by Firrell et. alSimilar results were observed by Goldsmith and Spain Previous studies have shown that step changes in conduit size can affect WBC margination oH = 20% and measurements were made 1.2 mm downstream of the channel inlet just below the narrow section (1 mm) of the expansion. In blood suspended in RPMI 1640, straight channels exhibited better margination than the expansion (p = 0.03). However, when blood was allowed to aggregate , there was a significant improvement of margination in the expansion compared to a straight channel (p<0.01). This result is only in partial agreement with Schmid-Schönbein and coworkers et. al.in vivo observations by Pearson and coworkers To test the hypothesis that expansion geometries support more margination than straight channels, we directly compared margination in straight (25 µm and 50 µm) and expanding (25–50 µm) channels . These ewγ; n is the number of channels in the array (10) and d is the depth of the channel (10 µm). The channels with high shear rate exhibited low margination in all three suspension media (p<0.01). This result is consistent with previous studies in tubes and vessels Margination in pseudo-2D sudden expansions is affected by shear rate along the width γw; . In our d = 10 µm, 15 µm, 25 µm and 50 µm, and quantified margination at the two sidewalls. For devices with walls higher than 15 µm, lateral margination was reduced  wherein WBC margination is higher than 90% when suspended in plasma or dextran 500 (oH = 10%) or a high (oH = 40%) hematocrit results in less margination. Change in hematocrit alters wall shear rate and the RBC velocity profile Our experiments show that RBC aggregation and conduit expansions encourage WBC margination and verify that important aspects of blood rheology are retained in microfluidic channels. An illustration of the general trends is shown in tran 500 . A low  can be much higher than those within the plane of the device (wγ). Since blood cells can be damaged or activated by high shear stresses, the average shear in the vertical (z) direction is limiting, and determines the maximum allowable flow rate. At very high flow rates , cell sedimentation and sludging become significant, causing spatial and temporal variations in hematocrit in the experimental system. A number of steps can be taken to minimize these problems. For example, by working with arrays of parallel channels, flow changes in any given segment are not greatly affected if other, parallel paths are occluded, and hematocrit variation can be minimized by decreasing the inlet tubing length and mixing the sample reservoir frequently.However, PDMS microchannels also have limitations that must be considered for blood flow analyses. For example, when perfusing blood through microfluidic devices, shear rate is an important parameter. Soft lithography-based PDMS devices typically have channels with rectangular cross section, so the network lies within a pseudo-2D geometry. In these low aspect ratio channels, shear rates in the vertical direction  except for those in We designed straight channel and sudden expansion channel arrays. There were 10 parallel channels in each 5 mm long array. All experiments were conducted with channels that were 10 µm deep . The Mylar transparencies were printed on a 50,000 dpi resolution printer  and were taped to a blank glass plate using Kapton tape. We used SU8-2010, 2025 and 2075  master templates fabricated on Si (100) wafers  using photolithography. The photolithography system used was a Karl Suss MA 6 mask aligner . The devices were fabricated using soft lithography of PDMS Blood was obtained from buffy coats collected at the Massachusetts General Hospital blood bank and used within 48 hours. Any changes in blood rheology caused by blood storage conditions did not significantly affect WBC margination: freshly drawn blood and blood stored at 4°C for 48 hours supported the same levels of margination in 50 and 75 µm channels . The hemQ) in the array of channels such that zγ∼400 sec−1 except in p values of the Student's t-test for a level of significance (α) of 0.05 for statistical comparison. The error bars on the figures denote sample standard deviations.We computed Figure S1Comparison of WBC margination in blood bank blood and freshly drawn blood.(0.04 MB PDF)Click here for additional data file.Figure S2q) within Microfluidic Channel ArrayVariation of Flow Rate ((0.05 MB PDF)Click here for additional data file.Video S1Movie showing Fluorescent WBCs marginated to the sidewalls of a 50 µm wide microchannel (20×magnification). The RBC hematocrit was 20%.(2.33 MB MOV)Click here for additional data file.


Sex inhibits the evolution of other putative evolvability mechanisms, such as mutator alleles. This paper explores whether sex also prevents natural selection from favoring modifier alleles that facilitate [+PSI] formation. Sex may permit the spread of “cheater” alleles that acquire the benefits of [+PSI] through mating without incurring the cost of producing [+PSI] at times when it is not adaptive. Using recent quantitative estimates of the frequency of sex in Saccharomyces paradoxus, we calculate that natural selection for evolvability can drive the evolution of the [+PSI] system, so long as yeast populations occasionally require complex adaptations involving synergistic epistasis between two loci. If adaptations are always simple and require substitution at only a single locus, then the [+PSI] system is not favored by natural selection. Obligate sex might inhibit the evolution of [+PSI]-like systems in other species.The [ +PSI] prion, unlike mutator alleles, will evolve to promote evolvability in sexual yeast species. Together with previous laboratory studies of [+PSI]–mediated adaptation, and with bioinformatic studies consistent with [+PSI]–mediated adaptation in the wild, our theoretical results firmly establish [+PSI] as a model system for the evolution of evolvability. We also shed light on the importance of complex adaptations involving multiple genes. Adaptations involving multiple simultaneous genes drive the evolution of evolvability in this system. This work is an important proof of principle, showing that evolvability can sometimes evolve under realistic conditions.Can evolvability evolve? One obvious way to evolve faster is via mutator alleles that increase the mutation rate. Unfortunately, recombination will rapidly separate a mutator allele from the advantageous alleles that it creates. Mutators, therefore, gain very little benefit from promoting adaptations and are thought not to evolve in sexual organisms. Here we find that the [ A simple point mutation at the stop codon will produce much higher levels. Let the ancestral allele at locus i with an intact stop codon be designated appended gene product - sensu Masel and Bergman +PSI] promotes evolvability by acting as a stopgap mechanism. [+PSI] spontaneously appears far more often than stop codon mutations  at either one locus  or two loci . Our “agp” notation and our parameter estimates are based on the assumption that adaptation comes from addition to a protein C-terminal through stop codon readthrough. The same formalism can, however, still be applied if the variation revealed by [+PSI] is instead mediated via nonstop mRNA decay agp loci, whose frequencies are initialized at this time. After switching back to environment 1, this set of agp loci is no longer tracked.In environment 2, , yielding no benefit to a +prf allele. If, however, two different readthrough products are simultaneously involved in a complex adaptation, then it becomes exceedingly unlikely that both +PSI] will have an advantage, since it will cause simultaneous readthrough at both loci, reaping synergistic benefits and promoting complex adaptations. Competing paths of direct vs. [+PSI] mediated adaptation are shown in If only a single agp and prf loci. Haploids must have different alleles (α vs. a) at the mating-type mat locus in order to conjugate, and so we also track the mat locus for its potential effect on inbreeding. We do not model mutation at the mat and prf loci, except implicitly through the possibility of mother-daughter haplo-selfing (see Reproduction below). There is free recombination between all loci. Individuals therefore have either three or four genetic loci, depending on whether we are modeling simple or complex adaptations with one or two agp loci, respectively. Each of the three to four loci has two alleles, plus there are also two possible cytoplasmic states ([+PSI] versus [−psi]).We track individual genotypes at the −10 per base pair per cell division −10 = 1.3×10−9 per cell division.The point mutation rate in Saccharomyces is around 5×10agp loci. The back mutation rate is primarily important only for setting agp allele frequencies at mutation-selection-drift equilibrium in environment 1 , and the case where it is completely recessive (h = 0) relative to the 0prf allele. +prf individuals form [+PSI] with probability m = 10−5 per generation +PSI] is lost with probability m′ per generation during cell division. Empirical work shows that m′<0.0002 m′≈m.We explore both the case where the m increases by as much as 60-fold in response to environmental stress +prf to promote evolvability. Here we make the conservative assumption that m does not depend on the degree of adaptation to the current environment.Note that +PSI] or by point mutations in stop codons are likely to incur a fitness cost. This cost could be related to gain or loss of function, and hence specific to the gene in question, or it could be a more general metabolic cost. Here, in order to develop a general, parameterized model, we assume a metabolic cost.In both environments, readthrough products at any locus induced either by [psi−δ = 0.003 and PSI+δ = 0.01 in [−psi] and [+PSI] cells respectively iE be the level of readthrough at locus i. iE is equal to jδ for psi−δ)/2 for j∈{psi−, PSI+}. The unit concentrations for all equations below is now given relative to a typical expression level of a gene defined as E = 1.In environment 2, the metabolic cost of readthrough is ameliorated because of the adaptive effects of a substrate-dimer reaction involving readthrough products at one or two loci. Readthrough probabilities are L is either 1 or 2, depending on whether simple or complex adaptation is assumed, and dβ is a constant that weights the metabolic cost of readthrough at the potentially adaptive loci relative to the metabolic cost of readthrough across the whole genome. Since there are ∼5000 genes in Saccharomyces, we assume that dβ = 1/5000. Equation 1 yields a fitness of 1 in the absence of readthrough, decaying exponentially towards zero as levels of readthrough increase. The parameter α controls the strength of selection against readthrough.The fitness of an individual in environment 1 isagp loci. We assume that the readthrough +Agp gene product has adaptive function when in the form of a dimer. For simple adaptations, this is an +Agp homodimer. For complex adaptations, this is an bβ is a parameter controlling the magnitude of the adaptive effects and t1/2 is the half-life of a substrate acted on by a catalytic +Agp dimer. The first term represents the metabolic cost of readthrough, and is identical to fitness in environment 1. bβ is set such that the relative fitness of [+PSI] homozygous agp+ individuals is 1+s2 where s2 = 0.001, 0.01 or 0.1. Fixing s2 in this way allows appropriate comparisons between the 1-locus and 2-locus models. t1/2 captures how the strength of adaptation depends on the extent of readthrough at each of the L loci. The biochemical model for calculating t1/2 depends on the +Agp dimer concentration and is presented in the In environment 2, an individual's fitness depends both on the metabolic cost above, and on a benefit accruing from readthrough at +PSI]. This estimate depends on the frequency of [+PSI] as a rare polymorphism in wild, [+PSI]-competent Saccharomyces populations. Following expression of a Sup35-GFP fusion protein, a few cells from wild populations show aggregates almost immediately +PSI] cells containing Sup35 aggregates at a frequency of ε = 1%. If some of these aggregates are false positives, then the true value of ε could be lower. Assuming populations are in epimutation-selection balance, the strength of selection against [+PSI] is sexp is the probability an offspring is formed sexually. Given that an individual is formed sexually, autop is the probability it is formed via automixis and amphip is the probability it is formed via amphimixis (see below). Although Equation 3 is complex, its inference of the strength of selection against [+PSI] depends largely just on the observed [+PSI] frequency ε and the rate of [+PSI] appearance mMasel and Griswold 1s against [+PSI] in environment 1, α is given bys1 to the fitness of [+PSI] individuals relative to −psi individuals. We useε to calculate s1 and hence α, and α to run our simulations. Since there is uncertainty in the estimate of the equilibrium frequency ε of [+PSI] when deleterious, we explore cases when ε is 0.01%, 0.1% and 1%.Given selection +prf and 0prf alleles by initializing a population in environment 1 with a 0.5 frequency of each allele, and simulating evolution for 5/Ω12 generations to determine how often +prf increases in frequency.We analyze evolutionary competition between +PSI] frequencies, and also to initialize agp frequencies at the moment when the switch to environment 2 occurs  and so the approximation of linkage equilibrium is conservative relative to inferring whether +prf can be maintained.We use mutation-selection-drift balance theory to initialize  state is slightly more complex. Both sexual and asexual reproduction consist of cell division followed by cell growth. During cell division, [+PSI] is lost with probability m′. During subsequent cell growth, [+PSI] appears spontaneously in +prf cells with probability m. When reproduction is sexual, both contributing individuals first have the opportunity to lose [+PSI] during meiosis with probability m′. The new diploid individual is then [−psi] only if both parent cells are [−psi]. This allows 0prf lineages to capture the benefits of [+PSI]. During diploid cell growth following mating, [+PSI] has a single opportunity to appear with probability m in +prf cells.Propagation of  is deleterious and times when it is adaptive. A sample run showing +prf fixation is shown for illustrative purposes in +prf very slowly declines in frequency until the switch from environment 1 to 2 occurs. A [+PSI] selective sweep then immediately begins, with +prf hitchhiking to high frequency. Later, first one and then both rare +agp mutants appear. Once recombination has combined the two, a [−psi] revertent sweeps through the population. By this stage +prf has already become fixed.The simulated yeast population experiences a fluctuating environment between times when -mediated adaptations is strong. From these results, it seems unlikely that a [+PSI]-like evolvability system could be favored by natural selection in an obligately sexual species under the conditions considered here.We see in +prf is dominant (h = 1). When +prf is completely recessive (h = 0), sex provides even less of a barrier to the evolution of evolvability , we see in +prf increases in frequency when Ω12>2×10−6, corresponding instead to Ω12eN>10. This still corresponds to an exceptionally mild and plausible absolute requirement on the rate of environmental change.Opportunities for adaptation must also arise at a minimum frequency for capacitance to evolve . Previou+prf frequency of 0.5 is a very artificial starting condition, and was chosen for computational efficiency. To test the sensitivity of our results to this starting condition, we also did an invasion analysis starting with a single new +prf mutant Click here for additional data file.Figure S2+PSI] in environment one. This could inhibit [+PSI]-mediated adaptation, but the effect is negligible unless selection for [+PSI] in environment two is very weak. Ω12 = Ω21 = 10−5, h = 1.Values of ε lower than the upper bound ε = 0.01 do not change our results. Lower values of ε imply stronger selection against [(8.80 MB TIF)Click here for additional data file.Text S1Initial allele frequencies.(0.03 MB PDF)Click here for additional data file.Text S2Adaptive gene product activity.(0.03 MB PDF)Click here for additional data file.