How growth regulators provoke context-specific signals is a fundamental question in developmental biology. In plants, both auxin and brassinosteroids (BRs) promote cell expansion, and it was thought that they activated this process through independent mechanisms. In this work, we describe a shared auxin:BR pathway required for seedling growth. Genetic, physiological, and genomic analyses demonstrate that response from one pathway requires the function of the other, and that this interdependence does not act at the level of hormone biosynthetic control. Increased auxin levels saturate the BR-stimulated growth response and greatly reduce BR effects on gene expression. Integration of these two pathways is downstream from BES1 and Aux/IAA proteins, the last known regulatory factors acting downstream of each hormone, and is likely to occur directly on the promoters of auxin:BR target genes. We have developed a new approach to identify potential regulatory elements acting in each hormone pathway, as well as in the shared auxin:BR pathway. We show that one element highly overrepresented in the promoters of auxin- and BR-induced genes is responsive to both hormones and requires BR biosynthesis for normal expression. This work fundamentally alters our view of BR and auxin signaling and describes a powerful new approach to identify regulatory elements required for response to specific stimuli. Although distinct sets of growth regulators - auxin and brassinosteroids - are required for cell expansion; rather than being independent signals, the response from each pathway requires the other The continuous shaping of plant form is a marvel of signal integration. In early seedling development this is particularly clear, as environmental cues, such as light, profoundly alter the innate morphogenetic program. How diverse pathways merge to determine a discrete cellular growth response is largely unknown. Auxin, the first plant hormone identified, has been implicated in patterning or growth of virtually every plant tissue from earliest embryo to developing fruit . BrassinArabidopsis , a plasma-membrane-localized receptor serine/threonine kinase, is essential for BR perception and accounts for most BR-binding activity in bidopsis . A Shaggbidopsis . When BRbidopsis . Upon BRbidopsis .In contrast to BRs, no auxin receptor has been identified. However, exposure to auxin is known to promote rapid turnover of nuclear Aux/IAA proteins by ubiquitin-mediated targeting to the 26S proteasome . Aux/IAAArabidopsis genome, rekindled interest in the interaction between auxin and BRs, as it was found that a significant percentage of the BR genomic response comprised genes annotated as auxin responsive  length as a quantitative measure of growth. Both hormones are known to induce cell elongation, and exogenous BR treatment has been shown to increase hypocotyl length  and ARF8 (At5g37020) were negatively regulated by BR treatment (see Tables PIN3 (At1g70940), PIN4 (At2g01420), PIN7 (At1g23080), and an AUX1-like gene (At1g77690). Auxin induced the expression of BRI1 and a close paralog, BRL3 (At3g13380), and repressed the expression of another BRI1-like gene, VH1/ BRL2 (At2g01950) (While auxin treatment had no effect on ARF gene expression, transcripts of 2g01950) . It is p2g01950) B.det2 plants, which are hypersensitive to exogenous BR treatment, were insensitive to growth at elevated temperature  plants were no longer affected by BR treatment in a yucca background , those with increased transcript levels following auxin treatment only , and those genes whose transcripts were induced following treatment with either hormone . Known plant promoter elements and their annotations were downloaded from PLACE . This approach offers a significant advantage over other background models used to assess enrichment. Permuted distributions reflect real expected frequencies and do not rely on assumptions about genome architecture. In addition, the normal distribution of site frequencies observed with large numbers of permutations allows for the use of powerful parametric statistical methods. Moreover, the ease of filtering based on relative probabilities makes this approach ideally suited to comparisons of promoters regulated in different conditions. In this study, matches were considered significant if a motif was overrepresented in a given set (p < 0.1) and present in at least 10% of group promoters. This analysis identified several motifs specifically enriched in a given group . However, the A, B, and AB groups showed significant enrichment of the core ARF-binding element TGTC in their promoters, perhaps reflecting some sequence divergence between Arabidopsis and soybean, where the element was first identified. A well-characterized synthetic element containing the ARFAT called DR5  gene, which is known to function in BR response , ethylene, ABA, and cytokinin have all been shown to affect hypocotyl elongation , 0.8% phytagar (Gibco-BRL), and one of five concentrations of BL (0, 1, 10, 100, or 1,000 nM). Seeds on plates were then stratified in the dark at 4 °C for 2 d. Plants were grown in approximately 35 μmol mGUS staining protocol was as described in Arabidopsis seedlings were immersed in 1 μM BL in 0.5× Murashige Minimal Organics Medium  or medium alone for 2.5 h before they were harvested for total RNA preparation. Total RNA from the treated seedlings was used for preparing probes for the microarray experiments, which were carried out according to the protocols provided by the gene chip manufacturer Affymetrix . All experiments used two independent biological replicates. Details of the auxin experiment have been described previously  greater than 0. Data are available at Gene Expression Omnibus; see Nine-day-old, light-grown eviously . Data aneviously . Genes wPlants were treated with hormones as above using treatments of either 1 μM BL, 1 μM indole-3-acetic-acid (auxin), both hormones, or a mock treatment. Total RNA was extracted using a Qiagen  RNAeasy kit and first-strand cDNA was synthesized using an Invitrogen Superscript First-Strand cDNA Synthesis kit. cDNAs were diluted 20-fold and combined with SYBR master mix  for PCR. Primers were as follows: At5g64770 (5′-CTTCTCATACTCTTCATTTCCTCTCCTACT-3′, 5′-TTCTCGTAAGCTTCGTGCTTGA-3′), At1g18400 (5′-CTAGCGGCGTCTCCGATAAT-3′, 5′-AAGAACCTGTTTCAGTGGCAATAAC-3′), At1g10550 (5′-AAGCTTCCCGCTGGATTTG-3′, 5′-TTGATAAATAGAAAGCAACCACAACAC-3′), and At4g30290 (5′-TCCCTGGTAACTCTGCTGGAA-3′, 5′-CCGGAGATTTAAGATAGAATGTTGTGA-3′). At5g15400 (ubiquitin) was used to normalize all values (5′-TGCGCTGCCAGATAATACACTATT-3′, 5′-TGCTGCCCAACATCAGGTT-3′). PCR reactions were performed in triplicate and analyzed using an ABI PRISMA 7700. A standard curve was constructed for each primer using an equal mixture of all cDNAs.We used a Perl script to extract the 500 bp of sequence preceding the 5′ end of each annotated transcription unit in the AGI pseudomolecules annotation (14-May-2003) downloaded from NCBI. These putative promoter sequences begin immediately upstream of the 5′ UTR for transcription units with an annotated 5′ UTR, and upstream of the annotated translational start for the remainder.n genes and extracting 500-bp promoter sequences for the sampled set of genes. Known plant promoter elements and their annotation were downloaded from PLACE  minus the mean count from the 1,000 surrogates (Csurr) relative to the SD from the 1,000 surrogates (SDsurr) [i.e., Z = (Ctrue − Csurr)/SDsurr]. Thus for each motif the p value we calculated was the probability to the right of the observed count calculated on the null distribution derived from sampling promoters randomly from the genome. We considered a motif to be significantly overrepresented if this probability was less than 0.1. These calculations were implemented using Perl scripts and a relational database (MySQL).We analyzed putative promoter regions upstream of auxin- and BR-regulated genes to identify overrepresented promoter elements. One thousand surrogates of each promoter set were created by randomly shuffling the list of genes represented on the Affymetrix ATH1 arrays and then sampling om PLACE . For eacTable S1yucca mutant are shown as compared to WT and following BL treatments. The comparisons from left to right are WT BL- versus mock-treated, WT IAA- versus mock-treated, yucca mock-treated versus WT mock-treated, and yucca BL- versus mock-treated. nc, no change.Effects of increased auxin levels in the (160 KB XLS).Click here for additional data file.Table S2yucca mutant are shown as compared to WT and following BL treatments. The comparisons from left to right are WT BL- versus mock-treated, WT IAA- versus mock-treated, yucca mock-treated versus WT mock-treated, and yucca BL- versus mock-treated. nc, no change.Effects of increased auxin levels in the (169 KB XLS).Click here for additional data file.Table S3ave, average; se, standard error.(64 KB XLS).Click here for additional data file.Table S4ave, average; se, standard error.(64 KB XLS).Click here for additional data file.Table S5ave, average; se, standard error.(63 KB XLS).Click here for additional data file.Table S6ave, average; se, standard error.(26 KB XLS).Click here for additional data file.http://www.ncbi.nlm.nih.gov/geo/) accession numbers for the genes and gene products discussed in this paper are WTBR1 (GSM13423), WTBR2 (GSM13424), WTmock1 (GSM13420), WTmock2 (GSM13421), wtzm1 (GSM13430), wtzm2 (GSM13432), wtzmIAA1 (GSM13433), wtzmIAA2 (GSM13434) and, yuccaBR1 (GSM13428), yuccaBR2 (GSM13429), yuccamock1 (GSM13426), and yuccamock2 (GSM13427).The associated experimental descriptions are available at accession numbers GSE862 (BR effects on WT and yucca seedlings) and GSE863 (auxin effects on seedlings).The Gene Expression Omnibus (


We studied food consumption and nutrient intake in subjects with depressed mood, anxiety and insomnia as indices of compromised mental wellbeing.The study population consisted of 29,133 male smokers aged 50 to 69 years who entered the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study in 1985–1988. This was a placebo-controlled trial to test whether supplementation with alpha-tocopherol or beta-carotene prevents lung cancer. At baseline 27,111 men completed a diet history questionnaire from which food and alcohol consumption and nutrient intake were calculated. The questionnaire on background and medical history included three symptoms on mental wellbeing, anxiety, depression and insomnia experienced in the past four months.Energy intake was higher in men who reported anxiety or depressed mood, and those reporting any such symptoms consumed more alcohol. Subjects reporting anxiety or depressed mood had higher intake of omega-3 fatty acids and omega-6 fatty acids.Our findings conflict with the previous reports of beneficial effects of omega-3 fatty acids on mood. Diet has an effect on mood and cognitive function . There iA double-blind placebo-controlled trial with 30 patients showed that omega-3 essential fatty acid supplements alleviated symptoms in patients with bipolar disorder . In a reA number of studies have shown that acute tryptophan depletion produces depressive symptoms and results in worsening of mood . Folic aIn general, a low-fat diet may have negative effects on mood , and altDepressed subjects tend to consume more carbohydrates in their diets than non-depressed individuals , and theWe set out to study whether food consumption and intake of nutrients in subjects with depressed mood, anxiety and insomnia differed from those in subjects without any such symptoms.This study was based on the cohort of a randomized, double-blind, placebo-controlled primary prevention trial testing the hypothesis that daily supplementation with α-tocopherol or β-carotene reduces the incidence of lung and other cancers . The stuAt baseline, subjects completed a questionnaire on their background and medical history, including three questions on mental wellbeing. These items concerned anxiety, depressed mood and insomnia experienced in the past four months. Height and weight were also measured, and a blood sample was drawn for determining total and high-density lipoprotein (HDL) cholesterol concentrations. Diet and alcohol consumption was assessed from a self-administered dietary history questionnaire , which aDietary nutrient data were analyzed by linking the questionnaire data to the food composition database of the National Public Health Institute, Finland. For analysis, we considered three main groups: principal nutrients, specific nutrients selected on the basis of a priori hypotheses, and certain foods. The principal nutrients were energy, carbohydrates, proteins and fats. The hypothesis-based nutrients were omega-3 and omega-6 fatty acids, lysine, serine, tryptophan, and two vitamins, vitamin D and folic acid. Omega-3 fatty acids from fish consist of long-chain fatty acids, while the omega-3 fatty acids in vegetables are shorter-chain molecules. The food items included were fish, milk, meat, vegetables, margarine, coffee and alcohol. We also evaluated the total energy intake.The trial involved three follow-up visits annually. At each follow-up visit the participants were asked whether they had felt anxiety, depression, or insomnia since the preceding visit (Have you felt feelings of depression in last three months? Have you felt feelings of anxiety in last three months? Have you had insomnia in last three months?). To identify subjects who suffered chronically from these symptoms we took into account the symptoms reported throughout the first follow-up year, i.e. at baseline and the three follow-up visits (at baseline, 4 months, 8 months and 12 months). Men reporting anxiety, depression, insomnia, or all these symptoms at all four visits were included in these analyses.As potential risk factors, baseline age, body-mass index (BMI), energy intake, alcohol consumption, education level, marital status and smoking were entered into regression models as covariates. Dietary factors were adjusted for energy intake in the models .At study entry, 4314 (16%) men reported depressed mood in the past four months, 6498 24%) feelings of anxiety, and 5550 (21%) insomnia. The mean intake of energy was 1 to 3% greater and consumption of alcohol 30 to 33% greater in subjects with any such symptoms, compared with symptom-free individuals  subjects with depressed mood also had feelings of anxiety, it may be that anxiety is the dominant symptom, and the greater intake of omega-3 and omega-6 fatty acids is primarily related to feelings of anxiety.Previously, it has been suggested that omega-3 fatty acids may alleviate the effects of depressive symptoms but not those of mania . RecentlSubjects with any or all of the symptoms consumed more alcohol than the symptom-free subjects. Subjects with all three symptoms consumed most alcohol of all, and they received 6% of their total energy from alcohol, compared with 4% in subjects with no symptoms. Energy from alcohol, however, did not explain the differences in the mean intake of energy between groups. Body-mass index was lower, despite a higher caloric intake, in subjects with any of the symptoms compared with symptom-free subjects.Subjects reporting insomnia drank more milk than symptom-free subjects, but less coffee. Warm milk has long been taken as a self-medication for insomnia, and our finding among those with insomnia accords with this traditional habit. In addition, they avoided consuming large amounts of coffee, which is known to have impact of sleep. We also found that subjects reporting depressed mood consumed more carbohydrates than subjects with no symptoms. This finding is consistent with the attempt by depressed subjects to alleviate the carbohydrate craving associated with symptoms of depression.Tryptophan intake showed no association with mental wellbeing in our study population. Interestingly, a number of negative studies has been published recently, suggesting that the effects of tryptophan depletion on mood are inconsistent -24, and There are some limitations in our study. Our study was a cross-sectional study, and it cannot provide causal evidence on the association between the diet and symptoms of depression, anxiety or insomnia. The study participants included only men, aged 50 to 69 years, and all were smokers. Our exclusion criteria limit the generalization of our findings, but the study still provides valid and reliable data on a community-based, homogenous sample of older men.Dietary intake and alcohol consumption were assessed with a validated food use questionnaire to measure the habitual dietary intake over the previous year as completely as possible. For most nutrients, both the reproducibility and the validity of this method are 0.6 to 0.7 . For exaThe assessment of self-reported depression was based on a single item only that might have compromised the specificity, but not sensitivity. For example, two questions only may be as effective as more detailed screening instruments in detecting probable cases of major depression . One of The scientific examination of relationships between nutrition and mental wellbeing is a relatively new area of study. Most of the studies focus on the use of dietary supplements, which provide more concentrated amounts of specific nutrients than most food sources. There are few data evaluating food consumption and nutrient intake among subjects with compromised mental health. Our main finding was that we did not find any association between omega-3 fatty acids from fish and mental wellbeing. In general, more attention need to be paid to the intake of nutrients in patients suffering from symptoms of depression, anxiety or insomnia. Further studies are needed to clarify complex associations between the diet and mental wellbeing, and to elucidate their mechanisms of action.


Recent studies have identified long, non-coding genomic regions that are perfectly conserved between human and mouse, termed ultra-conserved regions (UCRs). Here, we focus on UCRs that cluster around genes involved in early vertebrate development; genes conserved over 450 million years of vertebrate evolution.Evolutionarily conserved sequences within or adjoining orthologous genes often serve as critical Based on a high resolution detection procedure, our UCR set enables novel insights into vertebrate genome organization and regulation of developmentally important genes. We find that the genomic positions of deeply conserved UCRs are strongly associated with the locations of genes encoding key regulators of development, with particularly strong positional correlation to transcription factor-encoding genes. Of particular importance is the observation that most UCRs are clustered into arrays that span hundreds of kilobases around their presumptive target genes. Such a hallmark signature is present around several uncharacterized human genes predicted to encode developmentally important DNA-binding proteins.The genomic organization of UCRs, combined with previous findings, suggests that UCRs act as essential long-range modulators of gene expression. The exceptional sequence conservation and clustered structure suggests that UCR-mediated molecular events involve greater complexity than traditional DNA binding by transcription factors. The high-resolution UCR collection presented here provides a wealth of target sequences for future experimental studies to determine the nature of the biochemical mechanisms involved in the preservation of arrays of nearly identical non-coding sequences over the course of vertebrate evolution. The study reports a significant association of a non-transcribed subset of those elements with DNA-binding proteins; an equivalent observation has been made independently by Boffeli et al.[et al. [HoxD gene cluster that are highly conserved across vertebrate genomes.Comparative genome sequence analysis, often termed phylogenetic footprinting, has proven successful for the identification of y regions,2. Recenons[DACH , SOX9 [4x bigene ,6, and He [HOX-D ,8 clustee [HOX-D ). A receli et al. for a li.[et al.  describeIn this paper, we define a set of UCRs using high-resolution criteria that detect segments conserved between the human, mouse and pufferfish genomes. Analysis of this set provides insights into a previously unrealized organizational structure of UCRs in vertebrate genomes. We conclusively show that clusters of UCRs are globally associated with many of the genes that act as master regulators during vertebrate development. The clustered distribution of these regions along chromosomes and, importantly, around their presumptive target genes suggests that gene regulation involves the coordinated action of numerous, widely dispersed elements.We initiated this study by applying comparative genomics to identify putative regulatory regions for a number of evolutionary conserved homeodomain transcription factors that control neural cell fate determination ,13. Whenet al. [To test whether the association of UCRs with regulatory genes reflected a global genomic trend, we identified a comprehensive set of human/mouse/pufferfish UCRs for detailed analysis. We defined minimum requirements for a UCR (see Methods) and performed a genome-scale computational analysis that retrieved 3583 human/mouse/pufferfish UCRs. Since one of the requirements is that the UCRs are not overlapping actively transcribed genomic regions, they would belong to type II UCRs defined by Bejerano et al. .et al.[The median UCR length was 125 bp, but extreme lengths (>1000 bp) were observed. Qualitative assessment of "genescapes", the gene structures, surrounding UCRs revealed them to be present either in introns, in dense clusters around a group of genes or in 'gene deserts' . There appeared to be a strong association between locations of our set of UCRs and genes encoding transcription factors – even stronger than that reported by Bejerano et al. [see AddTo quantitatively assess the characteristics of genes proximal to UCRs, we analyzed the over-representation of gene annotations. We retrieved the InterPro  domain ai.e. the HoxD cluster, Nkx6.1, Nkx2.2 and Pbx3) [for detailed annotated lists of genes associated with UCR clusters, see Additional files  with links to the UCSC genome browser [In order to visualize the distribution of UCR locations across the human genome, we generated a UCR density map for each chromosome see . Figure  browser .IRX) gene clusters. IRX genes are situated in two clusters of three genes each, present on human chromosomes 5 and 16 [We performed a global pairwise comparison of all UCRs, in order to determine if UCR duplication was common across evolution. We discovered only five sets of duplicated UCRs, all of which are adjacent to corresponding duplicated genes. For example, duplicated UCRs are present in the introns of SOX5 (on chromosome 12) and SOX6 (on chromosome 11), two highly similar genes involved in chondrocyte differentiation . Of spec5 and 16 . SimilarThese UCRs occur in arrays of highly conserved regulatory elements spanning large chromosomal regions. The clusters are co-localized with genes encoding key proteins for the regulation of development, with a particular correlation with genes encoding transcription factors. The strength of association between UCRs and diverse classes of DNA binding transcription factors validates that a relatively simple definition of UCRs captures a biologically meaningful set of functional sequences. The presence of non-coding UCRs is predictive for the presence of genes implicated in development, differentiation and malignancies. The list presented in [The human genome contains numerous ultra-conserved regulatory sequences that are shared broadly across vertebrates. ented in  hints atExceptional mechanisms are brought to bear to retain UCRs over hundreds of millions of years of parallel evolution. UCRs are more strongly conserved than sequences encoding identical proteins, and exhibit sequence identity exceeding essentially all known cis-regulatory sequences. The retention properties suggest that UCRs have important functions in the vertebrate genome.The observed UCRs could fall into multiple functional categories, including enhancers of transcription, regulators of chromatin structure and unknown genes for non-coding transcripts. A small subset of UCRs have been identified previously as enhancers of transcription ,3.Drosophila melanogaster [The high conservation and length of UCRs compared to binding sites for single transcription factors suggests that the mode of regulation must involve more than the binding of small number of transcription factors. Homeotypic clusters of binding sites, as seen in developmental genes in nogaster , represeIRX gene clusters. Most of the UCRs have no similarity between the two clusters, with the exception of a set of four UCRs that have retained both mutual sequence similarity and spatial position  resulting in the decrease of mutational frequency in UCRs, or negative pressure consistent with evolutionary selection against such mutations. Given the breadth of possibilities, we leave postulation until further data emerges.et al.[Since Bejerano et al. focused We defined UCRs as non-protein coding genomic regions having a sequence identity > 95% over a 50 bp sliding window of length in human/mouse comparison  window which was progressively slid over each chromosome in 100 kb intervals. The same approach was used to estimate the gene density; specifically by summing the number of bases within the window that aligned with human mRNA (from the UCSC Genome Browser database).Distances between a given gene and UCR on the same chromosome were defined as the shortest distance between the starting points and/or endpoints of UCR and gene in the human genome (UCSC assembly hg15), using EnsEMBL  gene annThe distances from genes within a set  to the closest UCRs were calculated as above. The expected fraction of gene-UCR distances smaller than 8 kb was estimated by simulation: UCR genome coordinates were randomly chosen and distances measured as above. The simulation process was repeated 1000 times and the average fraction reported. In order to estimate if the observed distribution was significantly different from the expected, we used the chi-squared test.For each UCR, the closest upstream and downstream gene within 2 Mbp was identified . EnsEMBL InterPro  domain a. Since the number of tests is considerable, we corrected for multiple sampling using the conservative Bonferroni method [For each domain found in the UCR-proximal genes, we tested the null-hypothesis that the sample and background sets are drawn from the same population versus the alternative hypothesis that the sample set has a higher frequency of the domain, using Fisher's Exact Test  from thei method , in whici method .We used the distances between consecutive UCRs as a statistic indicating clustering. A neutral background distance distribution was created by assigning UCRs genome coordinates randomly, and subsequently measuring distances between consecutive UCRs. This process was repeated 1000 times. We compared the distance distribution between naturally occurring UCRs and the background using the Kolmogorov-Smirnov test , which aAll possible pairs of UCRs were aligned using NCBI BLASTN  with sta3 base pairsUCR – ultraconserved non-coding region; bp – basepairs; kbp – 10AS collected the data and performed most steps of bioinformatic and statistical analysis presented in the paper. He produced all the Figures in the paper and Table Genescape around 50 randomly selected UCRs. Selected UCRs are shown as yellow triangles, other UCRs as light yellow triangles. Genes are colored after domain (red = Homeobox, green = C2H2 Zink fingers in green, pink = Nuclear receptors, Blue = forkhead).Click here for fileGenescape around 50 randomly selected genes. UCRs are shown as as light yellow triangles. Color coding of genes as above.Click here for fileComplete list of protein domains in genes flanking UCRs. Each tested domain is listed along with corrected and uncorrected P-value as in Table Click here for fileComplete list of protein domains in genes with UCR(s) in intron(s) Each tested domain is listed along with corrected and uncorrected P-value as in Table Click here for fileUCR distribution in the human genome UCR density (pink) and gene density (blue) is shown for each chromosome. Densities are calculated as described in Methods.Click here for fileGenes associated with enumerated UCR clusters from Figure Click here for fileExtended list of UCR clusters An extended, but less annotated, version of in [on of in Click here for file


Drosophila.You might say that caspases are obsessed with death. The primary agents of programmed cell death, or apoptosis, caspases kill cells by destroying proteins that sustain cellular processes. Apoptosis, a highly controlled sequence of events that eliminates dangerous or unnecessary cells, contributes to a wide variety of developmental and physiological processes—in a developing embryo, apoptosis creates the space between fingers and adjusts nerve cell populations to match the number of cells they target; in an adult, apoptosis counters cell proliferation to maintain tissue size and density. Now it appears that caspases may also play a role in creating life. As Bruce Hay, Jun Huh, and colleagues report, multiple caspases and caspase regulators are required for the proper formation of free-swimming sperm in the fruitfly Caspases, which typically exist in a quiescent state in nearly all cells, are regulated through a complex network of activators and inhibitors. Once activated, a “caspase cascade” ultimately cleaves and irreversibly alters the function of essential cellular proteins, leading to apoptosis. A few of the dozen-plus known caspases appear to contribute to inflammation responses, but the vast majority are enlisted to kill cells. Not surprisingly, cells keep caspase activation under tight wraps. That's why it's intriguing that multiple caspases normally associated with the induction of cell death participate in this nonapoptotic process.During spermatogenesis, germline precursor cells—the cells that generate sex cells—give rise to 64 haploid spermatids.  Spermatids are connected by intracellular “bridges” that, along with most other cytoplasmic components, must be expelled in a process called “individualization” to create terminally differentiated free-swimming sperm. Protein structures known as investment cones surround each spermatid nucleus and sweep out the neighboring cytoplasm, bridges, and organelles, forming a bulge that eventually detaches as a “waste bag” as it reaches the sperm tail. This process—elimination of cytoplasm and membrane packaging of individual spermatids—also occurs in mammals. Many types of human infertility result when it is disrupted.To explore how caspases affect this process, Hay's group studied the consequences of inhibiting caspase activity (or the activity of specific caspase activators) in the male germline cells of fruitflies. In both cases, they observed that the bulges and waste bags were either abnormally small or absent and that the normal path of investment cone movement was disrupted. The researchers also inspected the flies to look for structural differences and found that spermatids in both mutant strains remained connected by cytoplasmic bridges and retained residual cytoplasm. Together, the authors conclude, these results demonstrate that individualization depends on caspase activity.Hay's team went on to characterize the pathways that activate caspases during sperm individualization. They found that in one pathway, two key activators of caspase-dependent cell death—Ark and Hid —promote the activity or stabilization of the caspase Dronc. A second caspase, Dredd, and its activator Fadd  were also found to be important. Double mutants that removed both Dronc and Dredd activity had more severe defects in individualization than mutants that removed only one or the other, suggesting that these caspases have distinct roles in this process. Interestingly, Drice—the downstream caspase activated just as individualization begins —was not affected by inhibition of Dronc and Dredd. This result, along with the fact that Dronc and Drice were activated at different times and places, suggests that some other mechanism activates Drice. Different apoptosis-related caspases and caspase regulators, the authors conclude, are recruited through different pathways at distinct points in time and space to create individually packaged, free-swimming sperm, a distinctly nonapoptotic process.Drosophila mutants, for example, resemble “cytoplasmic droplet sperm,” a condition seen in infertile men. Insights into the molecular basis of caspase activation in sperm individuation could provide clues to male infertility and suggest possible treatments. Given the widespread role of programmed cell death in supporting processes fundamental to life, perhaps it's not surprising that the agents of apoptosis also support the creation of life.Studies in mice suggest that individualization may occur similarly in mammals, with activation of apoptotic caspase cascades resulting in free-swimming sperm and loss of specific caspase activators causing infertility and defective spermatogenesis. The abnormal differentiation and residual cytoplasm seen in caspase-inhibited


Epidemiological studies suggest that statins, a class of cholesterol-lowering drugs, may lower the risk for Alzheimer disease. The mechanism for this effect is unclear. Alzheimer disease is characterized by accumulation of amyloid deposits in the brain. These deposits are composed of amyloid-beta (Aβ) peptide, a protein fragment that is cleaved off from the amyloid precursor protein APP. APP can be cleaved in two different ways. Amyloidogenic (“amyloid generating”) cleavage by an enzyme called beta-secretase yields “sticky” Aβ peptides that aggregate to form deposits, whereas non-amyloidogenic cleavage by alpha-secretases generates soluble peptides that do not form deposits. Studies in animal models and cell culture suggest that statins might modulate APP processing and shift the balance toward “healthy” (non-amyloidogenic) cleavage.In their quest to understand how statins affect APP processing, Sam Gandy and colleagues focused on a molecule called ROCK1, a kinase enzyme that had recently been implicated in APP processing. The theoretical link between statins and ROCK1 goes as follows: statins inhibit the isoprenoid pathway, isoprenoids are regulators of the enzyme Rho, and Rho in turn activates ROCK1. And while such potential connections could be drawn for any number of molecules, Gandy and colleagues went on to test whether statins exert their effect on APP cleavage by interfering with the isoprenoid/Rho/ROCK1 pathway.Working in mouse neuroblastoma cells, they confirmed that two different statins increased healthy cleavage of APP. When they directly interfered with the isoprenoid/Rho/ROCK1 pathway by adding a drug that inhibits Rho activation, they saw effects similar to those of the statins . The same effects were seen when they transfected the cells with a dominant negative form of ROCK1 ; this outcome shows that the pathway can influence APP cleavage. Most conclusively, when they added a version of ROCK1 that was constitutively  active, they reduced basal levels and abolished statin-stimulated levels of healthy cleavage.Taken together, these results suggest that statins influence APP processing, at least in part, by modulating the isoprenoid pathway and inactivating the ROCK1 kinase. Future studies are necessary to determine whether this mechanism is actually responsible for the apparent clinical benefits of statins. Another question worth exploring further is whether ROCK1 might be a suitable target for therapeutic interventions that aim to decrease harmful, and promote healthy, cleavage of APP.


Research in General Practice requires the participation of General practitioners (GPs). In Germany there is little tradition of research in this field, and GPs are not used to be participants in research. Little is known about German GPs attitudes towards research. Therefore the aim of our study was to assess the willingness of German General Practitioners to participate in primary care research and their attitude towards research in general practice. The results should enable a more successful approach to GPs in further studies.Cross sectional study using semi-structured interviews with a random sample of 76 General Practitioners who participate in the teaching of medical students at the University of Heidelberg.Despite little experience, over 85 % of GPs appreciated research in their field. Important reasons for scepticism about research were the gap between theoretical research and practical work of GPs and the domination of research by specialists. Main barriers for participation are clinical workload, administrative overload and the newly introduced Disease Management Programs. The highest motivation for GPs to participate in research emanates from the will to substantiate their quality of care with solid research data.Financial incentives and personal support e.g. with study nurses are certainly necessary to establish a research culture and to overcome main barriers against participation. The most successful approach to motivate GPs to participate is to convince them that research documents their quality of care. This data may reflect the facts on which the financial resources are provided in the future health care system. Compared to other European countries, Germany still has little of a research tradition in general practice. Increasingly policy-makers have realized that the continuity and the efficacy of the healthcare system have to be improved. For this a well-developed primary medical care system is needed. In recent years a number of new chairs of general practice have been established and a national funding programme was created in order to promote General Practice as an academic discipline in Germany. The University of Heidelberg, which is the oldest university in Germany, is among the beneficiaries of these developments. Despite of a well established network of teaching practices, the research group for general practice and health services research was only created in 2002 . This grWe performed a cross sectional observational study collecting qualitative data. The Ethical Commission of the University of Heidelberg approved the study.A random sample of 76 GPs in the area of Heidelberg was approached for the study. The GPs were selected by choosing every third of an alphabetical list of 250 practices. These GPs were associated with the university by frequently teaching students in their practices. Due to old data, in six cases the GPs did not practice any more. So finally 76 GPs were included. All of the selected GPs were in practice for more than five years. Former studies indicated that relevance of the topic has a positive predictive value for the recruitment rate. Therefore we selected a topic with a high clinical relevance in daily practice: osteoarthritis ,4. BasedNo letters were returned to the university because of wrong addresses. A reminder or anything similar did not follow the first letter. One week after the letter, every GP was called by the principal investigator and was asked – after giving him again information on the study – if she or he wanted to participate. This approach was chosen to get qualitative information of all approached GPs about their willingness to participate and their opinion in general. This way of data collecting has already been used in this field of research and enables not only a high rate of data response, it is also a feasible way of collecting qualitative data . If the We were mainly focused on qualitative information. Therefore the statements of the GPs were grouped and coded by two separate researchers and then discussed in order to agree on the selected categorisation according to the guidelines for qualitative researchers .A total of 18 GPs (23.8%) of the approached GPs was female, 58 GPs (72.2 %) were male. Only two GPs faxed their agreement-sheet within the first week, before they were phoned and interviewed by the principal investigator. During the telephone calls 25 GPs (32.8 %) agreed to participate and promised to fax the sheet. Out of this group 5 GPs (18.5%) sent their fax during the subsequent two weeks. A total of 8 (10.5 %) faxes were returned. Five female (27.7 %) and 22 male GPs agreed (37.9 %) to participate. A total of 27 GPs agreed to participate ultimately.Table As can be seen in table There were three main conclusions that can be drawn out of our interview results. Firstly, the research topic improving the quality of care for patients suffering from osteoarthritis was considered as highly relevant by the interviewed GPs. This is concordant to our assumptions based on epidemiological data, which led to the fictitious research topic. The same reasoning causes GPs to seek support in the daily treatment of patients with osteoarthritis. Consequently this will be subject of future research projects.Secondly, most of the GPs appreciate research in general practice, but a few were very sceptical. German GPs still don't realise it as a professional obligation as their colleagues in countries like e.g. the Netherlands or the United Kingdom, with a much longer tradition in research, do .The third main result of our survey has not yet been shown in former studies. It is the fact that the willingness for participating in research emanates mainly out of the motivation to improve the reputation of family medicine in general by documenting the high quality of care with data attained in solid surveys. This may reflect the increasing self-confidence of German GPs, which are about to expend the influence in the health care system, and their awareness that an own research culture helps to enhance this. Facing decreasing financial resources in the Health care system, GPs may also be aware that a solid database documenting the quality of care will get more important for the distribution of financial resources in the near future.The revealed barriers against participating in studies mentioned in our telephone survey are in line with results from previous studies in other countries . AccordiPrevious studies were mainly focused on formal or external barriers for GPs against participating in research, or revealed approaches that cannot easily be transferred, as e.g. the enrollment of friendly GPs . What thThe author(s) declare that they have no competing interests.TR conceived and performed the study and draft the manuscript. JS participated in the study design. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Acinetobacter baumanii organisms in Sivas, Turkey over 3 years. We used disk diffusion or standardized microdilution to test the isolates against 18 antibiotics.Gram-negative bacteria cause most nosocomial respiratory infections. At the University of Cumhuriyet, we examined 328 respiratory isolates of Enterobacteriaceae and Klebsiella spp (35%), A. baumanii (27%), and Escherichia coli (15%). Imipenem was the most active agent, inhibiting 90% of Enterobacteriaceae and A. baumanii organisms. We considered approximately 12% of Klebsiella pneumoniae and 21% of E. coli isolates to be possible producers of extended-spectrum beta-lactamase. K. pneumoniae isolates of the extended-spectrum beta-lactamase phenotype were more resistant to imipenem, ciprofloxacin, and tetracycline in our study than they are in other regions of the world.We cultured organisms from sputum (54%), tracheal aspirate (25%), and bronchial lavage fluid (21%). The most common organisms were Our results suggest that imipenem resistance in our region is growing. Pseudomonas aeruginosa in our region [Nosocomial bacterial pneumonia is frequently polymicrobial, with gram-negative bacilli predominating . Becauser region . The curKlebsiella spp (35%), A. baumanii (27%), and E. coli (15%). We also isolated rare organisms such as Stenotrophomonas maltophilia, Burkholderia spp, and Hafnia alvei. All studied Enterobacteriaceae (except Enterobacter spp) were far more susceptible to ticarcillin-clavulanate than to ticarcillin alone, which suggests that the primary mechanism of resistance in these organisms is β-lactamase production.Table K. pneumoniae accounted for 79% of Klebsiella isolates. Klebsiella spp were generally more susceptible to the tested antimicrobials than were Enterobacter spp, Serratia spp, or E. coli. The overall resistance rates to the third-generation cephalosporins (cefotaxime, ceftazidime, and ceftriaxone) were as follows: Klebsiella spp, 10%–19%; Serratia spp, 16%–33%; and Enterobacter spp, 22%–45%. Serratia spp were less resistant to third-generation cephalosporins than Enterobacter spp. E. coli isolates resistant to piperacillin, gentamicin, and the fluoroquinolones accounted for only 4% of all E. coli isolates. Imipenem was the most active agent against our isolates.A. baumanii. Tobramycin was more effective against A. baumanii than against Enterobacteriaceae. Tobramycin and imipenem were the most active agents against both gentamicin- and ciprofloxacin-resistant A. baumanii (Table After imipenem, ciprofloxacin, and the aminoglycosides, tetracycline was the most active agent against ii Table .E. coli isolates (20.8%) and 11 K. pneumoniae isolates (12.2%). All K. pneumoniae and E. coli isolates with the ESBL phenotype were resistant to tetracycline. Regarding K. pneumoniae isolates, 2 were susceptible to tobramycin, 3 to gentamicin, and 4 to ciprofloxacin, but 8 were susceptible to amikacin and imipenem. Regarding E. coli isolates, 4 were susceptible to tobramycin, 7 to gentamicin, 5 to ciprofloxacin, and 8 to amikacin, but all were susceptible to imipenem.We observed the ESBL phenotype in 10 Klebsiella isolates were more susceptible to third-generation cephalosporins , aztreonam (44.0% vs. 65.6%), and ticarcillin-clavulanate (37.0% vs. 60.3%). Klebsiella spp were 81.6% susceptible to ceftazidime in our study; these rates are 96.6% in North America [Contrary to the findings of the Turkish antimicrobial resistance study group , our KleAcinetobacter spp in respiratory specimens may reflect colonization and not necessarily infection [Acinetobacter infection is the lower respiratory tract, especially in mechanically ventilated patients [Acinetobacter spp were the second most frequent gram-negative bacilli isolated from patients with pneumonia in Latin America [A. baumanii isolates. Susceptibility to imipenem was >95% in Canada [A. baumanii will stimulate the use of carbapenems and possibly increase carbapenem resistance in our region.Although the isolation of nfection , the mospatients . Acineto America . In our n Canada , India [n Canada , and Chin Canada . The higE. coli isolates were susceptible to ciprofloxacin. However, this rate was greater in Europe (95.2%) [E. coli's susceptibility to ceftazidime was >95% in Europe [E. coli isolates. Conversely, E. coli strains were not resistant to imipenem in Europe [Only 75% of our  (95.2%) , North A (95.2%) , and Latn Europe , North An Europe , China [n Europe  but onlyn Europe . Imipenen Europe , Latin An Europe , China [n Europe .Enterobacter spp showed high rates of resistance to broad-spectrum penicillins with or without β-lactamase inhibitors (41.7% resistance to ticarcillin-clavulanate) and third-generation cephalosporins (45.2% resistance to ceftazidime). The high rates of ceftazidime resistance among Enterobacter spp suggests a high prevalence of stably derepressed AmpC cephalosporinase-producing strains. Interestingly, resistance to third-generation cephalosporins, aztreonam, and ticarcillin-clavulanate was higher in our study (28.1%–48.0%) than with the Turkish antimicrobial resistance study group (13.3%–38.3%) [Enterobacter or Serratia isolates were resistant to imipenem. In our study, however, the rates of susceptibility to imipenem were 86.2% for Enterobacter spp and 76.5% for Serratia spp. Imipenem susceptibility for these two species was >95% in other parts of the world [Serratia spp were at least 95% susceptible to ceftazidime in the United States [y group 1.3%–38.3%he world ,13. Mored States , Canada d States , India [K. pneumoniae isolates were 45.4% in Latin America, 24.6% in the Western Pacific, 22.6% in Europe, 7.6% in the United States, and 4.9% in Canada [E. coli isolates were 8.5% in Latin America, 7.9% in the Western Pacific, 5.3% in Europe, 4.2% in Canada, and 3.3% in the United States [E. coli isolates decreased susceptibility to the aminoglycosides, tetracycline, and ciprofloxacin but not imipenem, suggesting the presence of other resistance genes in ESBL-encoding plasmids. Despite the high percentage of ESBL production in E. coli isolates, antibiotics remained reasonably effective with these isolates. Imipenem was active against all ESBL-producing E. coli isolates. E. coli remained 30.0% resistant to gentamicin; this resistance rate was 75.9% in the Western Pacific, 57.8% in Latin America, 25.7% in Europe, and 21.1% in the United States [From 1997 to 1999, ESBL detection rates in n Canada ; this ran Canada . During d States ; this rad States .K. pneumoniae isolates were resistant to imipenem in the United States, Latin America, the Western Pacific region, and Canada [K. pneumoniae strains was 61.1% in Canada, 55.1% in the Western Pacific, 52.0% in Latin America, 49.5% in Europe, and 44.4% in the United States [K. pneumoniae strains was 44.2% in the Western Pacific, 34.6% in the United States, 24.3% in Europe, 23.1% in Latin America, 22.2% in Canada, and 63.6% in our study.Only 0.5%–0.7% of ESBL-producing d Canada . Our ratd States , but thiE. coli isolate. It was resistant to ampicillin, ticarcillin, and piperacillin but susceptible to ceftazidime, ceftriaxone, and aztreonam. This profile suggests an oxacillinase with carbapenemase properties. This finding is interesting because class D enzymes have been found only in Acinetobacter spp [Klebsiella spp were resistant to all β-lactams, including aztreonam. These species were probably expressed a metallo-β-lactamase with additional mechanisms  [We found only one imipenem-resistant cter spp . Two imiduction) .The absence of a confirmation test for the ESBL phenotype limits the impact of our results. On the other hand, it is known that supplemented media (blood) can alter the zone diameters for several agents and bacterial species. Despite these limitations, our data can be used for local therapeutic choices.P. aeruginosa during the same period [We previously presented the antibiotic susceptibility patterns of 249 respiratory isolates of A. baumanii. We accepted all consecutive isolates because we did not attempt to distinguish actual pathogens from colonizing strains. Specimen types consisted of sputum (54.2%), transtracheal/endotracheal aspirates (24.6%), and bronchial lavage fluid (21.2%). We cultured sputum samples that showed no oral contamination in the presence of sputum purulence or a suspected lower respiratory infection.We collected our data from 01/01/1999 to 01/01/2002 at the microbiology laboratory of the University of Cumhuriyet. We processed the data to eliminate duplicate registrations. We excluded any isolates collected within 7 days when they came from the same specimen source of the same patient. We initially identified the isolates using such routine methods as colonial/microscopic morphology and enzymatic characteristics. We confirmed species identification with API-bioMerieux products. We retrospectively analyzed antibiotic susceptibility patterns in 238 respiratory isolates of Enterobacteriaceae members and 90 respiratory isolates of E. coli ATCC 25922 and Staphylococcus aureus ATCC 25923 strains. The disks (Oxoid) contained the following antimicrobials: ampicillin (10 μg), ampicillin/sulbactam (20 μg), piperacillin (100 μg), aztreonam (30 μg), cefazolin (30 μg), cefuroxime (30 μg), cefotaxime (30 μg), ceftriaxone (30 μg), ceftazidime (30 μg), amikacin (30 μg), gentamicin (10 μg), tobramycin (10 μg), ciprofloxacin (5 μg), imipenem (10 μg), tetracycline (30 μg), and cotrimoxazole (25 μg). Until November 1999, our microbiology laboratory based susceptibility rates on disk zone sizes; thereafter, we used a coordinating laboratory to determine the minimal inhibitory concentrations (MICs) of these 18 antimicrobial agents, accomplished with a standardized microdilution technique (Sceptor System, Becton Dickinson Microbiology System). We used this system to determine MICs for all strains. We used the NCCLS criteria to identify possible extended-spectrum β-lactamase (ESBL)-producing strains of Klebsiella spp and E. coli when MICs were increased (2 mg/mL) with ceftazidime and/or ceftriaxone and/or aztreonam [We confirmed susceptibility to 18 antimicrobial agents using disk diffusion according to the National Committee for Clinical Laboratory Standards (NCCLS) guidelines , except susceptible. We labeled all resistant and intermediate isolates as resistant. We divided the number of resistant isolates by the total number of isolates that had undergone susceptibility testing.We classified our results into two categories. We labeled strains deemed susceptible by the disk diffusion method or microdilution technique as UG had primary responsibility for study design, collection of data, and writing the manuscript. IA, MZB, TE had intellectual contribution as well as the writing of manuscript. All authors read and approved the final manuscript.


MEN1 gene. The protein sequence has no significant homology to known consensus motifs. In vitro studies have shown menin binding to JunD, Pem, Smad3, NF-kappaB, nm23H1, and RPA2 proteins. However, none of these binding studies have led to a convincing theory of how loss-of-menin leads to neoplasia.Multiple Endocrine Neoplasia type 1 (MEN1, OMIM 131100) is an autosomal dominant disorder characterized by endocrine tumors of the parathyroids, pancreatic islets and pituitary. The disease is caused by the functional loss of the tumor suppressor protein menin, coded by the Global gene expression studies on eight neuroendocrine tumors from MEN1 patients and 4 normal islet controls was performed utilizing Affymetrix U95Av2 chips. Overall hierarchical clustering placed all tumors in one group separate from the group of normal islets. Within the group of tumors, those of the same type were mostly clustered together. The clustering analysis also revealed 19 apoptosis-related genes that were under-expressed in the group of tumors. There were 193 genes that were increased/decreased by at least 2-fold in the tumors relative to the normal islets and that had a t-test significance value of p < = 0.005. Forty-five of these genes were increased and 148 were decreased in the tumors relative to the controls. One hundred and four of the genes could be classified as being involved in cell growth, cell death, or signal transduction. The results from 11 genes were selected for validation by quantitative RT-PCR. The average correlation coefficient was 0.655 (range 0.235–0.964).This is the first analysis of global gene expression in MEN1-associated neuroendocrine tumors. Many genes were identified which were differentially expressed in neuroendocrine tumors arising in patients with the MEN1 syndrome, as compared with normal human islet cells. The expression of a group of apoptosis-related genes was significantly suppressed, suggesting that these genes may play crucial roles in tumorigenesis in this syndrome. We identified a number of genes which are attractive candidates for further investigation into the mechanisms by which menin loss causes tumors in pancreatic islets. Of particular interest are: FGF9 which may stimulate the growth of prostate cancer, brain cancer and endometrium; and IER3 (IEX-1), PHLDA2 (TSS3), IAPP (amylin), and SST, all of which may play roles in apoptosis. MEN1 gene was classified as a tumor suppressor [MEN1 gene spans 9 kb of the genome, is comprised of 10 exons, and codes for a 610 amino acid protein termed menin [MEN1 gene were identified in our laboratory [Multiple Endocrine Neoplasia type 1 (MEN1, OMIM 131100) is an autosomal dominant disorder characterized by endocrine tumors of parathyroid, pancreatic islets and pituitary . Based oppressor ,3 and thppressor . The MENed menin . More thed menin . Recentlboratory . Menin iboratory .et al[Men1 was found to be expressed in testis (spermatogonia) at high levels [et al [et al [Studies on the function of menin have not yielded a clear picture as to the role of menin as a tumor suppressor; however, the results of these studies suggest some interesting possibilities. Two groups ,9, basedet al reportedet al. In posth levels . Lemmenss [et al  by screes [et al  suggest l [et al  observedl [et al  and a pul [et al  have beel [et al -21 and sDespite the above studies, a clear consensus of the molecular mechanisms leading to neoplasia, following the loss of menin, has not emerged. Very little is known about the gene expression changes in human neuroendocrine tumors following the loss of menin. Global gene expression analyses, using cDNA microarrays, have been used to classify other human tumors into clinically distinct categories -26. Wu  has discEight neuroendocrine tumors from six MEN1 patients were included in this study. The patient ages were 19, 22, 42, 51, 57, and 57 years at the time of surgery (Table The RNA isolated from 8 tumor specimens (6 patients) and 4 normal islet preparations was of acceptable quality for hybridization, as determined by preliminary small hybridizations on test chips. The dChip computer program returned data concerning the percent of genes judged to be present, and the percent of single and array outlier events Table . The expAverage expression and standard deviation was computed for each gene in both the group of 4 normal islets, and the group of 8 islet tumors and expressed as the coefficient of variation (CV). Genes with average expression levels less than 50 were excluded from this analysis. Figure The experimental groups were clustered figure  using a The genes were also clustered by the dChip software. A group of apoptosis-related genes was identified whose expression was significantly correlated with the Tumor/Normal assignment of the data. Twenty-four apoptosis-related genes represented by 26 different Affymetrix probes were identified in the overall hierarchical clustering. Nineteen of these genes were more highly expressed in the normal islets than in the islet tumors Figure . EighteeSince the Student's t-test was designed to compare only one parameter in two populations, the simultaneous measurement of multiple genes might lead to an excessive number of false positives. In order to empirically determine the potential false positive rate, we started with 923 genes which had a p value <=.05 and repeatedly scrambled the individual tests into groups 4 and 8 and then performed new t-tests. The average number of genes having a p value = < .05 in 20 such scrambles was 51 (5.5% of 923 genes). This was only slightly more than the 46 genes expected (0.05 × 923). We therefore concluded that there was little chance of excess false positives in repeatedly using the Student's t-test.In order to obtain a better picture of the nature of the tumors and normal islets in this study, the expression levels of the principal hormone RNA of pancreatic islets was examined Table . Tumors . There were 16 genes related to cell growth, 13 genes related to signal transduction, and 16 genes related to other functions which were increased in the group of tumors relative to the group of normal islets . The results are shown in Table Whether there were degradative processes acting on the tissues prior to or during or after the extraction of the RNA can be guessed by the quality of the RNA. Each RNA specimen in this study was tested on an Affymetrix test chip, and each was found to be acceptable. Additional quality assessment was made by the dChip software. Only one specimen, a normal control, had Array Outliers greater than 5%, suggesting that it was subnormal Table . HoweverAlthough, only solid tumor was utilized, there were undoubtedly a small percentage of blood, blood vessel, and connective tissue elements intermixed with the tumor tissue. Rarely, there might be a small amount of exocrine tissue. In the case of the normal islets used as controls, microscopic examination showed that greater than 90% of the tissue was islet. Any contaminants would probably have the effect of reducing the discriminant power to differentiate tumor from normal. Thus, t-test p values and fold changes would tend to under-represented and some, otherwise significant, genes might be missed. The actual data, represented by the hierarchical specimen clustering Figure , showed One might have expected more variation in the gene expression levels in the tumors than in the normal islets, since tumors are often heterogonous. However the data on the average CV of the genes in the normal and tumor groups suggested that there was no more variation in the tumors (average CV of 30%) than in the normals (average CV of 31%). The low CV in the tumors may relate to the single mode of tumor formation (induction by the loss of the menin tumor suppressor). However, there was increased variation noted when the tumors and normals were combined Figure . This waOf particular interest was the high proportion (3/8) of tumors expressing principally PP hormonal RNA. This was entirely consistent with pathological studies showing the preponderance of PP containing tumors in the pancreas of MEN1 patients . The facThe use of the Students t-test for comparison of multiple genes might be questioned because the test was designed for comparison of only two groups. In this study, we confirmed that comparison of 923 genes would not generate an excess number of false positive results. Nevertheless, in the group of 193 genes finally selected at a p < = .005, we can expect that 1 of those genes is a false positive.This study suggests that the overall effect of loss of function of menin is the suppression of gene expression. Nevertheless, there were 86 genes that were over-expressed in the tumors relative to the normals. Although we associate tumorigenesis with increased rates of growth, only two of eleven Cell Cycle and Cell Proliferation genes were increased in the tumors. Since tumor growth may also be significantly affected by rates of cell death, it is perhaps significant that there were no Cell Death genes significantly increased in the tumors relative to the controls.The correlation of GeneChip results with quantitative real-time PCR  of the genes delineated in this study are a direct and necessary affect of loss-of-menin tumorigenesis cannot be determined by this study alone. Firstly, the activity of many genes are regulated both by their levels of expression and by post-translation modifications, such as phosphorylation. Secondly, the microchips used in this study represent only about 1/3 of the total number of human genes. Thirdly, it is not certain that the initiating gene changes caused by loss-of-menin are persistent in the tumors that develop. However, there were some genes, which because of their association with growth or apoptosis are of special interest. The general suppression of apoptosis related genes noted in this study Figure  has beenet al,  who showet al, . Severalet al, -40. Soma in vivo -57.PHLDA in vivo , and may in vivo . IAPP  in the group of neuroendocrine tumors. The underexpression of these apoptosis-related genes may be related to neoplastic development or progression in these MEN1-related neuroendocrine tumors.Tumor specimens were obtained from patients with the MEN1 syndrome who had undergone surgery for islet-cell tumors of the pancreas. The specific germline mutations in the menin tumor suppressor gene were identified and previously reported  for eachApproximately 50 mg of tissue was removed from each frozen tumor specimen and homogenized with a mortar and pestle (Qiagen, Qiashredder Kit), and RNA was extracted using the Rneasy Mini Kit (Qiagen, Inc.), and quantified by UV absorbance. RNA was similarly isolated from the normal human islet preparations.The RNA was submitted to the GeneChip facility of the Siteman Cancer Center at Washington University School of Medicine. There, biotin labeled cRNA was prepared and hybridized to U95Av2 microarray chips (Affymetrix). The fluorescence of individual spots was then measured and the data returned on compact discs. We analyzed the gene expression data and made comparisons between groups using the dChip computer program . FollowiThe same preparations of total RNA that were used to probe the GeneChips were also used to prepare c-DNA for quantitative RT-PCR analysis of gene expression. C-DNA was first prepared using Superscript II reverse transcriptase (Invitrogen, Inc.). Primers for each gene were designed to produce products of 100 to 150 bp that spanned exon boundaries (when possible). The primer pairs are shown in table in vitro islet tumor cell line was utilized.The Sybr Green technique on an Applied Biosystems model GeneAmp 5700 instrument was utilized. Relative quantitation using a standard c-DNA preparation from an The author(s) declare that they have no competing interests.All authors contributed equally to this manuscript.


Acquired resistance to chemotherapy is a major obstacle to successful cancer treatment. Understanding the mechanisms by which tumors become resistant to a particular agent is key to identifying new drugs or combination regimens.Kinases are signaling molecules that control many aspects of cell behavior, including cell proliferation, i.e., whether and how fast cells divide. Abnormally active kinases promoting tumor growth are found in many cancers and are a focus of rational cancer drug design. One target for kinase inhibitors is the epidermal growth factor receptor (EGFR). Two EGFR inhibitors, gefitinib and erlotinib, showed therapeutic benefits in a subset of patients with non-small cell lung cancer. Recent work has helped us understand why some patients respond and some don't: responsive tumors usually harbor activating mutations in the EGFR gene, which somehow make the tumors sensitive to treatment. Nearly all patients whose tumors initially respond to EGFR inhibitors, however, eventually become resistant to the drugs and progress despite continued therapy.William Pao and colleagues examined tumors from six patients with non-small cell lung cancer who initially responded to gefitinib or erlotinib but subsequently relapsed. Tumors from all six patients carried activating mutations in the EGFR gene. In addition, in three out of the six cases, the resistant tumor cells carried an identical second mutation in the EGFR gene. Whereas the activating mutation was present in tumor cells before treatment with erlotinib or gefitinib, the second mutation was not found in pre-treatment biopsies from these patients, nor in over 150 lung cancer samples from patients who had not been treated with either drug. Additional cell culture studies supported the notion that the secondary mutation causes resistance to gefitinib or erlotinib. It is clear, though, that this is only one mechanism of resistance, because in the three other cases resistance occurred in the absence of the second mutation. What caused the resistance in those tumors is not known.10.1371/journal.pmed.0020075), the initial identification three years ago of resistance mutations against imatinib led to the rapid development of alternative kinase inhibitors that work even against tumors with the resistance mutation. Similarly, the results by Pao and colleagues should help researchers develop second generation drugs for lung cancer.All kinases share some common features, and a resistance mutation very similar to the one identified here has also been found in other kinase genes from tumors with acquired resistance to imatinib, another kinase inhibitor. As Gary Gilliland and colleagues point out in an accompanying Perspective (DOI:


Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g., comparison of sample means) to identify differentially expressed genes. This leads to the discovery of 'population-level' markers, i.e., genes with the expression patterns A > B and B > A. We introduce the PPST test that identifies genes where a significantly large subset of cases exhibit expression values beyond upper and lower thresholds observed in the control samples.Interestingly, the test identifies A > B and B < A pattern genes that are missed by population-level approaches, such as the t-test, and many genes that exhibit both significant overexpression and significant underexpression in statistically significantly large subsets of cancer patients (ABA pattern genes). These patterns tend to show distributions that are unique to individual genes, and are aptly visualized in a 'gene expression pattern grid'. The low degree of among-gene correlations in these genes suggests unique underlying genomic pathologies and high degree of unique tumor-specific differential expression. We compare the PPST and the ABA test to the parametric and non-parametric t-test by analyzing two independently published data sets from studies of progression in astrocytoma.The PPST test resulted findings similar to the nonparametric t-test with higher self-consistency. These tests and the gene expression pattern grid may be useful for the identification of therapeutic targets and diagnostic or prognostic markers that are present only in subsets of cancer patients, and provide a more complete portrait of differential expression in cancer. Studies of differential expression of individual genes often find genes that are up-regulated in some tumors, and down-regulated in others. Microarray studies typically seek to identify differentially expressed genes using use fold-change , t-testss3(ABA) statistics . These are listed in Table Our initial results are compelling in that they suggest that we can expect biomarkers of high clinical significance for subsets of patients to be important for distinct subsets of patients. This also suggests that clinical validation of the utility of biomarkers should examine panels of expression biomarkers, not individual biomarkers. Disruption of genomic function via these patterns cannot be studied in the population level biomarker framework for the simple reason that methods that compare, say, group means, will find no difference between the sample groups if the number of case samples found in the two tails are even approximately equal. This is a sensible approach even from within the framework of population-based hypothesis testing, because the PPST test can be expected to be more robust to one or two outliers that might mislead simple parametric tests. Note that a number of genes are 'nearly significant' under the t-test but are strongly significant under the PPST test for the AB/BA patterns  that the most commonly applied tests for identifying differentially expressed genes will miss important genes that are dysregulated in only a fraction of patients, and 2) that important aspects of differential expression may be, to a degree, highly individualistic in most cancers. Some potentially important genes with this form of unusual differential expression , and that for many other methods of analysis, are accessible online in the Cancer Gene Expression Data Analysis tool We have developed a test we call the Permutation Percentile Separability Test (PPST), which attempts to refute a null hypothesis that is slightly different from A = B, but which is capable of detecting AB, BA, ABA and BAB patterns. Under this test, we are interested in the question "are there are statistically significant number of samples in group A (e.g., tumor) that exhibit expression intensities beyond a particular percentile of the observed expression intensities in group B ?" and vice versa. By 'statistically significant' we mean that the number of samples that exhibit apparent overexpression (or underexpression) exceeds that expected under the null distribution.th percentile of the samples in the opposite group. This provides two scores, s1, and s2, for each gene (PPST scores). s1 is the number of samples in group A that are beyond the upper percentile (say, 95th) of group B plus the number of samples in group B that are below the lower 95th percentile of group A. This measure will tend to be large when all samples in both groups are significantly distinct from the alternate group in the same way (comparisons consistent with A > B). It can also be significant when a surprising number of samples in only one group varies from the expression levels in the alternate group. s2 is the number of samples with correspondingly opposite pattern (comparisons consistent with B > A). Sample class label permutations are used to generate an arbitrarily large number of permuted data sets. These scores s1 and s2 are calculated in each permuted data set to produce unique null distributions for each gene. For the sake of convenience of interpretation, we use -s2 when reporting s2 to denote underexpression. Genes with values of s1 beyond the specified acceptable Type 1 error risk (e.g., α = 5%) are determined to be significantly overexpressed in sample group A relative to B. Individuals in sample group A with expression intensity values over the 95th percentile of sample group B for a given gene may be considered overexpressed. Similarly, genes with values of s2 beyond the specified Type 1 risk for s2 are deemed underexpressed in sample group B relative to A. Varying the percentile threshold allows direct control over the false discovery rate.To test these hypotheses, we count the number of samples in both groups that are found beyond the nboth significant s1 and s2 scores in this comparison may be considered 'ABA pattern genes'  that can expected to be distributed nth percentile tailsboth in the upper and lower  of the intensity distribution of that gene in the other group (B); i.e., in the ABA (s3) or BAB (s4) pattern. These scores are not redundant to but rather allow for exploration of distribution-wise (upper and lower) false discovery rates. The application of the PPST test to find ABA patterns is called the 'ABA' test. Under the ABA test, differential expression of a gene may be deemed to be significant in both directions at once, i.e., simultaneously significantly over-expressed and under-expressed in a surprising number of patients in the case population. Both the PPST test and the ABA test will perform optimally when the variation in expression intensities in the normal sample population is well characterized.Genes that exhibit [.A collection of published microarray data sets we have placed 'on-tap' in the caGEDA  web application  were sub,53. Deta studied PPST: permutation percentile separability test.ABA test: a test that can detect genes with both A > B (gene is overexpressed in sample A compared to sample group B) and B < A (gene is overexpressed in sample B compared to sample group A) patterns.s1, s2, s3, s4: measures of the number of samples that exhibit expression intensities beyond a specified percentile in an alternate group; used as scores in the determination of significance under the PPST and ABA tests.JLW conceived of the PPST and ABA tests and executed the analyses, SP encoded the methods in caGEDA, TG provided direction, input and scientific motivation for pursuing a test with the capabilities of the PPST and ABA tests, MJB provided, direction, input and coordination of the research. All authors read and approved the final manuscript.


The error threshold puts a limit on the amount of information maintainable in Darwinian evolution. The error threshold was first formulated in terms of genotypes. However, if a genotype-phenotype map involves redundancy , the error threshold should be formulated in terms of phenotypes since there is no unique fittest genotype. A previous study formulated the error threshold in terms of phenotypes, and their results showed that a rather low degree of mutational neutrality can increase the error threshold unlimitedly.We obtain an analytical formulation of the phenotypic error threshold by considering the "additive assumption", in which base substitutions do not influence each other (no epistasis). Our formulation shows that an increase of the error threshold due to mutational neutrality is limited. Computer simulations of RNA evolution are conducted to verify our formulation, and the results show a good agreement between the analytical prediction and the simulations. The comparison with the previous formulation illustrates that it is important for the prediction of the error threshold to consider that the number of base substitutions per replication is rather large near the error threshold. To examine the additive assumption, a detailed analysis of additivity and epistasis in RNA folding of a particular sequence is performed. The results show a high degree of epistasis in RNA folding; furthermore, the analysis also elucidates the reason of the success of the additive assumption.We conclude that an increase of the error threshold by mutational neutrality is limited, and that the additive assumption achieves a good prediction of the error threshold in spite of a high degree of epistasis in RNA folding because the average number of base substitutions of sequences retaining the phenotype per replication is sufficiently small to avoid of the effect of epistasis. The error threshold is a limit on the permissible mutation rate for which "survival of the fittest" holds in Darwinian evolution . The errThe error threshold was first formulated in terms of genotypes. However if some changes in genotype do not alter the phenotype or the fitness , there is no unique genotype which can be stably maintained. Instead, the survival of a phenotype should be considered, and thus the error threshold should be formulated in terms of phenotypes .In this paper, first we formulate the phenotypic error threshold analytically by employing the additive assumption, in which base substitutions do not influence each other. Under the additive assumption, we obtain the probability that a replication does not alter the phenotype  as a function of the number of base substitutions and the fraction of "neutral substitutions". Our results show a qualitative difference from the previous formulation . Second,x) and the others called mutants (denoted by y). The population size is assumed to be large enough to express the abundance of the phenotypes by normalized concentration. The population dynamics of the phenotypes is described asThe quasispecies equation describes (prebiotic) replicator dynamics in well-mixed systems . We trandx/dt = σQx + σΛ (1 - Q)x - Dx - Φx,dy/dt = y + σ (1 - Λ)(1 - Q)x - Dy - Φy     (1)σ (> 1) is the replication rate of x ; Q is the replication accuracy of x. Λ is the fraction of neutral mutants of x. D is the degradation rate (or the death rate) assumed to be uniform over the phenotypes. Φ = (σ - D)x + (1 - D)y is the excess production (or the mean fitness). The terms - Φx and - Φy induce a selection pressure. We neglect back mutation from y to x (this simplification will be discussed in the next section). From Eq. 1, we obtain the survival condition of x as Q + Λ (1 - Q) >σ-1, for which the stationary value of x is larger than zero. From this inequality, we will deduce the phenotypic error threshold, i.e., the maximum error rate of replication for which x can be stably maintained in the system.where x. [A replicator is thought of as a polymer in our study. We refer to a monomer as a base, having RNA in mind. The formulation of the phenotypic error threshold itself is independent of this terminology.]We distinguish two classes of single base substitutions: neutral and deleterious substitutions. The class of a substitution is determined by the effect of the substitution on the phenotype when there are no other substitutions in the genotype: A substitution which retains the phenotype is a neutral substitution; otherwise it is a deleterious substitution. A beneficial substitution is not considered since the focus of the study is on the maintenance of Qe = Q + Λ (1 - Q), we assume that a mutant is neutral iff there is no deleterious substitution: The effect of a substitution on the phenotype is independent of the other substitutions; i.e., no epistasis is assumed (the additive assumption). Let λ denote the fraction of neutral substitutions in all possible single substitutions, and let d denote the number of substitutions per replication. Then, the probability that d substitutions are all neutral substitutions  is approximated by λd by assuming that the number of neutral substitutions in d substitutions follows the binomial distribution . This approximation is valid if the probability of correct replication per base (denoted by q) is sufficiently large so that d is small. Denoting the sequence length of replicators by N, the effective replication accuracy (Qe) is obtained asTo calculate the effective replication accuracy q and λ are uniform among the genotypes in x, that q is invariable over sequence positions, and that N is the same among the populations. [A similar formula was obtained in [Qe = (q + v(1 - q))N, where v is a parameter to be tuned to match the formula to the observed value of Qe. Therefore v in [by assuming that ained in  as Qe = ore v in  implicitq for which x can survive is derived from Q + Λ(1 - Q) >σ-1 and Eq. 2 asThe minimum qmin = (σN -1/- λ)/(1 - λ).     (3)qmin. As seen in Fig. λ. This is because if q decreases, the number of substitutions per replication (d) increases; hence the probability of neutral replication (λd) decreases (cf. [λ (= σN-1/), there is a singularity such that qmin becomes zero. However, this singularity is not plausible in two ways: (1) Such a large λ is not realistic (see below); (2) q at the singularity is so small that the binomial approximation is threatened. We studied the validity of the binomial approximation, and found that the inaccuracy in the binomial approximation is largest if in some positions of the sequence all possible single substitutions are neutral, but in the rest of the positions all possible single substitutions are deleterious; i.e., the distributions of neutral and deleterious substitutions over the sequence positions are completely separated. By taking this extreme example, qmin is calculated with the additive assumption but without the binomial approximation. As Fig. λ the binomial approximation is valid. qmin is underestimated by the binomial approximation only around the singularity (λ > 0.8), and thus the singularity is actually located at higher λ, which makes the singularity even less plausible. We conclude that the increase in the error threshold due to mutational neutrality is limited.The phenotypic error threshold is 1 - ses (cf.  and Eq. From Eq. 3, we obtain the information threshold, i.e., the maximum permissible sequence length asNmax = ln(σ-1)/ln(q + (1 - q)λ).     (4)λ. (Nmax reaches infinity only when λ increases to one.) However, this result does not mean that a longer sequence in fact can have a larger λ, and thus it can be maintained. We studied the relationship between λ and the sequence length by utilizing RNA folding, which is a well-studied prototype of genotype-phenotype map, where the genotype is the primary structure of an RNA sequence and the phenotype is the minimum free energy secondary structure of the RNA sequence. We utilized Vienna RNA package [λ for different sequence length was obtained through comparing the secondary structure of randomly created RNA sequences with that of all possible mutants with only one substitution. A substitution which retains the original secondary structure is considered as neutral; otherwise deleterious. As Fig. λ is a decreasing function of the sequence length. This relationship further limits an increase of information threshold due to mutational neutrality.As Fig.  package  to fold N = 76). RNA sequences which have the fittest phenotype replicate with the probability 0.01 per time step; all the other RNA sequences (mutants) replicate with the probability 0.001 per time step (thus σ = 10). The replication introduces mutations with a certain probability. Back mutations are not allowed to occur – the effect of back mutations is negligible if the sequence length is large enough [D = 0).We compare our analytical prediction with computer simulations. Our computer program simulates the evolution of RNA replicators in a well-mixed flow reactor (e.g. see ). In thee enough  (this waλ" (defined in Methods section – Non-uniform distribution of λ) of the fittest sequences increased from 0.307 to ca. 0.40 for the examined values of the error rate (data not shown).  The value of the representative λ fluctuates over the time (st. dev. = 0.01 at 1 - q = 0.0475).The results of the computer simulations showed that the "representative λ observed in the simulations after the evolution (λ = 0.4). As Fig. The equilibrium fraction of the fittest sequences of the computer simulations is compared to that of the analytical prediction over the different error rate in Fig. et al. [Reidys et al.  derived Qe = Q + λ (1 - Q). This equation shows an unlimited increase in the error threshold for λ ≥ σ-1  is valid only if either (1) a neutral set is uniformly distributed over the genotype space [a neutral set is a set of genotypes where all genotypes map to the same phenotype], or (2) q is so large that most mutants have d = 1. The uniform distribution of neutral sets in the genotype space is not applicable in RNA folding as shown later. The latter possibility is discussed next.from  = Q + λ  - Q. Thiq so that most mutants have d = 1 (e.g., [q) is close to the error threshold, mutants can have on average d > 1 even if λ = 0, for which the error threshold is at the lowest error rate  per replication  is lower than the average d per replication. However at the error threshold, even the average d per neutral replication is larger than one for λ > 0.32 . [A neuet al. [λ approximation". This extension divides a sequence in four sub-sequences in order to take into account the fact that the fraction of neutral substitutions varies over the sequence position. This extension still overestimates Qe though less so than Eq. 5 because the approximation now permits four substitutions per replication as a side effect of the subdivision. Note that this extension makes a fairly good prediction on the error threshold , in terms of additivity and epistasis. The objective of this study is to understand how the additive assumption achieves a good prediction in spite of a high degree of nonlinearity in RNA folding ,13.We compare the secondary structure of randomly sampled mutants to that of the tRNAphe. Similar to the previous section, a mutant is neutral if its secondary structure is the same as that of the tRNAphe; otherwise, it is deleterious. To evaluate the deviation from the additive assumption, we categorize mutants into four classes as shown in Table δ > 0 are only considered, positive epistasis occurs very rarely compared to additive deleterious case: No more than 0.5% of the mutants are neutral at d = 5 if they carry at least one deleterious substitution. If mutants with δ = 0 are only considered, negative epistasis is rather frequent relative to additive neutral case: As much as 35% of the mutants are deleterious at d = 5 even if they carry only neutral substitutions. However, replication with δ > 0 occurs far more frequently than replication with δ = 0: As much as 99.7% of the replication contains at least one deleterious substitution at d = 5 and λ = 0.307. Therefore, the relative frequency of epistasis is flipped around. Consequently, the additive assumption underestimates the degree of mutational neutrality.  and that observed in RNA folding  near the error threshold.The effect of epistasis is already noticeable when see Fig. . Since tQe) including the effect of epistasis in order to compare it with Qe calculated under the additive assumption. The first trial was to include a "trivial" epistasis in base paired regions (helices) as a part of the additive effect . However, the analysis showed that epistasis occurs mainly in a "non-trivial" way (data not shown), and thus it is not sufficient for our sake to include a trivial epistasis. We next took a probabilistic approach to calculate Qe with epistasis (see Methods section – Probabilistic approach). The results of this method agree with the observation . As Fig. d per neutral replication is ca. 1.5 at the error threshold, which is much smaller than the average d per replication (ca. 3.8). This means that the main contribution to Qe under the additive assumption is from the mutants of d = 1 or 2 at the error threshold. According to Fig. d = 1 or 2. Therefore, the additive assumption accurately estimates Qe and thus the error threshold. When the average d per neutral replication reaches 3, the additive assumption substantially underestimates Qe . The results showed that the average d per neutral replication  at the error threshold was indeed smaller (ca. 1.2 substitutions with λ ≈ 0.35) than in the previous case of the shorter sequence length (ca. 1.5 substitutions with λ ≈ 0.4), and the additive assumption still predicts the results of the simulations closely (data not shown).In the above examination of the additive assumption, there are two points which must be examined further: (1) The analysis of epistasis was performed on a yeast tRNAphe, which comprises the initial population of the RNA evolution simulations, but the results may differ if the analysis is done for a sequence which appears later in the RNA evolution simulations. Thus, we performed the same analysis to a sequence which was chosen from the population of the fittest sequences after the evolution in the simulations (at the 20000th time step). The results, however, did not change our conclusion (data not shown). (2) If the length of sequences is larger, the average • The phenotypic error threshold was formulated under the additive assumption. The formulation asserted that mutational neutrality increases the error threshold but the increase is limited.• The importance of considering multiple substitutions per replication at the error threshold was illustrated.Qe) and thus the error threshold.• The comparison with the computer simulations and the analysis of epistasis showed that the additive assumption correctly estimates the effective replication accuracy  epistasis in RNA folding is that the average number of substitutions per λ is not uniform over the genotypes sharing the same phenotype, the effective replication accuracy (Qe) depends on the distribution of the genotypes in the population. In this case, Qe is calculated under the additive assumption asIf XI denotes the population of the focal phenotype (I), and xi (resp. λi) is the population  of the genotype i. The set SI denotes the set of genotypes which have the phenotype I. If xi and λi are known, the representative λ of the phenotype can be calculated from the following equation aswhere λ and the population average of λ was very small in the computer simulations. The difference between the representative Qe was to include this epistasis as a part of the additive effects of mutations as described in [It is trivial that epistasis occurs between bases which make a pair (hydrogen bond) in the reference secondary structure. Our first trial to include epistasis in the calculation of ribed in . In thisμ(v, δ)), which depends on the number of neutral base substitutions (denoted by v) and on that of deleterious base substitutions (denoted by δ). Then, the probability of neutral replication is obtained  asSince  epistasis makes it difficult to predict what happens to the phenotype given a specific change in genotype, we take the following probabilistic approach: We assume that a mutant is neutral with a certain probability (denoted by d = v + δ. Qe is thereupon derived aswhere μ(v, δ) in the tRNAphe folding as shown in Fig. δ = 0 and v > 0, μ declines a little slower than exponentially as v increases due to negative epistasis  increases, saturates, and finally decreases as v increases  where β and γ are parameters. To obtain the parameters, we fitted Eq. 10 to the data in Fig. (d) was calculated from the above obtained parameters, and the calculated values match the observed ones (see the dashed line in Fig. where NT contributed to the entire part of the study. PHP contributed to the computer programing of the RNA evolution simulation. PH contributed to the conceptual development of the study and the manuscript preparation as the supervisor.