Registration of the fact of death is almost complete in the city of Chennai and not so in the rural Villupuram district in Tamilnadu, India. The cause of death is often inadequately recorded on the death certificate in developing countries like India. A special verbal autopsy (VA) study of 48 000 adult (aged ≥ 25 yrs) deaths in the city of Chennai (urban) during 1995–97 and 32 000 in rural Villupuram during 1997–98 was conducted to arrive at the probable underlying cause of death to estimate cause specific mortality.A ten day training on writing verbal autopsy (VA) report for adult deaths was given to non-medical graduates with at least 15 years of formal education. They interviewed surviving spouse/close associates of the deceased to write a verbal autopsy report in local language (Tamil) on the complaints, symptoms, signs, duration and treatment details of illness prior to death. Each report was reviewed centrally by two physicians independently. Random re-interviewing of 5% of the VA reports was done to check the reliability and reproducibility of the VA report. The validity of VA diagnosis was assessed only for cancer deaths.Verbal autopsy reduced the proportion of deaths attributed to unspecified and unknown causes from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu for adult deaths (≥ 25). The sensitivity of VA to identify cancer was 95% in the age group 25–69.A ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25–69 years) and less reliably for older ages (70+). Thus VA is practicable for deaths in early adult life or middle age and is of more limited value in old age. In developed countries, data on disease-specific mortality by age are readily available from national vital registration. In developing countries, where 80% of the world's deaths occur, estimation of cause of death is more difficult because the levels of coverage of vital registration and reliability of cause of death stated on the death certificate are generally low .A reliable assessment of disease-specific mortality rates is not yet possible in many parts of India, either because the underlying cause of the terminal illness was never known or because the relevant information was not recorded. For legal purposes death records do usually subdivide the causes of death into medical and non-medical  causes. But once non-medical causes have been excluded, specification of the underlying cause of a death from disease may be inaccurate, misclassified or missing for about 50% of adult deaths. For example, in Chennai, Tamilnadu, (south India) about half of those who died at home soon after the diagnosis of cancer  do not have cancer mentioned on their death certificate , and forElsewhere, 'Verbal Autopsy' i.e., 'systematic retrospective inquiry of family members about the symptoms and signs of illness prior to death' has been used to help determine the underlying medical cause of death, particularly in childhood ,3. For cA special study on 'verbal autopsy' of adult deaths was conducted in urban and rural areas in the state of Tamil Nadu, south India during 1998–2000. The aims of the study were (a) to develop a verbal autopsy instrument and test its utility to determine the underlying cause of death and (b) to estimate cause specific mortality using underlying cause of death arrived based on verbal autopsy reports. Now we report the type of training given to the field interviewers to interview and write the verbal autopsy report for adult deaths, the procedure followed to arrive at the probable underlying cause of death, the accuracy of the instrument developed and change in the proportions of cause of death based on the underlying cause of death arrived by reviewing 80 000 verbal autopsy reports.Male non-medical graduates with at least 15 years of formal education were selected. A ten days training was given on verbal autopsy interview techniques and writing verbal autopsy reports. There are four steps in training; 1). introduction to anatomy, signs and symptoms of various diseases, 2). mock interviews, 3). hands-on-training on writing verbal autopsy reports and 4). feed back session.This consisted of a basic three day introduction to anatomy, collecting data on history of past illness  from the respondent to write the verbal autopsy narrative report:• onset of illness prior to death: sudden or gradual• major symptom(s) and associated symptom(s) – in chronological order• If a symptom was present it was used as a filter to define what questions to be asked. For example, the filter symptom for heart attack was chest pain and the associated symptoms were breathlessness, sweating, vomiting and pain in the retrosternal area radiating to hand, shoulder, back etc. Cough for more than 4 weeks was a filter for lung cancer and tuberculosis. For each symptom, the duration should be recorded. Details of additional symptoms are built into the narrative in chronological order, by prompting, if necessary.• progress of the illness• any treatment received : Yes/No• If yes, type of treatment received• details of hospitalization prior to death:○ name of the hospital ,○ duration of hospitalization,○ whether discharged from the hospital against medical advice or not.○ Status at the time of discharge from the hospital: alive/dead• history of similar episodes and treatment(s) givenif available• abstract information related to the illness prior to death from the investigation reports done for any illness close to the time of death (within 6 months prior to the death) / hospital discharge summary etc, • If a death certificate is available, copy the cause of death given on the death certificate .• While recording history of adults with long standing illness, the description should include details that occurred in the month preceding the death, with other information recorded in the past history section (Appendix I in • For deaths that occur during pregnancy, delivery, or within six weeks of delivery: use Appendix II (A and B) in Additional file 1If the respondent is able to give the major symptoms and circumstances leading to death, then additional probing questions are asked about the associated symptoms using the symptoms/signs checklist (Appendix II(A & B) in ○ read out the filter symptom/sign of each module in the symptom/sign checklist○ check responses to each, and note down positive responses○ Where there is a positive response, additional details on that symptom and associated symptoms, if any, should be obtained.Thus, the methodology of collecting data in the open format using 'symptoms/signs checklist' is an interactive process, with the respondent taking the lead in providing the information, and the interviewer prompting where necessary for more details. The Field Interviewer gathers as much information as possible on the underlying cause of death from the respondent. It is imperative to get a logical and complete history of symptoms, signs, events, investigations and treatment, so that the medical reviewer gets sufficient information to assign a probable specific underlying cause of death.In the following two days, mock interviews were organized to illustrate techniques of probing a respondent to get the required information on cause of death as well as how to write the verbal autopsy report in local language (Tamil) in Appendix I in The third component of training included three days of hands on verbal autopsy training in the field. To limit distress over the terminal event, the field visit was carried out at least six months after death. Name of the deceased, father's name  or spouse name , age, gender, informant's name and address of the deceased at the time of death were given to field interviewers to locate the house of the deceased. The Field Interviewers carry Appendix I and II (symptoms/signs checklist) in The final component of training was feed back session for 2 days. This session involved teaching them how to include essential information in report writing. The feedback session mainly focused discussion on reports which did not have a specified underlying cause of death and reports with minimal information to arrive at the probable underlying cause of death; for example, a report may say that a person had a stroke ten days ago but did not specify the type of onset  or a report may say that the deceased had fever for ten days and died. It did not give details about the fever and other associated symptoms if any.This special verbal autopsy study was carried out in two areas in Tamilnadu. The Chennai city (urban) with a population of 4.2 million, and the Villupuram district , with a population of 2.5 million were chosen for this study. We have successfully traced 48 000 adult deaths (≥ 25 years at the time of death) in urban area and 32 000 adult deaths in rural Villupuram district and reviewed 80 000 verbal autopsy reports to arrive at the probable underlying cause of death.Information on deaths that occur in Chennai has been maintained manually by trained staff in Chennai Vital Statistics Department (VSD). The following data on deaths that occurred in Chennai during 1995 to 1997 were collected from the death registers in the Vital Statistics Department: deceased name, age, gender, marital status, father/spouse name, informant's name, occupation, place of death, address at the time of death, date of death and recorded cause of death (immediate, underlying and/or contributory). 72,000 deaths occurred during the study period of 1995–97. Of 72 000 found, 5000 deaths were attributed to external causes  in the death certificate, and were excluded. Of the remaining 67 000 deaths attributed in the VSD  to medical causes, 48 000 of the households were successfully visited during 1998–99 to try to assign cause of death by verbal autopsy.All formal and informal village records were to be sought to identify all deaths at any age during 1997–98. 41,000 such records were identified and 39 000 of the households were successfully visited during 1999–2000 to try to assign cause of death by verbal autopsy. Of 39 000 deaths, 7000 were before age 25.Feed back sessions were organized regularly throughout the study period to improve the quality of the verbal autopsy reports and 5% of the field visit reports were validated by re-interview one week after completion of the main interview, and blind to its results. This re-interviewing was done by one or other of two special interviewers because knowledge that a resurvey might well take place would ensure reliably motivated fieldwork at the initial survey, and also to check whether there were any systematic defects in the technique of any of the field workers: none were found. The underlying cause of death arrived based on re-interview data was not substantially different from the one arrived based on main interview data.th International Classification of Diseases, Injuries and Causes of Death [All verbal autopsy reports were centrally reviewed by two physicians independently in order to arrive at "probable underlying cause of death". Each made a diagnosis based on signs, symptoms and sequence of events prior to death given in the verbal autopsy report, which were then coded according to the 9of Death . The samIn Chennai city, the study was done in 1998–99 and the verbal autopsy reports were reviewed for 27 726 male deaths and 20 631 female deaths. Table In Villupuram district, verbal autopsy report was written for all deaths i.e., deaths due to medical and external causes. So verbal autopsy reports of 19 294 male deaths and 12 494 female deaths were reviewed. Deaths from unspecified medical causes and unknown causes decreased from 41% to 26% (p < 0.0001)  between the clinical diagnosis or clinical cause of death and postmortem findings -12 and m0–40% betWhere a cause recorded on the death certificate in the VSD differed from the underlying cause assigned by the VA, there was often no absolute way of knowing which was correct  except for cancer deaths which could be verified with the Chennai cancer registry records. Hence, the validity of VA diagnosis was assessed only for cancer deaths(ICD 9:140–208) by comparing with the stated cause of death in the VSD records and verifying with the Chennai cancer registry records and hospital medical records (only for cancer diagnosis). Chennai Population-Based Cancer Registry is a demographic registry in the network of the Indian Council of Medical Research, Govt. of India and has been functioning since 1982 at the Cancer Institute(WIA), Chennai. Cancer is not a notifiable disease in India. Hence registration has been done by active method. Cancer patients attending the Govt. hospitals are interviewed to collect data on age, sex, address, duration of stay in Chennai city, marital status, mother tongue and educational level. Interviews are done at the houses for those who have been missed by the registry staff during their (Govt.) hospital visit. The clinical data, such as date of cancer diagnosis, method of diagnosis, site of cancer, any spread of the disease, histology, treatment details and status  for all registered patients are abstracted from the hospital medical records. All data on cancer patients attending the private hospitals are abstracted from the hospital records. The mortality data available at the Vital Statistics Department are linked with the Chennai Cancer Registry data base. Therefore the cause of death arrived based on verbal autopsy report was compared with the hospital data on cancer patients available in the Chennai Cancer Registry data base.Table The Tamilnadu study on verbal autopsy ,16 used The verbal autopsy tool for adult deaths is an open narrative format uses the check list of symptoms and signs with filters to get more information on train of events or circumstances preceding the death. The sensitivity of this tool is 95% (94% compared to VSD records and 96% compared to Chennai population-based cancer registry) in the age group 25–69 during the study period. The validity of this verbal autopsy tool is influenced by the training given to the interviewers, on the immediate random checking of the 5% of interview data and reviewing of the field reports centrally by 2 physicians to arrive at the probable underlying cause of death which is better than that arrived by opinion-based algorithm . There iAs a result of using verbal autopsy method, adult deaths (≥ 25) from unspecified and unknown causes decreased from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu. Ten day training to write verbal autopsy reports followed by constant monitoring of the submitted reports resulted in arriving at the probable underlying cause of death for most of the deaths and to compute broad classification of the underlying causes of about 90% of deaths in early adult life or middle age: in old age, however, the proportion classifiable is substantially lower. The specific causes of death arrived based on verbal autopsy reports were used to estimate death rates for Chennai city  and to eThis methodology of arriving cause of death for adult deaths by verbal autopsy is now being adopted by the Registrar General of India, Govt. of India, for nationwide use in the Sample Registration System(SRS) that consists of 6671 units  spread across the country covering 1.1 million households and about 6 million population. The SRS is a large demographic survey of vital events occurring in a national random sample of urban and rural areas in India by the Registrar General of India (RGI) to provide annual estimates of age-specific birth and death rates at the national and state levels. This exercise on verbal autopsy for adult deaths along with the verbal autopsy questionnaire developed by the SRS collaborators for deaths that occur at ages less than 15 years and for maternal deaths is expected to yield reliable cause-specific death rates for India.A ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25–69 years) and less reliably for older ages (70+). Our experience shows that the open narrative, if well written, provides adequate information for assigning probable underlying cause of death for adult deaths.The authors declare that they have no competing interests.VG and RP participated in designing the study, analysis and preparation of the report. Development of verbal autopsy tool, training on verbal autopsy method, co-ordination of field work and data management were done by VG. Verbal autopsy review by VG and TS Kanaka, physician.The pre-publication history for this paper can be accessed here:Appendix I – used to write the history of past illness and verbal autopsy report for adult deaths (25 years or older) and in Appendix II – Symptoms/signs checklist for adult deaths (≥ 25 years) is givenClick here for file


The HE2 gene encodes a group of isoforms with similarities to the antimicrobial beta-defensins. We demonstrated earlier that the antimicrobial activity of HE2 proteins and peptides is salt resistant and structure dependent and involves permeabilization of bacterial membranes. In this study, we further characterize the antimicrobial properties of HE2 peptides in terms of the structural changes induced in E. coli and the inhibition of macromolecular synthesis.3H]thymidine, [5-3H]uridine and L-[4,5-3H(N)]leucine into DNA, RNA and protein. Statistical analyses using Student's t-test were performed using Sigma Plot software. Values shown are Mean ± S.D.E. coli treated with 50 micro g/ml of HE2alpha, HE2beta1 or HE2beta2 peptides for 30 and 60 min were visualized using transmission and scanning electron microscopy to investigate the impact of these peptides on bacterial internal and external structure. The effects of HE2alpha, HE2beta1 and HE2beta2 on E. coli macromolecular synthesis was assayed by incubating the bacteria with 2, 10 and 25 micro g/ml of the individual peptides for 0–60 min and measuring the incorporation of the radioactive precursors [methyl-E. coli treated with HE2alpha, HE2beta1 and HE2beta2 peptides as visualized by transmission electron microscopy showed extensive damage characterized by membrane blebbing, thickening of the membrane, highly granulated cytoplasm and appearance of vacuoles in contrast to the smooth and continuous membrane structure of the untreated bacteria. Similarly, bacteria observed by scanning electron microscopy after treating with HE2alpha, HE2beta1 or HE2beta2 peptides exhibited membrane blebbing and wrinkling, leakage of cellular contents, especially at the dividing septa, and external accumulation of fibrous materials. In addition, HE2alpha, HE2beta1 and HE2beta2 peptides inhibited E. coli DNA, RNA and protein synthesis.The morphological changes observed in E. coli treated with epididymal HE2 peptides provide further evidence for their membrane dependent mechanism of antibacterial action. HE2 C-terminal peptides can inhibit E. coli macromolecular synthesis, suggesting an additional mechanism of bacterial killing supplementary to membrane permeabilization. Antimicrobial proteins and peptides are widely expressed in both plants and animals. A variety of natural antibiotics belonging to different classes such as defensins, cathelicidins, cercopins and protease inhibitors  are founE. coli. Their antimicrobial activities are structure dependent and salt tolerant and their mechanism of action involves interacting with and permeabilizing bacterial membranes. However, structural evidence for the membrane changes in E. coli induced by these peptides is still lacking. Further, it is not still clear whether bacterial killing by HE2 peptides involves only membrane permeabilization or whether the peptides interact with specific targets inside the bacteria to inhibit metabolic processes as reported for other antimicrobial proteins is not yet demonstrated. In this study, using transmission and scanning electron microscopy, we provide further evidence that HE2 peptides induce significant structural changes in E. coli consistent with their membrane dependent mechanism of action as reported earlier. Further, we show that HE2 peptides inhibit E. coli DNA, RNA and protein synthesis suggesting that their antimicrobial action may also involve targets inside the bacteria as well as membrane permeabilization.In the epididymis, a major organ of the male reproductive tract, immature sperm released from the testis undergo sequential maturation to acquire forward motility and fertilizing ability. A wide variety of proteins including antimicrobial proteins released into the lumen of epididymis bind sperm and are thought to play an important role in epididymal immunity in addition to their role in sperm maturation . ExampleE. coli and purified as described previously [E. coli strain M15 (pREP4) was transformed with pQE30 vector  containing cDNA that codes for HE2β1 C-terminal peptide. Protein expression was induced with 1 mM isopropyl-β-D-thiogalactoside for 1 h at 37°C and the His-tagged recombinant peptide was purified using nickel-nitrilotriacetate agarose column . To avoid baseline expression of the protein prior to induction, 1% glucose was maintained in the bacterial medium and the induction time was kept to a minimum (1 h) to minimize the toxic effects of the peptide on E. coli. The peptide was dialyzed extensively against 10 mM sodium phosphate (pH 7.4) to remove urea.HE2α and HE2β2 C-terminal peptides were synthesized at the Peptide Synthesis Facility, University of North Carolina, Chapel Hill by standard f-moc solid phase procedures using Rainin symphony multiple peptide synthesizer (Rainin Instrument, Woburn, MA). The purified peptides eluted as single peaks upon reverse phase high performance liquid chromatography (HPLC) and were further demonstrated to have their corresponding molecular weight by MALDI-TOF mass spectrometry. HE2β1 C-terminal peptide was expressed in eviously . BrieflyE. coli resuspended in 10 mM sodium phosphate buffer (pH 7.4) were treated with 50 μg/ml HE2α, HE2β1 or HE2β2 for 30 and 60 min. After incubation, bacterial cells were washed with 10 mM sodium phosphate buffer (pH 7.4) and fixed with an equal volume of 4% glutaraldehyde in 0.1 M sodium cacodylate buffer, pH 7.4, followed by centrifugation at 1000 rpm for 10 minutes to concentrate the cells in a pellet. The fixed samples were stored overnight to several days at 4°C in the fixative solution. The pellet was rinsed in 0.1 M sodium cacodylate buffer several times, and post-fixed with a combination of 1.25% potassium ferrocyanide and 1% buffered osmium tetroxide for one hour at room temperature. Following dehydration with a graded series of ethanols (30–100%) and two changes of propylene oxide, the cell pellet was infiltrated and embedded in PolyBed 812 epoxy resin (Polysciences, Inc., Warrington, PA). Ultra thin sections (70 nm) were cut and mounted on copper grids followed by post staining with 4% uranyl acetate and 0.4% lead citrate. The sections were examined and photographed at an accelerating voltage of 80 kV using a LEO EM 910 transmission electron microscope (LEO Electron Microscopy, Inc., Thornwood, NY) equipped with a Gatan BioScan digital camera (Gatan, Inc., Pleasanton, CA).E. coli were studied using scanning electron microscopy as described earlier [The structural changes induced by HE2 peptides on  earlier . BacteriE. coli DNA, RNA and protein synthesis were studied as functions of incorporation of the radioactive precursors [methyl-3H]thymidine, [5-3H]uridine and L-[4,5-3H(N)]leucine respectively as described [6 mid-log phase E. coli resuspended in 10 mM sodium phosphate buffer (pH 7.4) were treated with varying concentrations of HE2 peptides and 2.5 μl/ml of either [methyl-3H]thymidine (20 Ci/mmol), [5-3H]uridine (25.5 Ci/mmol) or L-[4,5-3H(N)]leucine (59.5 Ci/mmol) for different time periods. After incubation, bacterial suspensions were added to 10% ice-cold trichloroacetic acid and allowed to stand in ice for 40 min. Samples were then collected on 2.4 cm GF/C glass microfiber filters (Fisher Scientific, Pittsburgh, PA) using vacuum filtration and washed thoroughly with 5% TCA and 70% ethanol. The filters were then dried and placed in scintillation vials containing 5 ml of EcoScint scintillation cocktail  and counts were obtained in a LKB 1214 Rackbeta liquid scintillation counter (LKB WALLACE, Turku, Finland) for 1 min for each filter. Statistical analyses using Student's t-test were performed using Sigma Plot software (SPSS Inc., Chicago, IL). Values shown are Mean ± S.D.The effects of HE2 peptides on escribed . 1 × 106E. coli exposed to HE2 peptides. The three images shown for each treatment were documented in different fields of view and are intended to represent the range of responses seen in the bacteria. In contrast to the smooth continuous double membrane structure clearly visible in untreated bacteria ]leucine into DNA, RNA and protein was studied in the presence of 2–25 μg/ml peptides. A dose and time dependent inhibition of DNA synthesis by HE2α peptide was observed  [E. coli treated with synthetic peptides of the antimicrobial protein apolipoprotein A-II [E. coli treated with the antimicrobial peptide tigerinin-1 [E. coli treated with HE2 peptides also revealed striking structural changes in their morphology. HE2 peptides caused membrane wrinkling, blebbing and leakage of fibrous material primarily at the dividing septa in E. coli. Such structural changes shown in Fig. E. coli involves annular constriction of all layers of the cell envelope and synthesis and assembly of new septal materials [Earlier we demonstrated that HE2 proteins and their C-terminal peptides exhibit salt tolerant and structural dependent antimicrobial activities and their mechanism involved permeabilization of both outer and inner bacterial membranes . In thisfensins) . Similarein A-II . Highly erinin-1  similar aterials . It is pE. coli. Inhibition of macromolecular synthesis was reported for bactenectins [3H]thymidine than [5-3H]uridine and L-[4,5-3H(N)]leucine, suggesting DNA synthesis is more sensitive to their antimicrobial action. It is possible that in bacteria that are extensively damaged by HE2 peptides, inhibition of macromolecular synthesis may result simply from the total breakdown of the cells. However the electron micrographs show that only some bacteria appear to be exuding cell contents after the 30 minute treatment. Thus during the first 10–20 minutes exposure to HE2 peptides, some peptides may be entering through pores too small for major cytoplasmic release. The early inhibition of DNA and RNA synthesis in bacteria where little loss of cell contents has occurred, may result from specific interaction of the synthetic machinery with HE2 peptides. Further studies are required to identify specific molecular targets within the bacteria and to establish whether HE2 interactions with these targets can be beneficial to the host by slowing bacterial proliferation.The mechanism of action of antimicrobial proteins is primarily thought to be membrane dependent involving membrane permeabilization and disruption. Structural characteristics of antimicrobial peptides tend to play an important role in their mechanism of action. For example, β-defensins are cationic in nature and with β-sheet rich amphipathic structures stabilized by the three disulfide motif . The catenectins , human nenectins , pleurocenectins  and the enectins . In thisIncreasing recognition of the ability of a number of proteins on the sperm surface to kill bacteria has led to the proposal that they may defend against microbial attack in both the male and female reproductive tracts. The cathelicidin hCAP18 on sperm is processed by the prostate-derived protease, gastricsin to release the active peptide ALL-38 and is found in the female reproductive tract after intercourse . A membeE. coli consistent with their membrane dependent mechanism of action [E. coli DNA, RNA and protein synthesis.In conclusion, we report that the epididymal antimicrobial peptides HE2α, HE2β1 and HE2β2 induce striking morphological changes in f action . In addiSY performed the electron microscopy studies, radioactive incorporation assays and wrote majority of the manuscript. KGH prepared the recombinant peptides. SHH and FSF supervised and coordinated the work and the preparation of the manuscript. All authors read, commented upon and approved the final manuscript.


Thus, evidence indicates that altered expression of MMPs may play a role in the aggressive phenotype seen in African American women.To date there are 26 human matrix metalloproteinases (MMPs) which are classified according to their substrate specificity and structural similarities. The four major subgroups of MMPs are gelatinases, interstitial collagenases, stromelysins, and membrane-type matrix metalloproteinases (MT-MMPs). This study investigates the expression of 26 MMPs, which have been shown to play a role in cancer metastasis. Breast tissues and cell lines derived from African American patients and Caucasian patients were assayed to demonstrate alterations in the transcription of genes primarily responsible for degrading the extracellular matrix (ECM). The expression levels of the extracellular matrix and adhesion molecules were analyzed using the gene array technology. Steady state levels of mRNAs were validated by RT-PCR analysis. Total RNA was isolated from tissue and cell lines and used in the RT-PCR assays. From this data, differential expression of MMPs between 6 breast cancer cell lines and 2 non-cancer breast cell lines was demonstrated. We have performed an  In 2003, it was estimated that approximately 1.3 million Americans would be diagnosed with invasive cancer. Of this group, racial/ethnic minorities account for a disproportionate number of these cancers . Thus a The extracellular matrix (ECM) is a complex structural entity surrounding and supporting organs and tissues of the body. The ECM plays a key role in cell-cell signaling, wound repair, cell adhesion and tissue function. Recent studies suggest that cell adhesion proteins located on breast cancer cells interact with the ECM . This inThe matrix metalloproteinases (MMPs) are a family of structurally and functionally related endoproteinases that are involved in the degradation of the ECM. Currently, there are 26 identified human matrix metalloproteinases, which are classified according to their substrate specificity and structural similarities . AbnormaMMP expression has bee shown to be elevated during development, pregnancy, and involution and has been shown to be related to tumor cell invasiveness . This stAll cell lines were purchased from American Type Culture Collection (Rockville, MD, USA). Cells were propagated in the recommended media and given new media every 2 to 3 days until 90% confluent (see table RNA was extracted from the cell line using the RNAqueous (Ambion, Austin, TX). Cells were collected by low speed centrifugation and lysed by adding 200 μl of Lysis/Binding Solution. An equal volume of 64% ethanol was added to the lysate. The lysate/ethanol mixture was transferred to the RNAqueous Filter Cartridge and centrifuged for 1 minute at 13,400 rpm. The flow through was discarded and 700 μl of Wash Solution 1 was added to the RNAqueous Filter Cartridge and centrifuged for 1 minute. The column was washed twice with 500 μl of Wash Solution 2/3 and eluted with 110 μl Elution Solution. Isolated RNA was quantitated using the UltraSpec 2000 (Pharmacia Biotech). All RNA samples were stored at -70°C in RNA elution solution until further use.The Extracellular Matrix and Adhesion Molecule gene arrays were obtained from SuperArray (Frederick, MD). The array membranes were pre-hybridized with GEA hybridization solution and denatured salmon sperm DNA at 60°C for two hours. For each RNA sample, a labeling mix consisting of 4 μl 5X GEA labeling buffer, 2 μl biotin-16-dUTP, 1 μl RNase inhibitor, 1 μl reverse transcriptase, and 2 μl RNase-free water was prepared and an aliquot of 3 μg of RNA was added to each respective thin-walled PCR tube. The cDNA labels were created using a cycle of 3 minutes at 70°C, 2 minutes at 42°C, and an additional 90 minutes at 42°C. Two microliters of stop buffer was added and the mix denatured at 94°C for 5 minutes. The labeled cDNA was added to the membrane and allowed to hybridize overnight. The membranes were washed with 2X SSC/0.1% SDS and 0.1X SSC/0.5% SDS, blocked with blocking solution, and the probes were detected using AP-Strepavidin, specific buffers, CDP-Star and subsequent exposure to X-ray film for 30 seconds to 5 minutes. The autoradiograms were analyzed using ScanAlyzer and GEArray Analyzer (SuperArray, Frederick, MD).4, 1.0 μl AMV reverse transcriptase, Tfl DNA polymerase and 1 μg of total RNA in a 0.5 ml thin walled Eppendorf tube on ice. The reaction mixes were then vortexed for 5 seconds and centrifuged. The PCR cycling profile was as follows: 48°C for 1 minute for reverse transcription of the RNA into cDNA, 94°C for 4 minutes to deactivate the reverse transcriptase, and 30 cycling sequences of denaturing at 94°C for 45 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 1 minute with a final extension at 72°C for 10 minutes. An aliquot of 20 μl of each RT-PCR reaction were run on 1.2% agarose gels, stained with ethidium bromide, photographed and subjected to densitometic measurements using the Chemi-Imager Tm 4000 (Alpha Innotech, Corporation, San Leandro, CA).The RT-PCR reactions were performed in a P/E GeneAmp 9700 thermocycler , using the Access RT-PCR system (Promega, Madison, WI). The reaction mixes were prepared by combining 27. 5 μl of nuclease free water, 10 μl of AMV, 1 μl Tfl 5X reaction buffer, 2 μl dNTP mix, 50 pM of upstream primer, 50 pM of downstream primer in 1.5 μl volume each, 3 μl 25 mM MgSOGene arrays were utilized to explore and compare the expression levels of extracellular matrix adhesion molecules in AA and CAU breast cancer cells. The array revealed elevated expression in 36% of the genes in AA samples when compared to their CAU counterparts. Of those elevated genes, 31% were from the cell membrane adhesion molecules group, 17% from the extracellular matrix proteins functional gene group, 37% from the proteases category, and 14% were protease inhibitors. Initial results of the gene array indicated a significant elevation of the proteases (data not shown). To further evaluate this, we proceeded with direct analysis of all known MMPs.Comparison of the individual relative densities between AA and CAU women revealed elevated expression in 12 of the 26 MMPs (3, 7, 8, 9, 11, 12, 13, 14, 15, 23B, 26, and 28) .Gene Array analysis of AA and CAU breast cancer cells indicates that there is altered expression of the genes in the Extracellular matrix and adhesion molecules, particularly the proteases. This group included 17 MMPs of which ten displayed elevated expression in AA women.RT-PCR was performed to confirm the results of the gene array. We observed elevated expression of 12 MMPs in AA cell lines when compared to their CAU counterparts. These include one gelatinase (MMP-9), two interstitial collagenases (MMP-8, and 13), 3 stromelysins (MMP-3, 7, 11), two MT-MMPs (MMP-14 and -15) and 4 uncategorized MMPs (MMP-12, 23B, 26, and 28). There was no MMP-17 expression detected in any of the cell lines (Figure Studies have shown that normal mammary gland expression of MMPs is low except during times of development, pregnancy, and involution ,15,16. HOur results suggest that there is altered expression of MMPs in cell lines derived from AA and CAU women. It also demonstrates that there is greater expression of MMPs in AA women than in CAU women. This investigation indicates that altered expression of MMPs may play a role in the aggressive phenotype seen in AA women. This evidence suggests that the elevated expression levels of 12 MMPs may be a contributing factor in the higher mortality rates of AA breast cancer patients. This study is significant because it may reveal biomarkers of metastasis in AA women. To date, this is the first study to extremely investigate MMP expression in cell lines derived from African American patients.MMP-Matrix metalloproteinases, MT-MMP-Membrane-type matrix metalloproteinases, AA-African American women, CAU-Caucasian, RT-PCR-Reverse transcriptase Polymerase Chain Reaction, ECM-Extracellular matrixJAM performed the microarrays and RT-PCR experiments, was involved in tissue culture and prepared the first draft of the manuscript. HFY was responsible for primer design, and performed data analysis and densitometric readings of the gene arrays and RT-PCR. KL maintained all cells and tissues and assisted in the editing of this manuscript. MJ provided cell lines, training in microarray performance and editing. AAD conceived the study and participated in its design, coordination and funding, as well as preparation of the manuscript. All authors read and approved the final manuscript.


Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen  A number of techniques have been developed that exploit combinations of protein features in training data and can predict protein-protein interactions when applied to novel proteins. Our study is motivated by a study by Jansen et al. [ provides more details about the quantification of these variables. Their Bayesian method predicts protein-protein interactions genome-wide by probabilistic integration of genomic features that are weakly associated with interactions . The model was used for two separate predictions of probabilistic interactomes (PI), one of which (PIE) is built on four high-throughput experimental interaction data sets, and the other (PIP) on the mRNA expression, Gene Ontology, MIPS functional and coessentiality data. Within the PIP sub-network, different genomic features are assumed to be independent in prior. In addition, this method involved discretizing the raw data into groups and representing the two mRNA expression profiles (cell cycle and Rosetta compendium data) by their first principal component for computational convenience.Proteins transmit regulatory signals throughout the cell, catalyze large numbers of chemical reactions, and are crucial for the stability of numerous cellular structures. Interactions among proteins are key for cell functioning and identifying such interactions is crucial for deciphering the fundamental molecular mechanisms of the cell. As relevant genomic information is exponentially increasing both in quantity and complexity, n et al. , who pron et al.  complexen et al.  as gold n et al.  and the n et al. , biologin et al.  and the n et al. , and hign et al. -10. The Our current study focuses on assessing the contributions of different types of genomic data towards predicting protein-protein interactions. This may help us to understand which genomic features have the closest biological relationship with protein-protein interactions and hence to construct a better prediction model. As prediction rules involving less relevant information may have lower prediction accuracy, our analysis can give us insights into how to construct more parsimonious models with comparable or better prediction accuracy. A potential disadvantage of the Bayesian network approach may be that the data discretization can obscure information contained in the raw genomic data. Thus, in addition to assessing the information content of the data sources, we also propose alternative non-Bayesian models that fully utilize the data without discretization. These methods, such as logistic regression and random forests, do not require prior knowledge, and we can evaluate the importance of the different genomic features in the context of these methods.. In doing so, we find that some of the features have stronger influence on the overall prediction. While this might be true for the larger problem as well, there are a number of caveats that one has to keep in mind, such as that the features that are present in this subset might not be the strongest in the whole set of 18 million protein pairs.To accurately and quantitatively assess the information contributions of different genomic features, we construct in essence a simplified problem that has some but not all of the elements of the original study. Here, we only look at a subset of the data from  comprisiHere, we construct models for predicting protein-protein interactions that, given the gold standards, are basically dichotomous classifiers. Multiple logistic regression  is one cThe logistic model has the advantage that it provides an estimated probability that a pair of proteins interact, and is readily available in standard statistical packages. In this paper, the logistic regression analysis was generated using PROC LOGISTIC in SAS/STAT software, Version 9 of the SAS System. Moreover, we can evaluate the importance of different genomic features by variable selections. Among many available schemes, we chose stepwise variable selection that is widely used in standard packages. Stepwise selection is a greedy search algorithm that selects variables with the best marginal prediction power given the current model. To quantify the importance of the predictor variables to the model fitting, we can use the deviance measure1 - log L0),-2 on all the features for random forest, logistic regression and the Bayesian network method. Figure Information assessment of different genomic data may help us understand their relationship with protein-protein interactions, and form a guideline for future model development.We saw that the MIPS and Gene Ontology functional similarities were the two most important information sources under both the logistic regression and random forests methods. Histograms of the MIPS and GO functional similarity data Figures  and 4 shErr) defined as follows.In the following paragraphs, we show quantitatively that the MIPS and Gene Ontology functional similarities are the dominating information contributors for predicting protein-protein interactions, while other genomic features have negligible benefit and can not provide credible predictions by themselves. We examine the performance of random forests using three different genomic feature sets: (i) all genomic features included, (ii) MIPS and Gene Ontology functional similarities only, and (iii) genomic features other than the MIPS and Gene Ontology functional similarities. The random forest performance is evaluated with the classification error  and f2(X) for the gold standard positives and negatives, respectively. Denote a classifier by C(X). Then the classification error can be written asI(A) is an indicator function equal to 1 when A is true and 0 otherwise. A minimal classification error Errmin can be computed by minimizing (1) across the space of X. It is easy to see thatwhere C(X) = I(f1(X) >f2(X)). With this formula, we can estimate the optimal (minimum) classification error based on any estimates of f1(X) and f2(X). In our study, f1(X) and f2(X) are estimated by their empirical density functions.is achieved at Table We also estimated the classification errors using the other genomic features within the random forest framework. Table Moreover, we compared the ROC curves Figure  of the rIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed in . Random We considered alternative non-Bayesian methods such as logistic regression and random forest for predicting protein-protein interactions. These existing methods do not require prior information needed for the Bayesian approach, and can fully utilize the raw data without discretization. The logistic model performs similarly as the Bayesian method in terms of classifications and, like the Bayesian method, produces estimated probabilities that two proteins interact. As a dichotomous classifier, the random forest method outperforms the other methods considered and efficiently uses the information, although it is computationally more expensive. In particular, its importance measure provides a more objective assessment of different genomic features on predicting protein-protein interactions than simply considering contributions to model fitting. These findings are motivation to look for other, more sensible data resources and superior models.We found that the data discretizations used in the Bayesian methods decreased classification performance. We note here that the genomic features datasets investigated here themselves are highly processed versions of the datasets they were derived from and that there may be better ways to take the original data into account.Another caveat is that the predictions might be just defining groups of proteins that have the same genomic properties as the protein complexes in the MIPS data. This does not necessarily mean that they really represent protein complexes. Rather, they may represent groups of proteins that have the same properties as protein complexes.In this analysis we have looked at the relative weights of various features in predicting protein-protein interactions based on the previous study in . We lookX1, X2, ..., Xn. Let Y = 1 when two proteins interact, i.e., they are in the same complex, and Y = 0 when not. The logistic model is of the formDenote the gold standards by random variable Y and the other genomic features by X consists of X1, X2, ..., Xn and their interaction terms.where the random vector The stepwise selection procedure starts from a null model. At each step, it adds a variable with the most significant score statistics among those not in the model, then sequentially removes the variable with the least score statistic among those in the model whose score statistics are not significant. The process terminates if no further variable can be added to the model or if the variable just entered into the model is the only variable removed in the subsequent elimination. Here, the score statistic measures the significance of the effect of a variable.sensitivity and specificity. Sensitivity is calculated by dividing the number of true positives (TP) through the number of all positives, which equals the sum of the true positives and the false negatives (FN); specificity is calculated by dividing the number of true negatives (TN) through the number of all negatives, which equals the sum of the true negatives and the false positives (FP).Receiving operator characteristic (ROC) curve  is a graSensitivity = TP/(TP + FN), Specificity = TN/(TN + FP).specificity on the X axis and sensitivity on the Y axis. A good classifier has its ROC curve climbing rapidly towards upper left hand corner of the graph. This can also be quantified by measuring the area under the curve. The closer the area is to 1.0, the better the classifier is; and the closer the area is to 0.5, the worse the classifier is.The ROC curve plot shows 1 - NL and BW conducted the major part of the data analysis, and created all the tables and figures, under the supervision of HZ. RJ and MG provided the data sets for the analysis and contributed to the discussion on the comparisons of different methods. All authors read and approved the final manuscript.The complete-information subset in ZIP file.Click here for file


The plastic response of fine roots to a changing environment is suggested to affect the growth and form of a plant. Here we show that the plasticity of fine root growth may increase plant productivity based on an experiment using young seedlings (14-week old) of loblolly pine. We use two contrasting pine ecotypes, "mesic" and "xeric", to investigate the adaptive significance of such a plastic response.The partitioning of biomass to fine roots is observed to reduce with increased nutrient availability. For the "mesic" ecotype, increased stem biomass as a consequence of more nutrients may be primarily due to reduced fine-root biomass partitioning. For the "xeric" ecotype, the favorable influence of the plasticity of fine root growth on stem growth results from increased allocation of biomass to foliage and decreased allocation to fine roots. An evolutionary genetic analysis indicates that the plasticity of fine root growth is inducible, whereas the plasticity of foliage is constitutive.Results promise to enhance a fundamental understanding of evolutionary changes of tree architecture under domestication and to design sound silvicultural and breeding measures for improving plant productivity. In anPhenotypic plasticity is the potential of an organism to alter its phenotype in changing environments -19. PhenLoblolly pine is the most important tree species for fiber production in the southern US . BecauseP < 0.001). These values increased to 102% and 199% for these two ecotypes, respectively, when the trees were treated for 14 weeks (P < 0.001).After 4 weeks of treatment, trees receiving the high nutrient treatment displayed 22% ("xeric") and 47% ("mesic") greater stem biomass than those under the low fertilizer treatment (P < 0.01), as compared to the significance level when the proportion was not held constant (P < 0.0001). These dependent relationships suggest that increased stem biomass due to better nutrient supplies was attributable to both increased foliage investment and decreased energetic costs of fine root construction.Allometric analysis was used to evaluate the influences of foliage and fine-root biomass partitioning on stem growth which arise from differences in nutrient supply. On both harvesting dates, the proportion of foliage biomass to total plant biomass increased markedly, whereas the proportion of fine root biomass decreased significantly, with better nutrient supplies. As an illustration, we use Figure py←1 = -0.09), whereas the plasticity of fine-root biomass partitioning, i.e., decreased partitioning of biomass to fine roots under higher fertilization, had a significant impact on the corresponding increase of stem biomass . Plasticity between different growth stages indicates the dependence of plastic responses on the timing and sequences of developmental events. Ecotypic variation in developmental plasticity represents different genetic bases involved in relevant developmental events [Foliage and fine roots have complementary roles in uptake of resources; the former in energy and carbon uptake and the latter in water and nutrient uptake ,22. Mechl events .Ecotypic differentiation of loblolly pine could be explained by limits of plasticity. Quantitative evolutionary genetic models predict that the phenotypic plasticity of a trait is costly or physiologically limiting when the trait is forced to respond to environmental variation ("passive" response). DeWitt et al.  delineatOur analysis suggests that the plasticity of fine-root biomass proportion is physiologically limiting, whereas the plasticity of foliage biomass proportion is not. The relationship of the shoot biomass residuals was positive with the degree of plasticity of foliage biomass proportion Fig. , but negForest tree form (biomass partitioning) is highly plastic in response to changes in nutrient levels. The carbon budgets for forest trees show a surprisingly large role of roots. Under low nutrient conditions that predominate in natural forests, 60–80% of photosynthate is allocated below ground, compared with 30% for high nutrient levels [Progress towards domestication in trees will be slowed by long generation times, but is likely to be based on the exploitation of interactions between genotypes and yield associated with various types of agronomic methods (e.g., fertilizer levels), as have been shown for herbaceous crop plants. However, the environmental uncertainties during the long life span of trees have caused some breeders to consider the value of plasticity as a trait itself. And plasticity could obscure the relationship between phenotype and genotype, making selection less efficient. Efforts to domesticate forest trees will be enhanced by a deeper knowledge of phenotypic plasticity .Our study of biomass partitioning in relation to varying nutritional levels in loblolly pine supports the previous hypothesis, proposed by Linder and Axelson , that thPinus taeda L.). We used two contrasting loblolly pine ecotypes from regions that differ in soil resource availability. One of the ecotypes, known as the "Lost Pines" of Texas, is adapted to droughty conditions and low soil fertility and is denoted by "xeric", whereas the other, Atlantic Coastal Plain, is adapted to more moderate conditions and is denoted by "mesic". Adaptive differentiation between the contrasting "xeric" and "mesic" ecotypes has been previously characterized [Phenotypic plasticity was evaluated for fine roots and biomass partitioning of a commercially important forest tree species, loblolly pine  and fine root biomass (py2←) to stem biomass through the change of nutritional levels were estimated by solving the following regular equations:Path analysis was used to identify the cause-effect relationships in a complex system . Path anpy 1←+ r12py 2←= ry1r12py 1←+ py 2←= ry2ry 1and ry 2are the family correlation coefficients of the plasticity of foliage biomass and fine-root biomass with the plasticity of stem biomass, respectively, and r12 is the family correlation coefficient between the plasticity of foliage biomass and fine-root biomass. Residuals were estimated to evaluate the degree of determination for the path analysis [where analysis . All datRW designed the study, carried out the experiment, analyzed the data and drafted the manuscript. JEG participated in the experiment. SEM participated in the design of the study. DMO participated in the design and coordination. All authors read and approved the final manuscript.


We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at:  One important goal in bioinformatics is to integrate data from disparate sources of heterogeneous biological information. Data integration allows us to assemble targeted data reagents for bioinformatics analyses, and to discover scientific relationships between data. Most public repositories of biological data focus on deriving and providing one particular type of data, be it biological sequences  the source data, 2) the ontology system, 3) the relational data models, 4) the APIs, and 5) the applications  controlled vocabulary . The Atl.The Atlas internal ontology exists largely to help describe Sequence Features as they exists in the GenBank Sequence Feature model, as this is the primary data source for features. Neither the Open Biological Ontologies (OBO)  relationsequence, which stores the sequence string and associated meta-data such as sequencing technique and molecule type; sequence identifiers, for which all identifiers that appear in the records are stored  [Feature table, one will note, does not explicitly contain source or type fields. This information is stored in other tables and can be pulled together dynamically as a GFF2 record is being constructed. For example, the BioID_type table contains the database source information in its db_source field and the internal Atlas Ontology table's term field which represents the feature type used in the GFF2 output. However, to reflect the fact that features in such output are now reconstructed from the Atlas system, we prefix the original source type with 'Atlas:', such as in 'Atlas:GenBank/RefSeq'. The reader will note that there are two different Ontology tables in Atlas. A more detailed explanation for the motivation for having two different kinds of Ontology tables is described in the previous Ontology section. However, in the context of sequence features, it is the internal Atlas Ontology table that is relevant.The schema for sequences is organized into three main parts: 2 (GFF2)  is suppoSequence table. Additional fields for: sequencing technique, tech, such as expressed sequence tags (ESTs); molecule type, moltype, such as DNA, RNA, protein, and nucleic acid; sequence length, length; the NCBI taxonomy identifier, taxonid; and the definition line, defline, are also stored in the Sequence table. Fields such as taxonid, tech, and moltype can be used separately, or in combination to produce customizable queries that return highly specific sets of data. Sequence identifiers, as with all other external identifiers, are managed through a layer of abstraction by associating them with internal identifiers within Atlas, which act as primary keys. Having a single internal identifier for a sequence allows us to relate all other identifiers found in the record to each other. In addition, ontologies for all types of identifiers currently found in GenBank ASN.1 data files, as well as relationships between these identifiers are modeled in the Bioid and Bioid_Bioid tables, respectively. As mentioned above, sequence features are also modeled in Atlas. For details please refer to the Ontology section below.The sequence string is stored in the For molecular interaction data, we developed a relational model compliant with the PSI-MI. Adopting a common interaction data model allowed us to unify data from different sources, and allows us to develop a set of common interaction retrieval APIs.Currently, HPRD, BIND, DIP, IntAct and MINT are included as interaction data sources. BIND, DIP, MINT and IntAct release their data in PSI-MI format. HPRD is releasing data in both PSI-MI standard format, and their own XML format. At the time of this publication, BIND released data as indexed flat files, ASN.1, XML, and PSI-MI format (level 2).Interactor, Interaction, Experiments and Dbxref. Interactor holds information about one of the interacting members in an interaction, such as the interactor's name, taxonomy, sequence, molecular type, features, subcellular localizations, and external identifiers. An Interaction consists of one or more interactors, and one or more experiments.The Atlas interaction model consists of four major entities: Experiment stores information about the experiments used to identify interactions. Finally, Dbxref is used to crosslink the external identifiers such PubMed id, RefSeq accession, HPRD id, BIND id, and Ontology id, for example .We integrated OMIM, LocusLink, Entrez Gene, HomoloGene and the annotation part of GO into the Atlas system in order to provide gene-related information. Conveniently, the OMIM and LocusLink data sources provide flat file tables which could be imported directly with the MySQL import function. Entrez Gene will eventually replace LocusLink, however in order to maintain a smooth transition and backward compatibility, we are maintaining populated relational models for both Entrez Gene and LocusLink until LocusLink is officially retired. Integration between HomoloGene and Sequence is achieved by relating the taxonomic, protein sequence and gene identifiers with Atlas' There are two classes of APIs in Atlas: loader and retrieval. Components of Atlas for which we have developed our own relational models, such as the Biological Sequences component, or the Molecular Interactions component, each have their own set of loader APIs. The loader APIs used to build the loading applications, populate instances of the relational models in the Atlas databases. Though most end-users will never need to use the loader APIs, they are critical to the implementation of the Atlas loading process, and are provided to the software development community. The other class of APIs are the retrieval APIs. These APIs serve to retrieve the data stored in Atlas. They are required for developing custom retrieval applications such as the Atlas toolbox applications. The loader API for Biological Sequences is implemented in C++ as it relies heavily on the NCBI C++ Toolkit  to parseThe API is constructed using object-oriented methodologies, employing objects to represent everything from low-level database connections to high-level data structures, and their access methods. This is illustrated in Figure Common in the design of the C++, Java, and Perl APIs, are a set of APIs written for MySQL database connectivity which handles the opening and closing of MySQL connections, as well as managing the execution of the SQL statements themselves. All subsequent APIs that interact with the Atlas database extend from this set of APIs.Both the data loader and the retrieval utilities share a common class responsible for low-level data transformations. This class includes methods that facilitate conversions between two internal Atlas identifiers, such as bioid_id to ontology_id, or methods that convert internal Atlas identifiers to externally referenced public identifiers, such as GenBank accession numbers, or GI numbers. Inheriting this shared identifier conversion class benefits both the loader APIs and the retrieval APIs, by providing them with the necessary tools to integrate information.Seq class. This class is inherited by both the SeqLoad class and SeqGet class, which define the loader methods, and retrieval methods, respectively. Another feature of the Biological Sequences API, is its ability to control stream output based on molecule types. API users simply specify which molecule type to filter by, through calls to higher-level retrieval methods, and SeqGet will then handle the logistics of stream management. Similarly with the Molecular Interactions component of Atlas, the InteractionDb class is inherited by the InteractionLoad class and the InteractionGet class, respectively defining the loader and retrieval methods which manipulate the data in memory.The Biological Sequences component of Atlas manages common identifiers, and hash maps in the Our Java interaction APIs, for example, are tightly coupled to our interaction data model with classes representing all the major schema objects such as Interaction, Feature, Dbxref, and Experiment. The APIs are works in progress and we continue to develop and improve them. We are considering even more tightly coupled API development by using XML schema code generators such as JAXB.All the source code is provided under the GNU General Public License (GPL), and therefore any developer can model future API development on numerous functions we have already implemented.The Atlas toolbox is a collection of applications that use the C++ API to perform common sequence and feature retrieval tasks. The applications are standard Unix command-line based tools that follow a command-line option-based interface for parameter entry. These are end-user applications and do not require any programming ability to use them. We have developed toolbox applications for sequence retrieval from accession and GI numbers, retrieval of sequences from all organisms beneath a given node in the NCBI taxonomy tree, retrieval of features given accession and GI numbers, retrieval of sub-sequences corresponding to specific features identified by qualifiers and their values, and retrieval of a set of interactions associated with a molecule given the accession number of an Interactor. Besides being useful tools, the toolbox applications' source code provides good examples of application development using the APIs. Software developers wishing to use the APIs can use these toolbox applications as a starting point for their own custom applications  by the NCBI , and areThe second class of loaders is interaction-based loaders. These loaders are exclusively implemented in Java. The datasets loaded by this class of loaders include BIND, HPRD, MINT, IntAct and DIP. All the interaction loaders are designed to parse the data in the way that best deals with that particular source data's structure and content (mostly XML). The interaction data is loaded using a common interaction object model, and the interaction loading APIs provide a flexible and extensible framework for future interaction data loading efforts. Currently, we are developing a PSI-MI level 2 data loader.Besides these classes of loaders, there is also a Java based loader that parses and loads UniProt sequence data. In addition, scripts are used to load datasets for which MySQL dumps, or tab-delimited database dumps are provided. This is handled using the MySQL import function, and eliminates the need to devise special parsers and loaders.GenBank and RefSeq are checked daily for incremental updates from the NCBI. Accession numbers are used to maintain the integrity of the data. New accession numbers reflect new records and will be inserted into the database. Updated sequences or records with same root accession number and patched annotations will replace existing records in the database. When new releases of GenBank/RefSeq are made available, all databases are purged and reloaded to remove retired records and to maintain referential integrity..Though we encourage the use of Atlas as an in-house repository, it can also act to serve the wider internet community. We provide a publicly available web interface to the Atlas databases to demonstrate some of its functionality. This offers basic access to GenBank, RefSeq, NCBI Taxonomy, Atlas Ontologies, BIND, HPRD, MINT, IntAct and DIP. Web interfaces to the Atlas toolbox applications: ac2gi, ac2seq, ac2tax, feat2seq, gi2ac, gi2feat, gi2seq, gi2tax, tax2seq, techtax2seq, tech2seq are available. In addition, interacting partners for proteins identified by accession numbers or GI numbers can be retrieved from any of the four interaction databases stored in Atlas. These web tools can be found at: The Atlas data warehouse offers maximum flexibility of data retrieval and integration. Users can access data in Atlas at the SQL, API and end-user application levels. Routine, pre-defined queries can be accessed through the APIs in Java, C++, and PERL (see API section, above), enabling developers to incorporate these queries in their software applications. Most of these queries have been used to build the Atlas toolbox, a set of end-user applications that run on the Unix command-line Table . IncludeSingle record queries are the simplest use case of the system. Users can input a GenBank or RefSeq accession number and/or GI number into the ac2seq and gi2seq toolbox applications to retrieve the relevant sequence record in Fasta, GenBank or ASN.1 format. Features on a particular sequence can also be retrieved independently with GenBank or RefSeq accession numbers and/or GI numbers. The single record queries can also be performed in batch mode where the user supplies a list of accession numbers or GI numbers and all data pertinent to the list of identifiers is then retrieved.Atlas provides tools to generate data reagents for genome analysis as well as a data model for storing biological features that have been annotated on the sequences. Coupled with Pegasys  and ApolAtlas provides users with the ability to generate custom sets of data to use as reagents. For example, using tax2seq, users can input a specific node of the NCBI taxonomy tree using its scientific name, or its NCBI taxonomy id and retrieve all nucleotide and amino acid sequences from organisms in the tree rooted at that node. This has special utility in genome analysis where specific sets of data from close relatives of the genome of interest enable comparative genomic methods for functional annotation. Furthermore, this type of taxonomy querying can be combined with the 'tech' field in the NCBI data model to produce sequences derived from different sequencing techniques such as expressed sequence tags (EST), genome survey sequence (GSS), sequence tagged sites (STS), high throughput genomic (HTG), etc. Compiling these specific data sets allows the user to perform more directed sequence similarity searches, for example, that yield more specific hits.Using the sequence data structure to model existing annotations in sequence records, Atlas can be used to store additional annotations created in Sequin  and ApolDeriving new associations from the information extracted from Atlas has proven to be particularly useful in developing a prototype system that infers interactions across species, detailed in "Ulysses – an Application for the Projection of Molecular Interactions across Species" (Kemmer D: in preparation, from the Wasserman and Ouellette laboratories).Given that the data for protein-protein interactions found within model organisms can be extremely sparse, Ulysses employs homology information to help bridge the gaps in the interaction data by projecting known interactions in one species onto other species for which those interactions are not known, and subsequently inferring potentially novel interactions in those species. Ulysses is able to perform its analyses and inferences, in part, by capitalizing on the integration, offered by Atlas, of HPRD, BIND, and HomoloGene. Atlas makes it possible to retrieve interactions for one species known to occur in another species, by integrating these datasets under one query space, and by providing the API and tools which make such queries simple.As an example, in both the MINT and DIP databases, protein C-C chemokine receptor type 3 (SwissProt accession number P51677) was found to interact with protein Small inducible cytokine A24 precursor (SwissProt accession number O00175) in human . Although referenced by different publications (, 44]), ), 44]), The Atlas system is also being used to determine yeast orthologs of genes that are implicated in human disease (Hieter P: in preparation). The inference being that human genes for which there are yeast orthologs represent essential genes which are candidates for human disease agents. Compiling the reagents for this custom database was straightforward using the Atlas tools. It takes advantage of the linkage between sequence identifiers, Taxonomy, HomoloGene, and OMIM.We have built a data warehouse of biological information with the goal of providing high-throughput, flexible access to the data by means of SQL queries, API-level queries, and end-user application-level queries. Our goal was to create a system that serves as a platform through which information from many sources of data can be interrogated, enabling biologists and computer scientists to easily carry out queries necessary for their research. The data warehouse facilitates complex queries on local instances of GenBank, RefSeq, UniProt, HPRD, BIND, NCBI Taxonomy, HomoloGene, Gene Ontology, OMIM, Entrez Gene, and LocusLink. With previously disparate data now unified in a relational model, SQL can be used to retrieve this consolidated information at once. Though Atlas can act to serve data publicly over the internet, its simple setup enables anyone or any institution to easily serve their own customized data warehouses to their own local users. Installing Atlas in-house to serve local users gives the data provider full control over the data they serve. Giving users access to the system on a high-bandwidth internal network offers convenience and high-performance for large queries, such as retrieving all human ESTs. Such data is then more readily retrieved with lower latency and higher bandwidth than attempting to retrieve the same data over the internet.One of the important strengths of the Atlas architecture is that it allows data integration at two levels. The first level uses a common data model to integrate similar types of data from different sources (e.g., GenBank or UniProt, and BIND or HPRD). The second level uses the APIs, ontologies, and tools to cross-reference disparate types of data.vertebrata), from the RefSeq database. With a single call to the taxonName2Sequences method, the user can accomplish this task. Within these API methods are SQL statements which first retrieve the taxonid from the Taxonomy database. Then using a recursive method, the taxon identifiers for all organisms beneath that given taxon node, are returned. All amino acid sequences for each of these taxon identifiers are then retrieved using taxonId2Sequences , thus leaving only one instance of a factoid in our database. However, as it would not necessarily be our goal to judge the correctness of data, this is perhaps a task better left to users of our system.Several other systems are available which have similar goals and provide good solutions to the problem of data integration. We have chosen to discuss Atlas in the context of three other systems: Entrez , SeqHounSeqHound  is a datEnsMart is a software system designed by EMBL-EBI  and the per se, but rather an engine that should be accessed 'in-house'. As with any locally-installable system of this nature, significant time and hardware resources are needed to make the system functional. The utility of the Atlas system will far outweigh the setup time required to get it up and running. Currently, API access to Atlas is limited to the users at the UBC Bioinformatics Centre, University of British Columbia, however the web tools are available worldwide.The Atlas system is designed to be locally installed and is not a data provider When working with sources of data from different data providers (for example UniProt and RefSeq), it is advantageous to create mappings from one data source to the other to prevent redundancy and to make associations between proteins to map annotations from one source to the other. We are investigating the idea of an identifier consolidation that can resolve mRNAs and proteins from different sources that are referring to the same protein product to a single identifier.We will constantly monitor and adjust any change of the data sources. In the near future, we will provide support for a PSI-MI level 2 release, and complete the migration of LocusLink to Entrez Gene. In addition, we are expanding Atlas to include other sources of data. We are currently adding MEDLINE, dbSNP and pathway data to support an integrative genomics and clinical informatics initiative, currently underway in our laboratory. With Atlas in hand, we are also working on an integration project that superimposes co-expression networks derived from microarray experiments and protein-protein interaction networks, to estimate the utility of co-expression networks in inferring protein interactions.Atlas is a data warehouse that enables high-throughput, flexible and complex queries on biological data. The system integrates sequences, molecular interactions, taxonomy and homology, and functional annotations on genes. The system functions as data infrastructure to support bioinformatics research and development. Atlas is currently being used in genome annotation projects, disease-gene association projects and inference of molecular interactions. We are releasing Atlas to the scientific community in the hope that it will foster creative ideas for how to make novel associations between disparate sources of data using existing public data sets.Atlas is available from the UBC Bioinformatics Centre, University of British Columbia. The Atlas package can be downloaded from the Atlas website at: The Atlas package contains the Atlas source code and represents the core of the project. The package is distributed under the GNU General Public License. Atlas is designed to run on Unix based systems. Please consult the user manual (available with the distribution) for detailed configuration, compilation and installation instructions. Additional packages are also provided at the website listed above. These packages include a snapshot of the NCBI C++ Toolkit (CVS version 20040505), a MySQL dump of sample data, and additional documentation. The NCBI C++ Toolkit, that is provided, is required only for those users who wish to build the loader applications or for those that require the utilities that convert ASN.1 format to GBFF, EMBL, and XML formats, etc. Those setting up the database will need to install MySQL Server 4.x. Atlas has been tested, specifically, with MySQL Server versions 4.0.9, 4.0.18 and 4.0.20, running on either Linux or AIX.The Atlas sequence-related binaries (toolbox applications and loader applications) are developed in C++ and therefore a C++ compatible compiler, such as the one included with the GNU GCC suite of tools, should be installed before attempting to build these binaries. We have tested the build process with GNU GCC versions 2.95.3, 2.96, 3.1 and 3.2. In addition, MySQL Client version 4.x and particularly its runtime library, libmysqlclient.a(so), is required. MySQL Client versions 4.0.14 and 4.1.0-alpha were tested. Details on the configuration and use of this library are outlined, in more detail, in the Atlas manual.For users that require Atlas tools that are based on Java, such as the loading and retrieval tools for LocusLink, BIND, HPRD, and HomoloGene datasets, a compatible Java interpreter must be installed. The API has been tested with J2SE 1.4.1 and J2SE 1.4.2. The Atlas Java API also requires BioJava version 1.4pre, or higher.For those using the Perl based Atlas tools, a compatible Perl interpreter must be installed. BioPerl version 1.4 must also be installed. Perl version 5.6.1 has been tested.Each of the packages have their own minimum system requirements. Specific memory, hard disk space and CPU requirements for each package are listed in the manual. As a general guideline, it is essential to have a generous amount of available memory, especially if one anticipates processing large sequences in memory. Another important factor is the amount of available hard disk space. The amount of sequence data to be loaded into Atlas will largely determine your disk space requirements. The Atlas database requires a minimum of 50 GB (RefSeq), plus adequate space for satellite databases. The satellite databases include such things as GO, LocusLink, HPRD, BIND, MINT, and DIP, which are relatively smaller datasets. Note that sequence data can greatly exceed these minimum estimates and the requirements should be carefully planned.SS was the architect of the system, developed the C++ APIs and wrote the first draft of this manuscript. YH was the database administrator responsible for schema design, data integrity and maintenance. TX contributed the Java APIs. MMSY contributed the PERL APIs. JL developed the C++ APIs, the toolbox and the user manual. BFFO was the principal investigator, conceived of the project and guided its development. JL, YH, MSSY and BFFO all contributed to the writing of this manuscript.


Prophylactic treatment with N-acetylcysteine (NAC) for 3 months or more is associated with a reduction in the frequency of exacerbations of chronic obstructive pulmonary disease (COPD). This raises the question of whether treatment with NAC during an acute exacerbation will hasten recovery from the exacerbation.1 ≤ 60% predicted, FEV1/VC ≤ 70% and ≥ 10 pack year smoking history. Subjects with asthma, heart failure, pneumonia and other respiratory diseases were excluded. All subjects received concurrent treatment with prednisone 40 mg/day, nebulised salbutamol 5 mg q.i.d and where appropriate antibiotics. FEV1, VC, SaO2 and breathlessness were measured 2 hours after a dose of nebulised salbutamol, at the same time each day. Breathlessness was measured on a seven point Likert scale.We have examined this in a randomised, double-blind, placebo controlled trial. Subjects, admitted to hospital with an acute exacerbation of COPD, were randomised within 24 h of admission to treatment with NAC 600 mg b.d. (n = 25) or matching placebo (n = 25). Treatment continued for 7 days or until discharge (whichever occurred first). To be eligible subjects had to be ≥ 50 years, have an FEV1 (% predicted) was 22% in the NAC group and 24% in the control group. There was no difference between the groups in the rate of change of FEV1, VC, SaO2 or breathlessness. Nor did the groups differ in the median length of stay in hospital (6 days for both groups).At baseline FEVAddition of NAC to treatment with corticosteroids and bronchodilators does not modify the outcome in acute exacerbations of COPD. Exacerbations are an important cause of morbidity in Chronic Obstructive Pulmonary Disease. Seemungal et al found that exacerbations were an important determinant of quality of life in COPD . In addi1 ≤ 60% predicted and FEV1/VC ≤ 0.7 at time of inclusion. Patients were excluded if they had any of the following conditions: asthma (as the primary diagnosis), heart failure, bronchiectasis, bronchial carcinoma, interstitial lung disease, pneumonia. They were also excluded if they were unable to comply with the study procedures because they did not speak English or were demented or if they had any other medical problems that in the opinion of the investigator would interfere with the conduct of the study.Patients were eligible for inclusion in the study if they had a physician diagnosis of COPD, were ≥ 50 years of age, had a smoking history ≥ 10 pack years and had been admitted to hospital with an acute exacerbation of their COPD in the previous 24 hours. In addition they were required to have FEVSubjects were treated with N-acetylcysteine 600 mg twice daily (b.d.) or matching placebo. Treatment was continued for 7 days or until discharge whichever occurred first. Effervescent N-acetylcysteine tablets were purchased from Zambon pharmaceuticals  and were repackaged in size 0 gelatine capsules. Each capsule contained 300 mg of N-acetylcysteine. Placebo capsules were prepared containing lactose as a filler. A randomisation schedule was drawn up by the hospital pharmacy and patients were allocated sequential randomisation numbers as they entered the study.In addition to N-acetylcysteine the patients received standard treatment for their exacerbation as specified by the hospital guidelines. This was oxygen therapy, prednisone 40 mg o.d. for one week and nebulised bronchodilators i.e. salbutamol 5 mg four times daily (q.i.d.) and ipratropium 0.5 mg q.i.d. Antibiotics were prescribed if the patients had increased volume and/or purulence of sputum. Mucolytics were not permitted during the trial except as study medicine.1, oxygen saturation and length of hospital stay. The study assessments were performed at the same time each day and two hours after the last dose of nebulised bronchodilator. Breathlessness was assessed prior to spirometry. Spirometry was performed according to American Thoracic Society criteria using a Vitalograph spirometer. Oxygen saturation was measured using a Nelcor N-20 pulse oximeter. Supplemental oxygen was stopped for 10 minutes before any of the measurements were performed. In addition the subjects were interrogated on each occasion about any possible adverse effects.The primary endpoint was breathlessness measured on a seven point Likert scale Table . SecondaThe study was conducted at Auckland Hospital between June 2001 and October 2001. The study was approved by the Auckland Ethics Committee and all participants provided written informed consent.The power calculation was based on a previous study where we treated subjects for acute exacerbations of COPD with theophylline for up to seven days . In thatThe two treatment groups were compared at baseline using Student's t-test for normally distributed variables, Wilcoxon test for non-parametric data and Fisher's exact test for categorical data.The effect of N-acetylcysteine on breathlessness, lung function and oxygen saturation was analysed by fitting the patient data to a random coefficient model using a mixed linear model approach (least squares regression). The baseline (Day 1) measurements were used as co-variates in the analysis. This allowed individual slopes and intercepts to be formed for each patient and their random variation incorporated in the model. This model adjusted for the varying number of observations available on the different patients. Non-normal dependent variables were rendered normal by transformation. Significant and main interaction effects were investigated by the method of Tukey.All tests were two-tailed and a 5% significance level was maintained throughout these analyses. The analyses were carried out using SAS Version 8.0 (SAS Institute Inc, Cary, NC, USA.)The life test procedure of SAS was used to compute nonparametric estimates of the length of stay function by the Kaplan-Meier method. Comparison between these functions was made using the Wilcoxon and log rank tests.Two hundred and ten patients who had been admitted to hospital with an exacerbation of COPD were screened for the study. Fifty subjects were randomised to treatment with 25 subjects receiving N-acetylcysteine and 25 subjects receiving placebo. The commonest reasons for exclusion were concomitant heart failure (n = 41) or pneumonia (n = 35).The two groups were similar at baseline in terms of age, smoking history, lung function, oxygen saturation and breathlessness Table . There w1, and SaO2 the rate of change was greater with placebo than with N-acetylcysteine but none of these differences were statistically significant. Table 1 and SaO2 from the beginning to end of the study.All of the subjects completed the study with none being withdrawn early. The rate of change in the Likert scores, lung function and oxygen saturation is shown in Table The Kaplan-Meier analysis showed a similar time course until discharge for the treatment and placebo arms Figure . NeitherThree subjects reported adverse events in each group. One of the subjects treated with N-acetylcysteine reported nausea compared with two of the subjects treated with placebo. There were no serious adverse events.N-acetylcysteine has been consistently shown to reduce the number of exacerbations of COPD when it is taken for 3 months or more. In contrast we failed to show any benefit when N-acetylcysteine was administered as a treatment for acute exacerbations of COPD. There are a number of possible explanations for the failure to see any benefit.We cannot exclude the possibility that there was a Type II error and that there is indeed a beneficial effect of N-acetylcysteine in the treatment of acute exacerbations. A larger study would be needed to rule out this possibility but there was less improvement in breathlessness, lung function and oxygen saturation with N-acetylcysteine than with placebo that argues against this explanation.Another possibility that needs to be considered is that we used too low a dose of N-acetylcysteine and that the concentrations of N-acetylcysteine in the lung were not high enough to exert adequate antioxidant or anti-inflammatory effects. N-acetylcysteine is metabolized to cysteine and this in turn acts as a precursor of reduced glutathione which is an antioxidant . BridgemIn contrast to N-acetylcysteine, prednisone and prednisolone are effective treatments for acute exacerbations of COPD. Davies et al studied 56 patients admitted to hospital with an exacerbation of COPD and found that prednisolone led to a greater improvement in lung function and shortened the hospital stay . Other s1 and VC. Sputum volumes, however, were greater with the combination of the mucolytic and antibiotic. Ricevuti et al treated 24 patients with an exacerbation of chronic bronchitis [There have been a number of other studies looking at the effect of mucolytics in acute exacerbations of chronic bronchitis although none of these studies used N-acetylcysteine. Each of these studies has limitations. Langlands treated 27 patients, who had been admitted to hospital with an exacerbation of chronic bronchitis, with bromhexine 8 mg t.i.d. for two weeks (13 patients received bromhexine and 14 received placebo) . In thisonchitis . Half ofOur study does not suggest that 600 mg b.d. of N-acetylcysteine is effective in the treatment of patients who are admitted to hospital with an acute exacerbation of COPD and who receive concurrent treatment with corticosteroids. In future studies it may be appropriate to use a higher dose of N-acetylcysteine and to compare N-acetylcysteine with placebo in patients with mild exacerbations who do not require treatment with corticosteroids. Consideration could also be given to comparing the effects of N-acetylcysteine with prednisone or prednisolone in patients who would usually be treated with oral corticosteroids.NAC N-acetylcysteineCOPD chronic obstructive pulmonary disease1 forced expiratory volume in one secondFEVVC vital capacity2 oxygen saturationSaOThe author(s) declare that they have no competing interests.PNB conceived the idea for the study and was responsible for the study design and writing the manuscript. He was also was involved in the conduct of the study and the analysis of the data. AM-D was involved in the conduct of the study, in the analysis of the data and with writing the manuscript. PJP was involved with the design and conduct of the study. TEM and RPY were involved with the conduct of the study. All of the authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: