They have been implicated in growth regulation and have been shown to be involved in both pre-mRNA and pre-rRNA processing. More recently, however, these proteins have been reported to act as transcriptional co-activators for estrogen-receptor alpha (ERα). Furthermore these proteins were shown to interact with co-activators p300/CBP and the RNA polymerase II holoenzyme. Taken together these reports suggest a role for p68 and p72 in transcriptional activation.In this report we show that p68 and p72 can, in some contexts, act as transcriptional repressors. Targeting of p68 or p72 to constitutive promoters leads to repression of transcription; this repression is promoter-specific. Moreover both p68 and p72 associate with histone deacetylase 1 (HDAC1), a well-established transcriptional repression protein.It is therefore clear that p68 and p72 are important transcriptional regulators, functioning as co-activators and/or co-repressors depending on the context of the promoter and the transcriptional complex in which they exist. AlthougDrosophila maleless (MLE) gene product [DNA helicases belong to a superfamily of proteins that is distantly related to DEAD/H box RNA helicases and includes the Werner syndrome protein (WRN)  and the  product  and is t product . RHA has product . Further product ; this st product . These rIn vitro, both proteins exhibit the RNA-dependent ATPase and RNA helicase activities characteristic of members of the DEAD box family [p68 is a prototypic member of the DEAD box family of proteins  and an ex family -14 and hx family . Moreovex family . This pr118, a residue phosphorylated by mitogen-activated protein kinase (MAPK). Interestingly the RNA helicase function of p68 appeared to be dispensable for this activity as a mutant p68 (Lys144 to Arg) in the ATP binding site (conserved motif I) retained the ability to co-activate ERα, although in a later study RNA binding appeared to be required [More recently p68 and p72 have been shown to be involved in a range of processes in the cell, including pre-mRNA and pre-rRNA processing and alternative splicing -18. p68 required . p72 wasrequired . Furtherrequired . More rerequired , suggestherpes virus thymidine kinase (TK) promoter but not the simian virus 40 promoter/enhancer. Moreover, while p72 can repress transcription from the Adenovirus major late promoter, p68 appears to have no effect, suggesting that these proteins do not behave in an identical way in all contexts. Furthermore we show, by co-immunoprecipitation, that both p68 and p72 interact with histone deacetylase 1 (HDAC1) and that HDAC1/p68/p72 co-elute by gel filtration, indicating that they exist in the same complex in the cell and suggesting a possible mechanism by which these proteins may exert their repressive effect.In this report we explore further potential mechanisms through which p68 and p72 may contribute to transcriptional regulation. We find that, in some contexts, p68 and p72 can also act as transcriptional repressors and that these proteins exhibit clear promoter specificity in this function. By directing GAL4-tagged p68/p72 (GAL4 DNA binding domain aa1-147) to promoters containing GAL4 binding sites, we show that both p68 and p72 can repress transcription from the Herpes virus thymidine kinase promoter/chloramphenicol acetyl transferase (CAT) reporter plasmid (TK-CAT) bearing 5 copies of the GAL4 binding site, and CAT activity was measured in standard assays. The amount of TK-CAT used had previously been titrated to give a basal level of activity within the linear range of measurement in the CAT assay system used (data not shown). Interestingly, we observed a marked decrease in CAT activity for both p68G4 and p72G4 , and the SV40 promoter/enhancer (SV40-CAT). In each case we used the same amount of p68G4/p72G4 DNA as previously and appropriate amounts of the reporter constructs that would give similar basal levels of CAT activity using the GAL4 control plasmid . To rule out this possibility, we used a similar TK-CAT promoter construct, which had, however, a 1.6 kb DNA 'spacer' between the GAL4 binding sites and the promoter (the inserted DNA is reported to have no effect on transcription ). Both GTo determine whether specific regions or domains of p68 and p72 act as transcriptional repressors, we performed CAT assays, again using the TK-CAT reporter, but with a range of deletion derivatives of p68/p72 covering the entire coding region. Deletion derivatives encompassing domains between amino acids 1–478 and 1–474 for p68 and p72 respectively repressed transcription in this assay while the C-terminal region for both proteins (aa 477–614 for p68 and 468–650 for p72) acted as a strong transcriptional activator Figure . ResidueMany studies have implicated histone deacetlyase (HDAC) proteins in active repression of transcription, and many transcriptional repressors have been shown to associate with HDACs . To testTo examine further the involvement of HDACs in transcriptional repression by p68/p72 we determined whether p68 and/or p72 co-immunoprecipitate a HDAC activity in cells. Plasmids expressing myc-tagged p68 and p72 were expressed in 293 cells (with myc tag plasmid vector as a negative control) and the tagged proteins were immunoprecipitated from cell lysates using the myc epitope antibody, 9E10. Equal amounts of immunoprecipitated protein bound to the antibody, as confirmed by western blotting Figure , were thThree classes of HDACs have been described. Class I HDACs, which include HDAC1, 2 and 8, are expressed in the nucleus and have been shown to bind several transcription factors and to mediate transcriptional repression . Since HWe have shown that the highly related DEAD box RNA helicases p68 and p72 act as repressors of transcription in a promoter-context manner. When targeted to the TK-CAT promoter-reporter construct they both strongly repress transcription Figure . FurtherUsing a series of deletion derivatives of p68 and p72 we identified three domains, within the core conserved among the DEAD box family of proteins, which can independently repress transcription Figure . Moreoveactive HDAC proteins is responsible for the difference observed upon the MLP promoter.Since HDAC proteins have been extensively implicated in the repression of transcription, it was important to examine whether these proteins are likely to play a role in transcriptional repression by p68 and/or p72. Firstly, the ability of the HDAC inhibitor, TSA, to relieve repression was examined. No effect was observed on repression of TK-CAT (data not shown) implying the involvement of a HDAC-independent mechanism in repression of the TK promoter. In contrast, repression of MLP-CAT by p72 was relieved two-fold compared with the vector control Figure  suggestiOur attempts at correlating the different repressive functions of p68 and p72 to specific domains of the respective proteins, using deletion derivatives, were unsuccessful, as the equivalent regions of both either caused transcriptional repression or activation. While this might suggest that both helicases repress transcription in the same manner, it is more likely that the HDAC recruitment by p72 may be an additional mechanism of repression, used at specific promoters. We also cannot rule out the recruitment of other repression complexes at this stage. Our findings that p68 and p72 differ in their ability to repress the MLP promoter and to recruit HDAC activity suggest that, at least in some contexts, p68 and p72 repress transcription by different mechanisms.In summary, it is clear that p68 and p72 act to repress transcription in a differential manner dependent upon promoter context. It will be important to determine which endogenous promoters are subject to repression by p68/p72 in a physiological context. However, until the signal transduction pathways, which target these proteins to the appropriate promoters, are elucidated it will be necessary to use a targeting system (such as GAL4) to undertake a molecular analysis of transcriptional repression by p68 and p72. Since it is now clear that p68/p72 can act both to activate and repress transcription future work will involve dissection of the transcriptional activation/repression complexes in which p68 and p72 are involved, as well as characterisation of the molecular 'switch' which determines whether these proteins will be part of transcriptional activation or repression complexes.We have shown that the highly related RNA helicases p68 and p72 can repress transcription in a promoter context-dependent manner. Both proteins associate with HDAC1, a well-established transcriptional repressor protein. Strikingly, however, p68 and p72 behave differently in their ability to repress transcription from different promoters and in their ability to recruit HDAC activity suggesting that they may, at least in some contexts, repress transcription by different mechanisms.2 at 37°C in DMEM with 10% FBS, 2 mM L-glutamine, 100 units/ml penicillin and 100 μg/ml streptomycin .U2OS human osteosarcoma cells and 293 human embryo kidney cells were maintained at 5% COBamHI/EcoRI fragments, in frame with the GAL4 tag. The CAT reporter constructs have been described previously: TK-CAT [A pcDNA3-GAL4 expression plasmid was used to express full-length and deletion derivatives of p68/p72/p82 tagged at the N-terminus with the DNA binding domain (amino acids 1–147) of GAL4 . The maj: TK-CAT , TK-Spac: TK-CAT , MLP-CAT: TK-CAT . The MLP: TK-CAT  was usedp68: The antibodies used were the mouse monoclonal antibody PAb 204 and the rabbit polyclonal antibody 2906, generated against the C-terminal 15 amino acids of p68 . PAb 2045 U2OS cells were seeded for transfections, which were performed as previously described [3×10escribed . Cells wNuclear extracts were prepared from U2OS and 293 cells essentially as described in  except t293 cell lysates were prepared in RIPA buffer [50 mM Tris-HCl (pH8.0), 150 mM NaCl, 1% Igepal, 0.1% SDS, 1% Na deoxycholate, 1 mM EDTA and protease inhibitor cocktail (Roche)]. After treatment with RNase/DNase, lysates were fractionated on a Pharmacia Superose 6 HR column in 50 mM Tris-HCl (pH7.5), 150 mM NaCl, 10% glycerol and 1 mM benzamidine, using a Pharmacia AKTA FPLC system. 0.5 ml fractions were collected and alternate fractions were analysed by western blotting. Molecular weight standards from Pharmacia were used to calibrate the column.Fleur de lys substrate, which contains an acetylated lysine side chain (Biomol) and was performed according to manufacturers instructions. Antibody-bound beads were washed in HDAC assay buffer prior to being added to the 96-well plate, to remove immunoprecipitation buffer. Reactions were incubated for 30 min at 37°C with or without the addition of 1 μM TSA. Samples were excited at 360 nm and emitted at 460 nm and were read in a fluorometer. Typically each assay was performed 3 times.293 cells were transfected with either pSG5-myc vector, pSG5-p68-myc, or pSG5-p72-myc. Cells were lysed in buffer B 48 hr after transfection. The lysate was then diluted in buffer A and myc-tagged proteins immunoprecipitated with 9E10 (myc epitope) antibody as described above. The HDAC assay employs BJW carried out the transcriptional and HDAC activity assays, the deletion mapping and the gel filtration, GJB and SMN carried out the co-immunoprecipitation experiments, DJG carried out the original experiments showing that p68 and p72 could act as transcriptional repressors, and NDP and FFP participated in the design of this study. FFP co-ordinated the study and prepared the final draft of the manuscript. All authors read and approved the manuscript.


Citizens increasingly use email in personal communication. It is not however clear to what extent physicians utilize it for patient communication. Our study was designed to examine physicians' activity in using email and to estimate the proportion of email messages missing from documentation in electronic patient records (EPR).All physicians  at the Finnish Student Health Service received a questionnaire by email, and were asked to print it and keep a daily tally of visits, phone calls and email messages over the study period of one working week (5.5. – 9.5.2003). The response rate was 70%. The data originating from the questionnaire were compared with statistical data from the EPR during the study period.The majority (79%, 41/52) of doctors reported using email with patients, averaging 8.6 (range: 0–96) email contacts and a percentage rate of "email / visit" 20% (range: 0–185%) in one working week. Doctors in the capital city region and those doctors who had a positive attitude toward email for patient communication were most active in email use. Up to 73% of email contacts were not documented in the EPR.The activity in using email with patients verified among Finnish physicians is compatible with recent study results elsewhere. The notable proportion of un-recorded email messages establishes the need for an electric communication system built into the EPR to improve the quality of patient care and to limit medico-legal risks. Citizens increasingly use email in personal communication . It is nInternational and national recommendations and guidelines have been published on email use between doctor and patient; the contents of the Finnish guidelines are well in line with European guidelines ,8. TheseUse of email between doctor and patient has been studied in one controlled and randomized and three cross-sectional studies ,5,9,10. Among Finnish citizens of working age young adults are the most active users of email and Internet ,11. Univfirstname.surname@yths.fi. Principles of communication by email with FSHS' employees and of other forms of electronic services  are available at the FSHS' website.The Finnish Student Health Service (FSHS) provides primary health care services to approximately 140.000 university students in Finland. The FSHS has health stations in 16 university cities. Services include health promotion, consultations with general practitioners and with other clinical specialists, mental health care, and dental care. Since 1993 FSHS has provided health counseling in Internet. Since 1999 all physicians have had an email account at their disposal in health stations and an email address of type: The Social Insurance Institution, the university cities, the State of Finland, and the students themselves finance FSHS services. Students pay an annual obligatory health care fee as a part of the Student Union's membership fee. There is no other fee for preventive services, visits to general practitioner or public health nurse, and laboratory or X-ray examinations prescribed during these visits. Use of Internet services is also free of charge. The FSHS employs 560 persons and 63% of the physicians are general practitioners. In this paper general practitioners also include specialists of general practice/family medicine, whereas "specialists" refers to clinical specialists other than psychiatrists or oral surgeons.The aim of the study was to seek answers to the following questions:1. How actively did physicians at student health care use email in communication with their patients?2. How much did they use email compared to phone calls and patient visits?3. Who were the active doctors using email with patients?4. What proportion of visits and phone calls could be candidates for substitution by email communication?5. Did the volume of visits, phone calls and email messages documented in the EPR of the FSHS during the study period differ from that of visits, phone calls and email messages registered in the study?All physicians (n = 82) in the FSHS' functionary register in April 2003 received a questionnaire by email. We excluded six physicians, who were not any more working for the FSHS and took exception to the two authors. The actual number of survey population was 74. The questionnaire see  includedThe first mailing of questionnaire took place 28.4.2003 and a reminder was sent 5.5.2003. Recipients were asked to print the survey form, fill it in by hand, and return it by internal mail. Overall 52 out of 74 (70%) physicians returned a completed survey.Respondents were grouped according to age, location, speciality licence, and type of employment Table . Facts o®). We collected the numbers of patient contacts documented at Medicus® for the study period using the statistical software Cognos®.All physicians at the FSHS utilize EPR  software. Statistical analyses were conducted using the proportion test for two independent groups, the χ2 test, Fisher's exact test, the unpaired t-test, the Mann-Whitney test and the Kruskal-Wallis test. All tests were made two-sided and p-values below .05 were regarded as statistically significant.We entered data using Microsoft ExcelOf all respondents 29% were men and 71% women .Respondents' general attitude toward email for patient communication was evaluated by the statement "Email contacts with patients facilitate my work." The group of "positives" was formed of the 56% of respondents who on a five step Likert scale replied: "same opinion" or "nearly same opinion."The "positives" reported more email contacts than others (email per visit: median18% versus median 3%, p < .001). All eleven who had reported more email contacts than phone calls belonged to the "positive" group.Doctors estimated that 2% (57/2296) of patient visits could have been replaced by email. Of phone calls 21% (196/948) could have been substituted with email. Respondents estimated that 10% (45/449) of patients' email messages required a personal consultation.The number of visits registered in the survey did not differ from the presumed number documented in the EPR Table .. The diEven if university students do not represent the whole population, they can act as "pilot population" representing adults of working age of a future information society. Our study population – doctors taking care of students – was small with only 52 respondents. Thus the results of the study cannot be indiscriminately generalized. Because of the small study population comparison of the subgroups may not be reliable.Although the study group was small, it well represented all the doctors at the FSHS, and the response rate was high. A further strength was that we compared the number of patient contacts documented in the questionnaires during the study week to the statistical data of contact numbers from the EPR at the same time. In other studies no corresponding comparison has been made.The doctors were asked to keep a daily tally of visits, phone calls and email messages, and to evaluate how many visits or phone calls could have been replaced by email. Many doctors undoubtedly did this simultaneously with patient work. Some doctors might have been in a hurry, they probably supplemented the questionnaire at the end of the day. To achieve a more accurate evaluation of visits and phone calls replaceable by email, a continuous assessment  could have been stressed even more in the instructions.The doctors at the FSHS do not have a specific electronic communication system focused on patient communication. They use their general, unprotected email system also to communicate with patients. A specific communication system used only for patient communication would enable an automatic collection of the patient communication data and create a more accurate database than our data collection method.Katz and colleagues have made the only controlled and randomized study concerning physicians' use of email . Our ownOur study revealed individual differences in the use of email in patient work. Differences in physicians' activity in using email have previously been reported in only one study . DerivinPhysicians reckoned that email could replace only 2% of visits. This confirms Sittig's evaluation in 2003 that email could possibly cover a small percentage of visits . IncreasWhen we compared contacts in the EPR with contacts registered daily on the questionnaires we found that the majority of email contacts were not registered in the EPR. This finding is supported by Gaster and colleagues who asked physicians themselves to describe how often they usually registered email contacts in patient records .FSHS provides specific electronic services for focused issues . Principles of recommended issues to use email between health providers and patients are available for students at FSHS' websites. We have had a presumption that email messages between FSHS' physicians and their patients mainly handle patient care. Nyström's congress report from 2004 supports our presumption. He explored 139 email messages from 103 individual patients at his GP practice at the FSHS and noticed that 77 % of email messages handled medical tests, and 16 % handled follow-ups of symptoms or illnesses [All university students in Finland have access to Internet and email at their universities. Use of email as communication method in health care does not in their case cause inequalities in health. A general tendency in the societies to provide also health services widely in electronic form (in Internet or by email) can contribute to inequalities for those who are not able to use modern technologies ,17.Doctors at the FSHS had an average of 8.6 email contacts with their patients during one week. The proportion of email contacts per visit was 20%. Physicians estimated that email contacts could substitute 2% of patients' visits and 21% of phone calls. Of email contacts 73% were not documented in the EPR.Our study indicates that email communication really constitutes a part of patient work. This should be taken into account in planning working time and daily timetables. Use of software not integrated with the EPR increases the physician's registering load and currently involves extra work. It is not possible to confirm the patient's identity reliably using two separate systems. A system allowing retrieval of patient's identity safely and with no need to register separately the email communication in the EPR would promote the patient's adequate treatment and reduce the physician's medico-legal risks.There is a need for a larger study on email utilization between patient and physician which better covers the medical profession. The consumer point of view should also be better taken into account. A content analysis of email messages for patient communication combined with assessments of email documentation in the EPR could have strengthened present study regarding the importance of email registration in patient records.In Finland the Ministry of Social Affairs and Health has instigated a major project to safeguard health care . One parThe author(s) declare that they have no competing interests.JC participated in the design of the study, coordinated it, keyed data into computer, performed the statistical analysis and drafted the manuscript with co-authors. MN conceived of the study, participated in the design of the study and wrote parts of manuscript in English. IV participated in the design of study and drafted the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:QuestionnaireClick here for file


Physical exercise has become a cornerstone of management of chronic obstructive pulmonary disease (COPD) because it leads to clinically relevant improvements of exercise capacity and health-related quality of life (HRQL). Despite the scarcity of randomised trials directly comparing exercise protocols, current guidelines recommend high intensity continuous exercise for lower extremities as the probably most effective exercise modality. However, for patients admitted to inpatient respiratory rehabilitation programmes, it is often difficult to initiate such an exercise programme because they are severely limited by dyspnoea and leg fatigue and therefore unable to perform continuous exercise at higher intensities and for periods longer than 30 minutes. Interval exercise may be an attractive alternative for these COPD patients because it allows high intensity exercise with recovery periods. The aim of this study is to assess if interval exercise compared to high intensity continuous exercise is not of inferior effectiveness in terms of HRQL and exercise capacity improvements but associated with better exercise tolerance in patients with moderate to severe COPD at the beginning of a respiratory rehabilitation.We will assign patients with moderately severe to severe COPD to either continuous exercise or interval exercise using a stratified randomisation. Patients will follow 12–15 exercise sessions during a comprehensive inpatient respiratory rehabilitation. Primary end point for effectiveness is HRQL as measured by the Chronic Respiratory Questionnaire (CRQ) two weeks after the end of rehabilitation and secondary endpoints include additional clinical outcomes such as functional exercise capacity, other HRQL measures, patients' experience of physical exercise as well as physiological measures of the effects of physical exercise such as cardiopulmonary exercise testing. Including expected drop-outs, we will need 52 patients per group to show differences corresponding to the minimal clinically important difference of the CRQ. Outcome assessors and investigators involved in data analysis will be blinded to group assignment until analyses have been carried out.Clinicians and the scientific community need evidence on the benefits and tolerance of exercise protocols available in clinical practice. The proposed trial will provide important and needed data on interval and continuous exercise for decision making in clinical practice. Impaired exercise capacity, dyspnoea and reduced health-related quality of life (HRQL) are common complaints of patients with chronic obstructive pulmonary disease (COPD). A major exercise-limiting factor in COPD is peripheral muscle dysfunction characterised by atrophic muscles and reduced fatigue resistance due morphological and metabolic alterations of peripheral muscles As much Respiratory rehabilitation with physical exercise improves exercise capacity and HRQL. AlthoughHowever, data on high intensity continuous exercise come from a trial that included 19 patients with mild COPD who were able to exercise for 45 minutes five times per week during an outpatient programme. For patiA solution to this dilemma may represent interval exercise where paPatients and clinicians will accept interval exercise to treat peripheral muscular dysfunction in COPD only if it is not of inferior effectiveness compared with continuous exercise and if it is indeed associated with better compliance resulting from less dyspnoea and leg fatigue during exercise. There is limited evidence from three randomised controlled trials comparing interval exercise and continuous exercise.-12 A sumThe studies indicated that both interval and continuous training improved exercise capacity, dyspnoea and HRQL and showed insignificant differences between interval and continuous exercise. However, insignificant differences do not allow concluding that interval or continuous are of clinically equivalent effectiveness These trThe investigators did not use steep ramp tests to determine exercise loads but normal incremental exercise tests. For interval exercise, muscle strength and anaerobic capacity is relevant because of the short high intensity intervals, but this is not measured by normal incremental exercise tests. In addition, training load tolerated during interval exercise may be underestimated when normal incremental exercise tests are used.Exercise tests to establish training intensity should consider the exercise mode. Meyer et al. studied interval exercise in several studies -16 in paMeyer et al. also assessed different ratios of work/recovery phases  and found that with these ratios and relatively short phases of high intensity exercise (10–30 seconds), lactate did not accumulate presumably because of lactate elimination during the recovery phases. IntervalBecause of the scarcity of evidence on the comparative effectiveness of interval exercise for COPD patients, additional trials are needed.,6 Our prAll consecutive patients admitted to a teaching rehabilitation clinic for an inpatient respiratory rehabilitation (Klinik Barmelweid, Barmelweid, Switzerland) will be assessed for study eligibility by senior staff physicians. If patients are deemed eligible after exercise testing, which is part of the usual rehabilitation program, senior physicians will inform patients about the study orally and in writing. If patients are willing to participate and provide written informed consent they will be randomly assigned to respiratory rehabilitation with either interval or high intensity continuous exercise. Both groups will perform 12–15 exercise sessions and follow the rest of the rehabilitation programme. Follow-up assessments will be done during, at the end of the rehabilitation programme as well as two and twelve weeks afterwards when patients are back in their home environment.The Ethikkommission of the Kantonsspital Aarau, Aargau, Switzerland, has approved the study protocol.We defined the following in- and exclusion criteria: Patients with COPD as defined by FEV1/FVC < 70% predicted, FEV1 < 50 % predicted after bronchodilation, with or without chronic symptoms (cough, sputum production) corresponding to a GOLD  stage III-IV and Germ2 < 55 mmHg or no = paO2 ≥ 55 mmHg).A third party not involved in the conduction of the trial will provide online central randomisation (DatInf GmbH, Tuebingen, Germany). A computerised 'minimisation' procedure will be used to avoid chance baseline imbalances in prognostically important variables MinimisaEvery time the study coordinator has been informed about an enrolled patients, he will enter the randomisation web site enter thSeparating patient enrolment and baseline assessments  from the randomisation procedure (study coordinator not involved in patient enrolment or rehabilitation programme) will ensure concealment of random allocation.The rehabilitation programme will start one day after baseline assessments and study enrolment. Exercise sessions and group lessons will take place five days a week and will consist of daily cycle ergometer training, breathing therapies (30 minutes per day), and guided walking of 15 to 30 minutes. Relaxation therapies (technique according to Jacobson) take place twice a week, patient education  three times a week, smoking cessation advice once a week or more if needed. Apart from physical exercise, the rehabilitation programme of approximately three weeks will be identical for both groups.The target workload for this group will be ≥ 70% of the maximum exercise capacity expressed in Watts and heart rate achieved during the incremental cycle ergometer test. Patients are usually not able to perform high intensity continuous exercise from the beginning and have to adapt to physical exercise. Physical therapists will increase training load as soon as possible to ≥ 70% of the maximum exercise capacity or as high as each individual patient tolerates. In each session , the performed workload (in Watts), if patients reached the target workload (in Watts), adjustments of workload and the requirement for oxygen. We define adherence to exercise as "full adherence" if patients follow at least 12 exercise sessions. We consider the training to be fully tolerated if patients are able to follow the exercise protocol for at least 9 exercise sessions for continuous exercise (taking into consideration the first three exercise session when patients increase training load up to 70% of maximum exercise capacity) and for at least 12 exercise sessions for interval exercise.Previous trials of respiratory rehabilitation or physical exercise did not report any adverse events. Nevertheless, we will record any adverse events such as injuries, cardiac events or increase of respiratory symptoms as they happen during the inpatient respiratory rehabilitation.At baseline and end of the rehabilitation, all patients will perform an incremental cycle ergometer test to the limit of tolerance (symptom based) under the supervision of a senior physician blinded to group assignment. Patients pedal unloaded for three minutes to warm up at 20 Watts. Then exercise load is increased by 7.5 Watts per minute until patients have to stop because of dyspnoea, leg pain, or criteria for stop testing . At the limit of tolerance, we will draw capillary blood samples to measures lactate concentrations and we will set the maximum exercise capacity expressed by Watts. The upper limit for the heart rate during exercise is set as the heart rate measured by the electrocardiogram at the maximum exercise capacity.2 output, minute ventilation, tidal volume and respiratory frequency.During testing, we will record gas exchange and ventilatory variables form calibrated signals derived from rapidly responding gas analyzers and a mass flow sensor. We will record breath by breath the following variables: Pulmonary oxygen uptake, pulmonary COAll patients will perform a steep ramp test at the beginning and end of the rehabilitation programme as described above.In order to characterize the patient included in the study, we will record their age, gender, lung function (FEV1, FEV1/FVC, diffusion capacity DLCO/VA, weight, height, smoking status at the beginning and end of rehabilitation as well as two and 12 weeks afterwards, duration of disease (time since diagnosis), co-morbidities such as hypertension, heart diseases, endocrine disorders, chronic rheumatological disorders and psychiatric disorders.The randomisation code will not be broken (investigators remain blinded to group assignment and will receive only codes, such as group 1 and 2) until a draft of the manuscript has been written. The authors will write two versions with alternative possible allocation patterns to avoid bias in the interpretation of the results. This approach is methodologically rigorous and limits introduction of bias during the interpretation of the data that some of the authors might have. We will Our null hypothesis is that high intensity continuous exercise is of clinically superior effectiveness compared with interval exercise to improve HRQL (δ ≥ 0.5 in CRQ domains scores). The alternative hypothesis is that interval exercise is not of clinically inferior effectiveness compared with high intensity continuous exercise.In the primary analysis, we will calculate the raw difference and 95% confidence intervals between groups in the mean follow-up score for the CRQ domains 2 weeks after completion of the respiratory rehabilitation programme. In an additional analysis, we will adjust for the base-line score and the four stratification variables using an analysis of covariance. We will use independent t-test to compare the change scores between groups.We will also use the confidence interval approach as recommended for equivalence and non-inferiority trials We will Most methodologists and statisticians agree that the boundaries of equivalence should be defined as the minimal important difference and below the differences observed in previous trials comparing active to control treatments.,39 The mWe will repeat the analysis with calculations of raw and adjusted differences for all other clinical and physiologic outcome measures and test for significant differences between groups using independent t-test if data are distributed normally.We will use both intention to treat and per protocol analysis to show equivalence in either case as recommended by others,39,46,47Our null hypothesis is that patients equally experience high intensity continuous exercise and interval exercise as measured by the Exercise Tolerance Questionnaire. The alternative hypothesis is that interval exercise is associated with the experience of less limiting symptoms compared with high intensity continuous exercise.We will use independent t-tests to compare the measures for exercise tolerance  between groups. Again, we will assess the raw differences between groups in the primary analysis and adjust for baseline scores and the four stratification variables in the additional analysis.Factors, which interfere with outcome measures, can distort the results if unevenly distributed between study groups. We use three approaches to control for confounders: First, we use randomisation to allocate patients. Second, we strengthen the randomisation using a computerised minimisation procedure with factors that are likely to influence the outcome measures (exercise capacity, pulmonary state and presence and absence of affective disorders). Third, we will collect a number of variables at baseline  that may modify the effect of exercise. We will compare the distribution of these potential effect modifiers between groups and statistically assess their influence on the outcome measures with multiple linear regression models.2 * 2 * SD2/DIFF2, where n = the sample size required in each group , SD = standard deviation of the outcome variable, DIFF = size of desired difference between groups. A and B depend on the desired significance level and desired power, respectively.For calculating the required sample size, we use the formula for comparison of 2 means: n = [A + B]We base our sample size calculations on the CRQ. We used empirical data from our previous trial., where wA priori sample size calculations usually provide only rough estimates. Therefore, we will recalculate sample size during the study when we have the data for 30 patients in each group. To this end, we will re-assess the standard deviation of the outcome variables and, if necessary, adjust the sample size accordingly (without breaking the randomisation code).We will implemented a series of measures to ensure high data quality.1. Site investigators will collect the data using standardized forms. All data will be collected and entered into a single database in the Horten Centre by one investigator. A second investigator will validate completeness and accuracy of data extraction by checking 20% of extracted patient data.2. Checklists with all the data to be collected will be provided for physical therapists and physicians.3. Teaching sessions will be held regularly for physical therapists and physicians involved in data collection aiming at robust data collection.4. Investigators meetings will be held monthly, or more frequently if needed, to discuss recruitment of patients, problems in conducting the study, acquisition of data, to check for consistency and completeness of data and for interim analyses.5. E-mail will serve as the first line of non-urgent communication between research team members.6. Monthly reports will be prepared by the principal investigator that will include the number of patients recruited, stage of follow up for each patient, notification regarding missing patient data and queries from data received.In the last 30 years, researchers made great efforts to study the effectiveness of respiratory rehabilitation compared to usual care. The meta-analyses of a recent systematic review[Research in respiratory rehabilitation should now focus on the evaluation of different exercise protocols. When an effective treatment such as respiratory rehabilitation is available, patients and clinicians are not confronted with the decision whether to start treatment or not, but with the decision on the most appropriate treatment. Therefore, clinicians need evidence from pragmatic randomised controlled trials directly comparing different exercise protocols at issue rather than evidence from (explanatory) trials comparing exercise with no exercise or usual care. The propThe evidence generated in the proposed clinical trial will also be relevant for the scientific community. From the 1970s up to the mid-1990s most investigators conducted explanatory clinical trials in order to better understand how and why physical exercise is effective in patients with COPD. After the recognition of its effectiveness the debate on the optimal exercise modality arose. HoweverThere is a need for randomised controlled trials to explore which exercise protocols are most effective for COPD patients. Another topic that has received little attention in respiratory rehabilitation trials so far is compliance to and subjective experience of physical exercise ExerciseBeside the paucity of data on the effectiveness and tolerance of different exercise protocols, there is also a need for trials that are methodologically sound and rigorous. Most of the studies on exercise or respiratory rehabilitation in patients with COPD did not report on details of exercise tests and protocols. In addition, study design related issues that introduce bias such as description of the randomisation procedure, concealment of allocation, sample size calculations or blinding have not been addressed frequently. Therefore, we try to use strong epidemiological methods to minimize bias in our trial.Our study could make an important contribution to the understanding of physical exercise in patients with COPD and have a significant impact on the structure of respiratory rehabilitation and exercise programmes.None declared.COPD = Chronic obstructive pulmonary diseaseHRQL = Health-related quality of lifeCRQ = Chronic Respiratory QuestionnaireHADS = Hospital Anxiety Depression ScoreFT = Feeling ThermometerMP drafted and revised the manuscript. All authors participated in development of research protocols and in the design of the study. MP, CZ and HS resolved statistical and methodological issues. All authors read and corrected draft versions of the manuscript and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Plasmodium falciparum are reported frequently in the East African highlands with high case fatality rates. There have been formal attempts to predict epidemics by the use of climatic variables that are predictors of transmission potential. However, little consensus has emerged about the relative importance and predictive value of different factors. Understanding the reasons for variation is crucial to determining specific and important indicators for epidemic prediction. The impact of temperature on the duration of a mosquito's life cycle and the sporogonic phase of the parasite could explain the inconsistent findings.Malaria epidemics due to Daily average number of cases was modeled using a robust Poisson regression with rainfall, minimum temperature and maximum temperatures as explanatory variables in a polynomial distributed lag model in 10 districts of Ethiopia. To improve reliability and generalizability within similar climatic conditions, we grouped the districts into two climatic zones, hot and cold.In cold districts, rainfall was associated with a delayed increase in malaria cases, while the association in the hot districts occurred at relatively shorter lags. In cold districts, minimum temperature was associated with malaria cases with a delayed effect. In hot districts, the effect of minimum temperature was non-significant at most lags, and much of its contribution was relatively immediate.The interaction between climatic factors and their biological influence on mosquito and parasite life cycle is a key factor in the association between weather and malaria. These factors should be considered in the development of malaria early warning system. Plasmodium falciparum are reported frequently in the East African highlands [Malaria epidemics due to ighlands -6. Immunighlands . Since tighlands -9 to preighlands ,11. Therighlands -20. Howeighlands -27. Woubighlands  found a ighlands . Moreoveighlands  found vaighlands  showed tighlands ,22,24,29The inconsistency of findings from different studies may be due in part to the interaction of weather factors affecting vector abundance and survival, and parasite maturation, key determinants of malaria transmission. Deposition of mosquito eggs, and their maturation into larvae and then into adults, requires aquatic breeding sites, and is, therefore, dependent on rainfall ,31. The Once female adult mosquitoes emerge, they look for a blood meal, and in the process they ingest malaria parasites (gametocytes) with the blood. The feeding frequency of mosquitoes increases with temperature, resulting in increased proportions of infective mosquitoes . The durThese data on the timing of the mosquito life cycle suggest that malaria cases should follow, at defined intervals, periods of increased temperature and increased rainfall. Moreover, because temperature accelerates several steps in the process of mosquito and parasite development, the time lag between the appearance of suitable weather conditions and the appearance of new malaria cases should shorten as temperature rises. Although based largely on laboratory findings, these data suggest quantitative hypotheses about the precise time lag between increases in temperature and rainfall, and increases in malaria cases. At an average temperature of 20°C, the aquatic phase of the mosquito will be completed in about 28 days (five days for eggs to hatch and 23 days for the larva to develop into adult stage); and sporogony is completed in 28 days Table . At thisThe purpose of the investigations reported here is to test these hypotheses using weekly data on weather and malaria cases. Specifically, we used Polynomial Distributed Lag Models (PDL) to determine the effect of weather factors and their lag distribution on malaria in relatively hot and cold environments using a data set consisting of weekly parasitologically confirmed malaria cases collected from health facilities and locally collected meteorological factors in 10 districts of Ethiopia for the years 1990 to 2000.Plasmodium species; the analysis was restricted to P. falciparum. The original data collected on the basis of Ethiopian weeks were normalized to obtain mean daily cases for each Ethiopian week [Microscopically confirmed malaria cases were collected from a health facility in each of ten districts of Ethiopia over an average of 10 years. Each of these health facilities serve people living in the surrounding localities with few exceptions coming from other places. The data were extracted from records of outpatient consultations for the years 1990 through 2000. These data were compiled by residency  and ian week .Daily meteorological data  recorded at the local weather stations nearest to the health facility were obtained from the National Meteorological Services Agency (NMSA) for the same period. The assumption is that the meteorological data from the local weather stations represent the surrounding localities whose inhabitants are served by the respective health facilities. These daily data were collapsed into weekly data to correspond with the weekly malaria cases. The weekly mean for minimum and maximum temperature and the total weekly amount of rainfall were calculated from the daily records.The data set consists of records with missing values for some of the variables . Because of the multiple time lags considered in the analysis (see below), discarding estimates with a single missing value results in multiple records lost for each missing value. To avoid this substantial loss of data, missing values of the independent variables (weather variables) were interpolated by fitting a linear regression using the value from the previous week and a dummy variable for that week. A new variable was created with original values for non-missing data points and interpolated values for missing data points.The daily average number of cases  was modeled using a robust Poisson regression (implemented in Splus), using weekly rainfall, minimum and maximum temperature as explanatory variables in a distributed lag model. The model was:E(Yst) denotes the expected value for the daily average number of malaria cases at site s on week t; ,  and Rt - i are the weekly minimum and maximum temperature and the rainfall i weeks previously; αs and βs represent the intercept and coefficient for time trend (t), which are specific to the site under consideration.where βi.As described in the introduction, biological considerations suggest that the lag between rainfall and associated malaria cases will be different from that between temperature and associated cases. Therefore, lags of 4–12 weeks were considered for rainfall, and relatively shorter lags of 3–10 weeks for the minimum and maximum temperature. The assumption is that changes in temperature and rainfall at a particular time do not have an important influence after 10 and 12 weeks respectively. The overall effect of a unit increase in minimum temperature (for example) is the sum of the coefficients A large number of variables  were introduced for the three weather factors in this model. The efficiency of the coefficient estimates may be affected due to the large number of parameters to be estimated and the possibility of multicollinearity between the lagged weather factors. When lag terms are put in the same model, correlation between measurements of weather on weeks close together will cause a high degree of collinearity that may result in unstable estimates.βi,γi,θi, forcing each of them to take the form of a (separate) 4th-degree polynomial in i. This reduces the number of degrees of freedom for each weather factor from the number of lags considered to five and circumvents some of the difficulties associated with estimation of coefficients for multiple lags, including instability of estimates due to collinearity of the different lags of the same variable. With this model the coefficients for lagged minimum temperature, for example, are assumed to take the form:A polynomial distributed lag (PDL) model  imposes φk is the parameters of the d-th degree (here d = 4) polynomial distributed lag. To estimate the parameters describing the polynomial lag φk equation (2) was substituted into the unconstrained distributed lag model (1) to obtain a constrained polynomial distributed lag model:where The effects of weather factors on the number of malaria cases were distributed over multiple weeks and the separate analysis for each district (not shown) indicated heterogeneity in magnitude and direction of the effects of the weather factors. Districts with similar climatic characteristics were grouped, in order to reduce the effect of random error and to produce more reliable and precise estimates of weather effects. Moreover, this approach will produce more generalizable results within similar climatic conditions. Thus, the districts were grouped into hot  and cold on the basis of altitude and temperature. The hot districts included Diredawa, Nazareth, Wolayita and Zeway; and the cold districts included Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma. Two separate PDL models were fitted to estimate the effects of different weather factors.A basic issue in epidemiological analysis is controlling for the effect of confounding factors. Malaria transmission is affected by different factors and shows a systematic variation over time. To control for other factors that may affect the long-term trend, a time variable was included in the model, which would remove the long-term wavelength patterns, leaving the deviations representing short fluctuations. Since the long-term trend and the numbers of weekly cases vary between districts, an interaction term between the time variable and district (dummy variable) was introduced.Urban areas may have other sources of breeding sites for mosquito not driven by rainfall. To examine the influence of these and other unmeasured factors that vary between urban and rural environments, separate models were fitted for urban and rural cases.P. falciparum cases from a health facility in each of 10 districts over an average of 10 years and meteorological data from local stations in each of the districts. Table The data set consists of microscopically confirmed The effect of rainfall and temperature on daily average microscopically confirmed cases was estimated by lag in the 10 districts grouped into two climatic zones, hot and cold Table . Figure Similarly, rainfall is significantly associated with malaria cases in the hot districts. Compared to the cold districts, a significant and positive effect of rainfall in the hot districts manifests at relatively shorter lags (six, seven, eight, nine and ten weeks) and remains positive afterwards but declines and becomes non-significant for the longer lags Figure . Much ofFigure In the hot districts, by contrast, the effect of minimum temperature on malaria cases is more complicated. At short lags, its effect is small and non-significantly positive Figure . A signiFigures To test the linearity of the association between the weather factors and malaria cases, a three dimensional relationship between weather factors, lag and the magnitude of effect at each lag was explored  and cold (Alaba, Awasa, Bahirdar, Debrezeit, Hosana and Jimma) environments in Ethiopia.The development of malaria early warning systems ,8 to preThe findings are largely consistent with hypotheses based on the relationship between weather factors and mosquito and parasite development. Rainfall is associated with malaria cases in both hot and cold districts with a lagged effect, and as expected, this lag is shorter in hot districts. The effect of rainfall on malaria is linear with saturating effects at higher rainfall levels  and a small increase in temperature substantially shortens the duration of these phases (to 37 days at 17°C). Similarly, the duration of the sporogony cycle will be short with increasing temperatures  correlation between weekly malaria cases and maximum temperature at shorter lags seen in hot districts may be due to its inhibitory and lethal effect on the survival of the parasites in the mosquitoes . The surAs with all observational studies of malaria incidence and weather, a limitation of this study is the likely presence of some confounding factors that may have influenced the number of malaria cases and may have been associated with weather. Existing interventions such as insecticide residual spraying and other methods are routinely applied and were not included in this analysis. The results would have been biased by such confounding factors if interventions were undertaken on the basis of weather, or if they were undertaken on the basis of incidence and their effect was differential depending on the weather. Another minor problem is with the assumption of a finite length for the delayed effect of the different weather factors. However, the lag length was chosen based on the inter-relationship between weather, mosquitoes and parasites Table  and it wWeather factors alone explain seasonal cycles but were not accurate in explaining the magnitude of unusually bad years Figure . This stThe findings are largely consistent with hypotheses, based on experimental data on mosquito and parasite development, about the interactions of climatic factors in determining the strength and lag structure of weather effects on falciparum malaria incidence. In the examined Ethiopian districts, weather-based predictors of malaria incidence are more useful in rural than in urban settings. These key points should be considered in the development of an early warning system for malaria.HDT and ML conceived the study, undertook statistical analysis and drafted the manuscript. AT initiated the study and made data available in collaboration with WHO and Ministry of Health of Ethiopia. JS made major contributions to the study design and statistical analysis. All authors contributed to the writing of the manuscript and approved the submitted version of the manuscript.Three dimensional relationships between weather, lags and magnitude of effect at each lagClick here for file


MONKEY is a new method for identifying conserved transcription-factor binding sites from multiple-sequence alignments. Saccharomyces, we illustrate how the significance of real sites increases with evolutionary distance and explore the relationship between conservation and function.We introduce a method (MONKEY) to identify conserved transcription-factor binding sites in multispecies alignments. MONKEY employs probabilistic models of factor specificity and binding-site evolution, on which basis we compute the likelihood that putative sites are conserved and assign statistical significance to each hit. Using genomes from the genus  Different types of genomic features have characteristic patterns of evolution that, when sequences from closely related organisms are available, can be exploited to annotate genomes . Methodsbona fide targets of the transcription factor [Sequence-specific DNA-binding proteins involved in transcriptional regulation (transcription factors) play a central role in many biological processes. Despite extensive biochemical and molecular analysis, it remains exceedingly difficult to predict where on the genome a given factor will bind. Transcription factors bind to degenerate families of short (6-20 base-pairs (bp)) sequences that occur frequently in the genome, yet only a small fraction of these sequences are actually n factor . A majorcis-regulation). Each of these methods explicitly or implicitly adopts one of several distinct definition of a conserved TFBS. These include a binding site in a reference genome that is perfectly or highly conserved [In the past few years, several methods have been introduced to identify conserved  TFBSs for a factor of known specificity  as fewer more distantly related ones. However, aside from these qualitative expectations, the dependence of the ability to identify conserved TFBSs on evolutionary distance and tree topology has not been rigorously investigated. Because the software MONKEY can be applied to multiple alignments of varying numbers of species and produces scores that can be meaningfully compared across different sets of species, we are now able to address these issues.We developed an approach to identify conserved TFBSs that combines probabilistic models of binding-site specificity -22 with MONKEY, our tool for identifying conserved TFBSs, takes as input a multiple sequence alignment, a tree describing the relationship of the aligned species, a model of a transcription factor's binding specificity and a model for background noncoding DNA. It returns, for each position in the alignment, a likelihood ratio comparing the probability that the position is a conserved binding site for the selected factor compared to the probability that the position is background.fib - represent the probability of observing the base b (A, C, G or T) at the ith position in a binding site of width w. For the model of the background, we use a single set of base frequencies gb.For the model of binding specificity, we use a traditional frequency matrix -22. The X of width w under the motif model (a frequency matrix, designated as motif) to the probability of having observed X under the background model (designated by bg), which can be easily reduced to:A widely used statistic for scoring the similarity of a single sequence to a frequency matrix is the log likelihood ratio comparing the probability of having observed a sequence Xib is an indicator variable which equals 1 if base b is observed at position i, and zero otherwise.where Mib = log(fib/gb), and find S for a particular w-mer by adding up the entries that correspond to the bases in the query sequence.This classifier can be motivated by the approximation that the data are distributed as a two-component mixture of sequences matching the frequency matrix and sequences drawn from a uniform background. In practice, we compute this score using a position-specific scoring matrix (PSSM) with entries, X and Y, we want to perform the same calculation on their common ancestor A. Since A is not observed, we consider all possible ancestral sequences by summing over them, weighting each by their probability given the data (X and Y), the phylogenetic tree (T) that relates the sequences, and a probabilistic evolutionary model [In extending this to a pair of aligned sequences ry model .X and Y are a conserved example of the binding site represented by the frequency matrix to the hypothesis that they have been drawn from the background:We can write a new score representing the log-likelihood ratio that compares the hypothesis that Rmotif and Rbg are rate matrices describing the substitution process of the binding site and background respectively. Using the conditional independence of the sequences X and Y on the ancestor, A, and writing TAX for the evolutionary distance separating sequence X from A, this becomes:where p(Xi|Ai, t) = eRt, that represents the probability of observing each base at position i in the extant sequence (X) given each base in the ancestral sequence (A) after t units of evolutionary time or distance, given some rate matrix, R [The class of evolutionary models used by MONKEY define a substitution matrix, atrix, R . Since t(X, Y, ..., Z), by replacing the probabilities of X and Y with the probability with the left and right branches of the tree below, and performing the calculation at the root. The probabilities of the left and right branches of the tree can be calculated recursively as has been described previously [This can be extended to more than two sequences, that is, eviously .Once again, for practical purposes we can convert these scores to a PSSM, whose entries are given for the pairwise case by:a and b in the two sequences. For multiple alignments of n species, each position requires 4n entries.where at each position we now index by the bases The use of evolutionary models is critical to the function of MONKEY. Myriad of such models exist, and in principle all can be used in MONKEY. For the background, it is natural to use a model appropriate for sites with no particular constraint, such as the average intergenic or synonymous rates. MONKEY allows the use of the JC  or HKY  models, In principle, the JC and HKY models can also be used for the motif, with rates set according to our expectation of the overall rate of evolution in functional binding sites, which has been estimated as two to three times slower than the average intergenic rate . Howeveri in the motif is given by:In using this model, we assume that sequences evolve under constant purifying selection to maintain a particular set of equilibrium base frequencies. The use of this model corresponds to a definition of a conserved TFBS as a sequence position where there has always been a binding site for the transcription factor. Although the model does not strictly require that a binding site be present in each of the observed species, positions lacking such sites will have lower probabilities as they require the use of less probable substitutions. The rate of change from residue a to b at position Q is the (position independent) underlying mutation matrix, which we set equal to the background model (Q = Rbg), and f is the frequency matrix describing the specificity of the factor. Thus, for each position in the motif, the HB model predicts the rates of each type of substitution as a function of the frequency matrix, and the background model.where p-value for a score S in a single sequence requires the enumeration of all possible w-mers that have a score S or greater under the background model. For n aligned sequences this requires the enumeration all 4wn possible sets of aligned w-mers with scores S or greater under the background model. While the number of possible alignments of n w-mers can be unmanageably large for even small values of n and w, because we treat each position independently we can enumerate these possibilities efficiently using an algorithm developed for matrix searches of single sequences [To compare scores from different evolutionary distances and different factors, it is critical that we are able to assign significance to a particular value of the score. To do so, we need to compute the distribution of the score under the null hypothesis that the sequence is part of the background. Calculating a equences ,29.w numbers, one from each column of the matrix. The probability of observing exactly score S is the number of paths through the matrix whose entries add up to S, weighted by the probability of the path. By converting the matrix to integers, we can compute this probability for all values of S recursively. We initialize Pi(S) (the probability of observing score S after i columns in the matrix) by setting P0(S) = 1 for S = 0, and P0(S) = 0 for S ≠ 0. We then compute the values of the function for i = [1, w] as follows:Every observed score is a sum of c represents a column in the alignment, and the sum is over all 4n possible columns an alignment of n sequences. The probability distribution function (PDF) of scores is Pw(S), and from this the cumulative distribution function (CDF), the probability of observing a score of S or greater, can be directly computed. Although in principle we can compute the probabilities to arbitrary precision, because the time complexity increases with the number of possible scores, we limit the precision to within approximately 0.01 bits.For aligned sequences, p-values from 5,000 pairs of sequences evolved in a simulation  with those computed by this method, and shows that they agree closely. We have used this method to compute the CDFs for alignments of up to six species, and therefore can apply our method to most comparative genomics applications. We note, in addition, that the likelihood ratio scores are approximately Gaussian (data not shown). As the means and variance of the scores under each model can be computed efficiently  we can estimate p-values using a Gaussian approximation  to ps + w + (a + b), where a and b are the number of gaps in the aligned versions of sequences r and s in position p to p + w, where p is the position in the alignment of pr. For each subsequence of length w in the window, we calculate the percent identity to the reference sequence, and create an alignment of pr to pr + w (in the reference sequence) to the most similar word in the window of each other sequence. This locally optimized alignment is then scored. Note that if a and b are zero , no optimization is done. If a is too large (in most contexts greater than five) we exclude that region of the alignment from further. This heuristic encapsulates the idea that too many gaps are indicative of lack of constraint, but conservatively allows for a few gaps due to alignment or sequence imperfections.The idea is to assign a poor score to regions of the alignment with a large number of gaps, but to locally realign regions with a small number of gaps to identify conserved but misaligned binding sites. To do this, we scan along the ungapped version of one of the aligned sequences - the 'reference' sequence. For each position in the reference sequence Saccharomyces cerevisiae have recently been published and become models for the comparative identification of transcription factor binding sites [S. cerevisiae genes to their orthologs in S. paradoxus, S. mikatae, S. bayanus and S. kudriavzevii genomes using CLUSTALW  and sought to evaluate the effectiveness of MONKEY under different evolutionary models and distances.The genome sequences of several species closely related to the budding yeast ng sites ,11. We aS. cerevisiae genome could be divided into two reasonably sized sets: those known to be bound by the factor (positives) and those known not to be bound by the factor (negatives). Unfortunately, even in yeast, the number of such cases is limited. For many factors we can identify true positives by combining high- and low-throughput experimental data that supports the hypothesis that a particular position in the genome is bound by a given factor. A true negative set, however, must be constructed on the basis of lack of evidence that a sequence is functional, as the interpretation of negative results almost always is ambiguous. In the case of transcription factor binding sites this is particularly problematic, because DNA-binding proteins have overlapping specificity, and we may therefore observe conservation of a binding site because it is bound by another factor with similar specificity. After evaluating all factors with binding specificity in Saccharomyces cerevisiae Promoter Database (SCPD) [Ideally, we would use several diverse transcription factors with known binding specificity, where the set of matches to the factor's matrix in the e (SCPD) , we focubona fide binding sites, we calculated the p-values of the positive and negative sites for each factor, using MONKEY on alignments of all five genomes for Rpn4p and four species (with S. kudriavzevii excluded because too few sequences were available) for Gal4p. We compared the performance of MONKEY with the HB model to scores from S. cerevisiae alone and to a 'simple' score  that utilizes all the comparative data without an evolutionary model.To evaluate the performance of our evolutionary method in correctly identifying p-values to real sites (positives) and high p-values to spurious sites (negatives), and we therefore compared the p-values assigned by monkey based on the HB model to those based on the 'simple' score. Not surprisingly, both methods were a great improvement over searching in S. cerevisiae alone. Overall, when compared to each other, the HB score assigned lower p-values to the binding sites more often in the positive sets  and less often in the negative sets  than did the simple score. We note that some of the supposedly functional Rpn4p sites were assigned higher p-values in S. cerevisiae alone, suggesting that they are not in fact conserved; these will be discussed below.The results are summarized in Table p-values to increase over evolution. To explore the change in p-values over evolutionary distance, we scored the functional and nonfunctional sets of binding sites at a variety of evolutionary distances by creating alignments of different combinations of species . The median p-value of the positive set of TFBSs decreases monotonically with evolutionary distance, with the rate of decrease an approximately constant function of evolutionary distance  or background model (HKY), representing functional and nonfunctional binding sites, respectively . The expected p-values with different combinations of species to be primarily a function of the evolutionary distance spanned by these species, there will also be effects related to the number of species and the topology of the three. For example, in the limit of very long branch lengths, the evolutionary p-values are on the order of the power of the number of species and are independent of evolutionary distance. In contrast, in the limit of very short branch lengths, the evolutionary p-values depend only on the distance spanned by the comparison, as most of the information provided by additional species is redundant. However, because most comparisons that are actually carried out are far from either of these extremes, we sought to evaluate the effects of species numbers and tree topology for the Saccharomyces species analyzed here.The comparisons at the different evolutionary distances used in Figure p-values for all the distances analyzed in Figure First, we recomputed the expected S. cerevisiae and S. kudriavzevii is almost exactly equal to the evolutionary distance spanned by S. cerevisiae, S. paradoxus and S. mikatae  to identify our most confident predictions of conserved matches to the Rpn4p matrix. We chose the p-value cutoff of 1.85 × 10-8, which corresponds to a probability of 0.05 of observing one match at that level over the entire search (using a Bonferroni correction for multiple testing). After excluding divergently transcribed genes, there were 56 genes that contained putative binding sites at that p-value. Of 32 genes in our positive set that had sequence available for all five species, 30 had binding sites below this p-value. Of the 28 genes in the negative set for which sequences were available, only three had binding sites below this cutoff. In this  case we have ruled out nearly 90% of the negative set at the expense of less than 10% of the positives.At the distances spanned by all available sequence data, the UFD1) is annotated as involved in protein degradation, and three (YBR062C, YOR052C and YER163C) have unknown functions.Examining the expression patterns of these genes Figure  allows tFinally, and most surprising from the perspective of comparative annotation, is a third set of 14 genes, including one from our original positive set and three from our negative set, most of which show no evidence of the proteasomal expression pattern associated with Rpn4p Figure . It is ep-values of individual positive Rpn4p sites at different evolutionary distances. While most of the sites follow the trajectory predicted for sites evolving under the HB model, the p-values for four of the positive sites seem to be well-modeled by the 'background' or unconstrained model. This is surprising because we expect these binding sites to be functional, and therefore under purifying selection. One explanation is that some of these sites may have been misannotated as functional. For example, in addition to a nonconserved positive site, the upstream region of REH1 contains another binding site that is a weaker match to the Rpn4p matrix  for comparative sequence analysis capable of functioning on relatively large numbers of related species, and enabled the examination of several important questions in comparative genomics. While most previous approaches to this problem have used heuristics to define conserved and nonconserved TFBSs, with the probabilistic scores and S. cerevisiae  for sequencing multiple closely related sequences. First, there may simply be no extant species at the exact evolutionary distance desired. Second, the quality of DNA alignments is expected to be much higher for multiple closely related species than for more distant pairwise alignments - if alignment errors prevent correct assignment of orthology, conserved binding sites will not be identified. For the factors considered here, the pairwise comparison performed nearly as well as the multiple species comparison well beyond the evolutionary distances at which pairwise alignments are reliable , suggest species . SequencSaccharomyces species. In our search, 41 of 56 (73%) predicted sites were found near genes showing the expected expression pattern, and are therefore likely to be functional. Even at this level of stringency, however, there are highly conserved sequences that match the matrix, but do not appear to be near genes that are regulated by Rpn4p. It is very unlikely that these sites are conserved by chance. One possible explanation for this high degree of conservation is that these are functional sites, but that the expression of these genes is not accurately detected in high-throughput assays, or their function has not been accurately determined. A more likely possibility is that these sites are conserved because they perform other, unknown functions. Consistent with this hypothesis is the fact that many of these matches fall near other highly conserved sequences  we have also found nonconserved examples among the TFBSs in the SCPD database , all of which have at least some direct experimental support.In addition to the conserved sequences that are unlikely to represent d Figure . Once agCis-regulatory changes have been proposed to be an important source of genetic variation [Drosophila melanogster and D. pseudoobscura .Another potential explanation is that these binding sites are actually conserved, but were not aligned correctly. While this is difficult to rule out in general, in the few nonconserved cases for Rpn4p at least we could not find (by eye) errors in the alignments. Most interesting, of course, would be the situation where these nonconserved binding sites are not due to some error on our part, but rather represent a biological change in the functional constraints on these sequences, possibly resulting in a change in the regulation of the expression of these genes. Our results represent an upper bound on the number of TFBSs for which this has occurred. ariation . Gains aariation ,39, and vice versa. Our results suggest that development of methods that model the evolutionary relationships between species and the evolution of the genomic features of interest yield insight into the challenges for comparative genomics.We have developed a method to identify conserved TFBSs in sequence alignments from multiple related species that provides a quantitative framework for evaluating results. The method - implemented in the open-source software MONKEY - extends probabilistic models of binding specificity to multiple species with probabilistic models of evolution. We have found that a probabilistic evolutionary model  that assp-values shown in Figure w were generated according to the average intergenic base frequencies of the S. cerevisiae genome. These were then evolved according to the Jukes-Cantor substitution model, to a specified evolutionary distance. Likelihood ratio scores and p-values were then calculated for each of the pairs of sequences using the method implemented in MONKEY. Finally, all pairs of sequences were ranked by their scores, and the rank divided by the total number of pairs was taken as the empirical p-value.To generate the empirical S. cerevisiae genes to their orthologs in S. paradoxus, S. mikatae, S. bayanus and S. kudriavzevii by taking the 1,000 bp upstream of each gene, identifying the corresponding region from the other species using data in the Saccharomyces Genome Database [S. cerevisiae coding sequence. We used this strategy rather than simply aligning intergenic regions to control for differences in alignments that might arise from the use of variably sized regions.We aligned the upstream regions of all Database , aligninDatabase  and trimS. cerevisiae background frequencies (ACGT) = (0.3, 0.2, 0.2, 0.3) and the median of the branch lengths estimates as the 'background' evolutionary model. The trees with these branch lengths were used as input to MONKEY to calculate p-values. The distances in Figure S. cerevisiae and S. paradoxus (0.194); S. cerevisiae and S. mikatae (0.403); S. cerevisiae, S. paradoxus S. mikatae (0.477); S. cerevisiae and S. bayanus (0.559); S. cerevisiae, S. paradoxus, S. mikatae and S. bayanus (0.816); S. cerevisiae, S. paradoxus, S. mikatae, S. bayanus and S. kudriavzevii (1.090).To obtain estimates of the evolutionary distance spanned by each comparison, we ran PAML  on the eRpn4p: we used Rpn4p sites in proteasomal genes [al genes ,43 to bual genes ,31 and cθ is the 'uncentered correlation', a commonly used distance metric for gene-expression data [n, that are available for each gene. All matches to the Rpn4p matrix (S. cerevisiae likelihood ratio score > 9) in the upstream region of a gene that matched the proteasomal expression pattern (t > 8) were considered to be true Rnp4p sites. The negative set consists of all sites that matched the Rpn4p matrix with a score greater than 9, and excluded sites in genes with even weak similarity to the proteasomal expression pattern (t > 0) or that were annotated [where ion data . Our sconnotated  as involGal4p: we used the matrix from SCPD [S. cerevisiae genome with a cutoff equal to the lowest score in the positive set and then eliminated any binding sites near genes that showed evidence for regulation in the systematic studies.rom SCPD   is calculated as above, and we define:where V:We can write a similar expression for the variance, In order to predict the scores for the genes in our positive and negative sets, we are interested in the case were we have observed a match to the motif in one species, but the constraints on its evolution are either those of the background or the motif. We can compute the expected scores under these assumptions as follows:p(Xi|motif) is the single species probability of observing the base Xi at position i in the specificity matrix (f), and using Bayes' theorem:where p(Xi, Yi) and p(Yi|Xi) with p(Xi, Yi, ..., Zi) and p(Yi, ..., Zi|Xi) and changing the sum over Yi to a sum over all the other leaves in the tree except the reference, in this case, Xi. For the functional set, we assumed the binding sites were evolving under the HB model [This calculation can be extended to the multiple species case, by replacing the distributions HB model , and forS. cerevisiae transcription factors. Additional data file Drosophila melanogaster genome, with the binding sites grouped according to whether the cluster has regulatory activity.Additional data file S. cerevisiae transcription factorsThe fraction of binding sites that are not conserved for several different Click here for additional data fileDrosophila melanogaster genome, with the binding sites grouped according to whether the cluster has regulatory activityThe conservation p-values of predicted binding sites in high-density binding site clusters in the Click here for additional data file


Activation of inflammatory pathways plays an important contributory role in coronary plaque instability and subsequent rupture, which can lead to the development of acute coronary syndrome (ACS). Elevated levels of serum inflammatory markers such as C-reactive protein (CRP) represent independent risk factors for further cardiovascular events. Recent evidence indicates that in addition to lowering cholesterol levels, statins also decrease levels of inflammatory markers. Previous controlled clinical trials reporting the positive effects of statins in participants with ACS were designed for very early secondary prevention. To our knowledge, no controlled trials have evaluated the potential benefits of statin therapy, beginning immediately at the time of hospital admission. A previous pilot study performed by our group focused on early initiation of cerivastatin therapy. We demonstrated a highly significant reduction in levels of inflammatory markers (CRP and interleukin-6). Based on these preliminary findings, we are conducting a clinical trial to evaluate the efficacy of another statin, fluvastatin, as an early intervention in patients with ACS.The FACS-trial (Fluvastatin in the therapy of Acute Coronary Syndrome) is a multicenter, randomized, double-blind, placebo-controlled study evaluating the effects of fluvastatin therapy initiated at the time of hospital admission. The study will enroll 1,000 participants admitted to hospital for ACS (both with and without ST elevation). The primary endpoint for the study is the influence of fluvastatin therapy on levels of inflammatory markers (CRP and interleukin-6) and on pregnancy associated plasma protein A (PAPP-A). A combined secondary endpoint is 30-day and one-year occurrence of death, nonfatal myocardial infarction, recurrent symptomatic ischemia, urgent revascularization, and cardiac arrest.The primary objective of the FACS trial is to demonstrate that statin therapy, when started immediately after hospital admission for ACS, results in reduction of inflammation and improvement of prognosis. This study may contribute to new knowledge regarding therapeutic strategies for patients suffering from ACS and may offer additional clinical indications for the use of statins. During the past decade, inflammation has often been cited as a major factor in the pathogenesis of atherosclerosis and its clinical sequelae, including ischemic heart disease. It was found that traditional risk factors such as hypertension, hypercholesterolemia, diabetes, and smoking could not fully account for the development of coronary stenosis in all patients suffering from ischemic heart disease. Intensive study of the pathogenesis of coronary plaque development and rupture led to the hypothesis that inflammatory factors contribute to this process. For example, T-lymphocytes and monocytes/macrophages have been repeatedly identified in plaque lesions; elevated levels of acute phase proteins (C-reactive protein, serum amyloid A, fibrinogen), cytokines (interleukin 1, interleukin 6, interleukin 8, tumor necrosis factor), and adhesive molecules (ICAM-1) correlate with the worse prognoses in patients with ischemic heart disease . FurtherActivation of the immune reaction in acute ischemic heart disease likely derives from: (i) pathological events occurring in the arterial wall where the lesion develops, leading to plaque rupture and the subsequent clinical consequences and (ii) myocardial necrosis, which triggers processes involved in removal of the necrotic mass and replacement with scar tissue. Whereas activation of plaque inflammation (as noted above) serves as a marker for plaque instability, elevation of inflammatory factors from the second source correlates with the extent of myocardial necrosis.Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors, lower cholesterol levels by decreasing the production of low-density lipoproteins (LDL) and up-regulating the expression of the LDL-receptor. These drugs are widely used in patients with hypercholesterolemia for primary and secondary prevention of coronary artery disease because of their efficacy in reducing cardiovascular morbidity and mortality -12. SurpA number of large clinical trials have been designed to investigate the effect of statins in treating acute coronary syndromes (ACS) -30 BecauLittle information is available on the efficacy of statins in treating ACS at an earlier phase, i.e., at the time of hospital admission. Recently published data from experimental projects -35 and fThe objectives of the FACS trial are to determine:(i) Whether initiation of fluvastatin therapy in patients with ACS immediately after hospital admission decreases levels of CRP, IL-6, and pregnancy-associated plasma protein A/ proform eosinophilic major basic protein (PAPP-A/proMBP), which represent indirect markers of plaque instability and indicators of poor prognosis; and(ii) Whether initiation of fluvastatin therapy decreases the occurrence of ischemic events  in patients with ACS.This is a prospective, 30-day, multicenter, randomized, double-blind, placebo-controlled study in 1,000 patients with ACS. Patients are enrolled from 10 sites in the Czech Republic and Slovakia. At each institution, the protocol and the informed consent form are reviewed and approved by the institutional ethics committee before study initiation. Eligible patients are randomized to one of two treatment groups immediately after hospital admission (within one hour). One group is assigned 80 mg/day fluvastatin (Lescol XL), with the other group receiving placebo. Participants are followed on an intention-to-treat basis. The primary endpoint relates to levels of CRP, IL-6, and PAPP-A/proMBP. The secondary endpoint is the occurrence of an ischemic event, defined as death, nonfatal myocardial infarction (MI), recurrent symptomatic myocardial ischemia, cardiac arrest with resuscitation, and urgent revascularization.This study will enroll high-risk patients admitted to the hospital for ACS. Eligible patients with ST elevation ACS must have resting chest pain less than 12 hours before admission and either ≥ 1 mm ST-segment elevation in 2 or more continuous leads or new left bundle branch block on ECG. Those with non-ST elevation ACS must have resting chest pain during the previous 48 hours and either ≥ 1 mm ST segment depression or negative T waves in 2 or more continuous leads.Subjects are excluded from study participation if they are <18 years of age or if they have concomitant active liver disease or persistent elevation of transaminases , a history of lipid-lowering therapy less than 30 days before the index event or a known allergy to fluvastatin or to any additives present in the drug. Other exclusions include inability to ingest oral medication, unwillingness to be followed for the duration of the study, muscle disease (e.g., myositis), and creatine kinase ≥ 5 times the upper limit of normal due to conditions other than myocardial infarction. Women of childbearing potential who are pregnant, nursing or who are not using effective contraception will also be excluded.After obtaining informed consent, blood samples are taken from patients for examination of inflammatory markers (CRP, IL-6, and PAPP-A/proMBP). Patients are then randomized to 80 mg fluvastatin (Lescol XL) or to placebo immediately p.o. Medical history and physical examination, standard 12-lead ECG, blood lipid profile, and liver function tests are performed as part of participants' routine admission care. Fluvastatin 80 mg or placebo are then taken once daily for 30 days. Follow-up measurement of inflammatory markers (CRP, IL-6, and PAPP-A/proMBP) is performed on day 2 and day 30. Follow-up visits are scheduled at pre-discharge, day 30, 90, 180 and 360. Blood liver function and creatine kinase tests are done at pre-discharge and at the 30-day visits. At day 30, the lipid profile is also examined, and study medication is withdrawn. All visits include assessment of ischemic events and recent medical history since the last follow-up visit, including use of concomitant medications  with respect to the primary endpoint (30-day decrease of CRP and IL-6) and the combined secondary endpoint . With 500 patients randomized to 80 mg fluvastatin (Lescol XL) and 500 patients randomized to placebo, the trial will have more than 80 % power to detect a decrease in CRP level by 1.36 μg/L and a decrease in IL-6 level by 1.09 ng/L. Calculations are based on a two-sample The FACS trial is the first multicenter, randomized, double-blind, placebo-controlled trial investigating the effects of fluvastatin therapy started immediately after hospital admission in patients with ACS.The author(s) declare that they have no competing interests.PO has made substantial contributions to the concept and design of the trial and has drafted the manuscript. DA, PH, JVej, JK, MMat, and JVes have made substantial contributions to the concept and design of the study and have been involved in revising the manuscript. PB, MW, OA, JS, EN, FH, MR, and JC have been involved in the acquisition and analysis of data. MMac and MK have given final approval of the version to be published.


Possible methods for distinguishing receptor binding models and analysing their parameters are considered.The conjugate gradients method is shown to be optimal for solving problems of the kind considered. Convergence with experimental data is rapidly achieved with the appropriate model but not with alternative models.Lack of convergence using the conjugate gradients method can be taken to indicate inconsistency between the receptor binding model and the experimental data. Thus, the conjugate gradients method can be used to distinguish among receptor binding models. Most medicinal preparations and biologically active substances do not penetrate into cells and must therefore exert their influence on intracellular processes by interaction with specific protein molecules at the cell surface -3, for wNon-cooperative interaction between ligand and receptor:R is the receptor molecule, L is the ligand molecule, RL is the ligand-receptor complex, and k+1 and k-1 are respectively the kinetic constants of formation and dissociation of the complex.where Cooperative interaction between ligand and receptorInteraction of one ligand with N types of binding sitesRL] are determined. The investigator has to solve two basic interrelated problems [Let us note that the ligand-receptor interaction can also involve a combination of all three of these schemes. The most frequently used method for studying ligand-receptor interactions is the radioreceptor method , based oproblems :1. discrimination among the ligand-receptor binding models (1–3 or modifications thereof);2. determination of parameters that adequately relate the model to the experimental data.From a pharmacological point of view, the most important parameters are the following:R0] , and[Kd = k-1/k+1 (dissociation constant) [onstant) The concentration of receptors and the dissociation constant can be changed. Modification of these parameter values can occur in many physiological and pathophysiological situations. For instance, the receptor concentration can reflect functional receptor modifications, and the dissociation constant can reflect genetic alterations of the receptor .To solve the two interrelated problems a series of graphic methods can be deployed, of which the most frequently used is the Scatchard method ,8. HowevRegression methods can be found for the measurement of ligand-receptor interaction constants . As a maLet us write the law of mass action for each ligand-receptor interaction scheme as:For the scheme (1)R] = [R0] - [RL], [L] = [L0] - [RL].But [So equation (4) can be rewritten:Rikkatty equations. It can be solved analytically with the help of a special substitution [R] = [R0] - [RL], [L] = [L0] - [RL] do not generate analytically soluble equations. Therefore, all equations of this form were solved numerically using the Runge-Kutta method [This differential equation relates to the class of titution , but in a method ,14.The differential equations are as follows:For scheme (2):For scheme (3):RL]u. Random error assuming the normal distribution law was superimposed on the magnitude of [RL]u, and was calculated at 5, 10, 20 or 100 points.Numerical solution of equations (5–7) was carried out to determine [RT]m was calculated using parameters other than [RL]u from models (1–3). These parameters were applied to the determination of [RL]u by the following functional minimization:The magnitude [RL]u - [RL]m)2.     (8)Φ = , Newton's method and its variants (the conjugate gradients method and coordinate descent method in various modifications) were used -17. The R0] and Kd cannot be <10-15 M or >10-5 M. Hence the iteration procedure could be improved by re-scaling these parameters logarithmically, making 10-15 M equivalent to -1 on the new scale and 10-5 M equivalent to 1.It is clear from the literature  that [R0R0] Kd can be seen. Therefore the magnification of the random error in evaluating the magnitude of [RL]u displaces the functional (8) global maximum from its true values. In a sufficiently large neighbourhood of the global maximum, the functional magnitude (8) is practically invariant. However, this modification becomes more essential for evaluating the ratio of the functional (8) to basis vector of values [RL]u. Therefore this ratio was used with the inhibiting criterion choice.The functional (8) contour plots are shown in fig. The Newton method converges only in the close neighbourhood of the global maximum. However, modifications of the Newton method using second derivatives allow convergence to the global maximum after 1–2 iterations (fig. RL]u was taken into account, the convergence of the conjugate gradients method varied less than that of the Newton method.The conjugate gradients method converged after 2–3 iterations fig. , line 2.The coordinate descent method required an indeterminately large number of iterations before satisfactory convergence was reached. Use of the exhausting coordinate descent method accelerated the convergence procedure, but the number of iterative steps remained large fig. , line 3.It can be shown that 5 points suffice to identify the parameters of model (1) using the conjugate gradients method, whereas this method required >10 points for identifying the parameters in a more complicated model. The Newton methods required >7 and 12 points respectively, and the coordinate descent method required >10 and 18 points.RL]m using an incorrect binding model. In particular  behaviour was analysed with respect to the evaluation of [see fig. , the funThus, the modification of the functional (8) contour plot from the type in fig. It appears that when an incorrect choice of the receptor binding model has been made, the conjugate gradients method does not lead to convergence, whereas in some cases the Newton method converges to one of the local minima. Therefore, lack of convergence using the conjugate gradients method suggests an incorrect choice of receptor binding model.even when the application of graphical methods is difficult or impossible. As seen here, lack of convergence in the conjugate gradients method indicates that an incorrect choice of model has been made. It is also shown that for the defining the parameters of the correct model, 5–10 data points are sufficient.Possible methods have been explored for discriminating among models for receptor binding model and for defining the relevant parameters. The procedure devised allows one to determine the receptor binding model and its parameters,