This implies that there should be a threshold altitude at which V̇O2max is less dependent on CaO2. To ascertain which are the mechanisms explaining the interactions between hypoxia, CaO2 and V̇O2max we measured systemic and leg O2 transport and utilization during incremental exercise to exhaustion in normoxia and with different degrees of acute hypoxia in eight rhEpo-treated subjects. Following prolonged rhEpo treatment, the gain in systemic V̇O2max observed in normoxia (6–7%) persisted during mild hypoxia (8% at inspired O2 fraction (FIO2) of 0.173) and was even larger during moderate hypoxia (14–17% at FIO2 = 0.153–0.134). When hypoxia was further augmented to FIO2 = 0.115, there was no rhEpo-induced enhancement of systemic V̇O2max or peak leg V̇O2. The mechanism highlighted by our data is that besides its strong influence on CaO2, rhEpo was found to enhance leg V̇O2max in normoxia through a preferential redistribution of cardiac output toward the exercising legs, whereas this advantageous effect disappeared during severe hypoxia, leaving augmented CaO2 alone insufficient for improving peak leg O2 delivery and V̇O2. Finally, that V̇O2max was largely dependent on CaO2 during moderate hypoxia but became abruptly CaO2-independent by slightly increasing the severity of hypoxia could be an indirect evidence of the appearance of central fatigue.Treatment with recombinant human erythropoietin (rhEpo) induces a rise in blood oxygen-carrying capacity (CaO Evidence of this is supported by numerous reports showing that prolonged rhEpo treatment induces a consistent increase in maximal oxygen uptake . Likewise, with altitude acclimatization, CaO2 and systemic oxygen delivery are restored close to sea-level values, however V̇O2max measured at high altitude is only barely ameliorated 22max is not as tightly dependent on CaO2 as in normoxic conditions, but is rather limited by the pressure gradient driving diffusion from the capillaries to the muscle mitochondria, due to the reduced arterial oxygen pressure (PaO2) 2 on the cardiovascular response to exercise have been carried out after prolonged exposure to hypoxia, where accelerated erythropoiesis and hypoxia coexist, it has not been possible to separate putative effects linked to altitude acclimatization from those elicited by the increase in CaO2.On the other hand, the ergogenic effect of rhEpo in a hypoxic environment is much less documented. While exercise capacity is consistently increased after rhEpo in normoxia, this is not the case with exercise in hypoxia, since neither rhEpo injections 2max evanishes. Determining this threshold may give further insights into the minimal level of hypoxia required to initiate some mechanisms counteracting the advantageous hematological adaptations conferred by rhEpo.The fact that recombinant Epo boosts maximal oxygen transport at sea level but not at high altitude implies the existence of an “altitude threshold” beyond which the ergogenic effect of rhEpo on V̇O2max vary with the severity of hypoxia. From the results obtained in response to this first question, our second objective was to gain insights into the mechanisms involved in the interactions between hypoxia, CaO2 and V̇O2max.The first objective of this study was to determine how does the rhEpo-induced rise in V̇O−1. The subjects were active in recreational sport activities such as jogging 1–2 times per week. During the entire study period, the subjects were asked not to deviate from their normal lifestyle, and this was well accepted by all. Written informed consent to participate in the study was obtained from all subjects. This study was carried out according to the Declaration of Helsinki and was approved by the Ethical Committee of Copenhagen and Frederiksberg Counties, Denmark. All the study took place at sea level in Copenhagen, Denmark.Eight Caucasian healthy male volunteers participated in the study. Their characteristics (mean±SD) were age: 27±7 yr, height: 180±4 cm, body mass: 83±7 kg, and maximal oxygen uptake: 4.1±0.2 l.min−1 orally, and this was maintained throughout the entire study period. The present rhEpo treatment is an exploratory model, which has been developed by the Copenhagen Muscle Research Centre both for physiological studies and also for doping testing purposes. Our rhEpo treatment, which aimed to mimic what could be a procedure of rhEpo misuse, was shown to increase red blood cell volume substantially (∼10%) while maintaining the hematocrit at around 50% (corresponding to the upper limit for entering some competitions) throughout the study period After baseline measurements, treatment with rhEpo  was started and lasted for fifteen weeks. On each occasion, 5000 IU was injected as follows. First two weeks: one injection every second day; third week: three injections on three consecutive days; week four to fifteen: one injection every week. All injections occurred between 08:00 and 10:00 a.m., and were preceded by 30 min of supine rest. Two weeks prior to rhEpo treatment all subjects received 100 mg iron.dayThe overall experiment design of the study is summarized on IO2 = 0.174), 2500 m (FIO2 = 0.153), 3500 m (FIO2 = 0.134) and 4500 m (FIO2 = 0.115). In this study, the altitude of 1500 m refers to “mild hypoxia”, the altitudes of 2500–3500 m to “moderate hypoxia” and the altitude of 4500 m to “severe hypoxia”. The order between oxygenation conditions was randomly balanced in a single-blind manner. Each subject completed the five exercise tests over a period of 3–4 consecutive days with a maximum of two incremental tests per day, and if so with a recovery period of at least 120 min between the two tests. During baseline measurements, the non-invasive experiment (five tests) took place one week before the invasive experiment (two tests). On the experimental days the subjects reported to the laboratory at 08:00 a.m., and a catheter was inserted into an arm vein for lactate determination. Arterialized blood was obtained from a capillary blood sample drawn from a pre-warmed finger tip, by using an electrically heated pad . Respiratory variables were measured continuously, as explained below.In order to gain insights into the altitude threshold beyond which the effect of rhEpo on maximal oxygen uptake becomes marginal, seven of the eight subjects (subjects #1 to #7) underwent a non-invasive experiment on two separate occasions, i.e. before and after five weeks of rhEpo treatment. On each occasion, the subjects performed an incremental cycling exercise until exhaustion in five conditions, i.e. in normoxia and in four levels of acute normobaric hypoxia equivalent to the altitudes of 1500 m . The cardiac impedance device used in the present experiment has been previously validated during exercise in healthy subjects Validity of cardiac impedance for cardiac output measurement. In order to evaluate the validity of our cardiac impedance method for cardiac output measurement during the non-invasive study, cardiac output during incremental exercise in the invasive study was measured simultaneously by the cardiac impedance technique and the cardio-green dye dilution method. The agreement between the two techniques was assessed by the method of Bland and Altman −1 with 95% confidence interval ranging between −4.83 and 4.01 l.min−1.IO2 = 0.115). As for the non-invasive experiment, here the altitude of 4500 m refers to “severe hypoxia”. The order between oxygenation conditions was randomly balanced in a single-blind manner and the recovery time between the two exercise bouts was 120 min. On the experimental days the subjects reported to the laboratory at 08:00 a.m., and catheters were placed under local anesthesia (2% Lidocain) In order to gain insights into the control of maximal oxygen transport in hypoxia following prolonged rhEpo treatment, seven of the eight subjects (subjects #1–4 and #6–8) underwent an invasive experiment on two separate occasions, i.e. before and after fourteen weeks of rhEpo treatment. On each occasion, the subjects performed an incremental cycling exercise until exhaustion in two conditions, i.e. in normoxia and in normobaric hypoxia equivalent to an altitude of 4500 m (Fmax) was calculated with the formula max = Wcompl+40×(t/90), where complW is the last workload completed, t is the number of seconds that the final, not-completed workload was sustained and 40 (watts) is the workload increment. During the invasive experiment, a blood sample was obtained simultaneously from the femoral vein and artery, followed by the measurement of leg blood flow, and cardiac output at the 6th and 12th minutes at 100 W, within the last 45 s at each workload and at maximal exercise, as close as possible to exhaustion. In this specific study, only the measurements at the 12th minute at 100 W and maximal exercise are reported. Also during normoxic trials the values at 260 W were given as an estimate of the corresponding peak workload attained in hypoxia (∼280 W). During the non-invasive experiment, a blood sample was obtained simultaneously from the forearm vein site and the capillary sampling site at the 12th minute at 100 W and at maximal exercise. During all exercise tests, strong verbal encouragement was given to the subjects. The subjects practiced the incremental exercise until exhaustion twice before performing baseline experiments.The experimental protocol consisted of an incremental exercise bout during which the subjects cycled with a cadence close to 80 revolutions per minute on an electrically-braked ergometer , first at 100 watts (W) during 15 min, then by increasing workload by 40 W every 90 seconds (s) until the subject could not sustain the pedaling pace. The maximal workload (W2 (PO2) to be set a pre-determined level of normobaric hypoxia, the precision of the PO2 being ±0.82 mmHg. At rest and during exercise the subjects breathed through a face mask connected to two-way respiratory valve. The inspiratory port of the three-way valve was connected by a low resistance corrugated tube to a three-way stopcock hidden from the subjects, delivering either room air or normobaric hypoxia. During all the experiments in hypoxia, the subjects breathed hypoxic mixture at rest for 10 min before exercising.Normobaric hypoxia was simulated by diluting ambient air with nitrogen via a mixing chamber, the dilution being continuously regulated via an oxygen-pressure probe . This device allows the inspired partial pressure of O2, CO2 production (V̇CO2), and expired minute ventilation (V̇E) were measured continuously using an automated metabolic cart . Before each test ambient conditions were measured, then gas analyzer and flowmeter were calibrated by using high precision gases and a 3-litre syringe, respectively. The ventilatory variables were recorded as averages of 15 seconds. The highest 15-second measurement of V̇O2 was taken as representative of V̇O2max. The reason for using this short interval is that leg blood flow, leg V̇O2 and blood pressures were also assessed during a similar time interval.Pulmonary V̇OFemoral venous blood flow was measured by constant-infusion thermodilution, as described in detail elsewhere Systemic vascular conductance was calculated as the cardiac output divided by the mean arterial pressure. Leg vascular conductance was calculated as the quotient between leg blood flow and the pressure difference between the femoral artery and the femoral vein. All pressures were referred to the fourth parasternal intercostal space.2 and O2 tensions, lactate, and potassium . Capillary hematocrit values obtained with the gas analyzer (ABL 700) were tested against the micro-centrifugation method during a separate procedure. A satisfactory agreement was found between the two methods (results not shown). Blood gases were corrected for measured femoral vein blood temperature . Plasma norepinephrine concentration was measured by ELISA . Blood O2 content (ml.l−1) in femoral artery and vein (CaO2 and CfvO2 respectively) was computed from the saturation and [Hb], i.e. (1.34×[Hb]×SO2)+(0.003×PO2). Systemic oxygen delivery was computed as the product of cardiac output and CaO2, while leg O2 delivery was obtained multiplying leg blood flow by CaO2. Leg V̇O2 was calculated using the direct Fick method, i.e. as the product of leg blood flow and arterial-venous difference in oxygen content. The systemic a-v O2 difference was calculated using the measured whole body V̇O2 and cardiac output (indirect Fick method). Leg lactate and potassium releases were calculated as the product of leg blood flow and the venous-arterial difference of lactate and potassium, respectively.Blood was sampled anaerobically in heparinized syringes (or in capillary tubes (Radiometer) for the non-invasive experiment, according to the manufacturer's instructions) and immediately analyzed for hemoglobin concentration ([Hb]), hematocrit, oxygen saturation, blood pH, CORed blood cell volume was determined by a carbon monoxide rebreathing method twice before rhEpo treatment, and after 5 and 13 weeks of rhEpo treatment. This dataset has been reported elsewhere t test adjusted for multiple comparisons with the Newman-Keul's procedure. Statistics were done with the Statview software, version 5.0. . The values are reported as arithmetic means±SE. Differences were considered as significant for P<0.05.The Kolmogorov-Smirnov's test was applied to examine the normality in the distribution of data. Due to the small number of participants in our study (n = 7), for each parameter, all the data that were obtained at a given power output (i.e. at 100 W or at peak exercise) were pooled before data distribution was examined (n = 28). The Bartlett's test was used to evaluate the uniformity of variance between conditions. If variance was not found to be homogeneous, ANOVA was replaced by the Welch's test. Once normality and variance homogeneity were verified, the differences in the measured variables among experimental conditions and exercise levels were analyzed using two-way ANOVA with repeated measures, with rhEpo and power output as within-subjects factors. Two separated ANOVA analyses were performed, one for testing the effect of rhEpo in normoxia and the other one for testing the effect of rhEpo in hypoxia. Another two-way ANOVA (rhEpo×hypoxia) was applied to the measured variables 1) at 100 W and 2) at peak exercise. When F was significant in the ANOVA, planned pair-wise specific comparisons were carried out using Student's paired 2 of 15.7% after 5 weeks and of 14.5% after 13 weeks of rhEpo treatment.The resting hematological changes observed among our rhEpo-treated subjects have been reported elsewhere 2max of 5.8% after 5 weeks  at the systemic level with an augmentation of pulmonary V̇O 5 weeks  and of 7 5 weeks , and 2) reatment .2max observed in normoxia, of 5.8%, did not decline with increasing levels of inspired hypoxia up to 3500 m (2max reached 14.0% at 2500 m (P = 0.09 vs normoxia) and 17.5% at 3500 m (P<0.05 vs normoxia)  . Althougondition .2max, of 1.9% after 5 weeks  following rhEpo treatment in spite of elevated CaO2 levels raises the question of the mechanisms involved. The results here below, obtained from our invasive study, provide some insights into these mechanisms.The fact that V̇O2  after rhEpo  and in hypoxia (P<0.05)  . Systemiexercise . Similarreatment .2 delivery increased after rhEpo in normoxia only  than during normoxia (∼6%) following rhEpo. The observation of a similar V̇O2max response at two consecutive levels of simulated altitudes (2500 m and 3500 m) further supports the plausibility of our observation. Our results indicate that in normoxia, the improvement in V̇O2max was smaller than the concomitant rise in O2 delivery (∼18%), while during moderate hypoxia (FIO2 = 0.134–0.153), the improvement in V̇O2max matched the increase in O2 delivery (∼15%). Whatever the mechanism underlying this tighter coupling might be, we suggest that O2 utilization responds more “efficiently” to higher O2 supply when the O2 transport system must cope with moderate arterial hypoxemia.A surprising observation was that the improvement in V̇O2max during severe acute hypoxia. This confirms recent data from our laboratory, showing no increase in V̇O2max at 4100 m following prolonged rhEpo treatment 2max at 4300 m after autologous blood re-infusion 2max is classically found barely changed with altitude acclimatization (>4000 m), in spite of a large improvement in CaO2or chronic hypoxia) V̇O2max appears to be independent of the enhancement of CaO2, the underlying mechanisms are different. Indeed, prolonged rhEpo treatment stimulates erythropoiesis without increasing hypoxia-inducible factor (HIF-1) levels, while during prolonged hypoxia HIF-1 accumulation triggers Epo gene activation therefore promoting erythropoiesis but also targets many other genes involved in various physiological responses, which may also have a negative effect on V̇O2max. In this regard it has been shown that transgenic mice lacking prolyl hydroxylases which otherwise always have HIF-1α downregulate aerobic potential while increasing glycolytic capacity The invasive and non-invasive experiments performed in this study show that rhEpo treatment had no positive effect on V̇O2max after acclimatization 2. Such cardiac output response was in line with other data showing no change in maximal cardiac output during acute hypoxia of similar severity (4000–4500 m) without CaO2 manipulation 2max in severe hypoxia, thus raising the question of the mechanism(s) abolishing the potential advantage conferred by high O2 transport capacity.One major difference between the present data and altitude-acclimatization studies relies on the responses of cardiac output and leg blood flow. With chronic exposure to hypobaric hypoxia (>4000 m), maximal cardiac output is blunted 2 delivery and leg V̇O2. Although this blood flow priority given to the active tissue implied a flow limitation into the other vascular beds, it is suggested that oxygenation in non-leg tissues - due to higher CaO2- was nevertheless sufficient to allow for vital organs such as heart, respiratory muscles or brain to ensure their homeostasis. Since at maximal exercise there is a competition between tissues for a limited amount of O2, a strong activation of the sympathetic system is required to avoid hypotension 2 demand and blood flow distribution One possible mechanism, supported by our experimental data, is related to blood flow regulation. In normoxia, rhEpo treatment allowed for a higher fraction of maximal cardiac output to be directed toward exercising muscles, resulting in higher peak leg O2 delivery and leg V̇O2 unchanged. It is worth noting that at that time, systemic fractional O2 extraction was reduced, unlike leg O2 delivery, therefore suggesting a limitation in O2 extraction into non-leg tissues. Although we could not establish any causal relationship between systemic O2 extraction and blood flow redistribution, we speculate that the lack of blood flow redistribution toward exercising muscles following rhEpo in hypoxia could be related to the low O2 extraction observed in non-leg tissues. Had blood flow decreased to non-leg tissue areas also in hypoxia, where O2 extraction was already diminished in spite of arterial hypoxemia, it is possible that O2 delivery/utilization in some organs  and hence local O2 homeostasis would have been compromised. Beyond the causes of this lower systemic O2 extraction, which remain unclear, our data showed an increase in mixed venous O2 content during hypoxic peak exercise, from 22 ml.l−1 before rhEpo to 45 ml.l−1 after rhEpo, suggesting higher central venous oxygenation levels. Pulmonary gas exchange was not altered by the increase in hemoglobin concentration elicited by rhEpo treatment, and hence, alveolar-to-arterial O2 difference and arterial O2 saturation were similar to those observed before rhEpo. In agreement with this finding isovolemic hemodilution at altitude did not alter pulmonary gas exchange in altitude-acclimatized subjects However, the distribution of blood flow during exercise in severe acute hypoxia was not altered by rhEpo, leaving peak leg Oversus systemic cardiovascular events that occurred during maximal exercise following rhEpo. Indeed, we found that blood pressure during normoxic maximal exercise increased after rhEpo. Since such response was abolished after isovolemic hemodilution Data on blood pressure and vascular conductance can also shed light on leg 2max in severe acute hypoxia are: 1) a redistribution of blood flow causing a lower peak leg blood flow following rhEpo  a lower systemic O2 extraction.In summary, our data indicate that the reason why rhEpo does not boost V̇OhEpo see  and 7, s2max following rhEpo evanished within 1000 m of altitude, i.e. that rhEpo was able blunt V̇O2max decrement efficiently during moderate hypoxia (FIO2 = 0.134) but failed to do so during slightly more severe hypoxia (FIO2 = 0.115). One previous study is highly relevant to the present one, because it showed a dominance of central fatigue over peripheral muscle fatigue in influencing exercise performance, only if arterial saturation was below 70–75% 2 of ∼78%, task failure in our subjects was likely more dependent on peripheral than central factors, thus enabling increased CaO2 to exert its maximal influence on V̇O2max. By contrast, during exposure to a higher altitude of 4500 m, SaO2 of ∼70% was likely low enough to trigger central nervous system (CNS) hypoxia and central fatigue in our subjects, therefore accounting – at least in part – for our results. Nevertheless, since this previous study 2 independently of PaO2 (as in the present one), we cannot draw a definitive conclusion.One major finding of the present study is that the gain in V̇O22max seen either after 5 or after 14 weeks of rhEpo treatment, both in normoxia and hypoxia (4500 m), suggests the attainment of a plateau in the V̇O2max response within the first month of treatment, as previously shown during 8 weeks of rhEpo administration The timing difference for the non-invasive study (5 weeks) and the invasive study (14 weeks) during the course of rhEpo administration see  raises t−1) and provided a clinically acceptable evaluation of cardiac output in healthy subjects during an incremental exercise Another point to be mentioned is the validity of the non-invasive impedance technique we used in the present study. We acknowledge that the measurement of cardiac output during strenuous exercise with the thoracic cardiac impedance method is a questionable technique 2max in normoxic conditions but not during severe hypoxia. What the present study adds to the established knowledge is that rhEpo efficiently blunted the decrement of V̇O2max up to the altitude of 3500 m, supporting the idea that CaO2 is a limiting factor of V̇O2max during moderate hypoxia. Furthermore, our study demonstrates that rhEpo-stimulating effect on V̇O2max was mediated not only by increased CaO2 but also by preferential blood flow redistribution toward exercising muscle tissue. We do not exclude the possibility that this advantageous blood flow redistribution also occurred at moderate altitude, therefore explaining why V̇O2max was substantially improved in this condition. That such cardiovascular changes did not take place during severe hypoxia would aim to protect some vital organs from deleterious hypoxemia.What was already known in the field is that prolonged rhEpo treatment boosts V̇O


Both approaches were successful. In particular, the investigation demonstrated the utility of IGA data for efficiently sequencing genomes from clinical samples containing as little as 3 % HCMV DNA. Analysis of the genome sequences obtained showed that each of the strains grown in cell culture was a mutant. Certain of the mutations were shared among strains from independent clinical sources, thus suggesting that they may have arisen in a common ancestor during natural infection. Moreover, one of the strains (JP) sequenced directly from a clinical specimen was mutated in two genes, one of which encodes a proposed immune-evasion function, viral interleukin-10. These observations imply that HCMV mutants exist in human infections.We have assessed two approaches to sequencing complete human cytomegalovirus (HCMV) genomes (236 kbp) in DNA extracted from infected cell cultures  or clinical specimens (strains JP and 3301). The first approach involved amplifying genomes from the DNA samples as overlapping PCR products, sequencing these by the Sanger method, acquiring reads from a capillary instrument and assembling these using the Staden programs. The second approach involved generating sequence data from the DNA samples by using an Illumina Genome Analyzer (IGA), processing the filtered reads by reference-independent ( Human herpesvirus 5) has the largest genome of any known human virus, at 236 kbp in size , the sizes of UL and US are 193 019 and 35 482 bp, respectively, and those of a/a′, b/b′ and c/c′ are 578, 746 and 1959 bp, respectively.Human cytomegalovirus  involved generating numerous, overlapping PCR products from HCMV genomes and analysing them by Sanger sequencing. This overcomes the presence of cellular DNA by amplifying HCMV sequences selectively. It amounts to an extension of a method that has been used by many researchers to analyse selected regions of HCMV genomes, in some studies to a substantial extent (de novo) assembly (IA) deduces long contiguous sequences (contigs) from the reads alone. DA is likely to cover a genome more comprehensively than IA, and in a manner that does not contain erroneously joined regions. On the other hand, IA, by not requiring a reference, is not biased by presumptions made about the target sequence.The use of an Illumina Genome Analyzer (IGA) formed the basis of the second approach (IGA sequencing). This high-throughput instrument generates such a large number of sequence reads that a virus genome can be sequenced even when diluted substantially by cellular DNA. Genome sequences may be assembled from IGA data by two strategies, each of which has advantages. Reference-dependent assembly (DA) involves aligning the reads with a reference sequence, whereas reference-independent (We used DA previously to sequence strain AD169 variant UC (varUC) and strain Towne varL from samples that contained high proportions of HCMV DNA  or clustered mismatches were remedied by utilizing an alignment of previously determined HCMV sequences as a guide to producing a further improved reference by reiterative local assembly. DA using this reference left four small gaps in the read consensus. One was located at the left end of UL (including the junction with the adjacent b inverted repeat), one in a (G+C)-rich region near the origin of DNA replication, and two in tandem, direct repeats of undetermined lengths (one a 3 nt reiteration and the other a 1 nt reiteration or homopolymeric tract). These gaps were resolved by PCR sequencing. At the conclusion of the assembly process, a total of 3 % of the reads aligned with the final sequence.The reference used for strain HAN13 was generated from the genome determined by PCR sequencing, and, as anticipated, needed no improvements . The reference used for strain 3301 was constructed by inserting strain 3301 sequences generated by PCR sequencing into a strain Merlin backbone. The strain 3301 PCR sequences had either been published previously -rich, quasirepetitive sequences at the origin of DNA replication and in the By comparison with sequence data available for other HCMV strains, the six strains sequenced in the present study appeared to contain the full complement of wild-type HCMV genes, with no extensive deletions. However, several apparent mutations were identified as differences from other strains that were predicted to ablate gene function  and two directly from clinical material (JP and 3301).Two approaches were assessed. The first (PCR sequencing) involved amplifying HCMV genomes as sets of overlapping PCR products, sequencing them by the Sanger method, acquiring reads from a standard capillary instrument and assembling data using the Staden programs. Although it was possible to benefit from efficiencies of scale by processing strains in parallel, this approach was labour-intensive. Nonetheless, PCR sequencing yielded complete sequences for strains 3157, JP and HAN13, without prior knowledge of the target sequences or additional input from other methods. The proportions of HCMV DNA in the 3157 and JP samples are not known, and that in the HAN13 sample was estimated at 27 % (Table 3The second approach (IGA sequencing) promised greater efficiency than PCR sequencing, and involved processing IGA data by the DA or IA strategies. We had previously used DA to sequence samples of strains AD169 varUC and Towne varL, containing high proportions of DNA  was disappointing, as it resulted in large numbers of small contigs  often mutate during adaptation of HCMV to fibroblast cell culture , and the numbers of virus genomes per cell were measured by standard methods at 44 417, 13 345 and 10 000, respectively , 1.6 M betaine (Sigma-Aldrich) and 1 μl DNA polymerase. The PCR program consisted of heating the reaction mixture at 96 °C for 5 min, followed by 35 cycles at 94 °C for 15 s, 62 °C for 40 s and 68 °C for 1 min kbp−1, and holding at 4 °C.PCRs were carried out under two sets of conditions, using Advantage 2 DNA polymerase with the proprietary PCR buffer (Clontech). Details of the conditions applied to specific PCR products are available from the corresponding author. The first set of conditions was used to generate the majority of products, and involved a 50 μl reaction in 1× PCR buffer containing sample DNA , 0.2 μM each PCR primer (Sigma-Genosys), dNTPs (0.2 mM each) and 1 μl DNA polymerase (both from Clontech). The PCR program was conducted in a GeneAmp PCR system 9700 instrument (Applied Biosystems), and consisted of heating the reaction mixture at 95 °C for 2 min, followed by 35 cycles at 95 °C for 45 s, 60 °C for 30 s and 68 °C for 1 min kbpThe PCR products were isolated by agarose gel electrophoresis and purified by using a GENECLEAN Turbo kit . Most PCR products were sequenced directly, but some were cloned into pGEM-T (Promega) and, for each product, four to six plasmids were sequenced.Sequencing reactions were set up in 96-well PCR trays using proprietary reagents (Applied Biosystems). The 10 μl reactions in 0.875× PCR buffer consisted of purified PCR product , 0.32 μM sequencing primer (Sigma-Genosys) and 0.5 μl BigDyes (Applied Biosystems) containing DNA polymerase. The thermal program consisted of heating at 96 °C for 4 min, followed by 25 cycles at 96 °C for 10 s, 50 °C for 5 s and 60 °C for 4 min, and holding at 4 °C. Sequence data were generated at the BHF Glasgow Cardiovascular Research Centre (University of Glasgow) using an Applied Biosystems 3730 instrument.Sequence databases were compiled from the electropherogram files by using Pregap4, Gap4  was submitted.Sequence datasets for strains HAN13, HAN20, HAN38 and 3301, each consisting of several million single-end reads with associated quality data, were generated from DNA samples using an IGA operated according to the manufacturer's protocols at the GenePool (University of Edinburgh). Methodological information is available at ac and ba) at the genome termini, except for 50 nt regions proximal to the beginning of UL and the end of US, in order to minimize ambiguities due to the inverted repeats. The reads were aligned with the reference using Maq-0.6.8, and the alignment of reads with the reference and read consensus was inspected by using Maqview-0.2.1 (http://maq.sourceforge.net). The reference was then improved by reiterative DA, employing the whole sequence or substrings thereof , until the final reference matched the read alignment. As a last step, the read consensus was extracted and aligned with the reference by using mafft-6.240  filtering the reads by a range of treatments, (ii) IA of each resulting dataset to generate contigs consisting of HCMV or cellular sequences, (iii) Phrap assembly of all of the IA contigs combined from the assemblies to generate Phrap contigs, (iv) Gap4 assembly of the Phrap contigs against an HCMV genome scaffold to generate HCMV Gap4 contigs, and (v) DA of the IGA data using a reference constructed from the HCMV Gap4 contigs. The stages of this process were enabled and connected by using Perl scripts, which are available from the corresponding author.(i) The reads in each IGA dataset were examined for singletons (unique reads), overall quality, and quality at each base. They were filtered by removing singletons, removing reads below a minimum overall quality, truncating reads at the point where perfect quality ceased  or truncating reads to a set length, or combinations of these steps. The filtering treatments used for each dataset were chosen empirically on the basis of the IA output, with a preference for those that reduced the number of reads by approximately 20–60 %. Details of the filtering treatments used are listed in Table 5http://www.ebi.ac.uk/∼zerbino/velvet). Velvet uses a de Bruijn graph method to represent the reads in terms of words of hash length k, which is a number smaller than the read length (50 nt). The default value used was k=21, but k=25, 27 or 29 occasionally yielded longer contigs. Details of the values of k used are listed in Table 5(ii) Each unfiltered or filtered dataset was assembled by using Velvet-0.7.31 (http://www.phrap.org) with default parameters.(iii) The pooled, non-redundant IA contigs were assembled into larger contigs by using Phrap-1.080812 ((iv) The Phrap contigs were aligned with an HCMV genome scaffold using Gap4, thus retaining only those contigs (HCMV Phrap contigs) that originated from the virus genome. Two alternative scaffolds were used in processes denoted IAC and IAG. For IAC, a cognate scaffold was fashioned (at least in part) from the sequence of the relevant strain determined by other methods. For IAG, a generic scaffold was constructed from the strain Merlin sequence. The HCMV Phrap contigs often exhibited clusters of sequence differences from the scaffold adjoining their ends, probably as a result of erroneous alignments that had terminated the IA contigs. These mismatched bases were removed. HCMV Phrap contigs that overlapped each other were then joined, yielding a set of HCMV Gap4 contigs.(v) A reference consisting of the HCMV Gap4 contigs with any gaps patched by parts of the cognate or generic scaffold was used for DA. The reference was then improved and the sequence finished by reiterative DA and PCR sequencing, as described above.


Using the Genetic Analysis Workshop 15 simulated rheumatoid arthritis data and the structural equation modeling framework, we tested hypothesized "causal" rheumatoid arthritis model(s) by employing a novel latent gene construct approach that models individual genes as latent variables defined by multiple dense and non-dense single-nucleotide polymorphisms (SNPs). Our approach produced valid latent gene constructs, particularly with dense SNPs, which when coupled with other factors involved in rheumatoid arthritis, were able to generate good fitting models by certain goodness of fit indices. We observed that Gene F, C, DR, sex and smoking were significant predictors of rheumatoid arthritis but Genes A and E were not, which was generally, but not entirely, consistent with how the data were simulated. Our approach holds promise in unravelling complex diseases and improves upon current "one SNP (haplotype)-at-a-time" regression approaches by decreasing the number of statistical tests while minimizing problems with multicolinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity, and resolve multiplicative latent gene interactions. Moreover, because some  Advances in the technology used to interrogate variation in the human genome  chip panels) are rapidly generating vast amounts of genotyping data. However, to maximize the use of this information in unravelling the etiology of complex diseases such as rheumatoid arthritis (RA), statistical approaches are needed that simultaneously model multiple genes and multiple SNPs within a gene in a hierarchical manner that reflects their underlying role in a biological system(s). We used the Genetic Analysis Workshop 15 (GAW15) simulated RA data and the structural equation modeling (SEM) statistical framework, which solves systems of linear and non-linear equations, to test the overall fit of hypothesized "causal" RA model(s) formally by employing a novel latent gene approach that models individual genes as latent (not directly measurable) variables defined by multiple SNPs.measurement model that develops the relationships between the observed variables (indicators) and the latent (unobserved) variables; and, 2) a structural model that develops the relationships between the latent variables. The general form of the measurement model is as follows [The statistical theory involved in SEM is extensive so we only present the general concepts and notation needed to follow this work. SEM comprises two general sub-models: 1) a wherex = q × 1 is a vector of observed indicators in the exogenous latent variables (ξ);y = p × 1 is a vector of observed indicators in the endogenous latent variables (η);ξ = n × 1 is a vector of latent exogenous (independent) latent random variables;η = m × 1 is a vector of latent endogenous (dependent) latent random variables;δ = q × 1 is a vector of measurement errors for x;ε = p × 1 is a vector measurement errors for y;Λx = q × n is a matrix of coefficients relating x to ξ; and,Λy = p × n is a matrix of coefficients relating y to η.structural model is as follows [The general form of the  follows :whereB = m × m is a matrix of path coefficients for latent endogenous variables (η);Γ = m × n is a matrix of path coefficients for latent exogenous variables (ξ); and,ζ = m × 1 is a vector that represents the errors or disturbances in η.xi and λyi values collectively as λi values or measurement model "loadings" and βj and γj values as βj values or structural model "path coefficients".To simplify notation, we refer to λΣ, would be exactly reproduced by the covariance matrix determined by the model parameters, Σ(θ) (Ho: Σ = Σ(θ)). Thus, covariance-based SEM aims to test "causal" model theory by minimizing the difference between the sample covariance matrix (S) and the covariance matrix defined by the model parameters (Σ(θ)) using a fitting function. Maximum likelihood (ML) estimation fitting functions (FML) such as the following, where p is the number of observed variables, are often used for global optimization but require the rigid assumptions of multivariate normality and independence of observations [In SEM, the null hypothesis assumes that if the conceptualized model were correct, the population covariance matrix of the observed variables, rvations :WLS) should be used to obtain unbiased estimates, standard errors and model tests [Thus, for models with non-normal variables, a weighted least squares (WLS) fitting function  is corresponding vector from same-order implied matrix Σ(θ); and,θ is the t × 1 vector of free parameters.Variations to these fitting functions have been devised, including the robust weighted least-squares estimator (WLSMV), which allows for estimation of binary and categorical dependent variables ,3, and an1 = 1500) and including all unrelated controls (n2 = 2000). The SNPs were coded assuming an additive genetic model . Gender and smoking were dichotomous . Because IgM and anti-CCP (anti-cyclic citrinullated protein) values were only provided for cases, we arbitrarily set the values of these variables to zero for controls. Using the location of simulated risk loci provided in the answer key, we selected genotyped SNPs from the 10 K and chromosome 6 dense SNP chip panels upstream, downstream, and directly at (when available) the known location of each locus to build latent constructs for each gene. Because we did not explicitly know which SNPs were representative of each simulated locus, we also examined the linkage disequilibrium (LD) structure (Haploview v3.2) between the selected SNPs and performed factor analysis (FA) using SAS v8.2  to help devise viable gene constructs. FA was performed independent of disease status (with only the SNP data) and was used to generate eigenvalues, inspect scree plots and factor patterns, and determine the proportion of average variance explained (AVE). AVE is an indicator of the communality or validity of the construct [First, we randomly selected a training (Replicate 64) and a validation (Replicate 46) data set from the 100 replicates simulated. In an attempt to satisfy the requirement for independence of observations, the data files were reconstructed by randomly selecting one case from each affected sib pair n1 = 100 and inWe then built the full model(s) by constructing measurement and structural model equations using the "causal" RA model information provided in the answer key. However, to obtain scale determinancy and model identification, one of the loadings  must to be fixed to 1.0. When we were able to locate a SNP in the exact physical location of the simulated locus, we fixed that SNP's loading to 1.0. When the simulated locus fell between two SNPs, we arbitrarily selected one of them. We analyzed the models using the WLSMV estimator in Mplus v4.1  and evaluated the overall fit.The chi-square test evaluates whether the specified model is significantly different from the alternative model, which assumes the data are from a multivariate normal distribution with an unconstrained covariance matrix. However, it is affected by departures from normality and sample size . Therefore, when categorical or non-normal dependent variables are modeled, a modified chi-square test  or otherEven though we did not have explicit knowledge of which SNPs corresponded to each of the simulated genes, we were generally able to construct viable latent variables by using SNPs upstream, downstream, or directly at (where available) the physical location of each locus. Using eigenvalue (>1.0) and proportion of variance explained AVE > 0.50) criteria [0 criterip ≤ 0.05) and the inverse nature of this association may reflect the increased risk simulated with the wild-type "C" allele. Gene C was also highly correlated with DR (ρ = 0.895 ± 0.031), which was expected given that Locus C was simulated to be in complete LD with DR (D' = 1.0). We also found a strong positive path coefficient between Gene F and RA , which was expected because Locus F was simulated to confer risk from IgM on RA. However, the path coefficient between Gene D, which was simulated to be in "weak" LD with DR, and RA (β = 0.024 ± 0.027) was trivial in our model. We hypothesize the very low "D" risk allele frequency simulated contributed to this discrepancy. In addition, although the effects of DR were simulated to be controlled by Locus A, the path between Gene A and DR was negligible (β = 0.001 ± 0.022). This, however, may have been driven by our inability to devise a good construct for Locus A. When we added IgM (and/or anti-CCP), we did not obtain convergence, which was likely because of the skewed, edge effect distribution from assigning 0 values to controls.We analyzed a "full" model with all genes , gender and smoking as covariates, and RA as a dichotomous outcome Fig.  and obtaWe also evaluated RA severity as an ordinal outcome  using all of the variables in the "full" model described above and Gene G and Gene H, which were simulated to induce RA severity. This was not a good fitting model by any index evaluated . Although the loadings and path coefficients of the Gene A, B, C, D, and F constructs were similar, the path coefficients between Gene G (β = -0.010 ± 0.031) and Gene H (β = -0.005 ± 0.130) and RA severity were surprisingly low, with high standard errors. Using just one of the RA severity genes (Gene G or H) did not materially alter the fit indices but removing both Gene G and H resulted in a model with a much better fit that was similar to that found with RA as a dichotomous outcome . These results suggest neither Gene G nor H (as described by our SNP selection) affected RA severity, which appears inconsistent with how the data were simulated.D' = 1.0) and complete linkage (recombination fraction = 0) simulated between locus C and DR. However, when Gene C was used in lieu of DR in a model also containing Gene F, sex, smoking, and their paths on DR, the model did not fit as well , suggesting DR was a better predictor of RA than Gene C.We also examined subsets of the "full" model. In a "simpler" model including only latent gene constructs C and F, DR, sex and smoking , the fit indices were poorer  and the path coefficients between Gene C and RA (β = 0.656 ± 0.015) and between Gene F (β = 0.324 ± 0.039) and RA were inflated by 7.71% and 18.25%, respectively, compared to the "full" model. The measurement model loadings for the Gene C and Gene F constructs, however, were not very sensitive to removal of the other gene constructs. The majority of other simpler models also resulted in poorer model fit and inflated path coefficients compared to the "full" model except for one that included only Gene C, Gene F, DR, sex, smoking and their paths on RA, which resulted in a good fitting model by CFI (0.96) and RMSEA (0.05) standards as well as a better fit by WRMR standards (2.45). We surmised the improvement occurred because the genes removed were not adding substantive information  and because modeling Gene C and DR together made it difficult to define parameters representative of the data due to the complete LD  and WRMR (6.70) indices were similar but the CFI (0.92) was slightly lower in the validation data set. The most notable change in path coefficients was between Gene C and RA, which was slightly larger .Finally, we evaluated the "full" model depicted in Figure Although we only had knowledge of the physical locations of the simulated loci and not biologically plausible SNP sets, we obtained valid latent gene constructs using dense SNPs, which appeared to perform well in full models , we observed several models that had acceptable fit according to CFI and/or RMSEA but not WRMR criteria. CFI has been found to perform better than RMSEA, with a CFI value close to 0.96 providing acceptable rejection rates across models including those with binary outcomes such as RA when the sample size is ≥250 [a priori knowledge is needed, our approach may be best for candidate gene SNP panel data.We conclude that our latent gene approach holds promise in unravelling complex diseases such as RA and improves upon current "one-SNP(haplotype)-at-a-time" regression approaches by decreasing the number of statistical tests while minimizing problems with multi-colinearity and haplotype estimation algorithm error. Furthermore, when genes are modeled as latent constructs simultaneously with other key cofactors, the approach provides for enhanced control of confounding that should lead to less biased effect estimates among genes as well as between gene(s) and the complex disease. However, further study is needed to quantify bias, evaluate fit index disparity and resolve multiplicative latent gene interactions. Because some The author(s) declare that they have no competing interests.


This methodology can explore the full conformational space of a protein at the single-molecule level, detecting even poorly populated conformers and measuring their distribution in a variety of biologically important conditions. To the best of our knowledge, we present for the first time evidence of a conformational equilibrium that controls the population of a specific class of monomeric αSyn conformers, positively correlated with conditions known to promote the formation of aggregates. A new tool is thus made available to test directly the influence of mutations and pharmacological strategies on the conformational equilibrium of monomeric αSyn.Human α-Synuclein (αSyn) is a natively unfolded protein whose aggregation into amyloid fibrils is involved in the pathology of Parkinson disease. A full comprehension of the structure and dynamics of early intermediates leading to the aggregated states is an unsolved problem of essential importance to researchers attempting to decipher the molecular mechanisms of αSyn aggregation and formation of fibrils. Traditional bulk techniques used so far to solve this problem point to a direct correlation between αSyn's unique conformational properties and its propensity to aggregate, but these techniques can only provide ensemble-averaged information for monomers and oligomers alike. They therefore cannot characterize the full complexity of the conformational equilibria that trigger the aggregation process. We applied atomic force microscopy–based single-molecule mechanical unfolding methodology to study the conformational equilibrium of human wild-type and mutant αSyn. The conformational heterogeneity of monomeric αSyn was characterized at the single-molecule level. Three main classes of conformations, including disordered and “β-like” structures, were directly observed and quantified without any interference from oligomeric soluble forms. The relative abundance of the “β-like” structures significantly increased in different conditions promoting the aggregation of αSyn: the presence of Cu Natively unstructured proteins defy the classical “one sequence–one structure” paradigm of protein science. In pathological conditions, monomers of these proteins can aggregate in the cell, a process that underlies neurodegenerative diseases such as Alzheimer and Parkinson. A key step in the aggregation process—the formation of misfolded intermediates—remains obscure. To shed light on this process, we characterized the folding and conformational diversity of αSyn, a natively unstructured protein involved in Parkinson disease, by mechanically stretching single molecules of this protein and recording their mechanical properties. These experiments permitted us to observe directly and quantify three main classes of conformations that, under in vitro physiological conditions, exist simultaneously in the αSyn sample. We found that one class of conformations, “β-like” structures, is directly related to αSyn aggregation. In fact, their relative abundance increases drastically in three different conditions known to promote the formation of αSyn fibrils. We expect that a critical concentration of αSyn with a “β-like” structure must be reached to trigger fibril formation. This critical concentration is therefore controlled by a chemical equilibrium. Novel pharmacological strategies can now be tailored to act upstream, before the aggregation process ensues, by targeting this equilibrium. To this end, single-molecule force spectroscopy can be an effective tool to tailor and test new pharmacological agents. A single-molecule study detects structured and unstructured conformers in equilibrium in monomeric α-synuclein. The β-like conformers increase with pathological mutations and under other conditions known to promote aggregation. A significant fraction (possibly as much as 30%) of proteins and segments of proteins in eukaryotic proteomes has been found to lack, at least partially, a well-defined three-dimensional structure. Proteins belonging to this class are usually called natively unfolded proteins (NUPs) . NUPs haTheoretical studies depict the apparent structural disorder of NUPs as the result of the coexistence of a complex ensemble of conformers ensuing from a rugged energy landscape . Five clWe decided to approach the problem of the characterization of the conformers of α-synuclein (αSyn), which is a prototype of this class of proteins. αSyn is a 140–amino acid (aa) protein expressed primarily at the presynaptic terminals in the central nervous system, and it is thought to be physiologically involved in endoplasmic reticulum–Golgi vesicle trafficking . αSyn isThe fibrils spontaneously formed by αSyn by a nucleation-dependent mechanism are rich in β structure ,24. The 2+ and Al3+ Fluorescence emission spectra were recorded on a Perkin-Elmer LS 50 spectrofluorimeter equipped with a thermostated cell compartment and interfaced with a personal computer using the FL-WinLab program for Windows. Sample measurements were carried out using a HELLMA ultra-micro cell with Suprasil windows and an optical path length of 10 × 2 mm. Fluorescence spectra were obtained at 25 °C using an excitation wavelength of 288 nm, with an excitation bandwidth of 4 nm and emission bandwidth of 4 nm. Emission spectra were recorded between 290–380 nm at a scan rate of 60 nm/min.Due to the well-known structuring effects of divalent metal ions on αSyn , an accu−5 Torr. Before deposition, the mica was preheated to 350 °C by a heating stage mounted behind the mica to enhance the formation of terraced Au(111) domains. The typical evaporation rate was 3 Å/s, and the thickness of the gold films ranged around 300 nm. The mica temperature was maintained at 350 °C for 2 h after deposition for annealing. This method produced samples with flat Au(111) terraces. These films were fixed to a glass substrate with an epoxy . They were then separated at the gold–mica interface by peeling immediately before functionalization with the desired molecules. This procedure produced gold substrates with a flat surface morphology due to the templating effect of the atomically flat mica surface [Gold  was deposited onto freshly cleaved mica substrates  in a high-vacuum evaporator  at 10 surface ,69.2 experiments, the protein was deposited in a drop with the addition of a final concentration of 1 μM CuCl2 and left on the surface for about 20 min, and the experiments were carried out in 10 mM Tris/HCl with 1 μM CuCl2. Control experiments in DTT were made in 50 mM DTT Tris/HCl buffer.For each experiment, a 20 μl drop of 3S3 construct solution (160 μg/ml) was deposited on the freshly peeled gold surface for about 20 min. SMFS experiments were performed using a commercially available AFM system: Picoforce AFM with Nanoscope IIIa controller  using V-shaped silicon nitride cantilevers  with a spring constant calibrated by the thermal noise method . The pulHooke, a Python-based home coded force spectroscopy data analysis program . Force curves were analyzed fitting each peak with a simple WLC force versus extension model [L and the persistence length p (Equation 1). The I27 modules were characterized in terms of the length of the polypeptide chain extended after each unfolding event.The force curves were analyzed using the commercially available software from Digital Instrument (Nanoscope v6.12r2), custom Origin scripts and on model  with twop < 0.01.To assess the statistical validity of the comparison between data obtained in 10 mM Tris/HCl buffer and those obtained in other conditions, standard chi square tests were performed. The differences between the 10 mM Tris data set and the other data sets are significant, with Protocol S1Characterization of the rupture events attributed to mechanically weak interaction and an electrostatic model that explains the observed mechanically weak interactions are given.(4.9 MB DOC)Click here for additional data file.http://www.ebi.ac.uk/trembl/index.html) accession number for α-synuclein (αSyn) is P37840 (SYUA_HUMAN). The Protein Data Bank (http://www.pdb.org) entry for the I27 domains included in the polyprotein is 1tit. The Online Mendelian Inheritance in Man (OMIM) reference number for familial Parkinsonism is 163890.0002.The UniProt KB (


Acting as motors, pumps, switches, catalysts, and in numerous other roles, proteins perform nearly all the functions of a cell. This staggering functional diversity arises from linking just 20 amino acids in seemingly endless permutations to form polypeptide chains. The sequence of amino acids in the polypeptide chain determines a protein's precise 3-D structure and biological properties. Cells have evolved multiple mechanisms to ensure proper folding, but a number of molecular and biophysical events—such as changes in pH or temperature, mutations, and oxidation—can disrupt a protein's native shape.When polypeptides fail to achieve or maintain their proper conformation, they commonly aggregate into abnormal “amyloid” structures, which often appear as long, straight, unbranched threads—or fibrils—consisting of supercoiled “protofilaments.” Amyloid fibrils define a diverse group of degenerative conditions, including amyotrophic lateral sclerosis, prion diseases, and Alzheimer and Parkinson diseases. In Alzheimer disease, the amyloid fibrils are deposited extracellularly; however, in Parkinson and Huntington disease, similar amyloid fibrils accumulate in the cytoplasm and nucleus of the cell respectively. How amyloid formation promotes disease has generated considerable debate, though mounting evidence implicates the early protofibrillar aggregates as the toxic species.Drosophila to identify the intrinsic determinants of amyloid β (Aβ) pathogenicity in an animal model of Alzheimer disease. (Aβ peptide is a primary component of amyloid plaques in the brains of patients with Alzheimer disease.) Determining how amyloid formation causes disease requires a better understanding of the molecular and biophysical conditions that promote protein aggregation. But such an understanding has proven technically challenging, in part because protein misfolding and aggregation in test tubes can't replicate cellular pathways designed to mitigate the toxic effects of these events. Luheshi et al. circumvented this problem by integrating computational predictions of protein aggregation propensities with in vitro experiments to test the predictions and in vivo mutagenesis experiments to link predicted aggregation propensity with observed neurodegeneration in the flies.In a new study, Leila Luheshi et al. worked with the fruit fly 39, Aβ40, and so on. Expression of Aβ42 in the Drosophila central nervous system produces neuron-damaging deposits and leads to abnormal locomotor behavior and shortened life span, as well as learning and memory problems. The researchers computed the aggregation propensities for each of Aβ42's 798 possible single amino acid substitutions—based on the physical and chemical properties of the amino acids—along with those of the pathogenic variant E22G Aβ42, which is associated with the inherited form of Alzheimer disease.Aβ peptides have between 39 and 43 amino acid sequences , indicated as AβDrosophila central nervous system, and then measured the variants' effects on locomotion and longevity. Fly lines expressing the form of the Aβ peptide that has a low aggregation propensity and is associated with health in humans (Aβ40) had a life span that was equivalent to healthy flies. Those expressing the nonmutant (wild-type) Aβ42 peptide, which deposits rapidly in the human brain, died sooner; but worse still were the flies expressing the highly aggregatory E22G peptide. A few variant lines (F20E and 131E/E22G) showed better survival and mobility than the wild-type flies (as determined by the number of flies reaching the top of a pipette after 45 seconds), while other variants shortened the flies' life spans.They identified 17 Aβ mutational variants with wide-ranging aggregation propensities, expressed them in the Overall, the researchers found a clear correlation between a variant's predicted tendency to aggregate and its influence on fly longevity. The same relationship was seen between predicted aggregation propensity and locomotion (for a representative sample of variant lines), though a few variants did not follow this pattern. To understand why, the researchers ran additional experiments on two peptides—one whose neuronal effects matched its predicted aggregation propensity (F20E) and one whose didn't (131E/E22G).42 peptide's tendency to aggregate. The F20E Aβ42 peptide aggregated much more slowly than the wild-type Aβ42 did in test tubes; and where the variant left no deposits in fly brains after 20 days, the wild-type peptide left extensive deposits. Since the F20E peptide could form amyloid fibrils in the test tube (even though it did so slowly) but didn't leave deposits in the brain, the researchers concluded that the F20E mutation inhibited aggregation enough for cells to dispose of the aberrant peptide—thus accounting for the flies' enhanced survival and behavior.As predicted, F20E reduced the AβThe 131E/E22G peptide, however, showed no correlation between predicted aggregation propensity—expected to resemble that of the pathogenic E22G peptide—and its increased survival and mobility. Nonetheless, the peptide aggregated at rates similar to the Alzheimer variant in vitro as well as in the fly brains. But because the 131E/E22G peptide deposits were not accompanied by cavities in brain tissue—a telltale sign of neurodegeneration—the flies showed no neurological deficits.This finding fits with reports that the density of Aβ plaques in elderly patients with Alzheimer disease does not correlate with the severity of clinical symptoms—and that the soluble protofibrillar aggregates, not the mature amyloid plaques, cause neurodegeneration. Recomputing the propensities of each Aβ variant to form these protofibrillar species revealed not only an improved overall correlation with toxicity but it also brought the previously anomalous 131E/E22G variant in line with the prediction algorithm.Altogether, these results show that Aβ's toxic effects in a living organism can be predicted based on a computational analysis of its tendency to form protofibrillar aggregates. And even though cells have evolved multiple mechanisms to regulate folding, the researchers argue, it is the intrinsic tendency of the peptide's sequence to aggregate that governs its pathological propensity. Though the researchers focused on the peptide most closely associated with Alzheimer disease, they believe their approach will work for many other diseases as well.


The recommended schedule for this regimen for the phase II study was EpiDX 75 mg m−2, P 175 mg m−2, etoposide (E) 100 mg m−2 intravenous (fixed dose) days 1–3 with courses repeated every 21 days. The prophylactic use of colony-stimulating factors (CSFs) was not allowed. Twenty patients entered the phase II trial: median age was 61 years (range 50–70), median Eastern Cooperative Oncology Group performance status 0 (0–2); nine patients had visceral disease and 17 had more than two metastatic sites. A total of 100 courses were administered with a median of 5 (range 1–6) per patients. Main toxicity (NCI-CTC) was myelosuppression: neutropenia grades 3 and 4 in 16 and 35% of the courses, respectively. Seven episodes of febrile neutropenia were documented and one patient required hospital admission. Nonhaematological toxicity was moderate. Seven out of 19 evaluable patients achieved a complete response (37%), nine patients (47.3%) a partial response with an overall response rate of 84.2% ). In this poor prognostic population of patients the triplet epirubicin/paclitaxel/etoposide showed high antitumour activity with mild nonhaematological side effects. The use of CSFs should be able to improve the haematological profile.The aim of this study was to evaluate feasibility and toxicity of escalating doses of epirubicin and paclitaxel plus fixed dose of etoposide and to define the activity of the triplet in extensive disease small-cell lung cancer. Thirteen patients entered the phase I study: the maximum tolerated doses were epirubicin (EpiDX) 90 mg m Small-cell lung cancer (SCLC) accounts for about 20% of primary lung cancers and approximately 60% of SCLC patients have extensive disease (ED) at diagnosis  and cyclophosphamide, doxorubicin, etoposide (CDE). Cisplatin/etoposide (PE) is the reference regimen for the treatment of SCLC in the United States, while CDE represented one of the reference regimens in European countries.et al compared a 1-day intravenous (i.v.) injection of etoposide (500 mg m−2) to the same dose fractionated over 5 days, every 3 weeks; response rates and survival significantly favoured the 5-day schedule  of paclitaxel and epirubicin in combination with a fixed dose of i.v. etoposide as first-line therapy in ED–SCLC, and to evaluate the toxicity and activity of the triplet, particularly in terms of complete response.μl−1, haemoglobin ⩾10 g dl−1, platelet count ⩾100 000 μl−1), hepatic  and renal  functions were required. Left ventricular ejection fraction (LVEF) >50% at bidimensional ultrasonography or MUGA scan was mandatory. Patients with brain metastases were eligible. Patients were excluded if they had a positive history of any other type of cancer, with the exception of radically resected in situ cervical cancer and nonmelanoma skin cancer, congestive heart failure or other serious medical or psychiatric illnesses. Written informed consent was obtained from each patient according to local institution policies. The protocol was approved by the ethical committees of participating institutions and was conducted according to Good Clinical Practice guidelines and the Helsinki declaration of the World Medical Association. The study was supported by Bristol-Myers Italy.The phase I–II study was carried out in chemotherapy-naïve patients with histologically proven ED–SCLC, ECOG PS ⩽2 and aged 70 years or younger. Adequate pretreatment haematological   and repo−2 (as a 3-h infusion) on Day 1, epirubicin 60 mg m−2 (as a slow bolus injection immediately before paclitaxel) on day 1, etoposide 100 mg m−2 i.v. (1-h infusion immediately after Paclitaxel) on days 1–3. Premedication for paclitaxel consisted of 20 mg desamethasone by i.v. infusion 30 min before treatment; orphenadrine 40 mg i.m. plus i.v. cimetidine 300 mg 1 h before the start of treatment. The doses of epirubicin and paclitaxel were escalated in consecutive triplets of patients until dose-limiting toxicity (Patients entered the phase I part of this study in triplets. Starting doses were paclitaxel 155 mg mtoxicity . No intrμl−1 lasting more than 7 days or ANC⩽100 μl−1 for 3 days (72 h); febrile neutropenia; any grade ⩾3 (NCI-CTC) nonhaematological toxicity except for alopecia. When a DLT was observed in one out of three patients, three more patients were added at the same dose level. If an additional patient experienced a DLT, no further dose escalation was allowed and that dose level was considered as the MTD. The dose level immediately below MTD was the recommended dose for the phase II study.Dose-limiting toxicities (DLT) were defined as the occurrence of one of the following: absolute neutrophil count (ANC) ⩽500 μl. The prophylactic use of colony-stimulating factors (CSFs) was not allowed but suggested in case of febrile neutropenia. Patients were admitted to the hospital when febrile neutropenia lasted more than 72 h despite the use of adequate orally administered antibiotics, antimicotics and CSFs.In the case of grade 4 neutropenia ciprofloxacin 500 mg were administered orally twice a day and fluconazole 100 mg daily until ANC>1000 α error of 0.05 and a β error of 0.10, P0  and P1 (sufficiently promising true complete response rate) were set at 10 and 30%, respectively. In the first step, 18 patients had to be included: if ⩽2 complete responses were obtained, accrual was stopped, otherwise 18 more patients had to be registered. Drug combination was considered of interest if ⩾7 complete responses were observed out of 36 evaluable patients. Time to progression (TTP) was calculated from the date of registration to the date of clinical and/or radiological evidence of progression or death, whichever occurred first. Survival was calculated from registration to death or last follow-up. Survival and TTP were estimated using the Kaplan–Meier method , median ECOG PS 0 (range 0–2); the majority of patients had 2 or more metastatic sites. Demographics are summarized in −2; P, 175 mg m−2) since two patients experienced febrile neutropenia . Therefore, the recommended doses of the two drugs to be used in the phase II study were those of level B .Thirteen patients were registered: 18 courses were administered at dose level A (three patients), 18 at dose level B (three patients) and 32 at level C (seven patients); median number of courses administered per patient 6 (range 1–6). The major toxicity associated with this regimen was myelosuppression: a grade 4 neutropenia was observed in 11% of the courses in group B and in 47% of the courses in group C; no grade 4 neutropenia was observed in group A. No grade 4 thrombocytopenia or anaemia were observed. The MTD was found at dose level C  and no significant modification was observed after the sixth course of treatment in the whole patients' population.Even if activity was not the end point of the phase I part of the study, considering the fact that all patients enrolled were chemotherapy naïve, responses were assessed, with the same criteria of phase II, every three courses of treatment. Twelve out of 13 patients were evaluable (one patient in group C was lost to follow-up after the first course of therapy). Six patients (50%) showed a complete response (two patients from group B and four patients from group C) and six patients showed a partial response; the best response was obtained after three courses of treatment.A total of 100 courses were administered with a median of 5 per patient (range 1–6). Myelosuppression, in particular neutropenia, was the most common toxicity ; grade 3−2 (72% of the cycles). No prophylactic GCSFs were administered. Other haematological toxicities were grade 3 thrombocytopenia in 2% and grade 4 in 1% of the courses; only one episode of grade 4 anaemia was reported. There were no treatment-related deaths. One patient received platelets and three patients received blood cell transfusion.Seven episodes of febrile neutropenia were documented (30% of the patients) and one patient required hospital admission for parenteral antibiotic treatment. Patients having febrile neutropenia or grade 4 neutropenia  continued the treatment with paclitaxel reduced at 155 mg mNonhaematological toxicities were generally mild to moderate: only one episode of grade 3 diarrhoea was reported; grades 1 and 2 stomatitis occurred in two and three patients, respectively, grade 2 asthenia occurred in five patients and grade 2 vomiting in two patients. No clinically relevant neurotoxicity, cardiac dysfunction or hypersensitivity reactions were observed.In the first step six out of 18 evaluable patients obtained a complete response: the study proceeded to the second step in which one additional complete response was achieved =16.3–61.4)) According to the statistical design, accrual was stopped at this point  as we had already obtained the figures we needed. Nine (47.3%) partial responses were achieved with an overall response rate of 84.2% ((95% CI=60.4–96.6)). Three patients progressed during chemotherapy. One patient was not evaluable for response (interruption of treatment after the first course because of arrhythmia). Five out of 20 patients had brain metastases; after three courses of chemotherapy four patients obtained an objective response of brain metastases , one patient progressed during chemotherapy.Median time to progression was 6 months and median overall survival was 13 months.The treatment of ED-SCLC must be considered as palliative for the majority of individuals; although chemotherapy is able to achieve high remission rate and to improve tumour-related symptoms, very few patients may be cured with standard combination regimens . However, response to treatment, in particular, complete response, has been suggested as the major indicator of long-term survival   obtained by substituting cyclophosphamide with paclitaxel in a standard CDE regimen  often used in our Institution. The phase I study indicated the optimal doses of the three drugs to be combined and used in the subsequent phase II study; this was performed in different institutions in order to verify the applicability of the regimen in everyday practice. The primary objective of the study was the percentage of complete response rate considering its potential prognostic significance.The antitumour activity achieved with our combination was very high: the overall response rate was 84.2% (95% CI=60.4–96.6%) and the 37% (95% CI=16.3–61.4) complete response rate was really promising. It is worth noting that these percentages were obtained in patients with unfavourable prognostic features: 85% of our patients had two or more sites of metastases in addition to the primary tumour. As expected the most frequent toxic effect was myelosuppression with grade 3–4 neutropenia recorded in 51% of the courses and seven episodes of febrile neutropenia. Toxicity data seemed to be comparable with those reported with standard CDE combination  and slightly higher than those reported for the PE regimen  (Other haematological and nonhaematological toxicities, in our study, were mild to moderate and clinically irrelevant.vs a standard PE regimen in a large randomised phase III trial.In conclusion, our regimen seemed to be feasible and very active for the treatment of ED-SCLC: the multicentre nature of the present trial, in which nonspecialised hospitals have also been involved, and its feasibility in an outpatient setting, make this regimen worthy of future comparative trials. The introduction of prophylactic G-CSF could further improve the haematological toxicity profile of our regimen allowing an easier comparison


Eberwine(-like) amplification of mRNA adds distinct 6–10 bp nucleotide stretches to the 5′ end of amplified RNA transcripts. Analysis of over six thousand microarrays reveals that probes containing motifs complementary to these stretches are associated with aberrantly high signals up to a hundred fold the signal observed in unaffected probes. This is not observed when total RNA is used as target source. Different T7 primer sequences are used in different laboratories and platforms and consequently different T7 primer bias is observed in different datasets. This will hamper efforts to compare data sets across platforms. Amplification of messenger RNA is widely accepted as the method of choice to acquire sufficient copies of mRNA transcripts to enable microarray expression analysis of experimental samples Many versions of original “Eberwine” T7-primer sequence have been reported over the last decade We set out to investigate the consequence of the incorporation of the 3′ spacer sequence since in the final step in the amplification procedure it is transcribed by T7 RNA polymerase. As a consequence it becomes a leader sequence in all copies of amplified RNA . The 5′ Statistics tests were run using the R-project software package To study the effect of the presence of motifs in a probe sequence we analyzed three data sources all using amplified RNA  and one data source using total RNA (IV):“Operon (a)” The compendium of experiments present in the database of the Central Microarray Facility of the Netherlands Cancer Institute (data table can be found in http://microarrays.nki.nl/research/T7/) comprising a total of 6657 human full genome Operon (version 3.0) oligonucleotide microarrays (http://microarrays.nki.nl/services/oligo.html).“Agilent (a)” Data obtained from a set of 295 human tumors analysed on 25 K oligonucleotide microarrays from Agilent/Rosetta “Affymetrix”, “Agilent (b)”, “Illumina” and “Operon (b)” Datasets studied by the Microarray Quality Control project “Operon (c)” and “Operon (d)” Studies on the Operon V3.0 oligonucleotide platform (from the NCI and NMC respectively) that were done without RNA amplification as described in the Microarray Quality Control project (primer 1), also described as the “LinAmp1 primer” (primer 2) - with partly disclosed sequence  - is very similar  to the Affymetrix T7 primer. With respect to the 3′ spacer sequence primers (1) and (2) are identical. The Ambion primer (primer 3) having a 10 bp 3′spacer sequence that is different from the first two primers . And finally the Agilent primer (primer 4) having a 6 bp long 3′ spacer sequence that is fully overlapping with the Ambion primer.We set out to collect T7 primer sequences used in the field for which datasets are publicly available for analyses. We limited the study to the following T7-primers and looked in detail to their 3′spacer sequences relative to the original Eberwine primer sequence  : The Af5′GGGAGGCGG 3′, whereas the 3′ spacer of primer 3 is transcribed as a 10-mer sequence 5′GGGAGAAGAG 3′. Primer 4 introduces the short 6-mer leader sequence 5′GGGAGA 3′ into all copies of amplified RNA ” this is fraction has increased slightly to 1.6%. For “Operon” we find that 8.5% (2966 sequences) of the probes contain either the full 9-mer primer 1 motif or a 6-mer fragment of it. For the “Affymetrix” platform we find that 2,5% (3076 sequences) of all probe sequences and 37% of the 8204 probe sets contain a primer 1 motif sequence. On the Illumina platform 2% of the probe sequences contain the terminal 6-mer sequence TCTCCC from the primer 3 motif .Compared to the other platforms, the “Agilent (a)” platform has the lowest number of motif containing probes in their probe set: 1.3% (319 sequences) contain the relatively short 6-mer primer 4 motif piA the intensity of probe p in measurement i.Raw data (absolute intensities) was collected from 35K Operon arrays, printed in-house, and from 25K Agilent arrays and 22K Affymetrix U133 arrays from published literature. Using normalized hybridization data we calculated the mean probe intensity for each probe p is the set of probes, Smp the subset of Sp that contain motif m, s the size of Smp, n the number of measurements and The mean intensity per motif The intensity related motif bias is defined as the mean motif intensity divided by the standard deviation of the motif intensity:Rank related motif bias is defined as the rank related motif intensity divided by the standard deviation of the rank related motif intensity:To estimate the influence of the presence of T7 motif sequences in probes on the amount of amplified RNA that is observed to bind to these probes we calculated average intensity levels for subsets of probes. First, we selected those probes that do contain (parts of) the T7 motifs, and secondly we took those probes that do not contain such motifs (the ‘no-motif’ probes). For control purposes we also used subsets of probes either based on random selection of probes or on random motifs in probes. Finally, we compared the results obtained for primer motifs that were actually used in the studies under investigation . This mean is clearly shifted up relative to the mean of the no-motif probes (TCTCCC (note that primer 4 was not used in these experiments) displays a smaller mean intensity shift about half the shift observed for the primer 2 motif probes. These curves clearly show a difference in mean intensity but since the underlying distributions are not normal, a different metric was needed to calculate a statistic for this (see ranked motif bias below).For the probes of the “Operon (a)” dataset  with the 2 motif . A contr 2 motif  selectedInterestingly, shifts in mean probe intensities for data obtained from the same Operon V 3.0 probeset vary depending on the T7 primer used. For instance in the “Operon (b)” study , where pCCGCCTCCC is absent in the “Agilent (a)” platform and therefore not plotted in On the somewhat older “Agilent (a)” platform we find similar but less pronounced shifts in distribution curves, which still boils down to a higher intensity for primer 4 motif containing probes. Note that the 9-mer motif The “Affymetrix” study uses primer 1, and again we observe a shift of 3.6 for the corresponding motif containing probes . If we sTo investigate if the shift in mean intensities observed in amplified RNA studies affects the ratios observed for probes with T7 spacer sequences we compared two MAQC platforms, the “Operon (b)” and “Operon (d)” platform. Both types of microarrays use the same Operon oligonucleotide set and both were hybridized with identical samples. The only difference consists of using unamplified or amplified RNA. We selected probes from both platforms that were affected by T7 bias and compared the ratio measurements obtained for these probes. We observed that ratios for these probes are markedly closer to zero in the amplified RNA study compared to the unamplified RNA study . As a reCCTCCC (primer 1&2) appeared to be a significant outlier in the Affymetrix study (p = 1.4×10−10), the “Operon (a)” study (p = 9.4×10−10), the “Agilent (a)” study (p = 2.4×10−7) and the “Operon (b)” study (p = 4.7×10−6). Surprisingly, this motif also was an outlier in the “Operon(c)” study (p = 3.9×10−3). Motif TCTCCC however, (primer 3&4) was a significant outlier in the Affymetrix study (p = 5.2×10−3), the “Agilent (a)” study (p = 9.9×10−7), the “Operon (a)” study (p = 1.8×10−4 ) and the “Operon (b)” study (p = 3.9×10−9). The 9-mer motifs showed a very similar trend. An overview of all density plots and the associated p-values is shown in CCTCCC motif in the “Operon(c)” study, none of the motifs was a significant outlier in the Illumina study, the “Agilent (b)” study and the studies using unamplified RNA, “Operon (c)” and “Operon (d)”.To better compare subsets of probes in a normalized manner and to allow for statistical evaluation we converted the intensity axis into a rank-axis. We ranked the mean probe intensities Am) is high for abundant mRNAs and low for bias motifs (like the T7 motifs). If the motif really is the reason for the high intensity bias, the motif will invariably be associated with high Am. By calculating the intensity related motif bias (AM) by dividing the Am, and the rank related motif bias (RM) by dividing the Rm we introduced two methods for detecting motifs with associated intensity bias. The rank related motif bias (RM) showed to be superior in separating bias motifs from background motifs. An example of this is displayed in RM value. All primer related 3′ spacer sequence motifs could be traced back to peaks of rank related motif bias. Some additional peaks were also observed. These represent biases other than T7 primer related biases. To rule out that the peaks found in RM data against SAGE data of highly abundant genes. Out of the full repository of SAGE data from NCBI (ftp://ftp.ncbi.nlm.nih.gov/pub/sage/extr/tag_lib_freq.zip) and (http://www.ncbi.nlm.nih.gov/SAGE) the top 100 highest abundant gene products present in 4090 SAGE datasets were selected. For each motif we determined the fraction of abundant SAGE genes that are targeted by the corresponding subset of probes (FSAGE). There is no significant correlation between FSAGE versus the RM of the motifs (see http://microarray.nki.nl/research/T7/). We can thus conclude that the RM calculation is a good method for finding motifs linked to aberrant high signals. Moreover, the method is not hampered by motifs belonging to highly expressed genes.The T7 primer 3′ spacer sequence motifs are expected to have high mean intensities and therefore have a high mean probe rank. However, motifs that are exclusively part of abundantly expressed genes will also have high mean probe intensities and ranks. To discriminate motifs associated with highly abundant genes from those associated with bias motifs we reasoned that variations in observed The T7 primer used in amplification of mRNA for microarray applications is a potential source for bias in microarray data. The bias appears to be common to most microarray platforms that use amplification and mean intensities of one to nine percent of the probes increase aberrantly to levels up to a hundred fold the average level of unaffected probes. It is also dependent on the sequence of the T7 primer and it is absent from hybridizations where unamplified  RNA is used.We find that the older Agilent, the Operon V3.0 and the Affymetrix data sets are most affected and that the newer Agilent and Illumina platforms are virtually free from the phenomenon.all amplified (and subsequently labeled) molecules. As a consequence, in microarray hybridizations using this amplified RNA, these motifs are abundantly present. Apparently this high concentration drives probes with complementary sequences to bind amplified RNA, irrespective of the gene sequence present in the amplified RNA. This leads to a change in ratio for these probes more reflecting the total amounts of red and green in the hybridization mixture which, under normal circumstances, are present in equal amounts (1∶1). True gene expression differences therefore become masked by the T7 bias  sequences do not introduce hybridization artifacts on microarrays The intensity measured from a feature consists of two components: the gene expression and the bias. To which extent the T7 bias influences the measured ratio, assuming that the bias signal is equal for every affected feature, therefore depends on the signal coming from gene expression. As mentioned, high bias signals shift the log-ratio measured in affected features on dual color platforms towards 0 (no difference measured) and the spot is therefore easily lost from the analysis because it will not be selected as outlier anymore. In single channel measurements, on the contrary, T7 biased motifs features become over-estimations of gene expression because of the fact that the aberrant motif bias signals add up to the true gene expression signals. Here, the spots might have been noticed as consistently up regulated. Still, as in the case of Affymetrix, the spot is likely to become an outlier in the probe-set used to detect a single transcript and will therefore be discarded from the estimation of the averaged probe intensity for the transcript. Looking at actual probe data versus gene data confirms this assumption (data not shown).CCTCCC and TCTCCC) taken from the 3′ spacer sequences of the primers (see Our results clearly show that motifs present in the 3′ spacer sequences of T7 primers correlate strongly with the intensity bias observed on probes with complementary sequences and can induce intensity shifts up to a hundred fold the average level of unaffected probes. It is remarkable that the shift in mean probe intensity for the selected probes in mers see .The absence of an apparent intensity shift for subsets of probes containing complementary primer motifs in the “Agilent (b)” and “Illumina” platforms see  is remarSeveral oligonucleotide designs for gene expression analysis have been generated with probes containing motifs complementary to 3′ spacer sequences of the T7 primers. Improvements to prevent the occurrence of T7 motif bias can be found along two lines. One option is to eliminate sequence overlap from the T7 primers. In that case the 3′ spacer sequence could be completely removed or replaced by an uncomplimentary sequence. It is unclear at the moment if this is feasible in the sense that we don't know if a T7 enzyme will be functional using such a drastically modified primer. On the other hand, probes can be designed that lack complementary sequences to these T7 spacer motifs and thus eliminate the artifact. In the mean time, one could make use of specific blocking oligos that react with the motif sequences on the targets or on the probe sequences. Preliminary results  where single stranded oligos with complementary sequences to T7 spacer domains or to T7 motif domains from probe sequences were added to hybridization mixtures indicate that blocking oligos indeed reduce the T7 bias phenomenon. In fact, the approach is similar to the one taken to minimize side-effects of the stretch of T-nucleotides in the amplified RNA (a remnant of the poly(A) tail of the mRNA) by adding poly-d(A) in the hybridization buffer.all target molecules carry the 3′ spacer sequence, the hybridization kinetics on the features containing a complementary T7-bias domain (1 to 5% of the probe sequences) may be forced into the binding state. A secondary feature of the T7-bias domains is that they are GC-rich and distally located on the molecule which may increase binding affinity.The aberrant high signals we observe on features displaying T7-bias motifs are the result of excess binding of labeled target molecules. Our data show that this is the result of binding of the 3′spacer of these molecules to probes on the array. We hypothesize that the binding we observe is a reflection of the molar excess of the amplified material available for binding. Since RM-value calculation. We are currently investigating the nature and experimental causes for these other high RM-value associated motifs observed in the datasets under study.The method we describe for the calculation of motif bias is capable of detecting probes that are affected by T7 based intensity artifacts. Clearly, the 3′ spacer sequence in the T7 primer is associated with a higher than average rank related motif bias. It is however not the only motif that has a high value in this respect see . Until nTable S1Condensed ratio table. Data table where we indicate the Operon Identifier (oligoID), the Central Microarray Identifier (reporterID), the mean of intensities observed for this probe over the data in the CMFdatabase (mean) and the statistical values associated.(5.66 MB TXT)Click here for additional data file.Table S2Table of motif containing probes per platform.(0.03 MB DOC)Click here for additional data file.Table S3Statistical values  reflecting the chance that a particular motif belongs to a distribution of ranked motifs.(0.03 MB DOC)Click here for additional data file.Figure S1R of the motifs. SAGE data from NCBI (ftp://ftp.ncbi.nlm.nih.gov/pub/sage/extr/tag_lib_freq.zip) and (http://www.ncbi.nlm.nih.gov/SAGE). A selection made of the top 100 highest abundant  gene products present in 4090 SAGE datasets. Their sequence was split in motifs of different lengths and plotted against the MR of the same motifs. Panels A to D: plots for motifs that are 6, 7, 8 and 9-mers in length. Notice that there is no correlation between the two aspects of the same motif.M(R) score versus SAGE scores Figure showing that there is no correlation between the presence of a motif in a probe that binds abundant mRNAs and the T7-bias M(R) score of that motif. Dotplot of the SAGE tag-score versus the M(0.27 MB DOC)Click here for additional data file.Figure S2CCTCCC and TCTCCC that are significant outliers  in the studies that used the respective T7 primers . In the studies listed in panel C, these motifs aren't found as outliers (except for CCTCCC in “Operon (c)”. The corresponding 9-mer motifs show a very similar trend .Density plots of the motif mean ranked intensity for all 6-mer  and 9-mer  motifs in the platforms studied. Indicated is the position of the T7 bias motifs (2.63 MB TIF)Click here for additional data file.Figure S3Boxplot representation of the ratio distribution observed from an unamplified RNA study “Operon (d)” (A and B) versus an amplified RNA study “Operon (b)” (C and D). The spread of the ratios is shown for the T7 bias unaffected probes (A and C) versus the affected probes (B and D). For unamplified RNA an equal distribution is present for unaffected (A) and T7 bias affected probes (B). For amplified RNA however, the T7 bias affected probes (D) display a clear reduction in ratio distribution compared to the unaffected probes (C). Comparing A and C indicate that amplification in itself leads to increased ratio levels.(3.66 MB TIF)Click here for additional data file.


This formulation is repeated twice in the asymmetric unit as the rubidium cation lies on an inversion centre. Each anion or acid molecule is linked to two rubidium cations, while the rubidium cation has close contacts to four symmetry-equivalent organic ligands, with two different coordination modes towards this cation. In addition, each rubidium cation is coordinated by two water O atoms, raising the coordination number to eight. One of the carboxyl groups of the acid holds its H atom, which forms a hydrogen bond to a coordinated water molecule. The other carboxyl group is deprotonated in half of the ligands and protonated in the other half, taking part in a strong O—H⋯O hydrogen bond disordered over an inversion centre. The stabilization of the crystal structure is further assisted by O—H⋯O and O—H⋯N hydrogen-bonding interactions involving the water molecules and carboxylate O atoms.The structural unit of the title compound, [Rb(C DOI: 10.1107/S1600536809002001/hg2450Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials: