Salmonella leads to activation of signaling cascades that ultimately initiate the proinflammatory gene program. The transcription factor NF-κB is a key regulator/activator of this gene program and is potently activated. We explored the mechanism by which Salmonella activates NF-κB during infection of cultured intestinal epithelial cells and found that flagellin produced by the bacteria and contained on them leads to NF-κB activation in all the cells; invasion of cells by the bacteria is not required to activate NF-κB.Infection of intestinal epithelial cells by pathogenic Salmonella. Flagellin expression was required for Salmonella invasion of host cells and it activated NF-κB via toll-like receptor 5 (TLR5). Surprisingly, a number of cell lines found to be unresponsive to flagellin express TLR5 and expression of exogenous TLR5 in these cells induces NF-κB activity in response to flagellin challenge although not robustly. Conversely, overexpression of dominant-negative TLR5 alleles only partially blocks NF-κB activation by flagellin. These observations are consistent with the possibility of either a very stable TLR5 signaling complex, the existence of a low abundance flagellin co-receptor or required adapter, or both.Purified flagellin activated the mitogen activated protein kinase (MAPK), stress-activated protein kinase (SAPK) and Ikappa B kinase (IKK) signaling pathways that lead to expression of the proinflammatory gene program in a temporal fashion nearly identical to that of infection of intestinal epithelial cells by Salmonella mediated NF-κB and proinflammatory signaling and gene activation by this flagellated pathogen. In addition, expression of the fli C gene appears to play an important role in the proper functioning of the TTSS since mutants that fail to express fli C are defective in expressing a subset of Sip proteins and fail to invade host cells. Flagellin added in trans cannot restore the ability of the fli C mutant bacteria to invade intestinal epithelial cells. Lastly, TLR5 expression in weak and non-responding cells indicates that additional factors may be required for efficient signal propagation in response to flagellin recognition.These collective results provide the evidence that flagellin acts as the main determinant of  Salmonella and other enteroinvasive pathogenic bacteria such as enteroinvasive E. Coli, Shigella and Yersinia upon infection of IECs leads to the up-regulation of the expression of host genes, the products of which activate mucosal inflammatory and immune responses and alter epithelial cell functions aminomethane (Tris) was purchased from Fisher Scientific . Fetal calf serum was purchased from US Biotechnologies Inc. . Para-nitro-phenylphosphate (PNPP) was purchased from Aldrich Chemical . The Polyacrylamide gel electrophoresis (PAGE) supplies: acrylamide, bis-acrylamide, sodium dodecyl sulfate (SDS), TEMED, and ammonium persulfate were purchased from Bio-Rad Laboratories . Dulbecco's modified essential medium (DMEM), DMEM:F12, phosphate buffered saline (PBS), glutamine, penicillin G, streptomycin, amphotericin B, and Grace's Insect medium were purchased from Invitrogen . Luria Broth (LB) was purchased from Becton Dickson and Co . The protease inhibitors: aprotinin, bestatin, leupeptin, pepstatin A, and phenylmethylsulfonyl fluoride (PMSF) were purchased from Cal Biochem . Protease inhibitor cocktail contained 10 μg/ml aprotinin, 2.5 μg/ml leupeptin, 8.3 μg/ml bestatin, and 1.7 μg/ml pepstatin A. Phorbol 12-myristate 13 acetate (PMA), N-[2-hydroxyethyl]piperazine-N'-[2-ethanesulfonic acid] (Hepes), anisomycin, and 2-[N-morpholino]ethanesulfonic acid (MES) were purchased from Sigma Chemical . All other reagents were purchased from Sigma Chemical or Fisher Scientific unless stated otherwise.2 atmosphere. T84 colorectal carcinoma cells (ATCC CCL-248) were cultured in DMEM:F12 with 2 mM glutamine, 5% Fetal Calf Serum, 100 Units/ml Penicillin G, and 100 μg/ml Streptomycin at 37°C in a humidified 5% CO2 atmosphere. H5 insect cells (Invitrogen) were cultured in Grace's medium with 2 mM glutamine, 10% Fetal Calf Serum, 100 Units/ml Penicillin G, 100 μg/ml Streptomycin, and 0.25 μg/ml amphotericin B at 28°C. MyD88-/- & TLR2-/-/TLR4-/- double knockout cells were obtained from Shizuo Akira and Osamu Takeuchi  and grown in DMEM with 2 mM glutamine, 10% Fetal Calf Serum, 100 Units/ml Penicillin G, and 100 μg/ml Streptomycin at 37°C in a humidified 5% CO2 atmosphere.HT29 human intestinal  epithelial cells (ATCC HTB-38), HeLa cervical epithelial adenocarcoma cells (ATCC CCL-2), 293T kidney cells (CRL-11268), A549 lung carcinoma cells (ATCC-185), and T98G glioblastoma cells (ATCC CRL-1690) were cultured in DMEM with 2 mM glutamine, 10% Fetal Calf Serum, 100 Units/ml Penicillin G, and 100 μg/ml Streptomycin at 37°C in a humidified 5% COSalmonella typhimurium strain SJW1103  [Salmonella typhimurium and can only express the Phase I fliC flagellin, SJW86 (SJW1103 FliC::TN10), and SJW134 (SJW1103 FliC and FljB deletions) were obtained from Robert Macnab  and have been described [Salmonella serovar dublin strain 2229, strain SE1 (2229 SopE mutant), strain SB2 (2229 SopB mutant), and SE1SB2 (2229 SopE and SopB mutant) were obtained from Edward Galyov  and have been described [Salmonella strains for stimulation were grown in LB at 37°C without agitation for 16 hours, centrifuged at 6,000 × g for 1 minute, gently washed with PBS, and gently suspended in DMEM to maintain cells with attached flagella.bilized)  is a wilescribed . Salmoneescribed ,15. SalmSalmonella host is internalized by mammalian cells, obtained from Stanley Falkow  [Plasmid pFM10.1 (ampicillin resistance), encodes a green fluorescent protein (GFP) expressed after the ord, CA)  and was S. dublin 2229 or S. typhimurium SJW1103. Starter cultures were grown in Luria broth (LB) for 18 hours at 37°C with aeration, diluted 1:5000 in fresh LB, and grown for 12 hours under the same conditions. All subsequent procedures were performed at 4°C. Cells were removed from the medium by centrifugation at 10,000 × g for 5 min and discarded. The supernatant containing flagellin was filtered through a 0.8 micron filter  to remove residual cells. Supernatant was concentrated 100 fold using an Amicon 100 kiloDalton (kDa) cutoff membrane (Millipore). Initial studies used concentrated culture supernatant from S. dublin strain 2229 that was treated with DNase, RNase, Protease K, boiled for 20 min or 100 mM DTT at 37°C for 2 hours and used for stimulation of cultured cells.Native flagellin was harvested from S. typhimurium 1103 bacterial culture supernatant was washed 4 times by 1:10 dilution with 50 mM MES, pH 6.0, 50 mM NaCl and re-concentrated. Material not retained by the 100 kDa membrane was discarded. Washed culture supernatant was fractionated by gel permeation or anion exchange chromatography for analysis. For long-term storage, washed culture supernatant was supplemented with protease cocktail and stored at -20°C.Concentrated Fractionation by gel permeation chromatography was performed with a Superose 12HR column (Pharmacia) on a Bio-Logic system (Bio-Rad). One-half mililiter of 100× washed supernatant  was separated on the column at 0.4 ml/minute in 50 mM Hepes, pH 7.4, 200 mM NaCl. Fractions (0.5 ml) were collected, and 50 μl was fractionated by SDS-PAGE and stained with Bio-Safe Coomassie (Bio-Rad). Thirty microliters of each fraction was used for stimulation of HT29 cells (60 mm dishes) for 45 min and NF-κB DNA binding activity in the resulting whole cell extracts extracts were assayed by EMSA. The column was standardized with catalase (232 kDa), aldolase (158 kDa), abumin (67 kDa), ovalbumin (43 kDa), and Chymotripsinogen A (25 kDa), all obtained from Amersham-Pharmacia.Fractionation by anion exchange chromatography was performed with Poros HQ matrix  on a Bio-Logic system. Five mililiters of 100× washed supernatant  was separated at 1 ml/minute in 50 mM Hepes, pH 7.4, and a NaCl gradient from 50–500 mM. Fractions were collected and 5 μl of each fraction was examined by 10% SDS-PAGE. Proteins were fractionated on duplicate 10% SDS-PAGE precast gels (BioRad). One gel was stained with Bio-Safe Coomassie (Bio-Rad) and the protein bands were isolated for Mass Spectroscopy analysis (CCF Mass spectroscopy core facility) from the other identical non-stained gel, by electro-elution with a whole gel eluter (Bio-Rad) and SDS was removed with SDS-Out  per the manufacturers directions. Proteins isolated from bands B1 to B6 were acetone precipitated by addition of 20 μg Aprotinin and 5 μg of BSA to each eluted fraction, ice-cold acetone (-20°C) was added to 80%, mixed well and precipitated overnight at -20°C. Proteins were pelleted by centrifugation at 14,000 × g in the cold for 30 min, acetone/liquid was removed and the pellets washed 2× with 1 ml acetone (-20°C). After removal of the acetone, protein pellets were air dried and then resuspended and denatured in 5 μl of 6 M guanidinium hydrochloride (Gu-HCl) at room temperature for 30 min. Resuspended proteins were two-fold serially diluted in DMEM to a final Gu-HCl concentration of 55 mM to renature the proteins. Two hundred fifty microliters of individual renatured proteins/DMEM were added per ml to HT29 cells (60 mm dishes) and whole cell extracts were prepared 45 min after stimulation and were assayed for NF-κB DNA binding activity by EMSA.S. typhimurium 1103 containing flagellin was boiled for 20 minutes and precipitants removed by centrifugation at 15,000 × g. The supernatant containing flagellin was diluted 1:2 with 50 mM MES, pH 6.0, 50 mM NaCl and mixed with 2 ml Poros SP cation exchange matrix (PerSeptive Biosystems) per 1 liter of original culture. The Poros SP matrix was prepared as a 50% slurry and equilibrated with 50 mM MES, pH 6.0. The flagellin preparation and matrix were mixed on a roller at 12 to 14 RPM for 2 hours. The matrix along with bound contaminants was removed by filtration through a 0.85 micron filter and discarded, flagellin failed to bind to the cation exchange matrix at pH 6.0 and eluted in the flowthrough and was collected.The washed and concentrated culture supernatant from The pH of the flowthrough was adjusted by five-fold dilution of the sample with 50 mM Hepes, pH 7.8, 50 mM NaCl, and loaded onto a Poros HQ anion exchange column  equilibrated with 50 mM Hepes, pH 7.4, 50 mM NaCl. The column was washed with 2 volumes 50 mM Hepes, pH, 7.4, 50 mM NaCl, and eluted with a 10 column volume linear gradient of 50–500 mM NaCl in 50 mM Hepes, pH 7.4. Flagellin eluted from the column between 200–275 mM NaCl. Fractions containing flagellin were pooled and concentrated. The preparation was determined to be pure by electrophoresis of 5 μg protein by SDS-PAGE and stained with Bio-Safe Coomassie (Bio-Rad). Samples were stored at -80°C in 50 mM Hepes, pH 7.4, approx 225 mM NaCl, 10% glycerol and protease cocktail. A 4 liter preparation of culture supernatant yielded 2 mg purified flagellin.Gels were fixed and stained . All of the following procedures were performed by the CCF Mass spectroscopy core facility. Excised gel bands were reduced (100 mM DTT), and alkylated (100 mM iodoacetamide). Proteins in the gel bands were digested with modified trypsin  with an overnight incubation at 37°C. Tryptic peptides were extracted from the gel with 50% acetonitrile, 0.1% acetic acid, concentrated in a SpeedVac (Thermo Savant) to remove acetonitrile, and reconstituted to 20 uL with 0.1% acetic acid. Extracted peptides were subjected to reversed phase  liquid chromatography , coupled to a Finnigan LCQ DECA ion trap mass spectrometer for peptide sequencing, as described .IκBα amino acids 1 to 54 fused to GST or cJUN amino acids 1–79 fused to GST were prepared as previously described -39 and sSalmonella invasion. Cover slips were mounted with Vectashield mounting medium with DAPI , and cover slips sealed to slides.HT29 cells for microscopic examination were grown in 6 well plates on sterile cover slips to a density of 50–75%. Cells were stimulated as described above. After stimulation, cover slips with HT29 cells were washed 2 times with ice cold PBS and fixed with 4% w/v formalin at room temperature for 20 minutes. Cells were washed 4 times with PBS prior to mounting for visualization of Cells for antibody staining were treated with absolute methanol for 20 minutes following formalin fixation, then washed 3 times with PBS supplemented with 0.1% BSA (PBSB) and used directly or stored in the cold after azide was added to 0.02%. For p65(RelA) localization, cells on coverslips were blocked for 1 h at 37°C with PBS supplemented with 1% BSA. The PBSB was removed, washed once with PBSB and coverslips were placed cell-side down onto 150 μl of p65 antibody  diluted 1:1500 in PBSB on a square of parafilm and placed in a humidified chamber at 37°C for 1.5 h. Coverslips were removed and placed cell-side up in 6-well dishes and washed 3 × 5 min with PBSB. Coverslips were then removed and placed cell-side down onto 150 μl of FITC-labeled donkey anti-rabbit secondary antibody  (1:300 in PBSB) on a square of parafilm and placed in a humidified chamber at 37°C for 1.5 h. Coverslips were removed and placed cell-side up in 6-well dishes and washed 5 × 5 min with PBSB, removed and placed cell-side down onto slides mounted with Vectashield  with DAPI and then sealed. NF-κB localization was determined by indirect immunofluorescence. Samples were observed on a Leica DMR upright microscope  at 400× with oil immersion and equipped with FITC and UV filters. Images were collected with a MicroMax RS camera , and Image Pro plus, version 4.5, software . Color enhancements were performed with Image Pro plus software. Visible light plus color overlays for Fig. 8 Salmonella/ml at 37°C for desired times and extracts prepared as below. Cells harvested beyond one hour were washed with warm PBS and supplemented with warm DMEM, 2 mM glutamine, and 200 ug/ml gentamycin after 1 hour and returned to 37°C until extract preparation desired.Mouse embryo fibroblasts (MEFs) or HT29 cells were grown in DMEM as above to a density of 90% prior to stimulation. All cells were washed with warm PBS and supplemented with DMEM without serum or antibiotics in preparation for stimulation. Cells were stimulated with; 10 ng/ml TNFα, 1 μg/ml flagellin unless specified otherwise, 20 μg/ml Anisomycin, 12.5 ng/ml PMA, or 10Cells were washed with ice-cold PBS and all subsequent steps carried out at 4°C or on ice. Cells were scraped from the dish in ice-cold PBS, and collected by centrifugation at 1000 × g for 1 minute. Cells were lysed by suspension in 50 mM Tris-HCl, pH 7.6, 400 mM NaCl, 25 mM beta-glycerol phosphate, 25 mM NaF, 10 mM PNPP, 10 % glycerol, 0.5 mM sodium orthovanadate, 0.5% nonidet-40 (NP-40), 5 mM benzamidine, 2.5 mM metabisulfite, 1 mM PMSF, 1 mM DTT and protease inhibitor cocktail as described .NF-κB DNA binding assays were carried out as previously described ,35,38. AHT29 cells, 90–95% confluent in 35 mm round dishes, were prepared for stimulation as above and treated with a 1 ml suspension of Salmonella SJW1103 or SJW134 or left untreated in triplicate as above. After one hour, HT29 cells were washed 4× with warm PBS, supplemented with warm DMEM, 2 mM glutamine, and 200 μg/ml gentamycin, and incubated at 37°C for 4 hours. Cells were then harvested as above and lysed by suspension in 1 ml sterile distilled water. Ten-fold serial dilutions were prepared in PBS and 100 μl of each dilution was plated on LB agar plates and grown at 37°C for 20 hours. Colonies were counted and averaged.2). Immunopellets were resuspended in 30 μl Kinase buffer with 0.1 mM orthovanadate, 50 μM "cold" ATP, 5 μCi γ-32P-ATP, 2 mM DTT, and 2 μg of soluble GST-IκBα1–54 or GST-cJUN1-79, and incubated at 30°C for 30 minutes. Reactions were stopped by the addition of 15 μl 4× SDS-PAGE loading buffer, heated at 95°C for 5 minutes, and resolved on 10% SDS-PAGE gels by standard procedures. Gels were rinsed, stained with Bio-Safe Coomassie (Bio-Rad) to visualize protein bands, rinsed, photographed then dried and exposed to Kodak X-OMAT AR film  to detect substrate phosphorylation.Whole cell extracts (250 μg) were supplemented with 150 μl of Buffer A , and immuno precipitation kinase assays carried out as described  using eiProtein samples (40 μg) were resolved by SDS-PAGE on a 10% acrylamide gels by standard procedures, and proteins transferred to PVDF membrane (Millipore) and probed with antibodies as described . MembranAll DN-TLRs were constructed using PCR. The universal 5' primer consisted of a 5'KPN I restriction site followed by sequences encoding the kozak sequence, translational start site, and preprotrypsin leader sequence of pCMV-1 (Sigma) that all the wild-type TLRs were initially cloned into. The 3' anti-sense (AS) primers were human TLRgene-specific primers (sequences available upon request) that created a stop codon immediately after a conserved tryptophan in Box 9 of the TLR TIR homology domain according to Bazan , thus crRenilla luciferase normalization reporter and 1.85 μg pCDNA3.1 plasmid DNA as bulk filler DNA) and fire-fly luciferase expression was normalized to Renilla luciferase expression using the dual-luciferase assay . Fold inductions were calculated and values between experiments did not vary more than 15%, a representative experiment is presented. Transfection of 293T cells was performed with lipofectamine 2000 (Invitrogen) in 6-well dishes in triplicate as per the manufacture's protocol. TLR expression plasmids were added at 2 μg/well, and NF-κB and normalization control plasmids were as above with HT29 cells and pCDNA3.1 plasmid DNA as bulk filler DNA to a final DNA mass of 4 μg/well. Fold inductions were calculated and values between experiments (N of 3) did not vary more than 10%, a representative experiment is presented.HT29 cells were transfected with Lipofectamine Plus (Invitrogen) as previously described . In tran2PCR was performed with an iCycler (Bio-Rad) to quantify TLR1 through TLR10 mRNA, 18S rRNA, and GAPDH mRNA. RT2PCR (25 ul reaction volume) was performed with the appropriate primers (SuperArray) per manufacturers instructions in triplicate with HotStart Taq DNA polymerase (SuperArray) at 95°C for 15 min to activate Taq and amplified for 40 cycles . RT2PCR was performed on the minus RT controls with TLR5 primers to detect DNA contamination. Real-time PCR analysis was performed using SYBR-green (Perkin-Elmer) according to manufacture's instructions with the specific primer pairs indicated above and primer pairs for 18S ribosomal RNA as reference RNA (Classic 18S primer pairs – Ambion Inc). Cycle time (Ct) was measured using the iCycler™ and its associated software (Bio-Rad). Relative transcript quantities were calculated by the ΔΔCt method using 18S ribosomal RNA as a reference amplified from samples using the Classic 18S primer pairs from Ambion, Inc . Normalized samples were then expressed relative to the average ΔCt value for untreated controls to obtain relative fold-change in expression levels. Fold change in mRNA expression was expressed as 2ΔΔCt. ΔCt is the difference in threshold cycles for the TLR mRNAs and 18S rRNA. ΔΔCt is the difference between ΔCt non-simulated control and ΔCt stimulated sample. Values for fold-induction varied less than 5% among replicates.Cells N = 3) were stimulated 3 hours at 37°C with TNFα or FliC or left untreated and harvested for total RNA isolation. Total cellular RNA was extracted from cells with Trizol reagent (Invitrogen) [ were stiSalmonella invasion protein; PMA, phorbol 12-myristate 13 acetate; PNPP, para nitrophenyl phosphate; TK, thymidine kinase; BF, bright field; NP-40, nonidet-40; NRS, normal rabbit serum; IN, input; Ct, cycle time.The abbreviations used are: FBS, fetal bovine serum; IL-1, interlukin-1, SDS, sodium dodecyl sulfate; PAGE, polyacrylamide gel electrophoresis; EMSA, electromobility shift assay; IB, immunoblot; KA, kinase assay; GST, glutathione S-transferase; PBS, phosphate-buffered saline; TNFα, tumor necrosis factor α; NF-κB, nuclear factor kappa B; IKK, Ikappa B kinase; IκB, Ikappa B; PCR, polymerase chain assay; RT-PCR, reverse transcription polymerase chain assay; Gu-HCl, guanidinium hydrochloride ; MAPK, mitogen activated protein kinase; SAPK, stress-activated protein kinase; ERK, extracellular regulated kinase; TLR, toll-like receptor; DN, dominant-negative; JNK, Jun N-terminal kinase; AP-1, activator protein-1; MEF, mouse embryo fibroblast; WCE, whole cell extract; IEC, intestinal epithelial cell; MCP1, macrophage chemoattractant protein 1; TTSS, type III secretion system; Sip, TT and AD initiated the study and performed the majority of the experiments and contributed equally and were assisted by NK and JL. MD constructed a number of DN-TLRs and JD developed the study, provided funding support, oversaw the project and also constructed a number of mutant TLRs.


Profile-based analysis of multiple sequence alignments (MSA) allows for accurate comparison of protein families. Here, we address the problems of detecting statistically confident dissimilarities between (1) MSA position and a set of predicted residue frequencies, and (2) between two MSA positions. These problems are important for (i) evaluation and optimization of methods predicting residue occurrence at protein positions; (ii) detection of potentially misaligned regions in automatically produced alignments and their further refinement; and (iii) detection of sites that determine functional or structural specificity in two related families.For problems (1) and (2), we propose analytical estimates of P-value and apply them to the detection of significant positional dissimilarities in various experimental situations. (a) We compare structure-based predictions of residue propensities at a protein position to the actual residue frequencies in the MSA of homologs. (b) We evaluate our method by the ability to detect erroneous position matches produced by an automatic sequence aligner. (c) We compare MSA positions that correspond to residues aligned by automatic structure aligners. (d) We compare MSA positions that are aligned by high-quality manual superposition of structures. Detected dissimilarities reveal shortcomings of the automatic methods for residue frequency prediction and alignment construction. For the high-quality structural alignments, the dissimilarities suggest sites of potential functional or structural importance.The proposed computational method is of significant potential value for the analysis of protein families. Profile-based methods of sequence analysis use multiple sequence alignments (MSA) to extract information about conserved features of a protein family, which are impossible to decipher from a single sequence. Such methods increase both the sensitivity of homology detection and the quality of produced alignments -10, mainin silico sequence design that generates native-like sequences from a structural template. Detection of discrepancies between the model and the real data would assist the analysis of the model's performance and its further improvement. To our knowledge, such statistical assessment has not been proposed up to date.Statistical analysis at the level of individual MSA positions may be used to compare residue frequencies predicted from some model to the actually observed residue usage at the given position in sequence homologs. The model may represent, for example, a method for Several approaches have been proposed to detect potential regions of low alignment quality in sequence-sequence and sequence-profile alignments. These approaches range from identifying low-scoring regions in pairwise alignment  to more When the analyzed alignment is highly reliable, detecting positions of significant dissimilarity may reveal sites that determine functional or structural specificity of otherwise similar proteins. Several approaches have been proposed that use comparison of multiple sequence alignments in order to predict such sites -21. HoweP-value Estimation for Alignment Columns), to the analysis of real MSA.In this work, we consider approximate analytical estimates of P-value in two settings: (1) comparison of an alignment column to an emission vector of residue probabilities, and (2) comparison of two alignment columns. These estimates allow detecting cases where the null hypothesis (assumption of similarity) can be confidently rejected. We performed simulation experiments that show consistency of the estimates with the statistical model, and applied our method, PEAC  is generated by given vector of emission probabilities f. If this hypothesis is rejected, then the set of emission probabilities is inadequate for the description of the residue content in this alignment column.given alignment column (vector of residue counts ρ(n | f), which is difficult for analytical consideration. To calculate the P-value, we use the multivariate Gaussian approximation of the multinomial distribution, based on the assumption of large statistical samples :The assumed null model of random columns corresponds to a multinomial form of x = {xi} is a random d-dimensional vector of residue counts of size , f is emission vector of residue frequencies,  is the mean vector of residue counts, Σ = ||cov|| is the covariance matrix. This approximation of p.d.f. allows for the analytical expression for the P-value (Appendix 1 [see where ix 1 see ): is a regularized gamma-function, d is dimensionality of vector f. Thus, P-value is described by a χ2 distribution with (d - 1) degrees of freedom.where f = {fi}120 to generate a large number Ω = 107 of random columns of a fixed size N, i.e. Ω sets of N residues drawn randomly according to probabilities fi. For each random column, residue counts n = {ni}120 were derived and the multinomial probability of its generation was calculated as , where  is the multinomial coefficient. All Ω generated columns were sorted by ρmult in the ascending order. For a given P-value P*, the column number ΩP0 was chosen from this sorted list. This column corresponded approximately to the multinomial P-value P*. This P-value was compared to our estimate Pestim (formula (2)) calculated for the chosen column in the Gaussian approximation of multinomial distribution. For each value P* we performed 10 independent simulations and plotted average values of Pestim against P*, which showed their general consistency. Figure f derived from real alignment columns, and for three typical column sizes N. The accuracy of estimates becomes poorer for lower column sizes and more skewed frequency sets ) with P-values based on the multinomial model. In particular, we used a set of residue frequencies ets Fig. . Howeverm* and n* are generated by a single vector of emission probabilities. As the prior distribution of emission vectors, we use the maximum likelihood (ML) estimate based on m* and n*. Such prior should produce the conservative upper estimate of the P-value. Rejection of hypothesis H0(2) would mean that the two alignment columns are highly dissimilar.two observed columns The P-value for this hypothesis is calculated in three steps:n, m}, we produce the ML estimate of the p.d.f. for emission vectors f that can generate both columns simultaneously. We assumed a simple form of multivariate Gaussian distribution and calculated ML estimates of its mean  and variance values  (formulae B5).a). Given the two vectors of residue counts {θ(f) as the prior to calculate the posterior probability ρ) that a pair of random columns {n, m} is produced by any single emission vector f. Similarly to problem 1, we use multivariate Gaussian approximation of the multinomial distribution that assumes large total residue counts in the generated columns. The posterior probability density can be calculated asb). We use this p.d.f. ρ) = ∫ρ  θ (f) df     (3)c). Using (3), we calculate P-value as the integral (Appendix 2 see ):θ(f) is a ML estimate based on the observed alignment columns. The partial integral ∫ρ  dm dn can be calculated analytically for any emission vector f, but analytical calculation of full integral (4) is problematic. However, an approximate estimate of this value would suffice, since (i) expression (4) already contains approximations introduced by estimates of θ(f), ρ) and ρ); and (ii) we are interested in a conservative estimate of the upper P-value limit. Hence, we calculate an approximate upper estimate of P-value :erf(x) is error function, andwhere f was used to produce a column of size N by random draw according to these frequencies. Having the vector residue counts n in this column, we produced another vector of counts m that made our estimated P-value Pestim equal to the specified value P0. To produce this vector, we considered sets of residue counts as points in multidimensional and randomly chose a straight line passing through the point n. On this randomly directed line, we found the point m as the solution of equation Pestim = P0, where Pestim is defined by formula (5). Thus, we generated a pair of columns that corresponded to the specified P-value according to the PEAC estimate. We compared this estimate to the actual P-value P* calculated for the generation of m and n by the original vector f. As shown by the plot of P* against Pestim : (1) a given alignment column is generated by a given set of emission residue frequencies; and (2) two given alignment columns are generated by a single set of residue frequencies. We applied both types of estimates to the analysis of real multiple alignments, detecting cases of significant dissimilarity where the null hypotheses were confidently rejected.-320 and 1.0, with the median being approximately 0.01.Using our method, we assessed the consistency between predictions of residue frequencies based on structural considerations, and the frequencies in multiple alignments of sequence homologs. Specifically, we prepared a dataset of 1695 PDB structures and made predictions of residue propensities at each position, based on local structural environment. In parallel, the sequences corresponding to these structures were used as queries for PSI-BLAST searches, and profiles of detected confident sequence homologs were constructed (see Methods). The effective residue frequencies at profile positions were compared to the structure-based predictions, and P-values for each position were estimated using PEAC. The histogram of produced P-values for all positions is shown in Fig. P < 10-100). These sites were located mainly in the secondary structure elements, most frequently at their ends, and corresponded to unusual local distortions of 3D conformations. We compared residue content in the corresponding subset of alignment columns to the whole dataset. As shown in Fig. To analyze the cases of most pronounced discrepancy between our structure-based predictions and residue frequencies observed among sequence homologs, we chose ~1000 protein positions (0.3% of the whole dataset) that had lowest P-values  of their carboxyl caps (data not shown). When we excluded glutamates and aspartates whose charge could be neutralized by contacts with positively charged arginine, lysine or histidine, the remainig portion of the set was still comprised of mostly buried residues  In automatically produced alignments of sequences or structures, consideration of profiles of confident homologs helps to detect inconsistencies. According to our observations, these inconsistencies are caused mainly by alignment errors. (ii) In the high-quality structure based alignments, where structural equivalence of residues is confident, the low P-values may indicate functional specificity of spatially aligned residues.As an example of application (i), we evaluated our method by ability to predict erroneous residue matches produced by an automatic sequence aligner , as com-2.We then sorted all ClustalW positional matches by ascending P-values and classified them as true or false predictions of ClustalW errors. For our purpose, the ClustalW matches different from those in BaliBase were considered true positive predictions; whereas correct matches were considered false positives. Having the ranked list of true and false positive predictions, we generated sensitivity curve  collected pairs of protein domains that are structurally similar according to the DALI alignments  in the F-2 and the median was approximately 0.1 10-2. For identities around 25% . Using Insight II suit for molecular modeling and simulation (Accelrys), we performed a detailed manual analysis of structural superposition for a portion of the corresponding structural alignments. We found that the majority of inspected positions were apparently misaligned. Approximately 80% of these residues were located within 5 positions from a gap introduced in the structural alignment. Vicinities of gaps generally correspond to less similar fragments of aligned structures, which are more difficult to superimpose and where alignment errors can occur more frequently.We considered residue contents of the MSA columns corresponding to these low-P-value position pairs, and compared these contents to the average residue frequencies in the whole MSA datasets. In the set corresponding to 15 ± 1% sequence identity, the most pronounced difference was a higher frequency of aspartate at the position pairs with low P-values Fig. . In the We further concentrated on the aligned structural positions that showed unusual residue frequencies in the corresponding MSA columns. In the set corresponding to 15 ± 1% sequence identity, we considered positions with highly conserved aspartate, whereas in the set corresponding to 25 ± 1% sequence identity, we considered positions with high combined frequency of methionine, leucine and isoleucine. In an attempt to exclude apparently misaligned positions, we considered only those positions that were distanced more than 5 residues from gaps in the FSSP structural alignment. We selected and manually analyzed 16 of such positional matches. However, even among these selected matches most of the discrepancies were still caused by apparent alignment errors: 10 cases corresponded to structural misalignments , and 3 cases were caused by biased residue frequencies at profile positions, due to errors in PSI-BLAST alignments of sequence homologs. The remaining 3 position pairs did not involve apparent errors of either DALI or PSI-BLAST. These pairs might represent real differences in residue preferences at structurally equivalent positions.b). In glyoxalase II, D134 binds zinc atoms directly [Figure directly , whereasdirectly  Fig. 7Cb. In glya illustrate superposition errors as the typical source of low P-values for automatic structural alignments. DALI . However, such alignment corresponds to low positional P-values, indicating a significant difference between structure-based and sequence-based position similarity. The optimal profile-based alignment . These proteins possess the same α/β knot fold but belong to different SCOP families, SpoU-like RNA 2'-O ribose methyltransferase and Ybea-like, respectively. Using manually curated structure-based alignment of the two proteins and MSAs of their homologs detected by PSI-BLAST, we considered structurally equivalent positions that were well aligned in space . We found 24 such positions, the majority being concentrated in the region of dimer interface, which includes the 'knotted' C-terminal helix D  detection of potentially misaligned regions in automatically produced alignments and their further refinement; and (iii) detection of sites that determine functional or structural specificity in two related families.We proposed P-value estimates to assess statistical significance for (1) comparison of a single position in a multiple alignment to a set of emission residue frequencies; and (2) comparison of two alignment positions. Computational implementation of these estimates showed its potential value for several important tasks in sequence analysis: (i) evaluation and optimization of methods predicting propensities for residue occurrence at protein positions, such as protocols for neffPSIC for each symbol in the alignment column , and then applied the following transformation [Effective residue counts at alignment positions were calculated based on the PSIC  method. ormation :neff corresponds to the number of randomly aligned sequences with the average number of residue types per position equal to neffPSIC .-5. In the resulting multiple alignments of detected homologs, we purged sequences whose identity to the query was less than 25%, so that only confident sequence homologs were used for profile construction. We split query sequence into fragments of fixed length F. For each fragment, we extracted the corresponding segment of the multiple alignment and removed the sequences with deletions (gaps) in this fragment. For a query of length L we produced L-F+1 sub-alignments and derived effective residue counts as described above. In this work, we used the library of profile fragments of length F = 6, which provided accurate results when applied to the prediction of local structural environment from a sequence profile [We applied our method to compare structure-based predictions of residue probabilities to the actual residue frequencies observed among sequence homologs. For such a comparison, we produced sequence profiles that correspond to fragments of known 3D structures. Briefly, we used a non-redundant set of structures from PDB . SCOP ,46. entr profile .φ and ψ dihedral angles) at the given position and the preceding position, and solvent accessibility of the sidechain at the given position. For a given position we used the partition of Ramachandran plot into 15  classes proposed by Shortle [The equilibrium frequency of an amino acid at a position in protein structure reflects the energetic fitness of the sidechain in the local structural environment ,39. To e Shortle  For the As the second application, we estimated statistical significance of similarity between pairs of columns in multiple alignments. Namely, we used pairs of structurally similar proteins , proWe chose protein pairs with relatively low sequence identities, where detection of similarity between sequences is not straitforward. We focused on two identity ranges: 25 ± 1% (at the upper bound of twilight zone) and a lower range of 15 ± 1%. From each FSSP family, we extracted the parent sequence and all sequences of a significant structural similarity to the parent (Z-score greater than 5.0), with sequence identity to the parent within a given range. We found totally 494 and 1406 sequence pairs with identities 25 ± 1% and 15 ± 1%, respectively. These numbers were reduced by purging symmetric pairs and manual inspection of the remaining domains for the presence of repeats and low-complexity regions. For further analysis, we used 251 sequence pairs with identity 25 ± 1% and 340 pairs with identity 15 ± 1%, each pair representing a unique FSSP family. For each sequence, we ran 5 iterations of PSI-BLAST 2.2.1 against the NCBI nr database  and obtained multiple alignments of detected homologs. We then applied a procedure of the alignment processing similar to that implemented in PSI-BLAST [Solvent accessible surface area (ASA) for the residues of interest was determined using NACCESS package , which wRS carried out the theoretical considerations, computational experiments, analysis of the results and drafted the manuscript. NG conceived of the study, and participated in its design and coordination. Both authors read and approved the final manuscript."P-value for multivariate Gaussian distribution".Click here for file"Upper estimate of P-value for similarity between two alignment columns".Click here for file


The coronary artery calcium (CAC) score is an independent predictor of coronary heart disease. We sought to combine information from the CAC score with information from conventional cardiac risk factors to produce post-test risk estimates, and to determine whether the score may add clinically useful information.We measured the independent cross-sectional associations between conventional cardiac risk factors and the CAC score among asymptomatic persons referred for non-contrast electron beam computed tomography. Using the resulting multivariable models and published CAC score-specific relative risk estimates, we estimated post-test coronary heart disease risk in a number of different scenarios.Among 9341 asymptomatic study participants , we found that conventional coronary heart disease risk factors including age, male sex, self-reported hypertension, diabetes and high cholesterol were independent predictors of the CAC score, and we used the resulting multivariable models for predicting post-test risk in a variety of scenarios. Our models predicted, for example, that a 60-year-old non-smoking non-diabetic women with hypertension and high cholesterol would have a 47% chance of having a CAC score of zero, reducing her 10-year risk estimate from 15% (per Framingham) to 6–9%; if her score were over 100, however (a 17% chance), her risk estimate would be markedly higher (25–51% in 10 years). In low risk scenarios, the CAC score is very likely to be zero or low, and unlikely to change management.Combining information from the CAC score with information from conventional risk factors can change assessment of coronary heart disease risk to an extent that may be clinically important, especially when the pre-test 10-year risk estimate is intermediate. The attached spreadsheet makes these calculations easy. Aggressive primary prevention of coronary heart disease (CHD) is most appropriate in patients at relatively high risk of CHD events ,2. The cTo answer this question, we need to know the effects of age, sex and other CHD risk factors on the expected distribution of CAC scores. Several large cross-sectional studies have described the prevalence and extent of CAC among different age/sex groups ,8-10 witWe identified a large sample of men and women without clinical CHD who presented for electron beam computed tomography scanning. Using questionnaire data collected from these patients about smoking habits and medical history , we determined how conventional CHD risk factors, along with age and sex, affect CAC scores. We then developed a method for combining information from conventional risk factors and the CAC score , and we present several examples illustrating how that method may be applied in common clinical situations.All persons referred by their physician to an electron beam computed tomography (EBCT) scanning center in Nashville, Tennessee for measurement of coronary artery calcification between May 15, 1995 and December 31, 1997 were eligible for inclusion. Subjects with a history of CHD or complaining currently of any chest pain were excluded, as were subjects for whom CHD risk factor data were incomplete or missing. Only the first CAC score was included for those who received more than one EBCT scan.Current age, sex and presence of CHD risk factors were elicited by questionnaire from subjects and referring physicians. Each subject was labeled with hypertension, high cholesterol and/or diabetes mellitus if they answered affirmatively to the question, "Has your physician ever told you that you needed medicine for X?", or if their physician confirmed that such a condition was documented in their medical records. Patients were labeled as smokers if they currently smoked or had quit smoking within the preceding 3 months. No direct measurements of blood pressure, lipids or glucose were taken for the purposes of this study.We estimated the 10-year risk of a first CHD event using published mathematical models based on the Framingham study . For thi2) with density ≥ 130 Hounsfield units. The CAC score was calculated according to the method described by Agatston [Each subject underwent electron beam computed tomography scanning with an Imatron C-100 or C-150 scanner  after giving written informed consent. During a single breath hold, 40 consecutive slices of 3 mm thickness were obtained starting at the level of the carina and proceeding to the level of the diaphragm. Scans were obtained within 100 ms and were electrocardiographically triggered at 60–80% of the R-R interval. Coronary calcification was defined as a plaque of at least 3 consecutive pixels  age and sex, 2) age, sex, hypertension, high cholesterol, smoking, and diabetes, and 3) the Framingham 10-year CHD risk estimate. We examined whether the effects of age were linear  by testing a quadratic term in the model containing only age and sex. We evaluated the ability of each logistic model to discriminate subjects at high and low risk for CAC using the C-statistic, and estimated the proportion of variability in the extent of CAC explained in each linear regression model using the adjusted-R2 tests with 3 degrees of freedom to compare the expected frequencies based on each model with the observed frequencies. Lower p values, in this case, indicate a poorer fit of the model to the observed data. All statistical analyses were performed with Stata 7.0 .Finally, we used coefficients, intercepts and residual variance from logistic and linear models to estimate the probability that the CAC score of an individual with known risk factors would fall into each of four standard CAC score categories . We estimated these probabilities, using models containing the 10-year risk estimate as the only predictor, for a range of 10-year risk estimates. We also estimated these probabilities, using models with all CHD risk factor predictors, for the specific clinical scenario described in the Introduction (a 60-year-old woman with hypertension and high cholesterol) and for several other scenarios. We compared the actual distribution of CAC scores among 58–62-year-old women with hypertension and high cholesterol in our sample (n = 130) with predictions from 1) our two-stage model, 2) a one-stage model using Ln(CAC score + 1) as a continuous outcome in a linear regression model, and 3) a one-stage model using a censored normal distribution of cube-root transformed CAC scores (a Tobit regression model). This comparison was made both graphically and statistically, using XFirst, we calculated the Framingham 10-year CHD risk estimate  according to published models . Next, wWe identified 9341 persons without chest pain or a history of CHD presenting for their first EBCT scan between 4/15/95 and 12/31/97. Our sample was mostly middle-aged, but included persons as young as 35 years and as old as 88 years of age. Forty percent were women. The proportion with cardiac risk factors was high, though only 9% were diabetic (Table th–75th percentile: 0 – 87). The prevalence of zero scores ranged from 80% among women younger than 50 years to 5% among men 70 years old or older. After excluding zero scores, log-transformed CAC scores were approximately normally distributed, and appeared to be strongly associated with age and sex  was 135 (± 377), and the median was 4 (25Age and sex were strong predictors of the presence of CAC in logistic regression models Table . There w2 = 0.11) than the full model (R2 = 0.17).Among patients with non-zero CAC scores, age and sex remained strong predictors of the extent of coronary artery calcification, as measured by the Ln(CAC score) Table . Again, Using these models, we estimated the probability of measuring a CAC score in each of four standard CAC score categories  using the Framingham 10-year CHD risk estimate, a value easily calculated from conventional CHD risk factors using accessible web- or handheld computer-based software. These probabilities ranged widely based on the value of the 10-year risk estimate, with the probability of measuring a zero CAC score going from 75% (at a 10-year risk of 2.5%) to 13% (at a 10-year risk of 25%)  and high cholesterol  will have a 15% risk of suffering a CHD event in 10 years, according to the Framingham equation. If this women undergoes EBCT scanning, our models predict a 47% chance that her CAC score will be zero, a 36% chance that it will be between 1–100, a 12% chance that it will be between 101–400, and a 5% chance that it will be greater than 400. By integrating this information with previously published relative risk estimates see , we estiOur strategy outperformed two other modeling strategies in predicting the actual CAC distribution among the 58–62-year-old non-smoking non-diabetic women with hypertension and high cholesterol in our study sample n = 127)  are independent predictors of coronary artery calcification. This finding is consistent with previous studies -15. We aFinally, our analysis provides a guide for how to use the CAC score as a surrogate outcome when studying causes of coronary artery disease (a widely used study design -27). TheOur analysis has a number of limitations, perhaps the most important being a lack of clinical detail about participants. While we had information about conventional risk factors , the data were only available from a questionnaire, and were not confirmed by direct measurement. Only dichotomous indicators of such conditions were used. Furthermore, other conditions and indicators of high CHD risk such as family history of CHD, obesity, physical activity, income, education, and levels of C-reactive protein, triglycerides and Lp(a), for example, were unavailable. Whether such factors are important predictors of the presence and extent of coronary artery calcification is unknown. On the other hand, CHD risk assessment is often based on the same type of limited information we had available on each of our patients, so the models we present are perhaps more easily applicable to common clinical situations than models based on more detailed clinical data. Furthermore, a historical indicator of past exposure to high blood pressure or high cholesterol, as we had access to in this study, may actually be more useful as a predictor of CAC than treated blood pressure measured at one point in time. Another important limitation of this study is our lack of data on race/ethnicity – our results may not apply to all ethnic groups. Finally, our data are limited in application to CAC scores measured by electron beam computed tomography with 3 mm slice thickness and the described protocol. While CAC scores measured by the latest spiral computed tomography scanners appear to be similar to those generated by electron beam computed tomography , we cannThe Clinical Research Roundtable at the Institute of Medicine has identified translation of clinical research findings into improvements in medical care as the "next scientific frontier" . While oMP has received speaking and consulting fees from Bayer.MJP conceived the idea for the study, performed the analysis and drafted the manuscript. JAT and MP helped design and interpret the analysis. CM provided statistical guidance and interpretation. TQC recruited the patients and collected the data. WSB provided senior guidance in all aspects. All authors reviewed and commented on multiple drafts of the manuscript and approved the final draft.The pre-publication history for this paper can be accessed here:This spreadsheet is used for combining information from conventional risk factors and the coronary artery calcium score to estimate coronary heart disease risk in an individual patient. Step 1: Enter your patient's clinical information (the red numbers). Step 2: Choose an assumption about the coronary artery calcium score relative risks (optimistic or conservative). Step 3: Find the following results: 1) "Pre-test" 10-year risk of coronary heart disease (CHD) based on Framingham equations; 2) The probability of having a coronary artery calcium (CAC) score that falls within 4 standard CAC score categories; and 3) The "post-test" 10-year risk of CHD for each CAC score category. Step 4: Use the results to interpret a CAC score, or to decide whether or not to order a coronary artery calcium scan. If a score that would change your management is unlikely to occur, it may not be worth the money.Click here for file


Previous research indicated that women are more vulnerable than men to adverse psychological consequences of weight gain. Other research has suggested that weight gain experienced during antipsychotic therapy may also psychologically impact women more negatively. This study assessed the impact of acute treatment-emergent weight gain on clinical and functional outcomes of patients with schizophrenia by patient gender and antipsychotic treatment .Data were drawn from the acute phase (first 6-weeks) of a double-blind randomized clinical trial of olanzapine versus haloperidol in the treatment of 1296 men and 700 women with schizophrenia-spectrum disorders. The associations between weight change and change in core schizophrenia symptoms, depressive symptoms, and functional status were examined post-hoc for men and women and for each medication group. Core schizophrenia symptoms (positive and negative) were measured with the Brief Psychiatric Rating Scale (BPRS), depressive symptoms with the BPRS Anxiety/Depression Scale and the Montgomery-Asberg Depression Rating Scale, and functional status with the mental and physical component scores on the Medical Outcome Survey-Short Form 36. Statistical analysis included methods that controlled for treatment duration.Weight gain during 6-week treatment with olanzapine and haloperidol was significantly associated with improvements in core schizophrenia symptoms, depressive symptoms, mental functioning, and physical functioning for men and women alike. The conditional probability of clinical response (20% reduction in core schizophrenia symptom), given a clinically significant weight gain (at least 7% of baseline weight), showed that about half of the patients who lost weight responded to treatment, whereas three-quarters of the patients who had a clinically significant weight gain responded to treatment. The positive associations between therapeutic response and weight gain were similar for the olanzapine and haloperidol treatment groups. Improved outcomes were, however, more pronounced for the olanzapine-treated patients, and more olanzapine-treated patients gained weight.The findings of significant relationships between treatment-emergent weight gain and improvements in clinical and functional status at 6-weeks suggest that patients who have greater treatment-emergent weight gain are more likely to benefit from treatment with olanzapine or haloperidol regardless of gender. Because antipsychotic drugs are considered the core treatment modality for schizophrenia  the diffAlthough most of the literature on treatment-emergent weight gain tends to focus on this event as adverse, a growing body of research has demonstrated a significant link between beneficial therapeutic response and treatment-emergent weight gain. With the exception of a few studies that failed to find an association between weight gain and better clinical outcome -9, most The study of gender differences in the relationship between treatment-emergent weight gain and therapeutic response has gained limited attention and provided conflicting results. A brief report on weight gain during clozapine therapy indicated that greater weight gain was associated with clinical improvement among women, but not among men . In contWomen in the general population appear to be vulnerable to the adverse emotional and psychosocial consequences of weight gain. For women, obesity has been linked to lower life satisfaction, increased social isolation , and lowThe primary objective of this study was to expand on prior research and investigate whether the relations between acute weight gain during antipsychotic therapy and treatment outcomes differ based on patient gender and the specific antipsychotic used in the treatment regimen, olanzapine or haloperidol. This study also aimed to broaden the definition of therapeutic response by extending beyond positive and negative symptoms of schizophrenia to depressive symptoms and levels of mental and physical functioning, because these domains tend to deteriorate with weight gain among women in the general population.We used data of 1296 men and 700 women who participated in a randomized, double-blind, multi-center, clinical trial comparing olanzapine to haloperidol . ParticiParticipants were randomly assigned in 2:1 ratio . Although randomization was not stratified on gender or any other patient characteristics, it resulted in a 2:1 ratio for males (870/426) and for females (426/233). The olanzapine group (N = 1337) included 467 women and 870 men, and the haloperidol group (N = 659) was comprised of 233 women and 426 men.Participants were randomly assigned to olanzapine, 5 to 20 mg/day, or haloperidol, 5 to 20 mg/day. The type of antipsychotic medication used prior to enrollment was not assessed in the current study, but the likelihood of previous treatment with an atypical antipsychotic drug was very low, because the study was initiated in 1994 when only clozapine was available in some of the sites. Further, randomization worked for patient and illness characteristics , and theWe used data from the acute phase, the first 6 weeks of the study, for several reasons. First and foremost, this study was a 6-week randomized double blind clinical trial with a 46-week "responder maintenance period", in which only patients who responded to the acute 6-week treatment per predetermined response criteria were eligible to continue. Consequently, the study design did not permit a longer-term analysis on the link between weight gain and improvement because only patients who improved during the first 6-weeks phase were followed-up for a longer time period. Second, the 6-week period represents a relevant time frame often used in clinical practice to determine treatment outcome and decide on treatment discontinuation . For manDuring the 6-week acute phase, the mean modal dose was 13.2 mg/day (SD = 5.8) for olanzapine and 11.8 mg/day (SD = 5.6) for haloperidol. There were no discontinuations due to weight gain as an adverse event for any treatment group during the 6-week study period, and the rate of discontinuation for any cause was similar for women (62.7%) and men (60.8%), with a significantly smaller proportion of the patients in the olanzapine group (33.5%) than in the haloperidol group . In addition, the percentage of patients who discontinued treatment because of an adverse event or a lack of efficacy was significantly higher in the haloperidol group than in the olanzapine group. Further details on the parent study design and primary findings are available elsewhere .2).This investigation used measures of positive and negative symptoms ("core schizophrenia symptoms"), depressive symptoms, functional status, and body weight. Core symptoms of schizophrenia were assessed by the Positive Symptom and the Negative Symptom subscales on the BPRS  extracted from the Positive and Negative Syndrome Scale (PANSS) . Levels To enhance comparability of findings on different measures, the clinical measures were all standardized to z-scores. For the BPRS Core Symptoms, MADRS, and BPRS Anxiety and Depressive Subscale, this was done by subtracting the measure's overall mean and dividing by the measure's standard deviation at baseline. A single measure of depressive symptoms was calculated as the average of the standardized MADRS and BPRS Anxiety and Depressive Subscale. If a score was missing on either depression measure, the score on the available measure was used. The two depression measures were pooled because each is an independent and valid estimate of patients' level of depressive symptoms, and aggregating them should provide the best and most comprehensive estimate of depressive symptoms. Additionally, the pooling helped minimize loss of data, which are assumed not to be missing at random. The SF-36 Physical and Mental Component scores were converted from T-Scores to z-scores.Baseline comparisons used independent samples t-tests for continuous variables and chi-square tests for categorical variables. Effects of treatment and gender on independent variables were assessed using ANCOVA, with the baseline score as well as the number of weeks in the study as covariates. The relationship between change in weight and change in each outcome variable was assessed using separate multiple linear regression analyses, each with corresponding clinical change score as a dependent variable, the corresponding baseline score and number of weeks in the study as covariates, and the following independent variables: weight change, treatment group assignment, and gender. In an additional analysis, the interactions of these three independent variables were added to the regression models.The analyses included measures from baseline and the 6-week visit. Missing data were handled by carrying forward the last observation for all patients with at least one post-baseline assessment. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 11.0.Relative to men, women were older, more likely to be Caucasians, were more likely to be overweight or obese, had less severe positive symptoms, lower levels of physical functioning, and had higher levels of depressive symptoms Table . Women w2 = 51.8, p < 0.001).In order to illustrate the differences in weight gain by treatment group and gender, the patients were grouped into thirds based on their percentage of change in weight from baseline. Approximately one third of all patients (29.8%) lost weight (any decrease), one third (36.6%) had relatively stable weight (0% to <3% increase), and one-third (33.6%) gained weight (≥ 3% increase). The corresponding mean weight changes in kilograms were -2.1 kg, 0.9 kg, and 4.6 kg, for lost, stable, and increased weight groups, respectively. Figure 2. Within the olanzapine treatment group, but not the haloperidol treatment group, significantly more men than women experienced a potentially clinically meaningful weight gain  = 4.0, p = 0.045 for women and men in the olanzapine treatment group, and 2.3% vs. 3.5%; χ2 = 0.7, p = 0.40, for women and men in the haloperidol treatment group).Compared to women, men experienced greater increases in absolute weight  = 17.3, p < 0.001), were more likely to experience greater increases in BMI  = 5.8, p = 0.016), and were more likely to have an increase of at least 7% from baseline body weight . The results indicated that gender was not a significant variable . Therefore, gender was dropped from subsequent analyses.B = -0.038, t(1899) = 5.6, p < 0.001), in depressive symptoms  = 5.3, p < 0.001), in mental functioning  = 2.0, p = 0.047), and in physical functioning  = 2.3, p = 0.021). Because level of depressive symptoms was based on two depression measures, the MADRS and the BPRS depression/anxiety subscale, we repeated the analysis using each of these measure separately. Results were unchanged.Since men and women were not found to significantly differ on any of the clinical outcome measures and had a similar pattern of weight gain within each treatment group, we examined the association between weight change and change in treatment outcomes for all patients within each treatment group. Regression analyses demonstrated that for both olanzapine and haloperidol-treated patients, increases in weight were significantly associated with improvements in core schizophrenia symptoms, (B's) indicated that every one- kilogram increase in weight at 6-weeks was associated with approximately 0.03 standard deviations improvement in each clinical outcome parameter, when controlling for the effects of treatment group, gender, treatment group-gender interaction, baseline weight, the corresponding baseline outcome measure, and the number of weeks in the study.The regression coefficients  was about a third of 6.26 kg mean weight gain found at 39 weeks, when weight gain tends to plateau on olanzapine . SimilarAlthough weight gain appears to be greatest and most rapid during the first 6 weeks of treatment with clozapine , and durIt is of interest to note, however, that despite rapid weight gain during the 6-week period in our study, when weight gain is more likely to be noticed by the patients and their clinicians and thus may elicit a negative emotional response, the weight gain in this study was not only linked to improved clinical and functional status but also to reduced emotional distress as measured by the depression scales.Another limitation of the study is its lack of assessment of patients' adherence with medication. It is possible that weight gain and improvement occurred together because improvements occur mostly in patients who are medication adherent. Although this was not assessed in the present study, this possibility was previously studied by Meltzer et al. , who fouAnother study limitation is the correlational nature of the analyses, which precludes cause-effect relationship and allows for the possibility that the observed associations might be due to an unobserved variable or set of variables. Further, the relatively low correlations suggest that the association explains only a small proportion of the variance in treatment outcomes. Response to antipsychotic medications is a complex phenomenon that is associated by numerous relatively independent components  and weigNext, because the study included patients with a moderately severe level of symptomatology, the current findings may not generalize to patients with milder or residual symptoms of schizophrenia. However, the relationships among the severity of patients' baseline symptomatology, treatment-emergent weight gain and therapeutic response is currently unclear. And lastly, this study used the SF-36, a self-report measure of functional status, which was not designed to assess the potential impact of weight gain on patients' functional status or quality of life. Preliminary information on the first measure designed to specifically capture the impact of antipsychotic-emergent weight gain on patients' psychosocial functioning was only recently published . One wouWomen (and men) with schizophrenia who gained weight during treatment with olanzapine or haloperidol did not experience worsening of clinical or functional status. To the contrary, they had significant improvements in core symptoms of schizophrenia, depressive symptoms, and mental and physical level of functioning. Although excessive weight gain, regardless of origin, is of concern due to its association with physical health problems, the current findings suggest that patients who have greater treatment-emergent weight gain are more likely to benefit from treatment with olanzapine or haloperidol. Findings highlight the complexity inherent in medication management of schizophrenia patients and the need to balance treatment risks and benefits for each patient. In addition, further prospective studies will be required to assess the effects of weight gain, in both psychiatric and medical terms, on individuals treated for schizophrenia with various antipsychotic medications.The authors are employees of Eli Lilly and Company, Indianapolis, Indiana• HAS conceived of the study, participated in its design, the analytical plan, the interpretation of the results, and drafted the manuscript• MS participated in the design of the study, the analytical plan, the interpretation of the results, and performed the statistical analysis• ZZ and BK participated in the design of the study, the interpretation of the results, and the drafting of the manuscript.The pre-publication history for this paper can be accessed here:


Virtual environments (VE) are a powerful tool for various forms of rehabilitation. Coupling VE with high-speed networking [Tele-Immersion] that approaches speeds of 100 Gb/sec can greatly expand its influence in rehabilitation. Accordingly, these new networks will permit various peripherals attached to computers on this network to be connected and to act as fast as if connected to a local PC. This innovation may soon allow the development of previously unheard of networked rehabilitation systems. Rapid advances in this technology need to be coupled with an understanding of how human behavior is affected when immersed in the VE.This paper will discuss various forms of VE that are currently available for rehabilitation. The characteristic of these new networks and examine how such networks might be used for extending the rehabilitation clinic to remote areas will be explained. In addition, we will present data from an immersive dynamic virtual environment united with motion of a posture platform to record biomechanical and physiological responses to combined visual, vestibular, and proprioceptive inputs. A 6 degree-of-freedom force plate provides measurements of moments exerted on the base of support. Kinematic data from the head, trunk, and lower limb was collected using 3-D video motion analysis.Our data suggest that when there is a confluence of meaningful inputs, neither vision, vestibular, or proprioceptive inputs are suppressed in healthy adults; the postural response is modulated by all existing sensory signals in a non-additive fashion. Individual perception of the sensory structure appears to be a significant component of the response to these protocols and underlies much of the observed response variability.The ability to provide new technology for rehabilitation services is emerging as an important option for clinicians and patients. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Quantification of individual perceptual styles in the VE will support development of individualized treatment programs. The virtual environment can be a valuable tool for therapeutic interventions that require adaptation to complex, multimodal environments. Visual imaging is one of the major technological advances of the last decade. Although its impact in medicine and research is most strongly observed in the explosion of PET and fMRI studies in recent years , there hLet us first define what we consider a VE and consider the signals that need to be transmitted for such a system to operate remotely (TeleImmersion). VE is immersion of a person in a computer generated environment such that the person experiences stereovision, correct perspective for all objects regardless of their motion, and objects in the environment move in a natural fashion with subject motion. To achieve theses characteristics, certain technology must be utilized. To provide stereovision, slightly different images must be presented to the right and left eyes with little if any cross talk between the two images. In some systems this is provided by using field sequential stereo in combination with liquid crystal shutter glasses . In this system the right liquid crystal lens is clear while the left is opaque and the perspective scene generated on the screen is that for the right eye. Then the left eye lens is clear and the right is opaque and the left eye's view is displayed. This method of producing stereo has found its way into projection based systems ,5 and deRegardless of the system used, to keep all the stereo objects in the correct perspective and to keep them from being distorted when the person moves in the environment, it is necessary to track the movements of the person so that the computer can calculate a new perspective image given the reported location of the person's head/eyes. The tracking systems that are used to do this are varied. The most commonly used of these are the 6-degrees of freedom (DOF) magnetic tracking systems . With these systems a small sensor cube is placed on the subject and the location of the sensor within the magnetic field is detected. When the sensor is place on the head or glasses of the person the orientation of the head and therefore the location of the eyes can be presumed. Other non-magnetically based systems use a combination of acoustic location to delineate position and acceleration detection to obtain body coordinates in space. The combination results in 6 DOF for the location information . Other systems use cameras to track the person and then transform this information to the 6-DOF needed to maintain a proper image in the VE .So far we have confined our discussion to visual objects and have not considered the use of haptic or other forms of information to be integrated into the VE system . To provIn networked VEs several types of data need to be transmitted between collaborating sites: 1. the main data-set itself (this often consists of 3D geometry); 2. the changes to the data-set (these occur when collaborating users modify the geometry in some way – perhaps by moving the object or deforming it); 3. the virtual representation of the remote collaborator (this often is referred to as an avatar); 4. the video and/or audio channel (that facilitates face-to-face conversation.) Video has limited use in stereoscopic projection-based VEs because the large shutter glasses that the viewer uses to resolve the stereo tends to hide the viewers face from the camera. Furthermore most stereoscopic projection systems operate in dimly lit rooms which are usually too dark for effective use of video.The common model for data sharing in networked VEs is to have most of the main data-set replicated across all the sites and transmit only incremental changes. Furthermore the main data-set is often cached locally at each of the collaborating sites to reduce the need for having to retransmit the entire data-set each time the application is started. Classically TCP (Transmission Control Protocol – the protocol that is widely used on the Internet for reliable data delivery) has been the default protocol used to distribute the data-sets. TCP works well in low-bandwidth below 10 Mb/s) or short distance  networks. However for high-bandwidth long-distance networks, TCP's conservative transmission policy thwarts an application's attempt to move data expediently, regardless of the amount of bandwidth available on the network. This problem is known as the Long Fat Network (LFN) problem  Mb/s or , howeverChanges made to the 3D environment need to be propagated with absolute reliability and with minimal latency and jitter. Latency is the time it takes for a transmitted message to reach its destination. Jitter is the variation in the latency. Fully reliable protocols like TCP have too much latency and jitter because the protocol requires an acknowledgment to verify delivery. Park and Kenyon  have shoth of a second) by a more recent update.The virtual representation of a remote collaborator (avatar) is often captured as the position and orientation of the 3D tracking devices that are attached to the stereoscopic glasses and/or 3D input device (e.g. a wand). With simple inverse kinematics one is able to map this position and orientation information onto a 3D geometric puppet, creating lifelike movements . The 3D Audio and video data are similar in property to the avatar data in that they usually comprise an unending stream that is best transmitted via UDP to minimize latency and jitter. Often video and audio packets are time stamped so that they can be synchronized on the receiving end. When more than two sites are involved in collaboration it is more economical to send audio/video via multicast. In multicast the sender sends the data to a specific device or machine that then copies the data to the various people that are subscribers to the data. For example, a user send their data to a multicast address and the routers that receive the data send copies of the data to remote sites that are subscribed to the multicast address. One drawback of multicast is that it is often disabled on routers on the Internet as one can potentially inundate the entire Internet. An alternative approach is to use dedicated computers as "repeaters" that intercept packets and transmit copies only to receivers that are specifically registered with the repeater. This broadcast method tends to increase the latency and jitter of packets, especially as the number of collaborators increases.QoS refers to a network's ability to provide bandwidth and/or latency guarantees. QoS is crucial for applications such as networked VE, especially those involving haptics or tele-surgery, which are highly intolerant of latency and jitter. Early attempts to provide QoS (such as Integrated Services and Differentiated Services) have been good research prototypes but have completely failed to deploy across the wider Internet because telecommunications companies are not motivated to abide by each others QoS policies. It has been argued that QoS is unnecessary because in the future all the networks will be over-provisioned so that congestion or data loss that result in latency and jitter, will never occur. This has been found to be untrue in practice. Even with the enormous increase in bandwidth accrued during the dot-com explosion, the networks are still as unpredictable as they were a decade ago. Ample evidence is available from the online gaming community which often remarks about problems with bandwidth, latency and jitter during game sessions . These gFrustrated by the lack of QoS on the Internet, there is growing interest in bypassing the traditional routed Internet by using the available dark fiber in the ground. Dark fiber is optical fiber that has not yet been lit. Currently it is estimated that only about 5–10% of the available fiber has been lit, and each fiber has several terabits/s of capacity. The dot-com implosion has made this dark fiber and wavelengths of light in the fiber, very affordable. The newly emerging model is to construct a separate customer-owned network by purchasing or leasing the fiber from a telecommunications company, and installing one's own networking equipment at the endpoints. A number of federally supported national and international initiatives have been underway for the last few years to create customer-controlled networks explicitly for the scientific community. These include the National Lambda rail , StarLigThe ability to use virtual technology for rehabilitation is a function of cost, availability, and the kind of applications that can best utilize the network and provide rehabilitation services. Thus far, tele-rehabilitation research has focused on the use of low speed and inexpensive communication networks. While this work is important, the potential of new high-speed networks has not gathered as much attention. Consequently, we have little but imagined scenarios of how such networks might be utilized. Let us consider the case where a high-speed network connects a rehabilitation center and a remote clinic. The question is what kind of services can be provided remotely.The scenario that we envision is one where patients are required to appear at a rehabilitation center to receive therapy. Our scenario could work in several conditions. For example, a therapist at one location may want an opinion about the patient from a colleague at another location or, perhaps, the therapist can only visit the remote location once per week and with virtual technology the daily therapy could still be monitored by the therapist remotely. In our imagined condition we have a therapist at a rehabilitation center with VE, haptic and video devices and software to help analyze the incoming data  feeding to a remote clinic with identical equipment connected together through a dedicated high speed network. As displayed in Fig. A system as described above is possible today although expensive. The network characteristics that would be needed for each information channel would be as follows. A high-bandwidth connection would be needed for video and audio streamed to the plasma displays at each location, in addition to the high bandwidth a low latency and jitter connection would be needed for the Varrier Display system (VE). For a force feedback haptic device communicating between the patient and the therapist, a low network bandwidth could be used but the latency and jitter need to be low.After all possible consideration of how to best construct the virtual system, the next concern is how to associate the complex stimuli with the behavior of interest. The relative influence of particular scene characteristics, namely field of view (FOV), scene resolution, and scene content, are critical to our understanding of the effects of the VE on our response behaviors  and the Expectation of the visual scene characteristics will also influence responses in a VE. When subjects had some knowledge of the characteristics of a forthcoming visual displacement most reduced their postural readjustments, even when they did not exert active control over the visual motion . Thus weThe challenge is to determine whether the subject has become immersed in the environment, i.e., has established a sense of presence in the environment (see paper by Riva in this issue), and then to establish the correlation between the stimulus and response properties. The experience within the VE is multimodal, requiring participation of all sensory pathways as well as anticipatory processing and higher order decision making. Consequently, it is difficult to attribute resultant behaviors to any single event in the environment and responses across participants may be very variable. We have united an immersive dynamic virtual environment with motion of a posture platform  to recorIn our laboratory, a linear accelerator (sled) that could be translated in the anterior-posterior direction was controlled by D/A outputs from an on-line PC. The sled was placed 40 cm in front of a screen on which a virtual image was projected via a stereo-capable projector (Electrohome Marquis 8500) mounted behind the back-projection screen. The wall in our system consisted of back projection material measuring 1.2 m × 1.6 m. An Electrohome Marquis 8500 projector throws a full-color stereo workstation field (1024 × 768 stereo) at 200 Hz [maximum] onto the screen. A dual Pentum IV PC with a nVidia 900 graphics card created the imagery projected onto the wall. The field sequential stereo images generated by the PC were separated into right and left eye images using liquid crystal stereo shutter glasses worn by the subject . The shutter glasses limited the subject's horizontal FOV to 100° of binocular vision and 55° for the vertical direction. The correct perspective and stereo projections for the scene were computed using values for the current orientation of the head supplied by a position sensor  attached to the stereo shutter glasses (head). Consequently, virtual objects retained their true perspective and position in space regardless of the subjects' movement. The total display system latency from the time a subject moved to the time the new stereo image was displayed in the environment was 20–35 ms. The stereo update rate of the scene (how quickly a new image is generated by the graphics computer in the frame buffer) was 60 stereo frames/sec. Flock of birds data was sampled at 120 Hz.The scene consisted of a room containing round columns with patterned rugs and painted ceiling Fig. . The colSubjects gave informed consent according to the guidelines of the Institutional Review Board of Northwestern University Medical School to participate in this study. Subjects had no history of central or peripheral neurological disorders or problems related to movements of the spinal column  and a minimum of 20/40 corrected vision. All subjects were naive to the VE.We have tested 7 healthy young adults (aged 25–38 yrs) standing on the force platform (sled) with their hands crossed over their chest and their feet together in front of a screen on which a virtual image was projected. Either the support surface translated ± 15.7 cm/sec (± 10 cm displacement) in the a-p direction at 0.25 Hz, or the scene moved ± 3.8 m/sec (± 6.1 m displacement) fore-aft at 0.1 Hz, or both were translated at the same time for 205 sec. Trials were randomized for order. In all trials, 20 sec of data was collected before scene or sled motion began (pre-perturbation period). When only the sled was translated, the visual scene was visible but stationary, thus providing appropriate visual feedback equivalent to a stationary environment.Three-dimensional kinematic data from the head, trunk, and lower limb were collected at 120 Hz using video motion analysis . Infrared markers placed near the lower border of the left eye socket and the external auditory meatus of the ear  were used to define the Frankfort plane and to calculate head position. Other markers were placed on the back of the neck at the level of C7, the left greater trochanter, the left lateral femoral condyle, the left lateral malleolus, and on the translated surface. Markers placed at C7 and the greater trocanter were used to calculate trunk position, and shank position was the calculated from the markers on the lateral femoral condyle and the lateral malleolus.For trials where the sled moved, sled motion was subtracted from the linear motion of each segment prior to calculating segmental motion. Motion of the three segments was presented as relative segmental angles where motion of the trunk was removed from motion of the head to determine the motion of the head with respect to the trunk. Motion of the shank was removed from motion of the trunk to reveal motion of the trunk with respect to the shank. Motion of the shank was calculated with respect to the sled.The response to visual information was strongly potentiated by the presence of physical motion. Either stimulus alone produced marginal responses in most subjects. When combined, the response to visual stimulation was dramatically enhanced Fig. , perhapsUsing Principal Component Analysis we have determined the overall weighting of the input variables. In healthy young adults, some subjects consistently responded more robustly when receiving a single input, suggesting a proprioceptive (see S3 in Fig. Results from experiments in our laboratory using this sophisticated technology revealed a non-additive effect in the energy of the response with combined inputs. With single inputs, some subjects consistently selected a single segmental strategy. With multiple inputs, most produced fluctuating behaviors. Thus, individual perception of the sensory structure was a significant component of the postural response in the VE. By quantifying the relative sensory weighting of each individual's behavior in the VE, we should be better able to design individualized treatment plans to match their particular motor learning style. Developing treatment interventions in the virtual environment should carry over into the physical world so that functional independence will be increased for many individuals with physical limitations. In fact, there is evidence that the knowledge and skills acquired by disabled individuals in simulated environments can transfer to the real world -31.The ability for us to use this technology outside the area of research labs and bring these systems to clinics is just starting. However, the cost is high and the applications that can best be applied to rehabilitation are limited. The cost of such systems might be mitigated if this technology allowed therapists and patients to interact more frequently and/or resulted in better patient outcomes. Such issues are under study now at several institutions. This brings us to the idea of tele-rehabilitation, which would allow therapy to transcend the physical boundaries of the clinic and go wherever the communication system and the technology would allow . For exaThe ability to provide rehabilitation services to locations outside the clinic will be an important option for clinicians and patients in the near future. Effective therapy may best be supplied by the use of high technology systems such as VE and video, coupled to robots, and linked between locations by high-speed, low-latency, high-bandwidth networks. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies.The ability to provide rehabilitation services to locations outside the clinic is emerging as an important option for clinicians and patients. Effective therapy may best be supplied by the use of high technology systems such as VE and video, coupled to robots, and linked between locations by high-speed, low-latency, high-bandwidth networks. The use of data mining software would help analyze the incoming data to provide both the patient and the therapist with evaluation of the current treatment and modifications needed for future therapies. Although responses in the VE can vary significantly between individuals, these results can actually be used to benefit patients through the development of individualized treatments programs that will raise the level of successful rehabilitative outcomes. Further funding for research in this area will be needed to answer the questions that arise from the use of these technologies.