The mechanism(s) of nociceptive dysfunction and potential roles of opioid neurotransmitters are unresolved in the chronic pain syndromes of fibromyalgia and chronic low back pain.6-Phe7 (MEAP) and nociceptin immunoreactive materials were measured in the cerebrospinal fluid by radioimmunoassays.History and physical examinations, tender point examinations, and questionnaires were used to identify 14 fibromyalgia, 10 chronic low back pain and 6 normal control subjects. Lumbar punctures were performed. Met-enkephalin-ArgFibromyalgia  and low back pain  groups had significantly higher MEAP than the normal control group . MEAP was inversely correlated to systemic pain thresholds. Nociceptin was not different between groups. Systemic Complaints questionnaire responses were significantly ranked as fibromyalgia > back pain > normal. SF-36 domains demonstrated severe disability for the low back pain group, intermediate results in fibromyalgia, and high function in the normal group.Fibromyalgia was distinguished by higher cerebrospinal fluid MEAP, systemic complaints, and manual tender points; intermediate SF-36 scores; and lower pain thresholds compared to the low back pain and normal groups. MEAP and systemic pain thresholds were inversely correlated in low back pain subjects. Central nervous system opioid dysfunction may contribute to pain in fibromyalgia. Fibromyalgia (FM) is an enigmatic condition characterized by increased complaints of widespread pain with tenderness to palpation . The tenThe pain present in FM may induce antinociceptive neural mechanisms with the release of opioid peptides. This hypothesis was tested by measuring opioid peptides in cerebrospinal fluid, and comparing these levels to systemic pain thresholds, subjective complaints, and quality of life measures in 3 sets of volunteers. FM and Chronic Fatigue Syndrome often overlap . Chronic6-Gly7-Leu8, and Met-enk-Arg6-Phe7 (MEAP) [Two peptides were selected for measurements because they were involved in antinociceptive responses. Alternatively, dysfunction of their release could predispose to chronic pain. Preproenkephalin A is the precursor for leucine-enkephalin, methionine-enkephalin (Met-enk), Met-enk-Arg7 (MEAP) ,10. Noci7 (MEAP) .FM, LBP and Normal control subjects between the ages of 18 and 70 years were recruited to this IRB-approved protocol from rheumatology and orthopedics clinics, advertisements, and word of mouth. Normal subjects were pain and chronic fatigue free, had no diabetic, neurologic, inflammatory, autoimmune, or other chronic disorder that could predispose to pain, alterations in sensation, or known variations in cerebrospinal fluid composition. LBP inclusion criteria were (i) dominant pain complaint of low back pain, and (ii) imaging studies within the past 6 months. Exclusion criteria were: (a) evidence of a lumbar fracture or tumor to explain the pain, (b) any chronic illness that may affect functional status such as diabetes, cancer, chronic obstructive pulmonary disease, chronic inflammatory diseases, renal insufficiency or similar debilitating disorders, (c) previous back or neck surgery, and (d) FM or Chronic Fatigue Syndrome. FM subjects had a prior clinical diagnosis of FM including widespread pain affecting all 4 quadrants and the axial skeleton lasting at least 3 months that was not explained by any other chronic illness, and the presence of at least 11 of 18 tender points when manual, digital pressure of ~4 kg was applied [This self – report questionnaire containing 44 queries grouped into the following modules: (i) Fatigue; (ii) Musculoskeletal: morning stiffness, muscle pain, muscle spasms, dry eyes, dry mouth, fingers sensitive to the cold, fingers turn blue and/or white in the cold, swollen lymph nodes, swollen joints, fever; (iii) Chest: shortness of breath (SOB), SOB when hurrying on level ground or walking up a slight hill, SOB when walking with other people of own age on level ground, stop for breath when walking at own pace on level ground, SOB when washing or dressing, rapid heart rate, chest pain, irregular heart rate, palpitations; (iv) Headaches: migraine or tension type; (v) Neurological: numbness or tingling of hands or legs, inability to concentrate, problems with memory, dizziness; (v) Ear, Nose and Throat (ENT): problems with balance, hearing loss, ear pain, sensation of ear blockage or fullness, ringing in the ears, sinus pain; (vi) Bladder: urinary urgency, pelvic discomfort / pain / pressure, persistent bladder fullness after urination, dysuria; and (vii) Irritable Bowel Syndrome (Rome I criteria): abdominal pain relieved with bowel movement, abdominal pain with a change in frequency or consistency of stool, changes in stool consistency, changes in stool form (hard or loose/watery), changes in passing of stool, bloating or feeling of abdominal distention, passage of mucus, nausea or vomiting . SubjectMOS SF-36[Subjects completed the MOS SF-36. The dom2 rubber stopper making contact with the skin (Chatillon, etc.) [2 recorded. The average pressure causing pain was the Average Pain Threshold. FM and Normal subjects had manual digital pressure examinations of these points [All subjects had pressure testing at 9 bilateral sites 18 total) using a hand held dolorimeter  with a 1 cm8 total uLumbar punctures were performed using local anesthetic and 23G spinal catheters. Volumes of 4 to 8 ml were obtained, centrifuged, aliquoted, and immediately frozen at -80°C. Samples were shipped on dry ice to Dr. Lars Terenius for measurement of neuropeptides. Neuropeptides were extracted from 1 ml aliquots using C-18 SepPak cartridges, eluted, dried, and resuspended for validated radioimmunoassays for MEAP ,14 and nGeometric mean and 95% confidence intervals were determined for each neuropeptide, with arithmetic means and 95% CI's for all other variables. Differences between groups were assessed by ANOVA. Differences between means for each pair of groups were assessed by 2-tailed unpaired Student's t-tests with Bonferroni corrections for multiple comparisons. Significance was ascribed for p < 0.05.Lumbar punctures were performed on 14 FM , 10 LBP  and 6 Normal  subjects. The averages and ranges of ages for these 3 groups were similar . There were 4 African-Americans in the FM group, and 1 each in the LBP and Normal groups, and 1 Asian in the FM group. The remainder was Caucasian.Significant differences were found between FM and Normal results by 2-tailed unpaired Student's t-tests with p < 0.05 after Bonferroni corrections for multiple comparisons of this data. All of the FM subjects complained of fatigue figure . Other hDomain Scores for the Normal group were all near the predicted values of 100 figure . FM scor2) compared to the LBP  and Normal  groups (figure Dolorimetry identified significantly lower pressure pain thresholds for FM (1.51 kg/cms figure . The numThe 3 groups had significantly different mean CSF concentrations (p = 0.0014, ANOVA). The normal volunteers had significantly lower geometric mean concentrations  than the FM , and LBP  (figure Nociceptin – irm was not different between FM (4.27 pg/ml, 3.22 to 5.66, n = 14), LBP (4.52 pg/ml, 3.12 to 6.55, n = 10) and Normal (5.65 pg/ml, 2.65 to 12.04, n = 6) groups.2, which was coincidentally the lower 95% CI for the Normal group. MEAP – irm concentrations had a wide range in the FM subjects, but the geometric mean was significantly higher than for Normal subjects. Pain threshold and MEAP – irm concentrations did not have linear correlations in either the FM or Normal group. These data suggested that FM subjects were fundamentally different from Normal. When the pain threshold was below 2 to 2.3 kg/cm2, MEAP – irm levels increased approximately 4-fold compared to Normal. The combination of the widespread pain, systemic complaints, low pain threshold and high MEAP – irm concentrations in CSF was distinct from the low level of symptomatic complaints, normal (high) pain thresholds, and lower MEAP – irm levels found in the Normal group.These 2 variables were correlated  when all subjects were examined as a single group figure . This coThe LBP group had a chronic regional pain syndrome, no fibromyalgia, normal pain thresholds and systemic complaints, but severe disability SF-36 scores). Only the LBP group showed a linear correlation between pain threshold and MEAP – irm concentrations  of CFS were probably independent of those responsible for the elevated MEAP – irm levels in FM. Unfortunately, CFS subjects without FM could not be simultaneously tested to determine if their MEAP – irm levels were normal (as would be predicted).Nociceptin – irm levels were the same in our three groups. The levels were about 10% of that found in women during labor . It was The Normal group had Systemic Complaints and SF-36 scores in the normal ranges, high pain thresholds, low numbers of manual and dolorimetry-derived tender points and low cerebrospinal fluid MEAP and nociceptin concentrations. The LBP chronic regional pain group had similar Systemic Complaints scores, systemic pain thresholds and dolorimetry-defined tender point counts. However, most of their SF-36 results were near zero indicating the worst level of impairment of the 3 groups. MEAP – irm was just significantly elevated compared to the Normal group, and was correlated to the systemic pain threshold. The FM group was distinct since they had widespread pain complaints, the highest Systemic Complaints scores, the lowest pain thresholds, and highest numbers of tender points of the 3 groups. Their SF-36 scores were intermediate between the LBP and Normal groups. MEAP – irm concentrations were significantly higher in the FM than Normal group. The co-existence of Chronic Fatigue Syndrome with FM did not alter the MEAP – irm concentrations. This suggested that Chronic Fatigue Syndrome mechanism(s) did not involve preproenkephalin dysfunction. Nociceptin – irm levels were not different between these groups, and were lower than previously reported results from pregnant women in labor. Significant differences in MEAP – irm concentrations from previous studies may be due to the highly controlled definition of patients in this study, selection of control groups, and differences in peptide extraction methods.6-Phe7; SF-36, short-form of 36 questions with the following domains: PF, physical functioning; SF social functioning; PP, role limitation due to physical problems; EP, role limitation due to emotional problems; MH, mental health; E/F, energy / fatigue; P, pain; GP, general perception of health; CH, change in health; Systemic Complaints domains: MS, musculoskeletal; HA, headache; Neuro, neurological; ENT, ear, nose & throat; IBS, irritable bowel syndrome (Rome I criteria); SOB, shortness of breath.CSF, cerebrospinal fluid; FM, fibromyalgia; fMRI, functional magnetic resonance imaging; irm, immunoreactive material; LBP, low back pain; MEAP, Met-enk-ArgThe author(s) declare that they have no competing interests.JNB compiled the results and wrote the manuscript. DJC oversaw the clinical investigations that were performed by JC. GW was responsible for the sample repository, shipping, and collection of data. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Inhaled side-stream tobacco smoke brings in all of its harmful components impairing mechanisms that protect the airways and lungs. Chronic respiratory health consequences are a complex multi-step silent process. By the time clinical manifestations require medical attention, several structural and functional changes have already occurred. The respiratory system has to undergo an iterative process of injury, healing and remodeling with every exposure.To have a better understanding of the initial changes that take place when first exposed to environmental tobacco smoke, we have developed an exposure model, using the frog palate that closely represents the features of obstructive airways where ciliary dysfunction and mucus hypersecretion occur.Mucus transport was significantly reduced, even after exposure to the smoke of one cigarette (p < 0.05) and even further with 4-cigarettes exposure (p < 0.001). Morphometric and ultrastructural studies by SEM show extensive areas of tissue disruption. Gelatinase zymography shows activation of MMP9 in mucus from palates exposed to tobacco smoke.The clearance of mucus on the frog palate is significantly reduced after exposure to environmental tobacco smoke. Cilia and the extracellular matrix are anatomically disrupted. Tobacco smoke triggers an increased activity of matrix metalloproteinases associated with a substantial defoliation of ciliated epithelium. These studies enhance the knowledge of the changes in the mucociliary apparatus that occur initially after exposure to environmental tobacco smoke, with the goal of understanding how these changes relate to the genesis of chronic airway pathologies in humans. Respiratory diseases, infectious and non-infectious, are a prime cause of morbidity, mortality and health system utilization in many countries. Exposure to cigarette smoke is an important factor in causing as well as increasing complications in several pulmonary disorders. The mucociliary clearance constitutes the first line of defense to maintain the airways as free as possible of foreign bodies . ImpairmIn laboratory studies we have shown that the physical properties of mucus in nonsmokers are not altered by age or by restrictive pulmonary pathology. However, mucus properties show alterations when exposed to tobacco smoke and these alterations are noticeable in the very early stages of smoke exposure, even at an exposure level in the range of 1 to 5 cigarettes per day .The majority of studies on the outcome of tobacco use have been performed in the late phases of the process -7; the ain vivo and ex vivo epithelial injury models that replicate the features of airway diseases [Therefore our laboratory has been developing and using diseases -12. The ex vivo model, and in later studies use in vivo models.We also aim to have a better understanding of the mechanism by which ciliated epithelial cells are exfoliated after being exposed to tobacco smoke, as this may relate directly to impaired mucus clearance in several human airway diseases including chronic bronchitis and chronic obstructive pulmonary disease (COPD). Hence we decided first to test our novel Rana catesbiana, the upper portion of the head is removed following the procedures described in previous work [From a bullfrog, ous work ,11, by c2, and 15 NaHCO3 (220 mosm/l). The palate was placed inside the frog chamber, a wooden box with a glass top and fitted glass front, and manipulated trough glove openings. The humidity inside the box is maintained at 100% using an ultrasonic Pari nebulizer and the temperature is kept between 22° to 24°C by a rheostat-controlled, externally mounted light source. Before carrying out any measurement, the palate was allowed to stabilize inside the box for 15 minutes before testing.The FRS was prepared by mixing standard Ringer injection with sterile water (2:1). The composition of standard frog Ringer (in mmol/l) is 90 NaCl, 3 KCl, 2 CaClThe exposure chamber Figure  with a vThe exposure chamber was designed to allow a steady flow of side stream tobacco smoke to reach the epithelial tissue. The concentration of cigarette smoke inside the exposure chamber was maintained constant throughout the experiment by the presence of a mixing baffle on the outflow of the chamber. The concentration of tobacco smoke inside the chamber was not directly measured, but is estimated that the half palate was exposed to the smoke of each cigarette delivered, diluted in 180 liters of fresh air. As much as possible, the conditions  in the frog chamber and in the exposure chamber were maintained similar, with cigarette smoke being the independent variable.The palate was divided longitudinally in two halves along the midline as shown in Figure The palate was placed under a dissecting stereomicroscope provided with a reticulated eyepiece. Mucociliary clearance was determined by observing the movement of particles of charcoal powder gently deposited on a sample of mucus on the palate surface; its clearance was visually monitored and MTV determined. The displacement of 3 – 5 μL of endogenous frog mucus sample was calculated by dividing the distance traveled by the transit time across the 0.3 inch (7.62 mm) segment marked between 0.1 and 0.4 inches in the graduated eyepiece. At least five measurements of the time required for the mucus sample to travel the defined distance were made every time to obtain control and smoke exposure mucus transport velocity.We used cigarettes regularly available at commercial outlets of a representative brand in terms of customer preference and toxic emissions: nicotine 0.5 – 2.1 mg, tar 4 – 24 mg, carbon monoxide 5 – 25 mg, hydrogen cyanide 0.04 – 0.21 mg, benzene 0.025 – 0.069 mg, formaldehyde 0.018 – 0.1 mg. One cigarette took 17 minutes on average to burn completely in this preparation.After being exposed to one cigarette, the half palate was brought back to the frog chamber and MTV was measured in both control and exposed halves. A sample of mucus was collected from each half, and placed in separate containers and frozen at -80°C in liquid nitrogen followed by storage in a -80° freezer until analysis. The exposed half, brought back to the exposure chamber was further exposed to 3 more cigarettes . Following tobacco exposure, the half palate was again brought back to the frog chamber to measure mucus transport and collect tissue and mucus sample in both halves, control and exposed. One set of half palates (control and exposed to four cigarettes) was stored at 4°C overnight, and transport of mucus was reassessed the next day.Sample of mucus from both halves were immersed in glutaraldehyde 2.5%, and placed in properly identified separate containers for storage at 4°C for later Scanning Electron Microscope (SEM) studies. The epithelial tissue was carefully dissected and separated from the palate musculature. Samples of the palate tissue from each half were sectioned, frozen in liquid nitrogen and stored at -80°C for gelatinase zymography studies.2 0.5 mM, EDTA 10 mM, Tris-HCl 1 M, pH 6.8) was added to the ground samples (approximately 500 ul buffer per 10 mg tissue sample, 200 buffer per 10 mg mucus sample) that were sonicated for 30 seconds on ice and then centrifuged at 8000 rpm for 5 minutes at 4°C. The supernatant was collected for protein assay (BCA protein assay kit, Pierce). Normalizing the protein content as 5 μg, different amount of samples were loaded into the 7.5% separating zymography gel (30% acrylamide/ 0.8% bisacrylamide 3.75 ml, 4 × Tris-Cl/ SDS, pH 8.8 3.75 ml, H2O 6 ml, 2% gelatin A 1.5 ml, 10% ammonium persulfate 0.1 ml, TEMED 0.01 ml, for 4 gels) and were run at 100 volts for 20 minutes, 150 volts for 40 minutes. After electrophoresis, the gel was washed 3 times (20 minutes per a time) in 2.5% Triton X-100 at room temperature followed by incubation for 96 hours in zymography development buffer (0.15 M NaCl, 5 mM CaCl2, 0.05% Azide NaN3, 50 mM Tris-HCl pH 7.6). The gel was then stained for 2 hours with stain solution (Coomassie brilliant blue R-250 1 g/L, methanol: acetic acid: H2O= 2.5: 1: 6.5) followed by de-staining (ethanol: acetic acid: H2O= 1: 2: 22) overnight. The expression of the gelatinases was shown on the gel as clear bands against the dark background stained with Coomassie blue. The bands were compared with the gelatinase standards for MMP 2 and 9 running in the first lane of each gel. The density of the gels was measured in the Bio-Rad scanning densitometer.Samples of tissue, as well as mucus, were taken out of the freezer and ground to a powder in a mortar and pestal. Liquid nitrogen was added to keep the samples frozen. Homogenizing buffer , ten minutes at each step, followed by two additional periods of absolute ethanol (10 minutes each). The samples were further dehydrated by critical point drying at 31°C for 5 – 10 minutes, then mounted on a specimen holder for SEM and dried overnight in vacuum desiccators. In the final stage of preparation for viewing, the samples were sputter coated with gold (Edwards, model S150B Sputter Coater). Samples were viewed using SEM (Hitachi S-2500). Images were scanned directly to a computer and stored as image files for subsequent viewing and analysis.Data are expressed as mean ± standard deviation unless otherwise stated. A paired Student-T test was used for simple comparison. The level of significance was set at 5 %.The modified fresh frog palate exposure model was relatively easy to prepare and practical to handle. On gross examination, the surface of the palate exposed to side stream tobacco smoke did not show any macroscopic change in appearance after cigarette smoke exposure (CSE) compared to the control halves maintained in the normal chamber.MTV) was measured in both half palates prior to exposure of one half palate to cigarette smoke, and were identical (19.5 ± .03 mm/sec). Two additional MTV determinations were carried out in the control half after one and four cigarettes and after 24 h period of recovery during which time the palate was kept at 4°C. A paired T-test showed no statistical difference in mucus transport velocity among the control measurements during the entire experiment  immediately after exposure to the side stream smoke of one cigarette compared with the non-exposed half palate. Further exposure to the side stream smoke of three more cigarettes  significantly reduced MTV (p < 0.001), with no signs of recovery after 24 hours.We obtained a sample of mucus from the half palate exposed to four cigarettes and used it on the control half palate to measure MTV. Clearance was within the normal range. Immediately after, the same sample of mucus was tested on the exposed half palate and mucus clearance was again seen to be very slow. Mucus transport had basically ceased in some areas of the tobacco-smoke exposed palate.Scanning electron microscopy (SEM) studies to assess the integrity of the epithelium after exposure to side stream smoke of one cigarette showed areas where the layer of cilia looked disordered. However, we did not see loss of cilia or exfoliation of ciliated epithelial cells after careful examination under the SEM in lower and high power of the entire surface of the sample after this level of exposure.In Figure Gelatinase zymography showed increased activity of MMP-9 in mucus collected from the palates exposed to tobacco smoke of four cigarettes compared to mucus from control palates as shown in Figure Major findings in this study include: a) our amphibian mucociliary clearance model appears to be appropriate to study the early effects of tobacco smoke exposure; b) the clearance of mucus is significantly reduced after exposure to side stream tobacco smoke associated to dose response; c) ciliated cells are anatomically and physiologically acutely affected therefore drastically impairing mucus clearance. Acute irritation and inflammation of cilia due to exposure to tobacco smoke could explain this alteration; d) mucus seems to not be physiologically affected at this stage; and e) tobacco smoke triggers an increased activity of matrix metalloproteinases (in particular MMP9) associated with disruption of the extracellular matrix, most likely affecting cellular attachments to the basal membrane generating a substantial exfoliation of the ciliated cells.These findings might assist us in enhancing the knowledge of the changes in the mucociliary apparatus that occur early after exposure to environmental tobacco products, with the goal to understand how these changes relate to the development of chronic airway pathologies in humans.The frog palate has been used for several decades as a model to assess mucociliary clearance -17. Diffet al [This exposure model is an isolated system, theoretically free of interferences from other agents or systemic physiological responses resulting from other internal or external influences. In this injury model we randomly exposed either half palate to side stream tobacco smoke and used the opposite half palate as the control . Since there was no statistical difference in mucus transport between them, previously established in different sets of pairs of palates, one side was arbitrarily selected for control and the opposite half for exposure to tobacco smoke. This study follows an approach utilized previously by Zayas et al  that comet al [In assessing the mucus transport rate in both half palates before any experimental procedure and to insure that they functioned identically, we established baseline data for our experiment. This helped to reduce error variability due to sex, weight, age, as well as control for any seasonal effect on the mucus transport rate in the frog palates as demonstrated by Rubin et al .Since the palate is excised from the frog we may assume that any observed effect or response is a local and direct effect of exposure to side stream tobacco smoke, which is an advantage of our model. From the data obtained, we would conclude that our frog palate exposure model is suitable to study the acute effects of second hand tobacco smoke.We may also conclude that clearability of mucus seems not to be altered at this stage, at this level of exposure and in this particular exposure preparation or design. Therefore, at this stage mucus transport seems not to be functionally affected for being cleared in a normal fashion by non-exposed cilia. However, after more prolonged tobacco exposure there will be modifications in mucus properties, as seen and measured in smoking dogs where after two months of tobacco exposure, the galactose content and the viscoelastic properties of mucus were observed to present alterations. After further exposure the viscosity and elasticity properties of mucus were reversed to quasi-normal levels, but galactose content did not . Hence tMucus clearance is a function of cilia beat frequency and mucus viscoelastic properties. A change in mucus clearance may be due to one or the other component or both. In future studies, incorporating high speed digital imaging of the palate surface to determine cilia beat frequency will allow us to further differentiate between cilia related effects versus mucus related effects.Exposure to tobacco smoke with all its noxious agents and components will possibly allow us to separate and study in detail, the timing and appearance of different phases of the organism response. Our exposure model may allow us to individualize and study the inflammation phase resulting from the tobacco exposure. We can then focus on the injury phase occurring in the mucociliary system. Later we will explore mechanisms involved in the healing process. The remodeling of the ciliated epithelium following acute injury will be an important component of our studies and particularly how the remodeling is mediated. The time frame of the injury and recovery phases needs to be determined.Cilia do not work alone, but in association with other cilia to produce metachronal waves. Several metachronal waves may contribute to the propulsion of the mucus layer to flow over irregularities or non-ciliated areas. Our data indicates that acute tobacco exposure may have an initial and early irritation effect, possibly mediated by an unknown mechanism on the exposed palate that leads to inhibition of ciliary beat frequency or discoordination of metachronal waves. Such factors adversely affect clearance by imposing stasis or local eddies resulting in erratic clearance that may be the reason why scanning electron micrographs of palates after one cigarette showed cilia somewhat disordered, but not visibly disrupted.We have shown that tobacco smoke exposure interferes with mucociliary clearance. Sustained exposure may lead to loss of ciliated epithelium associated with activation of matrix metalloproteinases. Significant loss of cilia or ciliated epithelial cells results in disruption or even cessation of mucociliary clearance. Matrix metalloproteinases may be implicated in this injury through disruption of epithelial cell-to-cell or cell-to-basement membrane connections. Further clarification of the mechanisms involved will be undertaken in subsequent studies.Our exposure model can assess physiological, ultrastructural and molecular parameters in response to the initial deleterious effects of acute exposure to side stream tobacco smoke in an epithelial model homologous to the human airways. In these preliminary studies we did not attempt to mimic "human smoking conditions". However, our results show that MTV is affected even after exposure to one cigarette. Although concentration of smoke used in the present study is higher than likely encountered in typical environmental tobacco exposures, they are within an order of magnitude of those computed for exposure in poorly ventilated cars or homes. Future studies will try to replicate real conditions faced by non-smokers exposed to environmental tobacco smoke and to characterize the mediation and effectors of this acute injury.


Systemic lupus erythematosus is an age- and gender-associated autoimmune disorder. Previous studies suggested that defects in the hypothalamic/pituitary axis contributed to systemic lupus erythematosus disease progression which could also involve growth hormone, insulin-like growth factor-1 and somatostatin function. This study was designed to compare basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels in female systemic lupus erythematosus patients to a group of normal female subjects.Basal serum growth hormone, insulin-like growth factor-1 and somatostatin levels were measured by standard radioimmunoassay.2 = 3.03) in the entire group of normal subjects (i.e. 20 – 80 years). In contrast, serum insulin-like growth factor-1 levels were inversely correlated with age (adjusted r2 = 0.092). Of note, serum growth hormone was positively correlated with age (adjusted r2 = 0.269) in the 20 – 46 year range which overlapped with the age range of patients in the systemic lupus erythematosus group. In that regard, serum growth hormone levels were not significantly higher compared to either the entire group of normal subjects (20 – 80 yrs) or to normal subjects age-matched to the systemic lupus erythematosus patients. Serum insulin-like growth factor-1 levels were significantly elevated (p < 0.001) in systemic lupus erythematosus patients, but only when compared to the entire group of normal subjects. Serum somatostatin levels differed from normal subjects only in older (i.e. >55 yrs) systemic lupus erythematosus patients.Serum growth hormone levels failed to correlate with age (rThese results indicated that systemic lupus erythematosus was not characterized by a modulation of the growth hormone/insulin-like growth factor-1 paracrine axis when serum samples from systemic lupus erythematosus patients were compared to age- matched normal female subjects. These results in systemic lupus erythematosus differ from those previously reported in other musculoskeletal disorders such as rheumatoid arthritis, osteoarthritis, fibromyalgia, diffuse idiopathic skeletal hyperostosis and hypermobility syndrome where significantly higher serum growth hormone levels were found. Somatostatin levels in elderly systemic lupus erythematosus patients may provide a clinical marker of disease activity in these patients. Systemic lupus erythematosus (SLE) is the protean autoimmune disorder with strong familial penetrance. Immunologically, SLE is characterized by aberrations in T cell and B cell function ,2, over-We have shown that diverse rheumatic and musculoskeletal disorders, including osteoarthritis (OA) -12, diffInsulin-like growth factor-1 (IGF-1) synthesis is coupled to growth hormone via its capacity to stimulate hepatocyte IGF-1 production . In seveSomatostatin is a 14 amino acid polypeptide whose principal function is to regulate growth hormone release from the pituitary . ElevateThe present study was performed to determine the extent to which serum growth hormone, IGF-1 and somatostatin levels were modulated in patients with SLE. A linear regression analysis was performed to determine the relationship between age and serum growth hormone and IGF-1 levels in a group of normal female subjects so that these values could be employed for comparison to a group of predominantly young, female SLE patients.All studies were performed at University Hospitals of Cleveland (UHC) and the Wade Park Veterans Administration Medical Center (VAMC), Cleveland, Ohio. The UHC and VAMC Institutional Review Boards approved the study design with the research protocol, which included informed consent, being in keeping with the Declaration of Helsinki. Normal subjects and SLE patients were all volunteers. SLE Patients met the clinical and laboratory criteria for the diagnosis of SLE according to previously published classifications . PatientBlood drawn by venipuncture was clotted at room temperature, centrifuged, serum aliquots separated and stored at -70°C until assayed. Blood samples were generally collected during an identical 3–4 hr morning period to normalize the potential contribution of growth hormone pulses and serum glucose levels to serum growth hormone determinations -10. SeruBasal serum growth hormone and IGF-1 levels were determined by standard radioimmunoassay (RIA) (INCSTAR, Stillwater, MN) as previously described -10. The 2-value and regression line, respectively.The 2-tailed T-test was employed to analyze the differences in means of serum growth hormone, IGF-1 and somatostatin concentrations in groups of unequal size where p < 0.05 was significant. The population sample size was sufficient to detect a 20% difference in serum growth hormone and IGF-1 levels between control subjects and SLE patients and a 15% difference in serum somatostatin levels. The relationship between serum growth hormone and IGF-1 levels as a function of age was analyzed from scatter plots by linear regression analysis employing SPSS 11.1 (SPSS, Inc., Chicago, IL) and SigmaPlot 8.0 (SPSS, Inc.) to calculate the adjusted rGlucose and insulin concentration was determined in serum from normal female subjects and from patients with SLE. No normal female subjects were excluded from the study as a result of detecting hyperglycemia or hyperinsulinemia However, 2 SLE patients were excluded from the statistical analysis on this basis (data not shown).Over the course of this study, SLE patients received medical therapy with NSAIDs, prednisone (10–60 mg/day), hydroxychloroquine sulfate or methotrexate as well as combinations of these drugs. No SLE patients were treated with azathioprine or cyclophosphamide during this study.2 = 0.269) in this group of normal female subjects was found  between age  and basal serum growth hormone levels in the young female normal subjects was found  in SLE remains to be elucidated. In this regard, van Hagen  showed tThe therapeutic implications and diagnostic utility of serum growth hormone, IGF-1 and somatostatin measurements in SLE as well as in other musculoskeletal disorders appears central to assigning a role for these factors in disease progression. Serum growth hormone remained elevated in some DISH and OA patients where clinical symptoms were significant ,11,12. TThe authors declare that they have no competing interests.CWD participated in the design of the study and performed the clinical analysis. CJM participated in the design of the study and performed the statistical analysis. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence.A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of metabolic networks. Instead of using predicted genes or CDSs to query public databases, entries from public DNA or protein databases are used as queries to search a local database of the unannotated genome sequence to predict CDSs. Functions are assigned to the predicted CDSs simultaneously. The well-annotated genome of Salmonella typhimurium LT2 is used as an example to demonstrate the applicability of the method. 97.7% of the CDSs in the original annotation are correctly identified. The use of SWISS-PROT-TrEMBL databases resulted in an identification of 98.9% of CDSs that have EC-numbers in the published annotation. Furthermore, two versions of sequences of the bacterium Klebsiella pneumoniae with different genome coverage (3.9 and 7.9 fold, respectively) are examined. The results suggest that a 3.9-fold coverage of the bacterial genome could be sufficiently used for the in silico reconstruction of the metabolic network. Compared to other gene finding methods such as CRITICA our method is more suitable for exploiting sequences of low genome coverage. Based on the new method, a program called IdentiCS (Identification of Coding Sequences from Unfinished Genome Sequences) is delivered that combines the identification of CDSs with the reconstruction, comparison and visualization of metabolic networks (free to download at ).In this work a fast method is proposed to use unannotated genome sequence for predicting CDSs and for an The reversed querying process and the program IdentiCS allow a fast and adequate prediction protein coding sequences and reconstruction of the potential metabolic network from low coverage genome sequences of bacteria. The new method can accelerate the use of genomic data for studying cellular metabolism. In silico reconstruction of metabolic networks from genome sequences of organisms represents a starting point for a systematic analysis of metabolism [Knowledge about the metabolic network of an organism is essential for understanding its physiology and phenotypic behavior. A comprehensive understanding of the metabolic network at the system level is particularly important for both biotechnological and biomedical research and is now made possible by rapid advances in genome sequencing and functional genomics. tabolism -3. The ftabolism .Several methods and tools have been recently developed for the reconstruction, visualization and analysis of metabolic networks. These include general static metabolic network tools such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) , the BoeThe three-step method starting from gene finding has several drawbacks for the reconstruction of the metabolic network from incomplete or unfinished genome sequences. In unfinished sequences of a genome, especially in sequences with a low genome coverage (e.g. less than 4 fold), there may be many sequencing errors that do not warrant an accurate prediction of genes . For exain silico reconstruction of the metabolic network. The method is demonstrated with genome data from two organisms. A program combining automatic prediction and function assignment of CDSs with a visualization and comparison of metabolic networks of different organisms is also delivered.To avoid these problems, alternative methods are required for directly reconstructing metabolic networks from unfinished genome data. Sequencing and annotation are still time and resource consuming. An as early as possible exploitation of the genome data is of importance for functional genome research. In this work, we propose a method to identify coding sequences for proteins (particularly for metabolic enzymes) directly from unannotated low-coverage genomic data for The principle of the new method is schematically shown in Fig. S. typhimurium LT2 [S. typhimurium LT2 has 4449 CDSs, out of which 1218 have been annotated with enzyme EC numbers. These CDSs encompass 656 different unique EC numbers. The reliability of CDS prediction by the program IdentiCS is evaluated by using a nucleotide database (KEGG genome) and a combined protein database (SWISS-PROT + TrEMBL + TrEMBL update) separately. The annotated coding sequences or proteins of S. typhimurium were filtered out of these databases before sequence alignment. All CDSs having an E-value less than 10-10 were accepted and submitted for comparison with the KEGG annotation of S. typhimurium. The results are summarized in Table S. typhimurium are identified by using the KEGG genome database and the whole protein database SWISS-PROT and TrEMBL, respectively. The sensitivity on the nucleotide level (91.1% and 98.2% for the two databases respectively) is similar as on the CDS level. These results suggest that the SWISS-PROT-TrEMBL based approach is more preferable than the KEGG genome based approach for our method. It is understood that the combined protein database SWISS-PROT and TrEMBL contains almost all of the known protein sequences available in public databases (including proteins in silico translated from nucleotide sequences) while the KEGG database contains only a limited number of sequenced and annotated genomes. The difference becomes more significant if the organism studied is evolutionarily far from any organism whose genome is completely sequenced and annotated.To examine the applicability of the method proposed, the well-annotated genome sequences of rium LT2  are usedThe specificity of the method is about 81–82% on the CDS level and 87.2–94.9% on the nucleotide level for the KEGG genome database and the whole protein database SWISS-PROT and TrEMBL. The moderate specificity on CDS level is due to the relatively high amount of additionally predicted CDSs . It should be mentioned that all the additionally predicted CDSs have quite strong statistic significance . These additional CDSs may be missed in the original annotation and could in fact represent good candidates for an improved annotation of the genome.The inconsistence rate by IdentiCS is as low as 0.35% for the KEGG genome database and 0.64% for the SWISS-PROT and TrEMBL protein database, indicating the reliability of our method.In the above mentioned evaluation of the method, the cut-off E-value is less than 1E-10 for the CDS prediction. Further the effects of different scoring parameters  and their cut-off values on the CDS prediction by using the database SWISS-PROT and TrEMBL are examined. Fig. 2A,2B,2C) show the distribution of the true positive CDSs, false positive CDSs, sensitivity and specificity as function of bits score, E-value and identities respectively. The major part of the false positive CDSs is found in the regions of bits score less than ca. 80 and E-value larger than 1E-15. The specificity increases with the bits score and E-value in a form of a saturation curve while the sensitivity decreases almost linearly. This indicates that the prediction performance can be further optimized by choosing appropriate cut-off parameters. If the CDSs with bits score less than 75  are rejected, the false positive will decrease by 40% (from 987 to 602) while the false negative will increase from 110 to 144  of annotated EC-CDSs of E. coli based on the KEGG genome database. With the method proposed by Ma and Zeng [K. pneumoniae is found to be most similar to those of E. coli and S. typhimurium (data not shown). Thus, the predicted number of enzyme-encoding sequences for K. pneumoniae appears to be reasonable. With the same 3.9-fold coverage genome sequences of K. pneumoniae, the method of WIT predicted 2650 EC-CDSs which are twice the number of EC-CDSs in E. coli and S. typhimurium. The EC-CDSs predicted by WIT are significantly smaller and fragmented, possibly because of the presence of too many errors in the unfinished genome sequences. The fragmentation problem was overcome in our method that leads to a significant reduction in the number of identified EC-CDSs. The less false positive EC-CDSs will further simplify experimental design such as for microarray to examine the metabolic network.Both the KEGG genomes and SWISS-PROT-TrEMBL databases are used to identify enzyme-coding sequences from the 3.9-fold and 7.9-fold coverage genome sequences of and Zeng , the strin silico reconstruction of the metabolic network of K. pneumoniae. It would be of interest to examine if this also applies to other organisms or even lower genome coverage. The use of lower genome coverage sequences for studying cellular metabolism will greatly accelerate the exploitation of genome sequencing projects.A comparison of the unique EC numbers of EC-CDSs identified from the two different versions of genome sequences and by the different approaches reveals that the results of these different combinations share over 80% of common EC numbers Table . The WITK. pneumoniae as identified by the combination of 7.9-fold genome sequences and SWISS-PROT and TrEMBL databases are summarized in Table K. pneumoniae . From tFor a straightforward visualization of the biochemical reactions related to a specific organism and especially for comparing the metabolisms of different organisms, the KEGG metabolic maps act as a blueprint for visualization of metabolic networks in this work. Reconstruction, comparison and visualization of the metabolic network have been integrated in the program IdentiCS as a built-in component. It can work directly with the coding sequences and their functions predicted by IdentiCS or with existing annotation files such as the those in Microsoft Excel format or in GenBank flat file format. To use the KEGG maps for metabolic reconstruction, comparison and visualization information from the KEGG metabolic pathways is reformed into an Excel template. Metabolic network reconstruction is realized by mapping the identified enzymes (EC numbers) to the KEGG metabolic maps. Different similarity levels of the identified enzymes can be displayed in different colors. All identified enzymes in the map are marked and linked to their annotations. Web links to other Internet databases such as IUBMB , BRENDA S. typhimurium and K. pneumoniae demonstrated the applicability and reliability of the new method proposed for in silico identification of protein coding sequences from unannotated genome sequences. The use of protein sequence databases SWISS-PROT and TrEMBL is more favorable than the use of KEGG genome database for identifying coding sequences and thus for metabolic network reconstruction. Furthermore, the method allows an adequate reconstruction of the potential metabolic network from sequence data with low coverage (e.g. < 4 fold) of the bacterial genome as shown for K. pneumoniae. Together with the algorithms for the automatic annotation of sequences, the visualization and comparison of metabolic networks, the method and program developed in this work can accelerate the use of genomic data for studying cellular metabolism.The use of genome sequences from Salmonella typhimurium LT2 and Klebsiella pneumoniae. The genome of S. typhimurium LT2 has been completely sequenced and well annotated [S. typhimurium LT2 serve as a reference to evaluate the accuracy of the proposed method. The sequences and annotation for S. typhimurium LT2 were downloaded from KEGG [K. pneumoniae has been recently sequenced and the annotation is still in progress. Two different versions of the raw genome data of K. pneumoniae (3.9-fold whole genome shotgun coverage in 920 contigs and 7.9-fold coverage in 341 contigs) obtained from the Genome Sequencing Center of Washington University [The applicability of the method proposed above was examined with the genome sequences of two organisms, namely nnotated . Thus, trom KEGG  were obtained from the Swiss Institute of Bioinformatics . Not "faThe annotation process is based on similarity comparison as normally used in other annotation processes. The difference is that in our approach the gene or protein sequences from public databases are used as queries to search and locate similar ones in the raw genome sequences. When proteins from public database are used as queries, the tblastn algorithm in the BLAST program is applied that compares the query to all six translation frames of the unannotated DNA sequences. The dynamic translation of a small genomic database takes much less system resource than the translation of a large public database as in the conventional methods. Our method can thus be realized on a common PC system, especially when merely a subset of the public database is considered, for example for the purpose of identifying metabolic enzymes for metabolic network reconstruction.Because of sequence errors  and the local alignment nature of the BLAST algorithm, the BLAST research may report several small alignments between different parts of the query protein or gene and different parts of a genomic contig even if there should be only one alignment in the reality. In this situation, the tfasty34 program in the FASTA3 suite  should g1. they are coded on the same strand of the same genomic contig as the highest score fragment. In other words, all of these fragments must be translated either in positive or in negative frames.2. the alignments have an identity level not lower than 80% of the identity level of the highest score alignment.3. the generated larger sequence region has alignment gaps or extensions not more than 20% of its length.Since many queries can be similar to the same region on a genomic contig and sometimes they may have different function annotations, the program must judge and choose one annotation for this region. The decision is made by applying the following criteria:1. Each region normally has only one function. Here the region represents a piece of nucleotides either on the positive strand or on the negative strand of a DNA molecule. The same physical position on different strands of a DNA molecule can belong to different regions, and can therefore have a different function assignment. Although there are examples in some viruses that a region can code different proteins depending on the transcription frame, it happens very rarely in other organisms. The user can assign a tolerance value (e.g. 60 bp) to allow two successive regions to overlap each other to some extent.2. Highest similarity principle. If a query gene or protein has a similarity to a CDS higher than other queries, then the function of this query gene or protein is assigned as the annotation of the CDS. Bits score is used as a measure for similarity first. If two queries have the same bits score, then the identity level in percentage is taken as a second measure for similarity. If both bits score and identity are the same and these two entries have different function annotation (rarely occurred) then both of their functions are assigned to that region.3. Closest evolutionary relationship. If two or more query genes or proteins are comparably similar (e.g. the difference between their identity levels is lower than 5%) to a CDS but have different function, the evolutionary relationship between these organisms is further considered. The annotation of the organism that is mostly related to the studied organism from the viewpoint of metabolic evolution is transferred to the unknown CDS. The evolutionary relationship between different organisms and the one studied is established with the method of Ma and Zeng  after thIdentification of Coding Sequences from Raw Genome Sequences) can reconstruct the metabolic network of an organism with about 5 million base pairs of raw genome data. The computing time is less than 8 hours on a PC with 2.8 GHz Pentium 4 CPU and 512 MB memory. This program works together with Microsoft Excel under Windows environment.In this way, the coding sequences of a genome are identified and annotated at the same time. No second large-scale sequence alignment is needed. Once all the software and databases are prepared, our program which is called IdentiCS , false negative (FN) and false positive (FP) are used to calculate the sensitivity and specificity of CDS prediction in comparison with CDSs in the original annotation. The terms "sensitivity" and "specificity" are defined according to Burset and Guigo :We also evaluated the terms TP, FN and FP on nucleotide level according to Burset and Guigo  and calcJS is the developer of the method and the program. APZ supervised the study. Both authors read and approved the final manuscript.


The molecular basis of severe acute respiratory syndrome (SARS) coronavirus (CoV) induced pathology is still largely unclear. Many SARS patients suffer respiratory distress brought on by interstitial infiltration and frequently show peripheral blood lymphopenia and occasional leucopenia. One possible cause of this could be interstitial inflammation, following a localized host response. In this study, we therefore examine the immune response of SARS-CoV in human peripheral blood mononuclear cells (PBMCs) over the first 24 hours.in vitro with SARS-CoV and the viral replication kinetics was studied by real-time quantitative assays. SARS-CoV specific gene expression changes were examined by high-density oligonucleotide array analysis.PBMCs from normal healthy donors were inoculated We observed that SARS-CoV was capable of infecting and replicating in PBMCs and the kinetics of viral replication was variable among the donors. SARS-CoV antibody binding assays indicated that SARS specific antibodies inhibited SARS-CoV viral replication. Array data showed monocyte-macrophage cell activation, coagulation pathway upregulation and cytokine production together with lung trafficking chemokines such as IL8 and IL17, possibly activated through the TLR9 signaling pathway; that mimicked clinical features of the disease.The identification of human blood mononuclear cells as a direct target of SARS-CoV in the model system described here provides a new insight into disease pathology and a tool for investigating the host response and mechanisms of pathogenesis. Coronaviridae, and have the largest known single-stranded viral RNA genomes (27 to 32 kb). Coronaviruses, have both "early" and "late" phases of gene expression. Regulatory proteins are synthesized as "early" non-structural proteins, while the structural proteins are synthesized as "late" proteins. "Late" structural proteins are usually required in greater amounts thus, there is a necessity to regulate the expression of the viral genes quantitatively. After the viral entry via endocytosis or through specific receptors, the 5'-end of the viral genome is translated directly giving rise to twenty-three viral proteins, including the RNA dependent RNA polymerase (RdRp), and other functional products involved in transcription, replication, viral assembly and cell death. Coronaviruses can be classified into species and three major antigenic groups based on, serology, natural hosts, monoclonal antibody recognition and nucleotide sequencing [The causative agent for SARS has been identified as a novel coronavirus -3 with ggenomes 2 to 32 kbgenomes 2 to 32 kbquencing , althougHowever, SARS has rapidly caused a world-wide problem. The earliest known cases of SARS was reported in Guandong Province, China in November 2002, becoming more widespread by March 2003, when it was introduced to Canada, Singapore, Taiwan and Vietnam via Hong Kong. The largest number of infected patients has been in China with a worldwide incidence totalling more than 8,400 by July 2003. Infection by the virus induces high morbidity and mortality, the latter being estimated at 15% by the World Health Organisation. SARS is characterized by high fever, non-productive cough or dyspnea and in many cases may progress to generalized, interstitial infiltrates in the lung, thus needing intubation and mechanical ventilation , togethein vitro model system that will in the future allow detailed investigations of the host response to be made.We postulated that the pulmonary damage in SARS may not be a direct effect of the virus on the alveoli, but represents a secondary effect of cytokines or other factors proximal to but not from the lung tissue mediated by the host either as the primary or secondary response ,14. In t5 PBMCs were seeded into each well of a 24-well culture plate, 0.5 ml of complete RPMI-1640 (Life Technologies-Invitrogen, USA) added to each and cultured overnight at 37°C (5% CO2). A seed stock of SARS-CoV (strain SIN 2774) passaged in Vero E6 cells was used for infection. Vero E6 culture supernatants were added to each well in 50 μl volume at a concentration of 0.1 or 0.01 MOI (based on plaque forming units) and a control plate (media only). Each culture was set up in duplicate. After 4 hours of incubation, one set of the duplicate wells of the control plate and a 4 hours incubation plate were harvested while the rest received 0.5 ml media top-up and incubated for a further 2, 4, 6 and 8 days.PBMCs were obtained by Ficoll-Hypaque separation of whole blood. 2 × 10Harvesting was performed by gently flushing the wells with a Pasteur pipette to removed non-adherent cells, followed by a rinse of 1 ml RPMI. The rinsed fraction was pooled with the first harvest aliquot and spun at 1500 rpm. The cell pellet was washed twice with 2 ml RPMI to remove virus in the supernatant. 1.5 ml Trizol (Invitrogen, USA) was then added to the adherent cell fraction as well as the non-adherent cell fraction to lyse cells and stabilize the RNA. Extraction of total RNA was then performed following manufacturer's protocol and the resultant RNA dissolved in 40 μl water.® MGB probe: 5'-(6-FAM)AGAGCCATGCCTAACAT(NFQ)-3') in a one step PCR using master mix from Applied Biosystems (USA) according to manufacturers' recommendations. Reactions were performed using an ABI PRISM 7900 sequence detection system (48°C for 30 min, followed by 95°C for 10 min and 40 cycles of 95°C for 15 sec and 60°C for 1 min) and quantitation achieved using standard curves generated from in vitro transcribed RNA.The amount of SARS-CoV in each cell fraction was measured by real-time quantitative PCR assay. 2 μl of RNA was reverse transcribed and amplified in 20 μl using 0.9 μM each of forward (5'-GGTTGGGAT TATCCAAAATGTGA-3') and reverse (5'-AGAACAAGAGAGGCCATTATCCTAAG-3') primers, and 0.25 μM of TaqMan7 cells of mock-infected and infected cells were harvested and lysed using Trizol (Invitrogen, USA). Total RNA was isolated according to the manufacturer's recommendation. Quality of the total RNA was judged from the ratio between 28S and 18S RNA after agarose gel electrophoresis. 20 μg of total RNA was labeled with Cy-3 or Cy-5 using the Superscript II reverse-transcription kit (Invitrogen, USA) and hybridization was carried out overnight (16 hours) at 42°C on high-density oligonucleotide arrays (~19,200 gene features, Compugen) using universal human reference (Stratagene, USA) as a reference. Hybridized arrays were scanned at 5 μm resolution on a GenePix 4000A scanner (Axon Instruments) with variable photo-multiplier tube voltage to obtain maximal signal intensities, and the resulting images were analyzed via GenePix Pro v4.0 (Axon Instruments) as described in the manual.At each time point (4 hours, 8 hours, 12 hours, 24 hours), 5 × 10Raw data were analyzed on GenePix analysis software version 4.0 (Axon Instruments) and uploaded to a relational database. The logarithmic expression ratio for a spot on each array was normalized by subtracting the median logarithmic ratio for the same array. Data were filtered to exclude spots with a size of less than 25 μm and any poor quality or missing spots. Since the correlation of the overall data from reciprocal labeling was good, values obtained from reciprocal labeling experiments were averaged. In addition, the data were distilled to the set of gene features that were present at all 4 time points in both the viral infected samples and the negative controls. The results were represented as the logarithmic ratio of gene expression between the viral infected samples and their corresponding negative controls at the various time points. Application of these filters resulted in the inclusion of ~12,900 of the total ~19,200 gene features in subsequent analyses.f were translated so that their means were zero. Similar genes, whose translated gene-features exhibited same induction-repression pattern, were grouped together. Genes gi, gj were said to be similar if they satisfied the following condition:To discover patterns of gene expression, the values associated with each gene feature , where U(x) = 1 if x > 0, U (x) = 0 otherwise,git and gjt denote the translated values of gene features gi, gj at time t respectively; and N is the number of time points for which the expression of a gene was observed. Similar genes, based on the above criteria, are grouped together. Within each group, the genes were ordered in the descending order of their expression range (defined as the difference between the maximum and minimum ratios of gene expression). This algorithm is a special case of the Friendly Neighbor algorithm currently under development. The final plots were generated using the original expression ratios while preserving the clustering and ordering discovered by the above algorithm. To determine whether a gene observed to be responsive could appear merely by chance, 100,000 expression profiles were generated by randomly sampling the expression ratios from the entire dataset with replacement. The P value of a gene is the fraction of the random profiles whose logarithmic expression range is as good as, or better than that of the selected gene.where 3  of 0.1. The first sampling, taken from cells infected for 4 hours, showed an average copy number of 32 × 103 Fig.  and reprpatients  and cellpatients . An indipatients , in whic3 copies per well which is 4 times more than that attained by donor e, with 120 × 103 copies per well. Such variation strongly suggests that there is an underlying host-pathogen interaction influencing the kinetics of SARS-CoV replication efficiency. These in vitro observations may reflect the wide range of patient outcomes after SARS-CoV infection [The kinetics of viral replication was variable among the 6 donors Fig. . There wnfection .Antibody blocking experiments were also performed in which SARS-CoV was pre-incubated with convalescent patient sera for 30 minutes before introduction to the PBMCs and after a 4 day incubation period, the adherent cell fraction was harvested and assayed for SARS-CoV viral titer. Results clearly showed that even at high dilution, convalescent sera inhibited SARS viral replication (data not shown), presumably by blocking viral entry. This supports other reports indicating that SARS-CoV is not endocytosed through antibody mediated mechanisms and confirms a protective role for antibodies elicited either by the infection or through immunization ,20.in vitro with SARS-CoV (0.1 MOI) and harvested at 4 hours, 8 hours, 12 hours and 24 hours time intervals post-infection. As controls, uninfected aliquots of the same PBMCs were also harvested at the corresponding time points. Total RNA extracted from the PBMCs of the 3 individuals were pooled, labeled and hybridized to human oligonucleotide arrays consisting of ~19,200 gene features. Reciprocal dye swap replicate hybridizations were performed to minimize technical noise. Analysis of variance in expression levels for each gene across all the time points indicated the ~1200 genes which showed the largest variability  and their receptors (CCR4 and CCR7) were highly expressed in response to the infection Fig. , indicatin vitro infection  to be investigated. In future developments of the model, it will be interesting to compare the host response to different SARS-CoV isolates with inactivated preparations of the virus. In other diseases, in vitro models have revealed a number of key processes relevant to the clinical diseases [The emerging picture from this study implicates a central role for the immune response in the pathobiology of a SARS infection. While detailed diseases ,12,13 andiseases -42, furtNone declared.LFPN, MLH, ETL and REC conceived the study, its design and coordination, results analysis and drafted the manuscript. EEO and KFT carried out the virus infections. SYN, KRKM, VNV and JMC were involved in the array and statistical analysis. JT carried out the real-time PCR assays.The pre-publication history for this paper can be accessed here:List of all immune related genes after SARS-CoV infection Comprehensive list of 1087 immune related genes that were altered in PBMCs in response to SARS-CoV infection at 4 hours, 8 hours, 12 hours, and 24 hours. Genes were grouped and ordered using the algorithm described in Methods. Rows represent individual genes, columns represent individual time points. Each cell in the matrix represents the mean expression level from 3 subjects for a gene feature at a particular time point (non-infected PBMCs responses have been subtracted from infected responses). The red and green color bars reflect high and low expression levels respectively, while black indicates equivalent expression level. The magnitude of the log-transformed ratio is reflected by the degree of color saturation. The line graph indicates the average expression ratios for each group. The area above the axis indicates upregulation, while the area under the axis means downregulation.Click here for file


We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions.This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the r; mean error, ME; and root-mean-square-error, RMSE) using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278) and predicted high (>75% inhibition of gene expression) and low efficacy (<25%) AOs with a success rate of 83.3% and 82.9%, respectively, which is better than by previous approaches. A web server for AO prediction is available online at .In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo . The seqAntisense oligonucleotides contain 10–30 nucleotides complementary to a specific subsequence of an mRNA target, which are designed to bind to targets by standard Watson-Crick base pairing rules. The bound duplex can knockdown gene expression through a number of mechanisms. These are RNase-H mediated cleavage, inteference with translation or splicing and destabilization of the target mRNA -4. The AThere are many laboratory-based strategies for selecting AOs. A classical approach is the 'gene-walk' approach, in which 15 or more AOs are evaluated for a gene in order to find a sufficiently effective AO. Methods with higher reliability experimentally determine mRNA regions that are accessible to RNase-H clevage and therefore more likely to be an e'ective site for AOs -8. In geThere are many examples in the literature of experimental groups attempting to correlate AO sequence properties with efficacy. A correlation between binding energy (AO-RNA) and efficacy has been observed ,9. Partiβ-globin. In general, methods have not been evaluated on a broad range of gene targets. Another method is to look for motifs that occur more often in effective AOs. Ten sequence motifs have been identified with a correlation to AO efficacy in [AO selection can be based on either experimental or theoretical approaches (for a review, see ). Computicacy in , and recicacy in ,22 with In this context, the challenge is hence to discover general principles that hold across all AO studies. One approach to discover such principles is to explore a diverse range of sequence properties and incorporate the factors that affect AO efficacy into a computational model for AO design. This requires both a database of tested AOs, such as that produced by ,23, and a priori a well-suited problem to SVMs, given the low number of samples and high input space dimension [drastically affected by the input space dimensionality has sometimes led to the wrong idea that a feature selection is not necessary at all. The guiding principle of SVMs ensures certain robustness to outliers or abnormal samples in the distribution inherently, but the selection of the optimal subset of features is still an unsolved problem in the literature. We can state that in most applications, the success of machine learning is strongly affected by data quality (redundant, noisy or unreliable information) and thus a feature selection is not only recommendable but mandatory.This paper proposes the use of SVMs for prediction and analysis of AO efficacy. The collected database comprises 315 AO molecules including 68 features each, which induces imension . Neverthimension ,27. Addiimension ,22. The imension -32. The In this paper, we propose a two-stage strategy to tackle the problem:Feature selection. This task is carried out using three techniques: correlation analysis, the mutual information feature selection (MIFS) method, and the SVM-based recursive feature elimination (SVM-RFE).1. AO efficacy prediction. We develop standard and profiled SVMs to accomplish this task. Several measures of accuracy of the estimations and two cross-validation methods are used in order to attain both significant and robust results.2. In the present work, we have extended the database used in  by inclua priori representative parameters was derived from the information contained in the AO sequence collection, including values for: (1) base composition (Number of A/C/G/T, % GC content): (2) RNA-AO binding properties : (3) RNA-AO terminal properties (3' binding energy, 5' binding energy); (4) AO-AO binding properties ; and (5) 9 of the 10 verified sequence motifs correlated with efficacy from [A set of acy from . Bindingacy from . The calacy from  and a unacy from  and the acy from . Parametacy from . For breacy from . The datfilter [wrapper [The Feature Selection Problem (FSP) in a "learning from samples" approach can be defined as choosing a subset of features that achieves the lowest error according to a certain loss functional . Followifilter  and wrap[wrapper  methods.n input features  and output , or among pair-wise inputs ( and ) is the use of the correlation function. This is a good method to remove redundant features and to evaluate relationships, but fails when working with low number of samples, or when the assumed linear relationship is not present. When data is considered as the realization of random processes, it is possible to compute the relevance of variables with respect to each other by means of the mutual information (MI) function, which is defined as the difference between entropy of  and the conditional entropy of  given . The MI function is suitable for assessing the information content of features in tasks where methods like the correlation are prone to mistakes. In fact, the MI function measures a general dependence between features, instead of a linear dependence offered by the correlation function. In [ith input feature  included in the set, maximizes  over all remaining features  for some parameter β ∈  relationship between each of the tion. In , an algo, where xi is an input example, φ is a nonlinear mapping, w is a weight vector and b is the bias of the regression function. In the SVR, a fixed desired accuracy ε is specified a priori and thus one tries to fit a "tube" with radius ε to the training data. The standard SVR tries to minimize two factors: the norm of the squared weight vector, ||w||2, and the sum of permitted errors. These two factors are traded-off by using a fixed penalization parameter, C. We can formally state the SVR method as follows: given a labeled training data set {(xi, yi), i = 1,..., n}, where xi ∈ ℝd and yi ∈ ℝ, and a nonlinear mapping to a higher dimensional space φ: ℝd → ℝH where d ≤ H, find the minimum of the following functional with respect to w, ξi,  and b:Support Vector Machines are state-of-the-art tools for nonlinear input-output knowledge discovery . The Supsubject to: and C are, respectively, positive slack variables to deal training samples with a prediction error larger than ε (ε > 0) and the penalization applied to these ones. These two parameters are tuned by the user.where αi and  associated to each constraint. The dual functional obtained has to be minimized with respect to primal variables (w, ξi and ) and maximized with respect to dual variables (αi). The optimization of the obtained dual problem is usually solved through quadratic programming procedures [x can be expressed asThe usual procedure for solving the SVR introduces the linear restrictions (2)-(4) into (1) by means of Lagrange multipliers ocedures ,42, and support vectors and represent the most critical samples in the distribution.where only the non-zero Lagrange multipliers account in the solution. The corresponding input examples are called φ (x) but only a kernel function, which maps the samples into a high dimensional space. This kernel function appears in the form of dot products in (5), K (xi, xj) = φ(xi)·φ(xj) and can be viewed as a measure of similarity between samples. Therefore, in order to train the SVR model, one only has to select a kernel function, its free parameters, the parameter C, and the size of the ε-insensitivity zone. In this paper, we have only used the Gaussian  kernel, given by:An important characteristic of the SVR training methodology is that one does not need to know explicitly the form of the mapping K (xi, xj) = exp (-γ||xi - xj||2).     (6)a priori. The RBF kernel maps samples into a higher dimensional space so, unlike the linear kernel, it can handle efficiently cases in which the relation between the dependent and independent variables is non-linear. The RBF kernel has less numerical difficulties than sigmoid or linear kernels. In fact, sigmoid kernels behave like RBF for certain parameters [There are some reasons to select the RBF kernel rameters ,44 but urameters . Finallyrameters .m among n features (m <n) that maximizes the performance of the predictor for a given measure of accuracy. This is a wrapper method and involves high computational cost. The method is based on a backward sequential selection. One starts with all the features and removes one feature at a time until m features are left. Basically, in each iteration, one focuses on the weight vector, which constitutes the solution provided by the SVR and therefore, its analysis is of fundamental relevance to understand the importance of each input feature. The removed feature is the one whose removal minimizes the variation of ||W||2. Hence, the ranking criterion Rc for a given feature i is:The SVM-RFE algorithm has been recently proposed in  for seleKi) , keeping the highest correlation to efficacy. This analysis discarded 12 redundant features out of the 68 original ones, and additionally provided a ranking of the most correlated features to efficacy. We finally selected the 14 top ranked features according to this criterion, ranging in correlation from -0.35 (ΔG) to -0.16 (# Adenine). We selected this number of features for the purpose of a fair comparison with the best subset in [ (first and last columns, respectively). They are due to the facts that (1) we have included here very low efficacy oligos in the calculation, and that (2) because more features were added to the AO database, e.g. predicted secondary structure, oligos had to be discarded when the target RNA was unavailable.The first approach to the FSP consisted of performing a correlation analysis in order to identify redundant variables. We adopted a similar strategy followed in , i.e. toubset in . Table 1ubset in  with regβ = 0.75, which yielded a balanced estimation of both the MI with the output (AO efficacy), and the already-selected features. The more important features match the ones selected using the correlation function, but MIFS also included hairpin measurements. This is due to the fact that MIFS is not based on correlatedness but on mutual dependence criteria.A feature ranking according to the correlation coefficient can be useful to analyze input-output linear dependencies, but it is not good practice to rely only on this decision to build a model. As a second approach, we ran the MIFS method and selected a desired subset of best 14 features. We selected A third approach was the use of SVMs based on the RFE method. In this task, we trained an SVM to predict AO efficacy using all available features. It should be noted here that RFE is a wrapper method that involves a very high computational burden since the SVM must be retrained in each iteration with the selected features. The best model was selected by evaluating the RMSE (accuracy of the estimations) in the validation set through the 8-fold cross-validation method, which splits the data into eight parts, and uses seven parts for training and the eighth one for validation. The procedure is then repeated eight times. In our implementation, we included the possibility suggested in  by whichWe can also conclude that noticeable differences are observed between the obtained rankings. A possible explanation for discrepancies of this sort is the non-linear mapping that SVR methods perform. Explaining those input-output relationships is often difficult and biased conclusions are usually obtained. Different families of methods  perform different mappings due to their specific guiding principles  and thus, the interpretation of these methods is quite diffcult. In addition, different models (topologies, structures, kernels, membership functions) in a family would surely yield different results.C, ε, γ) As regards the penalization parameter, it is a common practice trying exponentially increase sequences of C (C = 10-2, 10-1,..., 103). In our case study, we achieved good results in the range of C ∈ [1,1000]. The insensitivity zone was varied linearly in the range [0.001, 0.3]. The γ parameter was exponentially varied in the range γ = 10-7,...,10-1. For each free parameter combination, we evaluated the performance of the predictors through several measurements: the correlation coefficient between actual and predicted efficacies (r), the mean error (ME), and the root-mean-square-error (RMSE). Additionally, we computed the rate of observed efficacies above a defined predicted threshold of 0.75 (SR>0.75) and below 0.25 (SR<0.25). These prediction ranges are of particular interest, since they stand for high and low AO efficacies, respectively. In fact, it is not only important to identify high efficacy oligos but also factors causing AOs to be completely ineffective ([0,0.25]). However, care must be taken as more noise can be present in the low efficacy region.A greedy search was carried out for the free parameters  [C and ε become sample-dependent. In [C and ε in complex pharmacokinetic problems. In this paper, our intention relaxing or tightening ε and C depending on the observed AO efficacy value. A proposal for this variation is illustrated in figure In AO prediction, we are most interested in predicting good oligos (high efficacy, > 0.75), and those that are bad (low efficacy, < 0.25). This previous knowledge about the problem can be introduced in the SVM formulation by tailoring specific  (P-SVR)  obviousldent. In ,47, we dε-insensitivity zone is reduced in these regions thus forcing a reduced error there. Some other profiles could be introduced in the training methodology without loss of generality.Additionally, the r) is almost unchanged. Using the P-SVR method  we gained substantially in RMSE, and also obtained a better balance between the success rates of high and low predictions. This indicates that the P-SVR improves the performance of standard SVR even without a dedicated feature selection method, and suggests that even better results could be obtained if P-SVR were embedded in the RFE feature selection procedure.Results for all approaches are shown in Table r indicator, which is a biased estimator of the accuracy. In fact, accuracy (RMSE) remains basically the same, and bias (ME) becomes positive and higher, which could induce some distrust for the model. When analyzing results from the P-SVR, we can observe a general improvement in all indicators, which is basically due to the fact that by tightening the "tube" around the interesting ranges, a higher number of support vectors is selected there , which induces a richer solution in the interesting zones. In addition, the profiled C parameter penalizes higher the committed errors in these zones, which is particularly interesting to deal with outlying samples in the distribution and to provide a smoother solution in these particular zones. The designed profile, nevertheless, could lead to an overfitted solution in the interesting zone if εi and Ci were not well-controlled. However, by using the-RNA cross-validation method, this threat is avoided and better results are finally obtained. Therefore, the combined strategy of P-SVR and -RNA cross-validation results in a balanced and robust predictor. Additional consequences can be extracted: (1) the correlation coefficient is relatively low for all methods but superior to the ones obtained in [F = 0.01, p = 0.811) or accuracy (F = 0.06, p = 0.567); (3) prediction is more accurate, in general terms, for the higher efficacy levels (SR>0.75, > SR<0.25), as also noted in [These outcomes are worth analyzing because one could expect worse results when using -RNA cross-validation since this method removes the possibility of cross-talk in the training phase between overlapping oligos. However, we have to stress here that, by training the SVR with -RNA cross-validation, one only improves the ained in ; 2) dif difr indnoted in ; and (4)>0.8 was reported to be 50%, and r = 0.30. The success of the P-SVR for the AO prediction problem suggests that it could be successfully applied to other prediction problems. A web server for AO prediction is available online at [In this paper, we have used standard and state-of-the-art methods for knowledge discovery in a relevant bioinformatics problem: the analysis and prediction of AO efficacy. We have engineered robust and accurate SVMs, and used filter and wrapper feature selection methods in order to build representative subsets of input features. Compared to , our resnline at .p-values from the Lagrange multipliers. This way, the user could get a set of best predictions back, then a second set that is more likely to be less accurate, and so on. This would allow the lab-user to choose the best ones first, but if they fail specificity controls they would have another set to work with.Our future work is concentrated to improving results with more careful design of profiles by the inclusion of fuzzy and rough sets. Additionally, we are exploring the possibility of providing confidence values for the predictions in the form of GCV carried out the training of the feature selection and regression methods. AC participated in model development and testing process, and developed the web-server. AJSL collaborated in model development and assessment. JDMG engineered the profile function. ES conceived and coordinated the study. All authors contributed to the manuscript preparation, and approved the final manuscript.


Conventional wisdom says that people deficient in one sense—such as vision or hearing—often acquire heightened acuity in another. And some studies support this notion by showing that areas of the brain known to control vision can respond to other forms of sensory stimuli in persons without sight. These adjustments, of course, take place over the lifetime of an individual. Now it appears that similar adjustments may occur over evolutionary time. Investigating the deterioration of olfactory receptor (OR) genes in primates, Yoav Gilad and his colleagues at the Max Planck Institute for Evolutionary Anthropology in Germany and the Weizmann Institute in Israel found a correlation between the loss of OR genes and the acquisition of full trichromatic color vision.OR genes—the molecular basis for the sense of smell—form the largest gene superfamily in mammalian genomes. But a high percentage of these genes are “pseudogenes,” DNA sequences that are remnants of genes that are no longer functional. Following an evolutionary “use-it-or-lose-it” rule, pseudogenes tend to evolve in larger gene families where there's no selective advantage in having, say, 100 versus 120 genes. While humans, nonhuman primates, and mice have roughly the same number of OR genes, in humans a much higher percentage of these are pseudogenes, at 60%, while nonhuman apes have about 30%, and the mouse has about 20%. Reliance on the sense of smell, it appears, decreases for animals that develop a dependence on other senses, such as hearing or sight, to survive. In characterizing this high proportion of pseudogenes, Yoav Gilad et al. asked: Is this characteristic of all primates? If not, at what point in primate evolution did the increase occur?Looking at 19 primate species—including one human, four apes, six Old World monkeys, seven New World monkeys, and one prosimian—Gilad et al. randomly sequenced 100 distinct OR genes from each of the species. The team found that Old World monkeys had roughly the same percentage of OR pseudogenes as nonhuman apes, but a much higher percentage than New World monkeys—except for one, the howler monkey. The percentage of OR pseudogenes in the howler monkey was much closer to that seen in the Old World monkeys and apes than in its New World cousins. The sense of smell, it appears, deteriorated both in the ape and Old World monkey lineage and in the howler monkey lineage. Since Old World monkeys, apes, and the howler monkey do not share an exclusive common ancestor, this deterioration must have evolved independently in both groups. Surprisingly, howler monkeys share another sensory feature with apes and Old World monkeys: trichromatic color vision.In trichromatic color vision, three retinal protein pigments, called opsins, absorb various wavelengths of light, which the brain processes to produce full-color images. Apes and Old World monkeys carry three opsin genes, and most New World monkeys carry only two, though females can sometimes have three. Only howler monkeys routinely have three genes occurring in both sexes. Thus, full trichromatic vision evolved twice in primates—once in the common ancestor of apes and Old World monkeys, about 23 million years ago, and once in the howler monkey lineage, about 7–16 million years ago. The evolution of color vision, the authors propose, coincided with a growing complement of OR pseudogenes and a deterioration of the sense of smell. Gilad et al. suggest that investigating the types of visual cues required for finding food may shed light on the nature of this connection.


The understanding of whole genome sequences in higher eukaryotes depends to a large degree on the reliable definition of transcription units including exon/intron structures, translated open reading frames (ORFs) and flanking untranslated regions. The best currently available chicken transcript catalog is the Ensembl build based on the mappings of a relatively small number of full length cDNAs and ESTs to the genome as well as genome sequence derived in silico gene predictions.We use Long Serial Analysis of Gene Expression (LongSAGE) in bursal lymphocytes and the DT40 cell line to verify the quality and completeness of the annotated transcripts. 53.6% of the more than 38,000 unique SAGE tags (unitags) match to full length bursal cDNAs, the Ensembl transcript build or the genome sequence. The majority of all matching unitags show single matches to the genome, but no matches to the genome derived Ensembl transcript build. Nevertheless, most of these tags map close to the 3' boundaries of annotated Ensembl transcripts.These results suggests that rather few genes are missing in the current Ensembl chicken transcript build, but that the 3' ends of many transcripts may not have been accurately predicted. The tags with no match in the transcript sequences can now be used to improve gene predictions, pinpoint the genomic location of entirely missed transcripts and optimize the accuracy of gene finder software. The definition of transcription units within a finished genome sequence in higher eukaryotes is challenging and relies on genome mapping of cDNAs and ESTs backed up by theoretical gene finder algorithms. An increasing number of gene sequences from model organisms have made the prediction of well conserved ORFs easier, but less conserved coding and untranslated regions are difficult to detect. The best way to unambiguously define transcription units is full length cDNAs, but large scale projects are expensive in terms of labor and costs. One also needs to bear in mind that some cDNAs elude detection, because unusual secondary structure or toxicity inhibits reverse transcription or cloning.SAGE investigates the transcription profile of a given cell sample by large scale sequencing of short cDNA tags derived from the bulk mRNA ,2. WhereCellular and molecular features of early B cell development  and lympThe whole chicken genome including a genome scale transcript build from Ensembl  and a co-5 or 4.69 * 10-5 respectively.Two SAGE libraries, named busage and dt40sage, were made from the bursa of Fabricius and DT40 cells using the LongSAGE technique which generates tags of 21 nucleotides in length and therefore decreases the likelihood of ambiguous matches ,4. Of thSuccessful mapping of SAGE tags to reference sequences is influenced by the quality of the sequences, the complexity of the reference sequence datasets and the prevalence of polymorphisms within the tag sequences. It was therefore decided to first search for matches within a bursal cDNA collection which represents the best possible reference dataset, as it was derived from the same tissue and genetic background as the busage library. Subsequently, unitags were mapped to the Ensembl transcript build and finally the chicken genome sequence. Unitags found in a previous dataset were not searched for any more in the next. To facilitate the searches, candidate tags starting with the CATG tetra-nucleotide were extracted from each reference dataset prior to analysis.As expected the highest rate of matching to total candidate tags was found for the bursal cDNA collection (3,030 of 26,044 candidate tags matched unitags), followed by the Ensembl transcript build (2,934 of 208,048) and the genome (14,505 of 9,091,924) .SAGE tags are expected at the position of the Most interesting from the perspective of gene discovery are the 13,427 unitags without transcript match, but with a single match in the genome  than the simulated tags providing strong evidence that most of the closely positioned tags are indeed related to the predicted transcripts  controlling procedure of Benjamin & Hochberg would admit the first 229 genes at an FDR of 5% . Twenty-To verify the validity of the SAGE data, semi-quantitative PCR was performed using primers close to the tags for 27 transcripts Figure . This coThe mapping of the SAGE tags to the recently released cDNA collections and the chicken genome has been useful to assess the completeness and accuracy of the current transcript catalog. On the positive side, it appears that the transcript build may have missed only a low percentage of genes, since relatively few tags map to genome regions far away from annotated transcription units. On the downside, fewer than 6,000 of over 19,000 tags with matches to reference sequences could be mapped to transcripts. The majority of the tags missed in transcripts are positioned downstream of annotated transcripts with a minority mapping upstream or within the genomic boundaries of transcripts. The most straightforward explanation for this is that many transcripts in the current version of the chicken transcriptome do not accurately reflect the 3' and the 5' ends of transcripts. This proposition is independently supported by the comparisons of the bursal full length cDNAs to the Ensembl transcript build which detected discrepancies to Ensembl annotated transcripts for approximately 50% of the cDNAs . AnotherAccurate definitions of the transcribed parts of the chicken genome is highly desirable not only to ascertain the correct ORFs, but also to identify transcription and translational control sequences often located in 5' and 3' untranslated regions. It should be interesting to use the genomic positions of the missed transcript tags in combination with current gene finder algorithms to improve transcript coverage. Many of the missed tags are close to already annotated exons facilitating this task. It should also be possible to use promising tag sequences to screen cDNA libraries for clones whose sequence will identify missed genes or exons. The riken1 bursal cDNA library is of excellent quality and should be suitable for this purpose.bona fide tags will be distinguished from artifacts by being replicated and even polymorphic tags will eventually be defined and assigned to their corresponding transcripts.Although the presented SAGE data provides valuable information about the expression levels of many genes in bursal cells and the DT40 cell line, the full potential of SAGE for gene expression profiling could not be exploited due to the difficulties in tag to gene assignment. Nevertheless, this first SAGE analysis in the chicken lays the basis for further studies. SAGE has the advantage that data from different experiments and laboratories are easily comparable as the tag sequences serve as a common standard. Accumulation of additional data will increasingly facilitate the interpretation of results because . The RNA bound to oligo(dT)25 magnetic beads was immediately used for the construction of a LongSAGE library [Total RNA from bursal tissue of chicken 20 day old CB-inbred chicks and from DT40 Cre1 cells  was extr library  followin library . High fi2O. This suspension was heated at 95°C for 10 minutes and stored at -20°C until further processing. The PCR amplification used primers from the plasmid backbone, M13 forward and reverse. Sequencing was performed using the Big Dye v3.1 ready reaction mix (Applied Biosystems) and a nested primer (SSP2) from the plasmid poly-linker. Reactions were analyzed on an ABI 3730 DNA Analyzer (Applied Biosystems). The raw sequencing files were processed as described previously [The pZero-1 (Invitrogen) plasmids containing SAGE ditags as multimeric inserts were transformed into E. coli. Zeocin resistant colonies transformed by the plasmids were grown at low density on agar plates, picked and directly suspended in 50 microliters of Heviously .The library insert sequences were searched for ditags in which the flanking CATG tetra-nucleotides are separated by a spacer sequence of more than 31 and less than 37 bases. Ditags of identical sequence were entered only once for each library to avoid the possibility of entering PCR amplification artifacts. The ditags were then divided into two SAGE tags of 21 bases including the CATG tetra-nucleotides. The combined SAGE tag collections of both libraries were normalized to generate a collection of unitags possessing unique tag sequences. A low number of tags  were found to be identical to the sequences of the linker tags used for the library construction and therefore were removed. Care was taken to minimize the possibility of tag sequence errors by using a high fidelity polymerase for the PCR amplification step of the library construction and by rejecting any ditag sequences which contained even a single ambiguous base call or a PHRED score lower than 10. It is possible that some unitags are due to sequencing errors, but these artificial tags are unlikely to match transcript or genome sequences. and iii) the chicken chromosome sequences . Candidate tags in the transcript datasets were extracted only in the sense orientation whereas both strands of the chromosome sequences were searched. The SAGE tags, unitags and candidate tags together with relevant information concerning their positions and frequencies were entered into tables of a relational database to facilitate further analysis.To map the unitags to reference sequences, candidate tags were extracted from i) full length bursal cDNA sequences , ii) the and .Unitag matches were sequentially searched for in the bursal cDNA collection, the Ensembl transcript build and the Genome. Once a match had been identified, that tag fell out of the remaining search process and only matches of identical sequences were accepted. To relate the position of matching unitags in the genome sequence to the Ensembl transcripts, the chromosome coordinates of the Ensembl transcripts and their orientation were extracted from their headers. The database table structure, all tabulated entries as well as the FOUNTAIN software  used forTo evaluate the significance of SAGE tag count differences between the libraries for each unitag, we used Fishers exact test  since itcDNA was synthesized from bursal tissue and DT40 Cre1 cell line using the SuperScript Preamplification System (Invitrogen). Primers were designed to amplify a region of a few hundred base pairs encompassing the SAGE unitag sequence of the reference transcript. PCR amplification was performed using the Expand Long Template PCR System (Roche) under the following conditions: 2 min initial incubation at 93°C; 20, 25, 30 and 35 cycles consisting of 10 sec at 93°C, 30 sec at 65°C and 5 min at 68°C with 20 sec elongation per cycle.JMB and MBW conceived the project. MBW, RBC, HA and JMB participated in the design of the study and its coordination. MBW constructed the libraries. MBW, RBC, NH, CJ, MS, MC and YDW performed clone management, sequencing and data analysis. HA performed confirmation PCR analysis. MBW, RBC, AMK, EE, VL and JMB performed bioinformatics and statistical analysis. AMK and JMB programmed the FOUNTAIN software package to include the SAGE analysis modules. MBW, RBC, AMK, HA, VL and JMB helped draft the manuscript. All authors read and approved the final manuscript.