Bordetella pertussis, Streptococcus pyogenes, Chlamydia pneumoniae and Mycoplasma pneumoniae) and 9 viral pathogens  produce highly variable clinical symptoms that cannot be used to identify the etiologic agent. Proper treatment, however, depends on correct identification of the pathogen involved as antibiotics provide little or no benefit with viral infections. Here we describe a rapid and sensitive genotyping assay and microarray for URI identification using standard amplification and hybridization techniques, with electrochemical detection (ECD) on a semiconductor-based oligonucleotide microarray. The assay was developed to detect four bacterial pathogens ( Chlamydia pneumoniae, Mycoplasma pneumoniae, Streptococcus pyogenes, S. pneumoniae, Bordetella pertussis, and Haemophilus influenzaeUpper respiratory tract infections (URI) can be caused by a variety of viruses, including rhinovirus, coronavirus, influenza A and B, parainfluenza, respiratory syncytial virus, adenovirus, metapneumovirus, and enterovirus; and by bacteria, including Streptococcus), patients with bacterial or viral infections for which specific therapies are available, and patients whose URI symptoms persist for over two weeks, can benefit from rapid detection. Rapid and accurate identification of URI agents at the point of care can reduce the inappropriate prescription of antibiotics and help identify problematic infections early.The rapid identification of certain viruses and bacteria is necessary for proper treatment of infection. Patients with a history of rheumatic fever  generally show sensitivities of 85% and direct immunofluorescence (DIF) assays show sensitivities of approximately 60 to 80% Because of their speed, specificity and sensitivity, genomic assays are complementary to serological assays and especially useful for identifying an unknown specimen where antibodies are not specific enough to differentiate closely related groups While traditional assays for pathogen detection and typing represent the gold standard, they alone, cannot meet the future needs for rapid, sensitive, specific and simple methods. The majority of these approaches do not provide sequence information, utilize expensive equipment that cannot be easily transported, require a high level of expertise to operate equipment, require extended periods of time, or are not easily modified. Here we describe a rapid and sensitive genotyping assay and array for upper respiratory pathogen identification using standard amplification and hybridization techniques and electrochemical detection (ECD) on a semiconductor-based oligonucleotide microarray. This platform was particularly suitable for developing this assay because it supports custom probe synthesis, which permits oligonucleotides of interest to be tested empirically. Multiple, highly reactive probes that are specific for a particular organism can then be put on the array for further iterative evaluation. Content of the final assay can also be customized to suit diagnostic needs in different geographic areas. Finally, the ability to use electrochemical detection with semiconductor microchips eliminates the need for expensive and delicate optical scanning equipment The assay was designed to identify 10 upper respiratory pathogens, with subtype identification in some cases (coronavirus and parainfluenza virus). We utilized multiple probes for each pathogen (approximately 30 to 100) that were designed from conserved regions of the respective genomes. Approximately 850 unique URI probes were included on the array with 12 to 13 replicates for each probe. The assumption for this methodology is that enough unique probes will produce specific signal in the presence of the labeled PCR products to result in a positive call. Multiple probes allow for genetic variability, especially with viral genomes, and also provide sequence information. In principle it is straightforward to substitute other organisms in these assays by synthesizing arrays with alternate probe specificity and alternate primer sets.One-tube RT-PCR was used for target amplification and labeling because of its sensitivity and its application to both DNA and RNA targets . Our PCRWe compared the sensitivity and specificity of biotin labeling by incorporation during amplification versus biotinylated reverse primers . AlthougWhile the microarray can be used with fluorescence detection, we used electrochemical detection (ECD), which relies on a redox reaction to generate electrical current (pA) on the array for measurement. Pseudo-images of two hybridized targets are shown in Results of array validation studies are shown in M. pneumoniae DNA. All pools produced sufficient PCR products for detection on arrays at a 1∶1,000 dilution of target DNA (1 pg). Pools B and C were effective on samples diluted 1∶10,000; but only pool C was effective at dilutions above 1∶10,000.Initial studies were conducted with two primer pools consisting of 19 primer pairs for DNA targets and 18 primer pairs for RNA targets  A. thaliana RCA gene primers for DNA targets, 2) A. thaliana Cab gene primers for RNA targets, and 3) A.thaliana rcbL gene primers for both DNA and RNA targets. The primer set for the rcbL gene was subsequently removed from the final assay to improve performance.Primers for assay positive control Bordetella pertussis and parainfluenza (PIV) types 1 and 3. The URI genotyping assay was also tested by including NA from the 5 organisms with DNA genomes and the 5 organisms with RNA genomes in separate PCR reactions, and then combining reactions for hybridization to arrays (Using optimized primer pools (1C and 2B) and probe sequences, sensitivity studies were conducted on 10-fold serial dilutions of NA from each target organism. Starting NA concentrations were determined spectrophotometrically and from vendor specifications . Exact co arrays . This diSignal specificity was determined by comparison of specific target signal to negative control probe signal and to signal from non-target probes. As shown in A variety of multiplexed assays for the identification of mixed targets have been reported that use electrophoresis, molecular beacons, or enzymatic assays for detection In this study, we describe a rapid and sensitive genotyping assay for URI. This genotyping assay uses the same protocol to detect both bacteria and viruses that produce upper respiratory clinical symptoms; and it relies on the simultaneous examination of numerous loci to differentiate one organism from another. Microarray technologies can provide a significant amount of information at disparate loci in a highly redundant fashion that is helpful when discriminating between similar or different organisms or compensate for cases where genomic variation can result in false negative signals. In this array design, probes were developed to be species and sub-species-specific and then crosschecked against databases to minimize the possibility of cross-hybridization of target with other probes or with contaminating host NA. Multiple probes per pathogen (approximately 30 to 100) were used to overcome potential variability in pathogen genomes and also to provide sequence information at disparate loci. PCR primers were designed to flank the conserved regions that contained multiple complementary probe sequences. They also were designed such that the one-tube RT-PCR reaction produced biotin-labeled, single-strand target for hybridization to arrays. Decreasing the number of primer pairs per multiplex RT-PCR also increased assay sensitivity. Greatest sensitivity was achieved with one primer pair per genome of interest.B. pertussis with a genome GC content of 67% and RSV with a GC content of 33%. Greater assay sensitivity was obtained by using 5′ biotinylated reverse primers in place of biotin-dCTP incorporation during amplification, as biotin-dCTP inhibits the PCR reactions (data not shown). However, with this increase in sensitivity, we also observed a slightly higher background signal and also a specific pattern of binding of free biotinylated primers to some probes. Removing microarray probes that bind to the free biotinylated primers can reduce this background artifact.Equal labeling for GC-rich and AT-rich genomes was promoted by including both biotin-dCTP and biotin-dATP in PCR reactions. This is especially relevant for Streptococcus pyogenes; Bordetella pertussis; Chlamydia pneumoniae; and Mycoplasma pneumonia. The arrays contain multiple probes per amplified region and multiple regions per organism that correspond to key distinguishing elements of each organism or subtype. However, to increase assay sensitivity, the final primer sets contained only one amplicon per organism. All virus and bacteria samples could be identified individually or in mixtures of 10 organisms within 5 hrs. Under optimal conditions, the assay was also sensitive, detecting 1 to 10 genomes, in most cases; and specific, detecting signal predominately on the pathogen probes of interest and not on probes for closely related species or serotypes as shown in The arrays were validated by hybridizing labeled target that was generated from RNA or DNA from all viruses and bacteria represented on the upper respiratory chip, which includes influenza A and B; parainfluenza types 1, 2, and 3; adenovirus 4; respiratory syncytial virus; coronavirus 229E, HK, and OC43; Studies to determine assay sensitivity were, in many cases, difficult to analyze because of complexities in quantifying target nucleic acid in a mixture with host nucleic acid. Most of the stock target bacterial DNA and viral RNA concentrations used in this study were based on PFU/CFU estimates provided by ATCC. Virus PFU were determined from cell culture lysates that would contain host cell nucleic acid. Given that one human cell contains approximately 7 pg of DNA, host cell nucleic acid contamination of samples could be very high. Thus, final results of assay sensitivity, based on PFU, could be underestimated in some cases.In this paper, we demonstrate the usefulness of ECD and microchips as a diagnostic platform for genotyping URI. In addition, this type of microarray can easily be subdivided into multiple sub-arrays; e.g., 4 sectors of 2240 electrode features per array, which allows multiple assays on one array and provides significant cost savings. Also, because of the platform's flexibility, the array content and primer pools can easily be changed to reflect the needs of clinicians in different localities and different seasons. The high manufacturability of semiconductors and their adaptability to inexpensive electrochemical microarray readers, which are small and field deployable, make these assays much more powerful than simple Real Time-PCR systems. In essence, the array provides a much higher level of confidence in a result akin to DNA sequencing; and, in addition, upfront bioinformatics and judicious choice of probes and loci allow a greatly simplified and speedier analysis of complex organisms or mixtures thereof. Ghindilis et al. The use of multiplexed RT-PCR combined with electrochemical detection on micro-arrays, as described here, has several advantages over traditional assays, including RT-PCR alone: 1) The ElectraSense® ECD scanner is small, portable, and rugged and can easily be automated or incorporated as a “sub-system” into other genomic analysis systems; 2) The ECD scanner is faster, easier to use, and less expensive than fluorescence scanners; 3) Multiplexing is simpler because the array sorts out multiple signals that can arise from miss-priming in complex samples; 4) There is no need to sequence PCR products because the binding of multiple probes to specific targets provides sequence-like information; 5) This format is flexible, allowing for multiple probes, selection of the most reactive probes through iterative tests, and thus increased probability of making correct calls at low target concentrations and with rare isolates/subtypes. This platform also allows one to easily include the detection of single nucleotide polymorphisms and short stretches of sequence within an assay; and 6) A low level of technical expertise is needed to run assays and analyze data.S. pyogenes, S. pneumoniae, H. influenzae and Moraxella catarrhalis). They detected these organisms in a group of healthy 0 to 6 year olds at approximately the same rate as in patients with acute upper respiratory infections. These organisms were also found in older healthy subjects (7 to 74 year olds) but at lower levels Disadvantages to using PCR for amplification are the possibility of contaminating reactions with amplicons from previous assays and amplifying genetic material from potential pathogens that are part of the normal flora. We are addressing amplicon contamination by developing a cartridge format for amplification, labeling, and hybridization of target. This cartridge contains the amplicons and is disposed of after analysis Rapid identification of upper respiratory pathogens will significantly decrease the time and cost for the identification of potential lethal virus and bacterial strains and lead to better treatment and management of infections. Microarray and biosensor technologies show great promise for virus and bacteria detection and genotyping and are needed for rapid effective treatment, environmental monitoring and the detection of bioterrorism agents Chlamydia pneumoniae: VR-1355) or genomic DNA (Mycoplasma pneumoniae: 15531D). Dr. Wanda Lyon provided nucleic acid (NA) for additional samples, including Influenza A, Bordetella pertussis, and Streptococcus pyogenes. Viral RNA was extracted from clinical samples and archived cultures with an RNAeasy kit  or from active cultures  with Trizol  following the manufacturer's protocol and precipitated with ethanol and sodium acetate  in the presence of yeast tRNA (Invitrogen) and glycogen . Genomic DNA from bacterial cultures was isolated with Qiagen DNeasy columns. Chlamydia pneumoniae DNA was isolated by resuspending the culture pellet in 475 µl Tris-EDTA, adding 25 µl 10% SDS (Sigma) and 2 µl or 20 mg/ml proteinase K  and incubating over night at 37°C. After heat inactivation at 70°C for 20 min, DNA was extracted once with phenyl/chloroform/isoamyl alcohol , twice with chloroform (Sigma), and precipitated with 3M sodium acetate and ethanol. Concentrations of RNA and DNA were measured with a NanoDrop ND-1000 Spectrophotometer . Positive control Arabidopsis DNA (RCA at 1 ng/µl) and RNA (CAP at 1 ng/µl) for RT-PCR reaction verification was obtained from Invitrogen . Positive control RNA and DNA were diluted (1∶20) in a solution of 20 µl yeast tRNA  and 4.8 µl BSA  in 475.2 µl 10 mM Tris pH 7.4 (dilution buffer) (Sigma).Viral and bacterial samples were obtained from ATCC either as cultures  that contain 12,544 individually addressable electrodes linked by the semiconductor circuitry. Contact pads on the array allow for electrical connectivity with an external device to control custom synthesis of unique probes at each electrode, and electrochemical detection with the ElectraSense® microarray reader (CombiMatrix Corp). Each electrode has a distinct DNA probe above it, and each electrode can be read electronically or fluorescently to determine the level of hybridization for a specific DNA sequence.Probe and primer design was achieved by aligning URI pathogen sequences obtained from GenBank and finding suitable regions for amplification and detection of each organism. A context database was then created for each organism to be typed. This was accomplished by collecting all available genomic sequences sharing the same genus or serotype as the sequence being investigated. Next, a structured BLAST database was created by incorporating genus and species information into the label of each context genomic sequence. For each organism to be typed, the gene sequences were extracted from the genomic record using the GenBank annotation. These gene sequences were then searched against the context database. All the matches between a gene of interest and a given context database genome were parsed to reflect each gene's conservation within that related genome. The specificity of each gene was reported in relation to the context database.Genes were chosen for probe design based on their specificity within the organism's home clade. A combination of sub-type-specific, species-specific as well as clade-specific genes were chosen. The desired specificity for gene choice depended on what one wanted to discern within the topography of the organism's home-clade. In general, for each organism of interest, genes were chosen whose specificities included unique as well as conserved sequences. If discernment was to be made between two closely related organisms, more specific genes that would allow discrimination were chosen. Five to ten genes were initially selected for each organism of interest. For each gene chosen, isothermal Tm 60°C probes were designed in half-probe-length steps across each sequence. Only good-quality probes, as defined by convention, were accepted. For example, probes could not contain repeat region greater than 6 bp in length, significant secondary structure, or GC percentage less than 35% or greater than 65%.After design, the probes were searched against the context database and their specificity was reported with respect to the context database. Regions within genomes that had the desired specificity were then selected. These regions were approximately 500 bp in length, and contained the probes that exhibited the desired specificity. The first and last probes of each region were designated as amplifying primers (the reverse primer was anti-sense), and the sequences in between were used as probes on the microarray. Where possible, PCR primers were chosen that were conserved within the clade. The forward primers were created with a Tm of approximately 50°C where as reverse primers were created with a Tm of approximately 65°C. This differential in temperature allowed for both amplification and single-strand target production. Bacterial and viral genes used for probe and primer selection are listed in Arabidopsis positive control mix, 0.5 µl enzyme mix, 2 µl RNA or DNA sample (diluted in positive control dilution mix (see above) and 4.3 µl dH2O. Thermal cycling parameters were: (50°C–30 min)×1 cycle; (94°C–3 min)×1 cycle; ×40 cycles; ×30 cycles and (72°C–5 min)×1 cycle. Amplification product was initially identified by loading 3.0 µl of each reaction product on 6% polyacrylamide gels (Invitrogen) and staining with SYBR Green I . For ECD assay sensitivity, 10-fold serial dilutions of NA samples (10-1 to 10-8) were made in buffer containing 40.0 µg yeast tRNA (Invitrogen) and 9.6 µg BSA (NEB) per 1.0 ml of dH2O.Two methods were used for labeling NA during RT-PCR: biotin-14-dCTP incorporation see  or biotiEach PCR reaction component, including magnesium, biotin and primer pool concentration, was tested individually at various concentrations. Primer pairs were tested individually with their respective template, and each primer sequence was tested “in silico”  for interactions with multiplexed primers to eliminate cross reactivity. PCR temperatures and dwell times were adjusted for optimal sensitivity. The composition of primer pools was tested by varying the number of primer sets for each target organism see .Initially, microarrays were incubated for 30 min at 45°C in 50 µl of a solution consisting of 5 ml of 2×hybridization solution (see below), 1 ml of 50×Denhardt's solution (Sigma) and 0.5 ml of 1% SDS (Sigma). For hybridization see , PCR reaFor the analysis of raw intensity data from arrays, we developed a simple sorting and averaging algorithm in Excel. The organization of the data was keyed to the probe name. (e.g.: Case 1) PosCntrl-ATRCA|Probe-031; Case 2) PosCntrl-Cab|Probe-004; Case 3) Corona-229E|Probe-019; and Case 4) Corona-HK|Probe-001). Probe sequence information was organized and stored according to organism type and sub-type; with the sub-type section of the probe name occurring before the main delimiter, the bar (“|”), and the type section of the probe name appearing before the dash (“-“), when present. Thus the types for Cases 1 and 2 are “PosCntrl”, and the sub-type for Cases 3 and 4 are “Corona-229E” and “Corona-HK”, respectively. The data could be further divided into individual amplicon-specific probes. Once broken down into their groupings, the average signal for each type and sub-type was calculated, presented and graphed as a simple bar graph in picoamps. For an average signal to be considered significant, it had to be greater than three standard deviations above the mean signal of the negative controls. This calculation is termed the Assay Cut-off and is graphed as a horizontal line along the bar graph for simplicity of presentation and interpretation. Through this method of analysis, multiple positive results may be detected in a single assay.


Nor were 17 dfe Quantitative Trait Loci (QTL) associated with fitness effects in over 1,400 recombinant lines. Our ability to detect measurable effects of inbreeding, media environment and the white mutation in the selection line backgrounds independent of dfe penetrance suggests that, within the limitations of laboratory tests of fitness, this large morphological change controlled by Hsp90 was selectable independent of strong, correlated and unconditionally deleterious effects—abundant, polygenic variation hidden by Hsp90 allows potentially deleterious alleles to be readily replaced during selection by less deleterious alleles with similar phenotypic effects. Hsp90 links environmental stress with the expression of developmental variation controlling unprecedented morphological plasticity. As outlined here and in the companion paper of this issue, the complex genetic architecture of Hsp90-buffered variation supports a remarkable modularity of Hsp90 effects on quantitative and qualitative phenotypes, consistent with the ‘Hsp90 capacitor hypothesis’ and standard quantitative genetic models of threshold traits.Hsp90 controls dramatic phenotypic transitions in a wide array of morphological features of many organisms. The genetic-background dependence of specific abnormalities and their response to laboratory selection suggested Hsp90 could be an ‘evolutionary capacitor’, allowing developmental variation to accumulate as neutral alleles under normal conditions and manifest selectable morphological differences during environmental stress. The relevance of Hsp90-buffered variation for evolution has been most often challenged by the idea that large morphological changes controlled by Hsp90 are unconditionally deleterious. To address this issue, we tested an Hsp90-buffered abnormality in Drosophila for unselected pleiotropic effects and correlated fitness costs. Up to 120-fold differences in penetrance among six highly related selection lines, started from an initially small number of flies and rapidly selected for and against a deformed eye trait ( Across diverse species, the Hsp90 chaperone buffers widespread morphological variation and controls characteristic developmental transitions, for example altered morphologies in laboratory and wild populations of Drosophila In the companion paper of this issue dfe). High lines , expressing dfe at high frequency, and genetically-related low lines , selected against dfe and expressing deformities only occasionally, were started from just a single deformed male and 3–4 unaffected females of the same genotype dfe was expressed at high frequency, even after the Hsp90 mutation that originally revealed the trait was lost due to almost certain selection against flies carrying Hsp90 mutations. Crossing Hsp90 mutations back into dfe lines further enhanced dfe penetrance, demonstrating that Hsp90, while neither necessary nor sufficient for expression of dfe, continued to buffer cryptic variation for the trait.Hsp90-buffered variation in Drosophila is distinguished by the sheer number and variety of adult morphologies controlled by the interaction of Hsp90 with hidden variation specific to different strain backgrounds dfe flies range from defects as mild as duplications of bristles at the eye margin and reduced size, to protuberances, more severe bristle duplications and deformity. We do not think the specific changes in eye shape of dfe lines would be adaptive in any novel environment imaginable. However, to address whether a rare beneficial change controlled by Hsp90 could have the potential to evolve free of deleterious fitness effects, we compared the performance of high versus low penetrance dfe lines in several tests of fitness. Importantly, all dfe flies were mutant for the white gene (1118w), which encodes a general transporter that is required for eye pigmentation and normal vision 1118w mutants have reduced competitive fitness Major developmental mutations and large morphological changes often have pleiotropic effects and serious fitness costs. The eyes of dfe penetrance, reduce genetic variation and enrich dfe polymorphisms  and low (LE1–3) lines was compared with mean measures of absolute fitness and competitive fitness relative to wild-type flies. As shown in dfe penetrance, at 25°C (panel A), but did not differ in any measure of fitness. Significant line effects were not systematically distributed to high or low, and were more likely attributable to the random effects of inbreeding and genetic drift than to fitness effects of the deformity  controls. While it is widely expected that such extreme deformities should decrease the viability of affected flies, this has not been our experience in breeding Hsp90 buffered abnormalities generally dfe trait were detected here.After 42 generations of selection, the high lines were further inbred to increase isms see . We firseformity . Perhapspe flies . Indeed,dfe selection lines relative to wild-type flies. Each of the white-eyed dfe lines was co-cultured in competition with Canton-S  flies. In crowded vials, wild-type flies out-competed any of the dfe lines, as expected due to potential inbreeding of dfe lines during selection and known deleterious fitness effects associated with the 1118w mutation. In the most extreme case . Even so, differences among the dfe lines did not translate into an overall difference between the high and low lines, and overall just 36% of surviving flies were from the dfe lines. When additional data were pooled over different rearing conditions and several transfers, including some 35,000 F1 flies in the study, the high or the low lines did not differ, and did equally poorly against wild-type  containing a small portion of the extensively inbred, and highly homozygous Samarkand (Sam) background in the context of the second and third chromosomes from each inbred high line. Reasoning that recombinant families with greater penetrance carry more dfe alleles, in dfe line was either not significant (HE2) or was even slightly positive , possibly indicating that alleles from the unrelated and extremely inbred genotype Sam, enriched in the recombinant families with lower penetrance, were deleterious in the context of more fit, heterozygous high line backgrounds (HE2/HE1 and HE2/HE3—see dfe quantitative trait loci (QTL) mapped at 5–10 cM resolution across the three high lines, no dfe QTL consistently co-localized with a fecundity QTL or was associated with fitness effects when tested at closely linked markers . In summary, while highly significant and expected effects of high versus low were measured for dfe penetrance, the high and low penetrance dfe lines did not differ in any of eight laboratory tests of fitness, countering widespread suggestions that Hsp90 buffered phenotypes in Drosophila necessarily suffer large, “unconditionally deleterious” fitness effect, the most frequent challenge to the ‘Hsp90 capacitor hypothesis’ To specifically examine the fitness effects of the genes controlling dfe and the most invariant quantitative traits dfe selection lines, abundant genes with small effects can interchangeably contribute to liability for the trait (probability of deformity). We suggest that the evolutionary dynamics of threshold traits under selection is qualitatively different from the evolution of continuous traits. At high frequency, conditionally cryptic alleles contribute interchangeably to threshold trait phenotypes, allowing different individuals in a highly affected population to carry different combinations of alleles and reducing inbreeding. The sheer abundance of trait polymorphisms would allow natural selection to eliminate polymorphisms with deleterious pleiotropic effects. For example, Hsp90 mutations themselves are lost coincident with artificial selection for deformity To our surprise, we found that Hsp90 buffering is remarkably specific to threshold traits, qualitative changes in morphology like Despite the central importance of adaptive evolution to understanding the evolution and emergence of biological diversity, the time and average number of mutations required for a novel adaptation to appear, and the expected size of allelic effects remain largely unresolved. Under the assumptions of gradualist models of evolutionary adaptation, the mapping of phenotype onto fitness is smooth and monotonic, with a single peak of maximum fitness. On such a fitness landscape, the changes that are most likely to be favourable are slow, small and quantitative, rather than rapid, large and qualitative www.picard.ch/downloads/downloads.htm). These Hsp90 targets are key proteins in cell cycle, protein synthesis, gene regulatory, chromatin remodeling, growth control and morphogenesis networks between which Hsp90 is a hub It has been suggested that many genes are ‘evolutionary capacitors’ Depending on genetic background, Hsp90 buffering can affect virtually any morphological feature of a presumably large range of organisms, extending from adult flies deformed eye selection was founded from F1 progeny of a cross between a heterozygous Hsp90 mutant . A single, heterozygous 1F male with an eye deformity was crossed to 3–4 related heterozygous siblings, yielding several more flies with eye deformities in the 2F generation. By the 4F generation, sufficient numbers of dfe flies were available to found six related, but independently maintained selection lines  increased among the high lines to the point that sufficient dfe polymorphisms were present that the trait no longer depended on the Hsp90 mutation, which is apparently deleterious even in the heterozygous state. By generation 15, none of the flies from either high or low lines carried the Hsp90 mutation 42F of selection. At generation 42F, we scored the offspring of each of 10 to 12 single mated females reared at 29.5°C, selected at random from affected flies in each high line. The next generation, 10 to 12 females were again chosen from the vial with the highest penetrance within each line, and this process was repeated for several generations. This ‘isofemale’ selection was essentially based on breeding values, rather than selection at the level of individual dfe flies. High lines HE2 and HE3 responded to several rounds of selection and are likely the most inbred; the mean penetrance of dfe increased among the vials, and the coefficient of variation decreased 4–5 fold. Line HE1 showed little response to this selection and a single isofemale line was chosen from the first round of selection. At 29° C high line penetrance and coefficients of variation were 0.43 (0.26), 0.73 (0.14) and 0.85 (0.07) for the inbred HE1, HE2 and HE3 lines respectively. During the inbreeding, artificial selection of the low lines was continued as usual without further inbreeding.The nt 19F2;  and anotdfe selection line and a wild-type control (Canton-S) was measured at 25°C and 60% humidity. Three 2–6 hour collections from independent groups of approximately 100 parents each were scored for each line and control. Each collection consisted of 100–140 embryos. Periodically, hatched larvae were removed from the collection plates. At 36 hours post-collection, empty and non-empty (unhatched) chorions were easily counted under a dissecting microscope. No further hatching was observed beyond 36 hours.The hatching success of embryos collected from each high and low Embryos laid in vials on standard cornmeal molasses agar during a 48-hour collection  were counted, and then allowed to develop to adulthood at 25°C at 60% humidity. Eclosing flies were scored at days 12, 15 and 17. Egg-to-adult survival was determined for each of 3 vials for each genotype as the ratio of embryos emerging as adult flies.dfe lines in competition with wild-type flies was measured at a high density of competing flies . Six 2–4 day old gravid females from each dfe line and six similarly-aged, non-virgin Canton-S competing females were placed in a vial for egg laying, with 10 vials (groups of flies) started for each genotype. To assure that the relative competitive of the parental females in this experiment were due to genetic rather than environmentally induced maternal, paternal or grandparent effects, all parental flies were simultaneously raised under equivalent density, sex ratio and age conditions. Every two days, the flies were transferred to new vials, for a total of four replicate vials per group of flies. The number of red (Canton-S) and white-eyed (dfe) adults emerging from the competition between dfe and wild-type larvae and parents was determined by scoring the vials on days 11,15, and 17. The ratio of white/total flies was used to measure relative fitness.Relative fitness of low and high Lifetime and early female fecundity were scored at 25°C and 60% humidity and measured as the number of viable adult offspring produced. Therefore, this measure included number of eggs/laid per female and their offsprings' viability. To control for differences in fertile lifespan in Experiment 1, both total lifetime fecundity  and dailLongevity was measured as days from eclosion through death in mated males and females housed singly up through four flies per vial and transferred to fresh, yeasted vials with every 2–5 days. Male and female longevity in Experiment 6 was measured for approximately 20 flies per line. Female longevity in Experiment 7 was measured for 103–107 flies per line.www.r-project.org). For ratios near 0 or 1, data were arcsin transformed to enable better statistical discrimination of differences between individuals and lines. Lifespan fit the expectations of gaussian distributions, therefore no transformation of lifespan data was necessary, however Chi-square significance values of log rank or Wilcoxon tests of survival were also determined and gave similar results .Penetrance at 25°C was scored in male and female progeny of flies tested for lifetime fecundity in Experiment 1. 53,625 progeny of 1,431 recombinant lines  were raiSamarkand I–236 (Sam) were introduced into the HE1, HE2 and HE3 selection line backgrounds by 2 generations of backcrossing (decreasing the average fraction of Sam to 12.5%). To increase the number and variety of meiotic breakpoints and the genetic map distance, subsequent generations of free meiotic recombination were performed. At generation B2, four sublines were started within each line and carried through 5 more generations with round-robin mating. The resulting recombinant isogenic families (i.e. genetic “clones”) contained unique pairs of recombinant autosomes, isolated in males using the attached-autosome balancer T(2∶3)B3 (Bloomington stock 2506) HE2 females and their genetically identical progeny were first scored for dfe penetrance and then stored at −80°C for genotyping. In HE2 recombinant families, the genotype of each marker was either HE2/HE2 or HE2/Sam, but in the other recombinant families, the genotypes were heterozygous between HE2 and each high line. Within the groups of recombinant families for any selection line, approximately 87.5% of the markers were expected to be high/high and 12.5% were expected to be high/Sam. Selected recombinant isogenic families were later genotyped at molecularly mapped STS sites (Drosophila genome project; Small recombinant fragments of the highly uniform wild-type background,


A significant number of human X-linked genes escape X chromosome inactivation and are thus expressed from both the active and inactive X chromosomes. The basis for escape from inactivation and the potential role of the X chromosome primary DNA sequence in determining a gene's X inactivation status is unclear. Using a combination of the X chromosome sequence and a comprehensive X inactivation profile of more than 600 genes, two independent yet complementary approaches were used to systematically investigate the relationship between X inactivation and DNA sequence features. First, statistical analyses revealed that a number of repeat features, including long interspersed nuclear element (LINE) and mammalian-wide interspersed repeat repetitive elements, are significantly enriched in regions surrounding transcription start sites of genes that are subject to inactivation, while Alu repetitive elements and short motifs containing ACG/CGT are significantly enriched in those that escape inactivation. Second, linear support vector machine classifiers constructed using primary DNA sequence features were used to correctly predict the X inactivation status for >80% of all X-linked genes. We further identified a small set of features that are important for accurate classification, among which LINE-1 and LINE-2 content show the greatest individual discriminatory power. Finally, as few as 12 features can be used for accurate support vector machine classification. Taken together, these results suggest that features of the underlying primary DNA sequence of the human X chromosome may influence the spreading and/or maintenance of X inactivation. Female mammals have two X chromosomes while males have one X and one Y chromosome. To equalize dosage of X chromosome genes in males and females, one X in female cells is inactivated, repressing the expression of most genes on the chromosome. Despite the chromosome-wide nature of X inactivation, at least 10%–15% of genes “escape” this inactivation in human females and are still expressed on the inactivated X. Whether a gene escapes or is subject to inactivation is thought to be determined epigenetically, and it is unknown to what extent, if at all, the underlying genomic DNA sequence of the chromosome plays a role. In this work, the authors show that the DNA sequence surrounding genes that escape inactivation is significantly different from the sequence surrounding genes that are subject to inactivation. In fact, a small number of DNA sequence features can be used to predict with high accuracy whether a gene will escape or be subject to this silencing. This establishes strong evidence that epigenetic regulation is, at least in part, dependent on genomic sequence and organization and provides a list of candidate sequence features whose role(s) in X inactivation can now be explored. XIST gene in the X inactivation center [XIST transcripts spread along the chromosome [XIST-independent manner .Recent studies have shown that this transcriptional repression is not complete. In both humans and mice, some X-linked genes escape inactivation to varying degrees . WhiInterestingly, the distribution of genes that escape inactivation on the human X chromosome is nonrandom , althougAs described above, X inactivation involves both an initial inactivation and the ongoing maintenance of this inactivated state. The lack of the complete silencing of some genes might result from the failure in the initial spreading of inactivation along the chromosome, the failure in a subsequent maintenance step after the initial inactivation, or a combination of the two . Thecis-acting elements may be long interspersed nuclear element 1  retrotransposons [Gartler and Riggs hypothesized that there may exist “way stations” on the X chromosome that aid the spreading of X inactivation . Subsequnsposons . The latnsposons . This consposons ; insteadnsposons . In the nsposons , may helIn this study, we have focused on primary DNA sequence features in regions around the complete set of human genes of known X inactivation status to further understand their potential role as signals in the spread and/or maintenance of X inactivation. Using the accurate and nearly complete X-chromosome sequence , we firsMultiple sequence features may influence X inactivation in an interdependent fashion. Determining these factors and their possibly combinatorial nature thus presents a complex problem. Machine learning classifiers such as those based on the support vector machine (SVM) algorithm have been successfully used to shed light on many complex biological problems, particularly gene expression analysis using microarray expression and/or sequence data –29. We fOur statistical analysis and SVM classification experiments together, summarized in q values, a measure of false discovery rate [q < 0.02 and 2,345 features at q < 0.05  between the rank-sum values calculated for the features in these two analyses.We considered the possibility that these results may simply reflect the unique evolutionary history of the X chromosome rather than a specific relationship to X inactivation. It has been noted that many sequence characteristics, including GC content and repeat content are different between XAR and XCR ,15. To iq < 0.05 . In addiq < 0.15 and 449 at q < 0.20 (2 = 0.72) and in the whole chromosome (R2 = 0.55), suggesting that similar differences are being found in all three analyses.To further explore a connection to evolutionary history, we analyzed the five unique strata that essentially separate the ancestral X chromosome sequence  from the sequence added later  ,15 as shq < 0.20 , but thiFrom these data, we conclude that significant features commonly identified in these analyses most likely represent global differences between the genomic environment of escaping and subject genes and not simply regional differences . Below w(q < 0.05), L1s and MIRs are among the most consistently enriched in regions surrounding the transcription starts of genes that are subject to X inactivation. In contrast, Alu elements are clearly the most consistently enriched in regions surrounding the transcription starts of genes that escape inactivation (Among those repeat sequence features found to have significantly different distributions chromosome-wide and in the XAR tivation . Furthertivation . These rq < 0.05) and also within stratum 3 (q < 0.2), shown in Only ten repeat sequence features are significant chromosome-wide and in the XAR (It has been reported that young L1 (subfamily L1Ps) members show greater enrichment on the X chromosome than older ones (subfamily L1Ms) , thus raq < 0.02, chromosome-wide) and 464 of the 515 5-mers (90.1%) that are enriched around escaping genes are GC rich, while all 27 3-mers and 275 of 316 5-mers (87.0%) enriched around subject genes are AT rich. Furthermore, 78.1% of 3-mers and 79.2% of 5-mers in regions around escaping genes contain a CG dinucleotide. Strikingly, among the short sequences that are very significantly enriched (q < 0.012) near genes that escape inactivation, the top 12 3-mers with respect to rank-sum values are CGT/ACG in various window sizes, while the top eight and 43 of the top 51 5-mers contain CGT/ACG  is consistently different between the two classes of genes . The ass CGT/ACG . SurprisAlthough the distributions of the above features are significantly different in the genomic contexts of genes subject to or escaping inactivation, no single feature or even combination of two features is sufficient to accurately discriminate between the two classes of genes. We therefore investigated whether we could classify genes using linear SVM classifiers trained with large sequenced-based feature vectors.Most genes in the XCR are subject to inactivation with few exceptions, while about half of the genes in the XAR escape inactivation . Thus, tNext, we performed leave-one-out CV experiments to determine whether there was a significant difference in accuracy. Using the same 5,596 features, the X inactivation status for 89/110 (81%) genes were correctly predicted, indicating that the overlaps in feature windows for nearby genes do not bias classification results . FurtherSince the majority of genes with functional Y-homologs escape inactivation , we creaIt has been noted previously that genes subject to or escaping inactivation are clustered regionally into domains . InteresTo investigate whether the same signals may be used for X inactivation in the XCR, we used SVM models trained on XAR nonborder genes and predicted the X inactivation status for genes in the XCR. As shown in Based on our initial statistical analysis, it is likely that most sequence features do not contain discriminatory information that would contribute to accurate classification. Therefore, we systematically eliminated probable noninformative features to determine the affect on the classification accuracy of linear SVMs . In addiIn an effort to compile a robust set of informative features, this feature selection process was performed 100 times. In each iteration, a randomly selected set of two-thirds of the XAR nonborder genes was used. On average, reduced sets of 53 features performed as well in classification tests as when utilizing the full complement of features .We identified features that appeared 20 or more times within these 100 reduced sets of 53 features  with thoInformative features that contribute to accurate classification are generally those that are consistently lower (or higher) in one class as compared with the other. To explore why the most consistently selected features might be desirable for classification, we compared their content in the windows around each of the 82 XAR nonborder genes, a subset of which are shown in p < 0.001 and p < 0.05, respectively; To build an efficient classifier with an even smaller set of features, we repeated this feature reduction experiment with all XAR nonborder genes to produce a set of 17 features . This sen = 324), most were predicted correctly with high confidence (>0.9 in n = 76), the majority were correctly predicted with probabilities >0.8  at Duke University.A University of California Santa Cruz (UCSC) Browser annotation track has been created using expression and prediction information to facilitate comparison with other genome annotations . This isWe have shown that the genomic environments of genes subject to and escaping X chromosome inactivation are significantly different, especially with respect to the content of LINEs, MIRs, Alu elements, and short sequence motifs containing the three-nucleotide sequence CGT/ACG. Based solely on primary DNA sequence, linear SVM classifiers can correctly predict 80% of all the genes and 87% of genes located in the interior of clusters of genes sharing the same X inactivation status. These results suggest that most, if not all, of the information necessary to determine X inactivation status is embedded in primary DNA sequence. Furthermore, our analyses indicate that this information can be represented by as few as 12 sequence features.Lyon proposed that L1 elements could serve as “booster elements” for spreading of X inactivation . MultiplHowever, LINE elements alone are not sufficient to correctly classify all samples, indicating that this feature by itself does not determine the X inactivation status of a region. For example, another frequently selected feature, MLT1K, classifies the escaping gene class almost perfectly due to its being found nearly exclusively around subject genes B but hasAlu repeats are primate specific and represent more than 10% of the human genome . They arThrough systematic feature reduction, we obtained a set of 12 features that alone contain sufficient information for accurate classification. It seems reasonable to hypothesize that some of these features may act as signals for some aspect of X inactivation. However, the feature reduction method we used does not guarantee an optimal final set of features. We can find six alternative 17-feature sets that achieve comparable classification rates as the 12-feature set . TherefoXIST itself, and this region may require a different mechanism to escape X inactivation. The other misclassified cluster was recently shown to be flanked by CCCTC-binding factor sites that may serve as “boundaries” to spreading of inactivation [SVM classifiers trained using genes from the XAR are able to correctly predict the status of 87% of XCR genes that are subject to X inactivation using all features from 50-kb and 100-kb windows , and 81%tivation . Since CThe exact relationship between primary DNA sequence and X chromosome inactivation is still not known. While the SVM classifiers created using the sequence features we selected are able to predict X inactivation status well, especially when only considering genes internal to clusters, they are not perfect. The most likely reasons for this include: (1) DNA sequence alone does not contain sufficient information to definitively determine X inactivation status; (2) the features identified in this study do not adequately characterize the sequence characteristics that determine X inactivation status; or (3) higher-order structures of chromosome or chromatin packaging provide long-range signals that are not adequately captured by consideration of relatively short-range windows of sequence in the vicinity of each gene, as performed here. For example, the repeat sequences and the 3-mers and 5-mers may not directly reflect structural properties of the underlying DNA that may facilitate or impede silencing. Further, it has been shown recently that the structural properties of DNA are involved in the positioning of nucleosomes on the genomic sequence, which in turn influences heterochromatin formation (reviewed in ).XIST RNA spreading and/or the ability to maintain a silenced state. Even without positing a specific role for the repeat elements directly, the base composition of certain repetitive elements such as Alu elements and L1s may contribute (L1s) or prevent (Alu) the proper packaging necessary for optimal spread and/or maintenance of inactivation. Their abundance on the X-chromosome could allow them to act as good proxies for this information, and thus allow the creation of accurate classifiers.We propose that structural characteristics underlying DNA in certain regions of the X chromosome may prevent the necessary chromatin packaging required to suppress gene expression. Thus, a sequence that is not amenable to high-density nucleosome array formation could interfere with Further study into the structural properties of chromosomal DNA and their relationship to both repeat sequences and X inactivation should allow for the evaluation of higher-order models containing potentially more informative aspects of the relevant sequence features. Such features may enable us to completely discriminate between regions that escape or are subject to X inactivation.Genes and ESTs (transcripts) considered for this analysis consist of those for which the X inactivation status is known, as previously described ,41. For The XAR is defined precisely as the first 46,700,000 bases in the build 35 assembly of the X chromosome. According to these criteria, there are 50 annotated genes in the XAR that escape inactivation and 60 that are subject to inactivation. In addition, there are 20 ESTs that escape and 18 ESTs subject to inactivation in this region. Of these 38 ESTs, 15 overlap transcribed regions of genes in the XAR and were discarded, leaving 13 that escape and ten that are subject. In the XCR, the above criteria identified 23 escape genes and 315 subject genes.Gene groups were created by calculating the overlap of every genes' 100-kb upstream (downstream) regions with neighboring genes' upstream (downstream) regions. Genes with an overlap exceeding 10 kb were placed in the same group. In a particular group, a gene did not necessarily overlap all of the other genes, just at least one other gene with respect to upstream or downstream sequence. A total of 62 groups were created for the 110 XAR genes.http://www.repeatmasker.org) and defined in Repbase (http://www.girinst.org), CpG islands, as well as of all 64 three-base and 1,024 five-base sequences, were extracted from the X chromosome sequence [http://genome.ucsc.edu).Content of 310 repeat families and subfamilies as annotated by RepeatMasker , genes with functioning Y-homologs , genes with Y-linked pseudogenes , and genes with no apparent Y-homolog or pseudogene .Our final dataset was a matrix with features as columns and genes as rows. In addition, the X inactivation status of each gene is represented as 1 for escaping and −1 for being subject to inactivation.U test. Features with zero values for all genes were excluded from all analyses. For each feature, we defined a weight W to characterize the difference in the distribution of that feature in regions of escaping and subject genes. Specifically, we defined W as follows:j,i∈em is the median rank for the escaping genes, j,i∈sm is the median rank for the subject genes, and jr is the Pearson correlation of the jth feature to X inactivation status. To assess the significance of W for a particular feature, a p value was calculated by randomly permuting gene labels 1,000 times and calculating a weight for each permutation. The null hypothesis was that the two samples (escaping and subject genes) are drawn from the same population. The p value assigned reflects the percentage of permutations whose weight was greater than the original W and reflects the probability that a W value of this magnitude was obtained by chance. To assess better the significance of these p values, q values were calculated and provide a measure of the false discovery rate . SVM training, prediction, weight calculation, and probability calculation were performed based on instructions that accompany the software. Namely, the sparse format was used, and the C value was dynamically calculated for each individual test based on the training data. Feature values were scaled for each experiment such that for each feature, values ranged between 0 and 1.LIBSVM version 2.71 software was used for SVM classification  of the held-out gene or all genes in the held-out group. Prediction accuracy was calculated based on results for all the genes in the set.Recursive feature selection was carried out as follows: starting with 5,596 sequence features from 50-kb and 100-kb windows, linear SVM classifiers were trained using a given set of genes. Features were sorted based on their weights in the resulting SVM classifier, and those whose weight was in the lowest 30th percentile were eliminated. This procedure was repeated in an iterative fashion to generate progressively smaller feature sets until the number of remaining features was less than ten. Accuracy of classification was assessed at each step by performing both leave-one-out CV on nonborder genes from the XAR and prediction of X inactivation status of genes from the XCR.X is the normalized matrix with the features as rows and genes as columns. The dendrogram in For the reduced set of 17 features for the 82 XAR nonborder genes see , values Feature vectors consisting of all 5,596 features from 50-kb and 100-kb windows and, separately, the reduced set of 12 features for the 82 XAR nonborder genes were normalized as described above. For each dataset, the top three principal components were calculated. Finally, the values for these three principal components for the 82 nonborder genes were projected onto a 3-D graph to visualize their separation.To determine the significance of the 12 selected features, 1,000 random sets of 12 features were drawn with replacement from the complete set of 5,596 features from 50-kb and 100-kb windows. The null hypothesis being tested was that the 12 selected features are randomly selected and therefore do not perform better than other randomly selected feature sets. Leave-one-out CV experiments were performed as described above for each of the random sets, and their prediction accuracies were determined.Figure S1The top plot represents the CV rate on XAR nonborder genes for each set of features. The bottom plot represents the prediction rate for XCR genes using SVM models constructed from XAR nonborder genes. At 17 features, both CV and prediction rates reach their respective maximum values.(90 KB PDF)Click here for additional data file.Figure S2x-axis in (A) and numbers in parentheses in (B):Features are represented by numbers; labels of 1, MLT1K 100 kb downstream (dw); 2, L1 100 kb dw; 3, AGGCA 50 kb upstream (up); 4, L2 100 kb dw; 5, MLT1K 100 kb up; 6, CGGTG 100 kb dw; 7, MIRb 100 kb up; 8, ATAGG 50 kb dw; 9, Charlie1 100 kb dw; 10, MER20 100 kb up; 11, TGACT 50 kb dw; 12, MLT2B3 100 kb dw; 13, ACCCC 50 kb dw; 14, MER3 50 kb up; 15, TCTGC 100 kb dw; 16, CTCAT 50 kb up; 17, GTTTG 50 kb up.(A) A dendrogram of ten hierarchical clusters. Features whose edges have the same color belong to the same cluster.(B) Plots showing the distribution of features within each cluster for 82 XAR nonborder genes.(443 KB PDF)Click here for additional data file.Figure S3Genes and ESTs are denoted by solid brown bars. The color intensity reflects the propensity for escaping inactivation, with the darkest brown indicating those that escape in all experiments (based on somatic cell hybrid data in ). Detail(23 KB PDF)Click here for additional data file.Table S1(574 KB XLS)Click here for additional data file.Table S2(50 KB XLS)Click here for additional data file.Table S3(40 KB XLS)Click here for additional data file.Table S4(21 KB XLS)Click here for additional data file.Table S5(18 KB XLS)Click here for additional data file.Table S6(86 KB XLS)Click here for additional data file.


Gadus morhua) with current tolerable weekly intake (TWI) levels. Under present conditions and climate change scenarios, methyl mercury increased in the ecosystem, translating into increased human exposure over time. However, we saw greater changes as a result of changing fishing mortalities. A large portion of the general human population exceed the TWI levels set by the World Health Organization , and they all exceed the reference dose (RfD) of 0.1 μg/kg bw/day set by the U.S. Environmental Protection Agency . As a result of an independent study documenting that Faroese children exposed prenatally to methyl mercury had reduced cognitive abilities, pregnant women have decreased their intake of whale meat and were below the TWI levels set by the WHO and the U.S. EPA. Cod had approximately 95% lower methyl mercury concentrations than did pilot whale. Thus, the high and harmful levels of methyl mercury in the diet of Faroe Islanders are driven by whale meat consumption, and the increasing impact of climate change is likely to exacerbate this situation. Significantly, base inflow rates of mercury into the environment would need to be reduced by approximately 50% to ensure levels of intake below the WHO TWI levels, given current levels of whale consumption.We modeled the flow of methyl mercury, a toxic global pollutant, in the Faroe Islands marine ecosystem and compared average human methyl mercury exposure from consumption of pilot whale meat and fish (cod,  Fisheries account for more than 95% of exports and 44.5% of gross domestic product, with demersal species  or a group of species . We initiated model simulations with a 100-year baseline run (t0 – t100) using only estimated mercury base inflow rate changes and environmental concentrations based on t100 – t200) to evaluate changes in fishing mortality rates and effects of increased sea temperatures due to climate change on methyl mercury bioaccumulation in all species/groups. We evaluated results in the context of human dietary consumption compared with standardized tolerable weekly intake (TWI) limits, based on the World Health Organization (WHO) equivalent [1.6 μg/kg body weight (bw); Q/P), and the von Bertalanffy growth coefficient (K).We modeled the transfer of methyl mercury through the food web in the marine ecosystem of the Faroe Islands using Ecotracer, a novel routine of the trophic ecosystem modeling approach Ecopath with Ecosim . A publihttp://ehp.niehs.nih.gov/docs/2005/7603/suppl.pdf).Using Ecotracer , we traca) initial concentrations for each species/group, including environmental concentrations; b) direct uptake rate parameters for each species/group; c) concentrations per biomass in immigrating organisms; and d) metabolism/decay rates for each species/group. Unassimilated food and instantaneous nonpredation death rates are estimated by the modeling routine.Within Ecotracer, the concentration of a contaminant in a given species or group of species is expressed as a function of gains from direct uptake from the environment and from the uptake from each food item as defined in the ecosystem model’s diet matrix, versus losses due to instantaneous decay rates, unassimilated food, predation, and instantaneous nonpredation death rates . Four co−6 g/m2 for latitudes 30° to 70° N, and approximately 0.68 × 10−6 g/m2 for latitudes 70° to 90° N  of methyl mercury to 3.312 g/km2  rates for marine mammals  because t0 – t100) and compared end concentrations with published field measurements. Transformation of literature values of mercury to methyl mercury were based on We followed the bottom-up flow of methyl mercury through ecosystem components (species/groups) with a 100-year baseline simulation  on the accumulation of methyl mercury in commercial fish species and pilot whales over a second 100-year period (t100 – t200). Changing F alters the production to biomass (P/B) ratio of a species/group (and hence mercury accumulation), because P/B can be defined as the total mortality rate, which is fishing mortality plus natural mortality  of methyl mercury asRiw is the daily intake (in grams) of seafood in weight w of species i, and Cil is the concentration of methyl mercury (micrograms per gram) in length class l of species i. Length class considerations are deemed important because of age-specific and growth-rate–specific increases in contaminant loads (K) have lower concentrations of methyl mercury than do slower-growing species (lower K), given the same size and environmental conditions.where nt loads . Thus, fQ/P (both derived from the model), and K , and K .t0 and t100, methyl mercury concentrations in all groups increased at a declining rate, whereas concentrations in the environment increased at an average rate of 0.004 g/km2 per year. Predicted concentrations at t100 for most species/groups fell within the ranges reported in the literature  scenario at t200 (F by 20% increased the methyl mercury concentrations in other toothed cetaceans (9.4%), pilot whales (16.0%), baleen whales (6.9%), saithe (6.0%), other deepwater fishes (8.0%), and mackerel (6.4%), relative to the status quo model at t200 , pilot whales (−12.4%), baleen whales (−4.7%), saithe (−3.8%), other deepwater fishes (−5.4%), and mackerel (−4.3%), relative to the status quo scenario at t200  and 4.4%  by tIndividual species/groups responded to these changes differently, with pilot whales  displayiWe deemed the human dietary intake of 12 g/person/day of pilot whale meat, as observed in the 1980s , to be tF is decreased substantially , this relationship has the potential to be a good predictor of methyl mercury concentrations for fish species  because it incorporates relevant life history and trophic indicators, such as TL, Q/P, and K.where Methyl mercury poses substantial health risks , and conF led to an increasing trend in methyl mercury concentrations, whereas increasing F had the opposite effect. Although our simulations suggested that increasing fishing mortality rates would lower the concentration of methyl mercury in species/groups, it would not be sufficient to decrease methyl mercury concentrations in whale meat substantially. The dominance of methyl mercury exposure through whale meat consumption will remain a problem, irrespective of potential changes to fishing pressures. Therefore, Faroe Islanders should seriously consider reducing whale meat consumption to levels comparable with those of pregnant women .In general, simulating decreases in Of additional concern are the likely effects of climate change, resulting in even higher concentrations of contaminants in the marine food supply of Faroe Islanders. The increasing methylation rate due to higher water temperatures will lead to continuous increases in concentrations of methyl mercury. This implies that Faroe Islanders may experience ever-increasing exposure levels, unless their dietary habits change to species with lower methyl mercury concentrations.We have demonstrated that Ecotracer is a capable tool for tracing contaminants through all functional groups of an ecosystem, requiring relatively few toxicologic input parameters to follow the food web flow of a contaminant through all levels of an ecosystem. Significantly, our approach also lends itself to the investigation of other contaminants with significant impacts on global human health, such as dioxins and polychlorinated biphenyls (PCBs).Orcinus orca), a species expected to have high levels of methyl mercury because of its position at the top of the food chain. In contrast, model concentrations for herring were below the value found in the literature. This may be due to the empirical data being from near-shore U.K. waters . Pooled groups are known to be often poorly represented in the underlying input data and may be missing data for key group components. For example, other toothed cetaceans were missing methyl mercury values for killer whales  but also are influenced by top-down factors . We also demonstrated that changes in fishing mortality can substantially alter the flow of methyl mercury in an ecosystem, by affecting the  mercury  and grow mercury .Methyl mercury affects human health as a result of direct discharges and atmospheric transport. This pollutant is of particular concern to indigenous peoples of the Arctic, who often rely heavily on marine resources, and especially marine mammals, for part of their traditional diets. For example, in Greenland, approximately 43% of blood samples taken from indigenous women of reproductive age had blood mercury levels exceeding guidelines . HoweverMethyl mercury will continue to be of global concern as long as there are ongoing anthropogenic inputs of mercury. Our ecosystem-scale simulations suggest that substantial reductions in mercury inputs (~ 50%) would be required to ensure safe exposure levels if people such as the Faroe Islanders wish to continue their cultural dietary traditions. Unfortunately, the United States in 2002 increased the disposal or release of mercury by 10% more than the previous year , whereas


Numerous studies have reported associations between fine particulate and sulfur oxide air pollution and human mortality. Yet there continues to be concern that public policy efforts to improve air quality may not produce actual improvement in human health.This study retrospectively explored a natural experiment associated with a copper smelter strike from 15 July 1967 through the beginning of April 1968.In the 1960s, copper smelters accounted for approximately 90% of all sulfate emissions in the four Southwest states of New Mexico, Arizona, Utah, and Nevada. Over the 8.5-month strike period, a regional improvement in visibility accompanied an approximately 60% decrease in concentrations of suspended sulfate particles. We collected monthly mortality counts for 1960–1975 and analyzed them using Poisson regression models.The strike-related estimated percent decrease in mortality was 2.5% , based on a Poisson regression model that controlled for time trends, mortality counts in bordering states, and nationwide mortality counts for influenza/pneumonia, cardiovascular, and other respiratory deaths.These results contribute to the growing body of evidence that ambient sulfate particulate matter and related air pollutants are adversely associated with human health and that the reduction in this pollution can result in reduced mortality. Only a few studies of natural experiments have evaluated large-scale mortality effects associated with sharp, prolonged reductions in air pollution. Reductions in mortality accompanied substantial reductions in PM concentrations associated with the closure of a steel mill in Utah Valley, Utah (Numerous epidemiologic studies have reported associations between ambient particulate matter (PM) air pollution and mortality . Most ofey, Utah , the baney, Utah , and a rey, Utah .3) decrease in concentrations of suspended sulfate particles. The primary objective of this study is to retrospectively analyze the mortality data and evaluate the evidence of strike-related reductions in mortality.In the present study we explored an interesting natural experiment associated with a nationwide copper smelter strike from 15 July 1967 through early April 1968 . The strThe primary study area in this analysis included the four Southwest states most influenced by the copper smelter strike: New Mexico, Arizona, Utah, and Nevada . As repoInternational Classification of Diseases (ICD) codes changed in January 1968 from the 7th [a) influenza/pneumonia 1960–1967 ; b) influenza/pneumonia 1968–1975 ; c) cardiovascular disease 1960–1967 ; d) cardiovascular disease 1968–1975 (ICD-8 codes 390–458); e) respiratory system 1960–1967 (ICD-7 codes 470–527); and f ) respiratory system 1968–1975 (ICD-8 codes 460–519).We collected mortality data from the National Center for Health Statistics’ yearly mortality reports of the United States from 1960 to 1975 . Monthly the 7th  to the 8We plotted mortality counts for the four Southwest states over time and over mortality counts for Eastern states, neighboring states, and bordering states. We also analyzed variation in death counts using 12 different Poisson regression models for the four Southwest states as a region as well as for each Southwest state individually. All of the models controlled for time trends in mortality. Because of potential nonlinearities in the time trends, the models included spline smoothers of time with 1–3 degrees of freedom using generalized cross-validation to select the best fit. To further control for non-event-related regional variability in mortality, models 1–4 included total mortality counts from all other U.S. states, the Eastern U.S. states, neighboring states, and bordering states , respecta) July 1967–March 1968 , b) August 1967–March 1968 (only the full strike months), and c) August 1967–April 1968 (full strike months plus 1-month lag). Statistical analyses were conducted using R and SAS software estimating the cubic spline smooths using the “gam” function in the MGCV package  associated with the copper smelter strike produced a small but measurable reduction in mortality (1.5–4.0%). The effect estimates were reasonably stable and robust to controlling for time trends, mortality trends in other areas of the United States, and nationwide monthly mortality counts for influenza/pneumonia, cardiovascular, and other respiratory deaths. The effect estimates were consistently reduced when mortality counts in bordering states were included in the models. Mortality counts from bordering states are more likely to have similar mortality trends and seasonality and other regional similarities, as well as common influences of regional communicable disease. Therefore, the use of mortality counts from bordering states as a control variable in the regression may be preferable to including mortality counts from more distant areas of the United States. However, because the air quality of the bordering states may also have been somewhat improved because of the smelter strike, using counts in the bordering states may result in overcontrolling and underestimating the mortality effect.The 8.5-month copper smelter strike in 1967–1968 provided a unique and interesting natural experiment to explore the mortality effects of air pollution. Relative to the natural experiment studies of Utah Valley  and Dubl3. Based on reported mortality reductions from the natural experiment studies of Utah Valley  associated with the copper smelter strike produced improvements not just in visibility but also in small but measurable reductions in mortality.In conclusion, the results of this study add to the growing body of evidence that ambient sulfate particulate matter and related air pollutants are associated with adverse human health effects and that the reduction in this pollution can reduce mortality. These results suggest that the approximately 60% reduction in regional sulfate concentrations (~ 2.5 μg/m3 has been corrected to ~ 2.5 μg/m3 in the introduction and the “Discussion.” Also in the “Discussion,” the decline in overall mortality originally was 0.2–0.7% and the estimated percent changes in mortality were 0.6–0.9% and 1.7–2.6%. They have been corrected here to 0.8–1.1%, 1.5%, and 4.0%, respectively.The concentration of sulfate particles noted in the manuscript originally published online as 1–1.5 μg/m


Stenogyne rugosa principally to identify genetic markers for the Hawaiian endemic mints.The endemic Hawaiian mints represent a major island radiation that likely originated from hybridization between two North American polyploid lineages. In contrast with the extensive morphological and ecological diversity among taxa, ribosomal DNA sequence variation has been found to be remarkably low. In the past few years, expressed sequence tag (EST) projects on plant species have generated a vast amount of publicly available sequence data that can be mined for simple sequence repeats (SSRs). However, these EST projects have largely focused on crop or otherwise economically important plants, and so far only few studies have been published on the use of intragenic SSRs in natural plant populations. We constructed an EST library from developing fleshy nutlets of Stenogyne fruit EST library consisted of 628 unique transcripts derived from 942 high quality ESTs, with 68% of unigenes matching Arabidopsis genes. Relative frequencies of Gene Ontology functional categories were broadly representative of the Arabidopsis proteome. Many unigenes were identified as putative homologs of genes that are active during plant reproductive development. A comparison between unigenes from Stenogyne and tomato (both asterid angiosperms) revealed many homologs that may be relevant for fruit development. Among the 628 unigenes, a total of 44 potentially useful microsatellite loci were predicted. Several of these were successfully tested for cross-transferability to other Hawaiian mint species, and at least five of these demonstrated interesting patterns of polymorphism across a large sample of Hawaiian mints as well as close North American relatives in the genus Stachys.The Stenogyne rugosa EST SSRs for evolutionary and phylogeographic studies of the Hawaiian endemic mints and their close relatives. Given that allelic repeat length variation in developmental genes of other organisms has been linked with morphological evolution, these SSRs may also prove useful for analyses of phenotypic differences among Hawaiian mints.Analysis of this relatively small EST library illustrated a broad GO functional representation. Many unigenes could be annotated to involvement in reproductive development. Furthermore, first tests of microsatellite primer pairs have proven promising for the use of  Stachys  and widespread genus belonging to the subfamily Lamioideae. Based on sequence variation in the rapidly-evolving nuclear ribosomal 5S non-transcribed spacer (5S-NTS), the Hawaiian mints are most closely related to temperate North American Stachys, whereas with chloroplast (cpDNA) sequence data, they group with a largely Mesoamerican Stachys lineage , H. haplostachya, has fragrant white flowers typical of insect-pollination and is found in the xerophytic shrubland of Hawai'i. The flowers of Phyllostegia resemble those of Haplostachys. The 32 species of Hawaiian Phyllostegia are herbs, lianas or subshrubs and are mainly found in mesic to wet forest habitats. In Stenogyne the flowers are axillary and the tubular corollas, displaying a range of sizes and colors, usually have a reduced lower lip, suggestive of bird-pollination. The 21 species of Stenogyne are mainly perennial vines found in lower elevation, mesic-wet forests to higher elevation, subalpine woodland. In contrast to this extensive morphological and ecological diversity among the Hawaiian taxa, DNA sequence variation has been found to be remarkably low, resulting in a lack of phylogenetic resolution among accessions of Phyllostegia and Stenogyne . One poArabidopsis could be annotated to specific orthologs in the Arabidopsis genome (see below).Concerning our 'positive' unigene identifications, it should be borne in mind that BLAST-based annotation of short EST unigenes can be misleading if best BLAST hits are more representative of domain homologies rather than homology to particular orthologs. However, at least among the five loci for which we analyzed microsatellite variation in depth, the four with putative counterparts in Arabidopsis genes that are active during plant reproductive development (Table ABNORMAL FLORAL ORGANS (AFO), ARABIDOPSIS THALIANA CENTRORADIALIS (ATC), FCA, GIGANTEA (GI), and LUMINIDEPENDENS (LD).Some of the unigenes from our EST library could be identified as putative homologs of nt Table . Among tAFO, which has also been named FILAMENTOUS FLOWER (FIL) or YABBY1, belongs to a small family of genes encoding transcription factors. The primary function of the YABBY gene family members is to specify abaxial cell fates in lateral organs produced by apical and flower meristems . Intereo fruits . ATC belm e.g., -24]. If . If FCA,s plants . Althougntenance , while trescence . If our  library . Generalnd MAF2) . It is nAFO, FCA, and ATC/TFL1 each belong to small gene families, whereas GI and LD are single-copy genes. For example, the GI homolog in our EST library contains three introns, and preliminary studies with GI intron sequence variation have shown promising nucleotide and indel (insertion/deletion) variation among a small number of Stenogyne and Phyllostegia species (unpublished data). Further studies of GI intron sequence variation not only among Hawaiian mints but also among other lamioid mint genera may demonstrate utility of the locus for phylogeny reconstruction.Another potential use for our EST resources is for further research on single or low-copy genes. Perhaps these genes could serve as markers for phylogenetic studies. Stenogyne and tomato unigenes revealed many homologs that may be relevant for fruit development. When annotating loci identified within Developmental Processes using the GO functional classification scheme , and itArabidopsis genes. The fact that three of the five repeat loci examined here were found in 3'-UTRs follows the general observation that UTRs harbor more SSRs than protein coding regions [The tetranucleotide repeats in markers 261039 and 260715 were found in the 3'-UTRs of single  regions .AFO (see above). One of our SSR primers was in the next-to-last exon, i.e., the primers span an intron, which explains the difference in the expected and the observed fragment lengths . As in the above cases, no primer match was found to other proteins closely related to this putative eukaryotic translation initiation factor 4A homolog. As expected, the observed allele frequency pattern suggests strong purifying selection, with over 80% of accessions harboring only one allele Fig. . ConsequInterestingly, the last three loci discussed  show the presence of rare alleles that have presumably 'escaped' from strong purifying selection. It is possible that these presumably deleterious alleles have been fixed by chance in colonizing polyploids.It is clear from the limited examples presented here that although SSR markers in transcribed regions may exhibit useful variability, their different evolutionary histories  can lead to markedly different patterns of polymorphism. As a consequence, when all five loci combined are analyzed hierarchically with parsimony or non-hierarchically as with PCA analyses, only little or no significant structure is found (results not shown). These results corroborate previous evidence for poor phylogenetic hierarchy in this island radiation , furtherFCA, ARGONAUTE, CRU3, and CHAPERONIN-60ALPHA  and 900 μL 100% EtOH was added and the mixture stored at 4°C for one hour before centrifugation at 10000 rpm and 4°C for 10 min. The pellet was then dissolved in ddH2O.Total RNA was extracted using a phenol – chloroform protocol: two grams of frozen material were ground to a fine powder in liquid nitrogen before 5 mL of RNA extraction buffer  and 5 mL phenol were added. The thawed mixture was transferred to a polypropylene Oakridge tube and centrifuged at 7000 rpm and 4°C for 15 min. The upper phase was extracted twice, once using an equal volume of phenol and a second time using an equal volume of chloroform, both times centrifuging at 7000 rpm and 4°C for 15 min. The upper phase was transferred to a new tube and 1/10 volume of 3 M NaOAc (pH 6–7) and three volumes of 100% EtOH were added, mixed, and stored at -20°C over night to allow nucleic acids to precipitate. Nucleic acids were pelleted by centrifugation at 7000 rpm and 4°C for 10 min. The pellet was dissolved in 2 mL of DEPC-treated ddHThe concentration of total RNA was measured using a Bio-Rad Fluorometer  and RiboGreen Dye , and mRNA was extracted from 0.4 mg of total RNA using the Poly (A) Purist mRNA purification Kit  following the manufacturer's instructions. The concentration of mRNA was measured as above.cDNA was synthesized from 5 μg of mRNA and a cDNA library generated using the ZAP-cDNA Gigapack III Gold Cloning Kit  following the manufacturer's instructions. The cDNA library was constructed using SOLR as host, with pBluescript as vector and cloning sites EcoRI/XhoI.Random 5' sequencing of cDNAs was done at the University of Florida ICBR Core Facility using ET Terminator . Sequence quality screening and assembling of ESTs into unique gene sequences (unigenes) was done as described in . See alsArabidopsis [Arabidopsis hits with an expectation value of 1e-10 or better were submitted to the GO annotation search tool at the TAIR website [Arabidopsis within the Biological Processes and Molecular Function GO categories was done using the whole Arabidopsis genome annotation tool on the TAIR website.A first annotation of the unigene sequences was done using BLAST in the GenBank NR database, and in the complete coding sequences from bidopsis . As a fubidopsis . All Ara website ,37, and Stenogyne fruit EST library with the tomato unigene set  was tor Tool . The GO tor Tool ,18 was utor Tool  was usedStenogyne rugosa PCR amplifications were optimized by testing different PCR reagents and annealing temperatures. The following protocol proved successful: 10 μL reaction volume using the AmpliTaq Gold DNA Polymerase kit , 0.2 mmol/L of a dNTP blend, 1 μmol/L of each primer, and 1 μL DNA and a PCR touch-down protocol with the following profile: 1) initial denaturation 95°C 10 min, 2) 10 cycles of 95°C 1 min, 60°C 1 min, decreasing annealing temperature 1°C/cycle, 72°C 1 min 30 sec, 3) 35 cycles of 95°C 1 min, 50°C 1 min, 72°C 1 min 30 sec, and 4) a final extension 72°C 10 min. Analysis of SSR variation was done using flourescently labeled forward primers, size standard ROX500, and an ABI3100 automated sequencer (Applied Biosystems).Each of the unigenes was analyzed for long microsatellite repeats using the free online tool SSR Primer , which iStenogyne rugosa , a set ® GeneMapper® Software v3.7 (Applied Biosystems). The polymorphic information content (PIC) was calculated using the Polymorphism Information Content Calculator [Arabidopsis proteome were performed at the TAIR website [Arabidopsis, respectively  using CLUSTALW. If the primers could be aligned equally well in several locations along Arabidopsis sequences, the locations that would give a product of expected size based on particular Stenogyne unigenes were regarded as most parsimonious. This procedure was repeated for next best BLAST hits  until paralogy could or could not be excluded.The amplification profile for each of the five loci was scored using the ABI PRISMlculator . Frequen website . Each ES website , and botin situ. ACS constructed the cDNA library under the supervision of PG, DGO, DES, and PSS. MJY participated in preparation of the library for sequencing. DES and PSS were responsible for producing the sequence data, and CL, ACS, JHLM, and VAA for the bioinformatic analyses. ACS acquired the GIGANTEA intron sequence data and CL performed the testing of microsatellite loci. CL and VAA conceived of the study, were responsible for its design and coordination, and drafted the manuscript. All authors read and approved the final draft.CL, ACS, and VAA carried out tissue collection


It has been recently observed that statins might slow the progression of aortic stenosis or sclerosis. Preliminary reports suggested a similar positive effect in reducing the degeneration of aortic valve bioprostheses even though this hypothesis should be further proven and supported by new data. In this review the present evidences of the possible effects of statins in this field are discussed. Nowadays many aortic valve prostheses are available in clinical practice: mechanical, biological and human tissue prostheses. None of the currently available prosthetic valves, however, approach the human valve in terms of hemodynamic function and/or freedom from complications. Mechanical prostheses have a longer durability and provide a generally good hemodynamic function, but are thrombogenic, requiring permanent anticoagulation with related risks in terms of morbidity and mortality ,2. On thAortic valve replacement is one of the most frequently performed cardiac surgery interventions in western countries and the proportion of biological valves used in the aortic position can be as high as 70% in patients over 70 years ,4. MoreoIt has been recently observed that hydroxymethylglutaryl coenzyme A reductase inhibitors might slow the progression of mild and moderate stenosis or sclerosis in native aortic valves -7. SeverIn addition, the last two studies on this topic, published this year, did not confirm the positive effect of hydroxymethylglutaryl coenzyme A reductase inhibitors on aortic stenosis progression Table  15,16].,16.15,16The problem of early calcification of bioprosthetic valves is well known. Indeed, at the time of commercial manufacturing , a treatFarivar and Cohn  have recDavid and Ivanov , analyzi2/year) and indexed effective orifice area (0.019 ± 0.031 vs 0.056 ± 0.086 cm2/m2/year) were also lower in statin-treated patients (p < 0.001 for both) , an ongoing study of the "Società Italiana di Ecografia Cardiovascolare", is aiming to create a large, prospective, observational investigation, involving many Echocardiograpic laboratories and thousands of patients, in order to give more definite answers from the real clinical world to this unsolved question. A substudy of the ASSIST study on aortic biological prostheses is also planned, in order to prospectively assess, in a large number of patients, the potential role of this pharmacologic treatment in reducing the rate of prostheses degeneration.The potential role of statin treatment in reducing the rate of aortic biological prostheses degeneration has not been clarified yet. Nowadays studies are discordant but a large, prospective, observational investigation is lacking. On going studies could answer some of the unsolved questions.NoneThe author(s) declare that they have no competing interests.


Abnormal hemostasis in cancer patients has previously been described, however the correlation between the plasma fibrinogen level and cancer metastasis and prognosis has not been reported in a large-scale clinical study.Preoperative plasma fibrinogen levels were retrospectively examined in 405 patients who underwent surgery for advanced gastric cancer. The association of fibrinogen levels with clinical/pathological findings and clinical outcome was evaluated.There was a positive correlation between plasma fibrinogen levels and the depth of invasion (p < 0.05). Hyperfibrinogenemia (>310 mg/dl) was independently associated with lymph node  and liver  metastasis, not with peritoneal metastasis in this series. Patients with hyperfibrinogenemia showed worse clinical outcome in T2 gastric cancer, however, there was no correlation of plasma fibrinogen level with prognosis in T3/T4 gastric cancer.Our results might support the idea that hyperfibrinogenemia can augment lymphatic and hematogeneous metastasis of advanced gastric cancer, which is major determinant of the prognosis in T2 gastric cancer. Therefore, in the situation without peritoneal involvement, hyperfibrinogenemia is a useful biomarker to predict the possible metastasis and worse clinical outcome in T2 gastric cancer. An increased frequency of thrombosis in patients with gastrointestinal cancer was first documented in 1865 -4. More Fibrinogen, an essential hemostatic factor, is converted to fibrin  by activated thrombin. Dvorak suggested that elevated fibrinogen concentrations were frequently observed in cancer patients with malignant disease . MoreoveA total of 442 patients with advanced gastric cancer underwent gastrectomy between January 1985 and December 1999 at the First Department of Surgery, University of Tokyo Hospital, Tokyo. Oral consent was obtained from each patient for blood test. Among them, plasma fibrinogen levels were evaluated before surgery in 424 patients. Because the plasma fibrinogen level is critically affected by the presence of inflammation, liver cirrhosis and chronic renal failure, five patients with apparent acute inflammatory disease , 10 patients with liver cirrhosis and four patients with chronic renal failure were excluded from the study to minimize confounding factors. The remaining 405 patients were enrolled in this study. We also evaluated fibrinogen levels in 387 patients with early gastric cancer and 124 patients with non-inflammatory benign disease who underwent surgery during the same period. Our protocol was approved by the ethics committee of faculty of medicine, the University of Tokyo.In this study we referred to the classifications established by the Japanese Research Society for Gastric Cancer  which deThe preoperative plasma fibrinogen level was measured from early morning samples taken before breakfast five to ten days before surgery. It had been determined by Clauss clotting method using DADE Thrombin Reagent™ and Coagrex-700™ automated coagulometer . The reference range of plasma fibrinogen level was defined as between 210 and 310 mg/dl according to the mean value ± 2 SD of healthy volunteers; plasma fibrinogen levels above 310 mg/dl were defined as hyperfibrinogenemia tentatively in this study.Statistical analysis was carried out using JMP 5.1 . Plasma fibrinogen levels were compared with one-way ANOVA followed by the SNK test. The association of fibrinogen levels with clinicopathological factors was assessed with Fisher's exact test. A multivariate stepwise logistic regression analysis was performed to identify independent variables that were correlated with hematogenous and lymphatic metastasis. The Kaplan-Meier method was used to estimate the distribution of survival curve, and log-rank test was used to compare the distributions between the groups with or without hyperfibrinogenemia. P < 0.05 was considered significant for all statistical analyses.The mean ± standard deviation (SD) plasma fibrinogen level in the 792 patients studied was 289.5 ± 81.3 mg/dl, which was not statistically different from patients with benign disease (278.1 ± 43.5)  Figure . In markA link between hyperfibrinogenemia and cardiovascular diseases such as coronary heart disease, stroke and peripheral vascular disease was previously revealed . SimilarRecent studies in fibrinogen deficient mice provide clear evidence that fibrinogen plays a crucial role in hematogenous and lymphatic metastasis of cancer cells . They shIn our study, we found that plasma fibrinogen level was associated with worse prognosis in T2 gastric cancer as well as lymphatic and hematogenous metastasis. The impact of hyperfibrinogenemia, however, was not found in T3/T4 gastric cancer. T3/T4 gastric cancers are serosal-infiltrating tumors, which enhances the potential to have occult metastatic foci on the peritoneum and/or form the overt peritoneal metastasis. As a fact, peritoneum is the major site of recurrence in T3/T4 gastric cancers -25, and Fibrinogen may enhance metastasis through several possible mechanisms. Firstly, the soluble form of fibrinogen could serve as the bridging molecule between tumor cells and host cells. Fibrinogen is a dimeric molecule with multiple integrin or non-integrin binding motifs, and malignant cells often express high levels of fibrinogen receptors, such as α5β1, αvβ3 integrins or ICAM-1 molecule. If fibrinogen were to bind to ICAM-1 on endothelial cells, it might promote stable adhesion of tumor cells to the endothelium of target organs. In addition, tumor cells and platelets can form large aggregates through the binding of fibrinogen, because platelet αIIbβ3 integrin receptors have a high affinity for fibrinogen. These aggregates effectively form microemboli in target organs, which can protect tumor cells from the innate immune system ,27. A stin vivo. During the conversion of prothrombin to thrombin by prothrombinase, the polypeptide prothrombin fragment 1+2 (F1+2) is released. The plasma F1+2 level is therefore a useful marker of thrombin generation. A number of studies have shown that F1+2 plasma levels are elevated in patients with specific types of malignancies [Recent studies have shown that thrombin might also be an important modulator of cancer metastasis gnancies ,28,29 bugnancies -32. Rahrgnancies . It therAlthough there is sufficient evidence to suggest that elevated fibrinogen levels might aid the development of metastatic lesions, we cannot ignore the possibility that this elevation is simply the result of the tumor mass spreading. Fibrinogen, which is one of the major acute phase proteins produced by the liver, is greatly enhanced in response to infection or other inflammatory disorders. Indeed, the fibrinogen level of all of the patients with preoperative acute inflammatory disorders was high. Inflammatory proteins, such as IL-6 or CRP, are reported to be higher in cancer patients compared to non-cancer patients. In our previous study, we found that CRP level did not show an independent association with lymphatic metastasis although plasma fibrinogen level did , suggestMetastasis is the unequivocal hallmark of cancer. The acquisition of metastatic ability leads to clinically incurable disease and resultingly worse clinical outcome for most cancer cell types. Our data, together with the results of basic experiments in previous studies, raises the idea that hyperfibrinogenemia causally imparts the hematogenous and lymphatic metastatic progression in patients with advanced gastric cancer, and is not simply the result of tumor progression. It also has a prognostic value in T2 gastric cancer, which might suggest hyperfibrinogenemia has the potential to be prognostic marker in other cancer type with less frequent peritoneal metastasis. Routinely examined plasma fibrinogen level is not only the factor to assess the perioperative hemorrhagic risk but also a useful biomarker to predict the possible metastasis and worse prognosis in T2 gastric cancer.thrombin-antithrombin III complexes: TAT, fibrinopeptide A: FPA, carcinoembryonic antigen: CEA, C-reactive protein: CRP, Lewis lung carcinoma: LLC, prothrombin fragment 1+2: F1+2, prothrombin time: PT.The author(s) declare that they have no competing interests.HY have been involved in preparing the manuscript for submission and made substantial contributions to conception and design, acquisition of data, and analyses and interpretation of data;JK contributed to analyses and interpretation of data and their interpretation and was involved in revising the manuscript for important intellectual content;NK and YY have been involved in plasma fibrinogen measurement and data analyses;HN participated in the design of the study and was involved in discussion of the results.The pre-publication history for this paper can be accessed here: