Drosophila consists of maternal and zygotic factors that generate, by transcriptional (cross-) regulation, expression patterns of increasing complexity along the anterior-posterior axis of the embryo. Using known binding site information for maternal and zygotic gap transcription factors, the computer algorithm Ahab recovers known segmentation control elements (modules) with excellent success and predicts many novel modules within the network and genome-wide. We show that novel module predictions are highly enriched in the network and typically clustered proximal to the promoter, not only upstream, but also in intronic space and downstream. When placed upstream of a reporter gene, they consistently drive patterned blastoderm expression, in most cases faithfully producing one or more pattern elements of the endogenous gene. Moreover, we demonstrate for the entire set of known and newly validated modules that Ahab's prediction of binding sites correlates well with the expression patterns produced by the modules, revealing basic rules governing their composition. Specifically, we show that maternal factors consistently act as activators and that gap factors act as repressors, except for the bimodal factor Hunchback. Our data suggest a simple context-dependent rule for its switch from repressive to activating function. Overall, the composition of modules appears well fitted to the spatiotemporal distribution of their positive and negative input factors. Finally, by comparing Ahab predictions with different categories of transcription factor input, we confirm the global regulatory structure of the segmentation gene network, but find odd skipped behaving like a primary pair-rule gene. The study expands our knowledge of the segmentation gene network by increasing the number of experimentally tested modules by 50%. For the first time, the entire set of validated modules is analyzed for binding site composition under a uniform set of criteria, permitting the definition of basic composition rules. The study demonstrates that computational methods are a powerful complement to experimental approaches in the analysis of transcription networks.The segmentation gene network of  Starting with known transcription binding site information these researchers use a the computer algorithm, Ahab, to recover known control elements and find novel modules within the genome The development of higher eukaryotes depends on the establishment of complex spatiotemporal patterns of gene expression. Thus, an important key to understanding development is to decode the transcriptional control of patterned gene expression.Drosophila has long been one of the prime paradigms for studying the role of transcription control in pattern formation  axis in the blastoderm embryo . We also predict 32 novel modules, and we expect at least some predictions with scores below 15 to be functional as well.Under these conditions, Ahab predicts 52 modules within the genomic region of the 29 genes of interest, an average of about two modules per gene. This hit rate represents a 5-fold enrichment compared to the genome-wide rate. Of the 52 predicted modules, 43 are located in intergenic regions, nine in introns, and none in coding regions, indicating a bias of the predictions toward transcriptional control regions. Of the 31 known modules, we recover 22 as significant predictions , and three overlap with free energy peaks just below the cutoff . In the cad, cap ‘n' collar (cnc), Dichaete (D), fork head (fkh), gt, kni, knirps-like (knrl), nubbin (nub), ocelliless (oc), POU domain protein 2 (pdm2), odd skipped (odd), and sloppy paired 2 (slp2). We used the free energy profiles to delineate the module and then tested its ability to drive blastoderm expression using a lacZ reporter construct , (−3), and (−6), all of which we tested; in addition, we tested one subthreshold peak further upstream, gt_(−10) (see gt pattern elements: the subthreshold gt_(−10) faithfully produces the anterior expression, gt_(−6) produces the anterior tip expression, and the gt_(−3) module produces the posterior expression (cf. gt_(−1) is bifunctional and produces both the anterior and the posterior expression domain.The gap gene −10) see A. We cansion cf. . Intereskni is expressed in two domains in the blastoderm, one at the anterior tip and one in the posterior, but only the module driving the posterior expression had previously been identified , and one in the first intron, kni_(+1). The kni_(−5) module faithfully produces the expression at the anterior tip, while the kni_(+1) module drives an imprecise kni pattern with an aberrant anterior and an abnormally widened posterior expression domain , which produces an unfaithful pair-rule-like pattern , and two for pdm2, pdm2_(+1) and(+3). nub_(−2) faithfully reproduces the posterior expression of the gene (see pdm2, pdm2_(+1) faithfully reproduces the posterior domain as well as the segmental expression of the gene, while pdm2_(+3) produces line-dependent variable patterns of blastoderm expression , which faithfully reproduces the pattern , which faithfully produces the early domain at the posterior end (see cnc is expressed in two domains, an anterior cap and a collar. Our single significant prediction, cnc_(+5), faithfully produces the pattern (see oc, oc_(+7), faithfully produces the single head gap domain of the endogenous gene , faithfully produces the early blastoderm pattern  and(−5). Both these modules drive expression in two stripes: odd_(−3) drives expression in stripes 3 and 6, while odd_(−5) drives expression in stripe 1 and a broader region encompassing stripes 5 and 6 of the endogenous pattern (see eve (eve_stripe3_7 and eve_ stripe4_6). Thus, at least four of the seven odd stripes are formed as individual stripes by maternal/gap input rather than as a complete seven-stripe pattern, indicating that odd has primary pair-rule character.Finally, the pair-rule genes: the head . We findtern see G. odd istern see E. This bOverall, our experimental validation demonstrates that Ahab is highly successful in predicting modules that drive patterned expression in the blastoderm. The algorithm finds missing modules that complement existing ones to collectively produce the expression pattern of a gene and identifies, with surprising accuracy, relevant modules in previously undissected control regions. Most of the modules faithfully produce pattern elements of the endogenous gene, suggesting that our delineation of modules, which is based on the free energy profile of the prediction, is generally quite accurate.Ahab's success in finding modules encouraged us to examine in greater detail its prediction of the binding site content of modules. We sought to examine whether the expression patterns of the previously known and our newly tested modules correlate with their composition.In its optimization procedure, Ahab fits all input factors simultaneously to the genomic region of interest, while experimental sites for transcription factors are typically determined in the absence of any competition. Ahab reports binding site composition in the form of integrated profile values, which tally the fractional occupancy of sites for a given factor, and are thus a measure of the strength of binding by this factor see . In ordeIn order to correlate the binding site composition with the ap expression pattern of the modules, we charted the previously known modules and all the newly validated modules with faithful expression and sorted them according to their expression along the ap axis see . We ask In anterior modules (driving expression at 50%–100% egg length ), Bcd sites are overrepresented and Cad sites underrepresented see , includiTo take a closer look at this relationship, we computed for each input factor and for every position along the ap axis the average number of binding sites found in the modules driving expression at that position. We plotted this number as a function of ap position and compared the resulting curve with the input factor distribution as determined by Reinitz and coworkers  Figure A. For ToThe situation regarding the gap factors is more complex. When examining the distributions of Hb, Gt, and Kr binding sites and comparing them with the input factor distributions, we clearly find an anticorrelative relationship: The number of sites is lower in regions where the cognate factor is present, and higher in regions where the factor is absent A. Remarkkni_kd, eve_stripe3_7, and eve_stripe4_6) (hb_anterior, Kr_CD1, and eve_stripe2) (D_(+4) module, which drives expression in a broad domain straddling the 50% EL line). Thus, the global distribution profile of Hb sites can largely be explained by introducing a simple contextual rule.However, we do find some modules that appear to be coextensively expressed with the presumptive repressors. One possible explanation is that the input factor has a different mode of action in these modules, that is, instead of repression it may mediate activation. Hb appears to be an example for such a switch in the mode of action. We find many modules with a small number of Hb sites that are coextensively expressed with Hb in the anterior, and it has been shown experimentally that Hb function is context dependent: Repressor function has been demonstrated for several posterior modules (e.g., ripe4_6) , while astripe2) . It is tstripe2) . We examcnc_(+5), oc_(+7), oc_otd_early, and hb_ant). In the case of Kr, context-dependent function has been suggested, but mostly based on tissue culture experiments (Kr_CD2, run_stripe3, nub_(+5), D_(+4)) are clearly expressed coextensively or overlapping with the Kr input factor. Since the average number of binding sites is low in these modules, it is possible that Kr acts as a repressor but that this manifests itself only in a reduced expression level. In fact, the Kr_CD2 module has been noted to be more weakly expressed than its sister module Kr_CD1, which lacks Kr sites  module is an example of the former: It drives expression in a posterior domain that is wider than the endogenous pattern (see kni_kd module, kni_(+1) contains the same types of binding sites, but with different profile values: The profile values for the activator Cad are higher and the ones for the repressors Hb, Kr, and Tll are lower (see pdm2_(+3) module is an example of weak and unstable expression . However, when we run Ahab over the aligned segmentation gene control regions in D. pseudoobscura, using D. melanogaster weight matrices as input, we recover as significant predictions about the same number of known modules as in D. melanogaster, indicating that there is substantial functional conservation , 56 for the pr run (score >15), and 69 for the mgpr run , in total 145 distinct putative modules, an average of approximately three per gene. Interestingly, the mg run and pr run peaks are completely nonoverlapping. We determined the relative contribution of maternal/gap and pair-rule input to each predicted module by evaluating its binding site composition as revealed by the mgpr run, i.e., using all input factors. Modules were classified into four types: maternal/gap driven, pair-rule driven, or driven by both but with bias towards maternal/gap input or pair rule input see .odd stands out as receiving unexpectedly strong maternal/gap input. odd is expressed in a pair-rule and then segment-polarity pattern , these positive results are likely to reflect the redundant and combinatorial nature of the input.The global analysis of the segmentation gene hierarchy shows that the prevalence of maternal/gap input strongly correlates with gap-like expression, while the prevalence of pair-rule input strongly correlates with segmental expression. Integrating the inputs over all modules within the control region of a gene provides a reliable indication of its type of expression pattern and position within the hierarchy. In fact, the integrated predictions are so accurate as to pinpoint abnormalities in the gene classification, such as the known head gap function of cis-regulatory modules. We find that the vast majority of the modules expressed in the early blastoderm contain maternal factor sites, which strongly suggests that the maternal gradient systems of Cad, Hb, Bcd, and Torso  have most, if not all, of the early zygotic patterning along the ap axis under their direct control. Together with the strong interdependence of the maternal gradient systems, this massively parallel output would explain the coordinated and long-range effects on segmentation gene expression patterns that are observed when maternal factors are titrated up or down through genetic manipulation.Ahab performs well not only in identifying modules, but also in predicting their composition, thus permitting an analysis of binding site composition under uniform criteria for the entire set of known and newly validated maternal/gap-driven modules. Gene expression studies in mutant embryos have revealed the global regulatory interactions within the segmentation gene network , but areFurther, by correlating the binding site content of modules driving expression at a given position with input factor distributions, we are able to infer the mode of action for six of the nine factors and to show that modules are generally well fitted to the distributions of their positive and negative input factors. The maternal factors act as activators within their domain of expression, while the gap factors act largely as repressors. This overall result confirms previously existing data and demonstrates that the rules gleaned earlier from rather small datasets generalize very well over the entire set. Interestingly, our data also provide support for the idea that Hb functions in a bimodal fashion and suggest a simple rule for its context-dependent switch from repression to activation. Modules with few Hb and many Bcd sites drive expression in the anterior half of the embryo, while modules with more Hb than Bcd sites do not. Depending on module composition and Bcd availability, Hb can thus activate transcription; this Bcd/Hb synergy could serve to bolster transcriptional activation in regions where Bcd levels taper off. For Kni and Tll, the mode of action cannot be assessed on the basis of the extant binding site information.The comparison of modules with faithful and unfaithful or unstable patterns provides some interesting additional clues for composition rules, such as the ratio and compatibility of activator and repressor sites. However, to address the question of how the precise domain boundaries are established within a given region of the embryo, a more detailed examination of composition rules and of the internal organization of modules will be needed, specifically of rules governing the number, affinity, spacing, and arrangement of binding sites. This analysis will require different types of experimentation as well as additional computational analysis.The performance of Ahab is influenced by a number of parameters, but the most important is the quality of the input factor weight matrices. To further improve weight matrices, more sites for undersampled factors will have to be collected (D-Stat and Gt), and existing sites for the unspecific factors will have to be scrutinized (Kni and Tll). More importantly, the relative affinity of binding sites for their factor will have to be measured in a more comprehensive fashion. The ideal experiment would measure, under identical conditions, the relative binding affinity of the consensus sequence to all possible single base mutations in the consensus binding site . For somD. melanogaster and D. pseudoobscura.With an Ahab run that recovers 70% of the known modules with predominant maternal/gap input, we predict another 32 putative modules in the control regions of gap and pair-rule genes. Most of these look plausible in terms of genomic location and composition, and as our validation shows, many drive blastoderm expression that faithfully reproduces the endogenous pattern of the gene. However, we also found modules whose expression does not match the endogenous pattern (unfaithful/unstable) or whose composition does not suggest any coherent expression pattern (e.g., no activator sites); among the latter are some predictions dominated by Kni and Tll sites, which are potentially problematic because of the unspecificity of their weight matrices. The apparently anomalous modules could drive expression at later stages of development or could simply be artifacts of improper delineation or missing relevant input. A more intriguing possibility is that some of these modules are in evolutionary transit—nascent or dying. Such modules might be held in check by relatively few point mutations (“pseudo” modules), by nearby insulator elements, or by restricted access to the basal promoter when competing with the functional modules. The effort to discover the true nature and function of these anomalous modules will be aided by the computational and experimental comparison of corresponding modules in Drosophila genome sequence Release 2 after masking tandem repeats in the genomic sequence as described in When possible, previously compiled position weight matrices were used: for Bcd, Hb, Cad, TorRE, Kr, Kni, and Tll , and forThe genomic position of known modules was derived from literature  or mappe8 randomizations, resulting in an estimate of p < 10−8 for the significance of the recovery of known modules by Ahab. When we remove from the calculation the 13 modules that were used for the construction of weight matrices , along with the kni, hb, and tll control regions, which contain no additional annotated modules, we find p = 4.9 × 10−6.To assess the significance of Ahab module predictions, we calculated the overlap between predictions and known modules in basepairs, and compared it with the overlap achieved when predictions are randomly placed within the delineated control regions (minus masked and coding sequence). We failed to match the actual overlap through 10The experimental binding sites that define our weight matrices are derived from a variety of in vitro experiments that typically neglect competition between transcription factors, whereas Ahab, in its prediction of binding sites, fits all factors simultaneously. To gauge whether Ahab can be used as a predictor of module composition, we examined what fraction of known binding sites the algorithm recovers. The only free parameter in the comparison is the profile value (between 0 and 1), which measures the fractional occupancy of a site by its factor; a profile value of 1 means that a site is always occupied by its factor. A site was scored as found if the prediction exceeded a certain profile value cutoff and overlapped the experimental footprint by more than 50%. We further examined whether Ahab misses known sites by misclassification. We found that Ahab generally does not misattribute the missing sites to another factor. A cogent example is provided by Cad and Hb, which have very similar binding sites containing an oligoT stretch. Surprisingly, none of the 21 Hb sites that were missed at a profile value cutoff of 0.5 were misclassified as Cad; conversely, only one of the 11 missed Cad sites was classified as Hb. This discrimination is far better than that achieved by a simple weight matrix scan over the same modules: for this scan, we counted information scores greater than five, which is the score of the weakest experimental binding sites, and overlaps between matrix and binding site of 50% or more. The matrix scans correctly classified 29/43 Hb sites and 16/21 Cad sites; but misclassified five Hb sites as Cad and two Cad sites as Hb. Taken together, Ahab finds the majority of known binding sites and rarely misclassifies; it is thus a reliable indicator of module composition. A complete listing of the integrated profile values reported by Ahab for known, newly validated, and predicted modules is available in D. melanogaster and D. pseudoobscura genomic sequence as described in D. pseudoobscura control regions, with D. melanogaster weight matrices as input and with cutoffs for significant predictions (15 in D. melanogaster) and subthreshold peaks  set to obtain equivalent numbers of predictions in D. pseudoobscura. D. pseudoobscura predictions were then mapped to D. melanogaster coordinates and examined for overlap with the known and predicted D. melanogaster modules.To assess the conservation of known and Ahab-predicted modules, we aligned To associate predictions from different Ahab runs, each run was processed and the highest point on the free energy plot within an interval of the window size was marked as a “peak.” Peaks are thus spaced by at least the window size. Peaks in two different runs correspond if they are closer than half the window size; their correspondence is unique and order independent. For the three-way comparison, the mg and pr runs were separately compared to the mgpr run. In no case did mg run and pr run peaks correspond without at least one of them matching a mgpr run peak.For the purposes of broadly classifying predicted modules as to type of input, we defined four classes: mostly maternal/gap input, mostly pair-rule, and mixed input but with a bias towards maternal/gap or pair-rule. Two classification methods were used. The first relied on a single Ahab run with all factors (mgpr run) and then compared the sum of the maternal/gap factor profile values for a given module with the sum of the pair-rule profile values, after normalization to make the mean and standard deviation of maternal/gap profile values over all peaks equal to the mean and standard deviation of all pair-rule profile values. An alternative scheme used the free energy plots for the three runs (mg, pr, and mgpr), identified corresponding peaks, and then compared their rank in the different runs. The two methods yielded very similar results.Since Ahab does not adapt its window size to the data, modules that are wider than the window size needed to be delineated to be captured accurately. To this end, we defined the start of the module as the first local maximum in the free energy plot that is above the cutoff. The end point was initialized as the other end of the corresponding Ahab window. The plot was then scanned from left to right, and when another local maximum or rise in window score above the cutoff was encountered, the end point of the module was reset as the end of the corresponding window. The sequence of all delineated predicted modules is available in Module predictions were tested as follows. The module was delineated within the genomic sequence as described above and further expanded to include good primer sites for touch-down PCR. Primers were designed following manufacturer's guidelines , restriction sites (Xba, Asp718) were added for subsequent cloning. Genomic PCR products were cloned into TOPO , sequenced to confirm identity, and subcloned into Casper hs43ßGAL . A FastalacZ probe. RNA in situ hybridizations were carried out as described by Noordermeer and Kopczynski (http://www.fruitfly.org/about/methods/RNAinsitu.html).Transgenic fly strains were generated using standard methods. For each construct three independent insertions were analyzed for expression patterns by RNA in situ hybridization with a http://flyex.ams.sunysb.edu  can be viewed at Dataset S1The dataset gives name, symbol, flybase identifier, and references for expression pattern, control region dissection, and binding site information.(178 KB DOC).Click here for additional data file.Dataset S2(8 KB TXT).Click here for additional data file.Dataset S3(80 KB TXT).Click here for additional data file.Dataset S4(37 KB TXT).Click here for additional data file.Dataset S5Data based on mg run, Fasta format.(53 KB TXT).Click here for additional data file.Dataset S6Input: Bcd, Hb, Kr, Gt, Kni, Tll, Cad, TorRE, and Dstat. Performed over defined sequences of known modules , tested (62 KB TXT).Click here for additional data file.Dataset S7Input: Bcd, Hb, Kr, Gt, Kni, Tll, Cad, TorRE, Dstat, H, Eve_HD, Eve_t2, Run, Ftz, Ftz-f1, Ttk, and Prd_HD. Performed over defined sequences of known modules , tested (77 KB TXT).Click here for additional data file.http://flybase.bio.indiana.edu) accession numbers for the genes and gene products discussed in this paper are Bcd (FBgn0000166), Cad (FBgn0000251), Capicua (FBgn0028386), cnc (FBgn0000338), D (FBgn0000411), D-Stat (FBgn0016917), Eve (FBgn0000606), fkh (FBgn0000659), Ftz (FBgn0001077), Ftz-f1 (FBgn0001078), Gt (FBgn0001150), H (FBgn0001168), Hb (FBgn0001180), Kni (FBgn0001320), knrl (FBgn0001323), Kr (FBgn0001325), nub (FBgn0002970), oc (FBgn0004102), odd (FBgn0002985), pdm2 (FBgn0004394), Prd (FBgn0003145), Run (FBgn0003300), slp2 (FBgn0004567), Tll (FBgn0003720), TorRE (cf. FBgn0003733), and Ttk (FBgn0003870).The FlyBase (


Lipoprotein lipase (LPL) is anchored at the vascular endothelium through interaction with heparan sulfate. It is not known how this enzyme is turned over but it has been suggested that it is slowly released into blood and then taken up and degraded in the liver. Heparin releases the enzyme into the circulating blood. Several lines of evidence indicate that this leads to accelerated flux of LPL to the liver and a temporary depletion of the enzyme in peripheral tissues.125I and injected. Already two min after injection about 33 % of the injected lipase was in the liver where it initially located along sinusoids. With time the immunostaining shifted to the hepatocytes, became granular and then faded, indicating internalization and degradation. When heparin was injected before the lipase, the initial immunostaining along sinusoids was weaker, whereas staining over Kupffer cells was enhanced. When the lipase was converted to inactive before injection, the fraction taken up in the liver increased and the lipase located mainly to the Kupffer cells.Rat livers were found to contain substantial amounts of LPL, most of which was catalytically inactive. After injection of heparin, LPL mass in liver increased for at least an hour. LPL activity also increased, but not in proportion to mass, indicating that the lipase soon lost its activity after being bound/taken up in the liver. To further study the uptake, bovine LPL was labeled with This study shows that there are heparin-insensitive binding sites for LPL on both hepatocytes and Kupffer cells. The latter may be the same sites as those that mediate uptake of inactive LPL. The results support the hypothesis that turnover of endothelial LPL occurs in part by transport to and degradation in the liver, and that this transport is accelerated after injection of heparin. Lipoprotein lipase (LPL) hydrolyses triglycerides in chylomicrons and VLDL and thereby makes fatty acids available for cellular uptake and use in metabolic processes ,2. RelatHeparin releases LPL from its endothelial binding sites into the circulating blood. The uptake in the liver is retarded, but not abolished ,14. ThisLPL activity in rat liver was 26 ± 1 mU/g (Table To study the distribution of endogenous LPL in rat liver we used affinity-purified chicken antibodies raised against bovine LPL. These antibodies have previously been used for ELISA of LPL in rat tissues . There wLPL activity and mass in plasma increased many-fold after heparin injection Table . The higHepatic lipase (HL) was also measured in the livers Table . As expeThe pattern of immunofluorescence for LPL after heparin was similar to that before heparin, with faint staining over most cells and more intense staining over scattered cells, some of which were ED2 positive (not shown).To explore the origin of the LPL released into plasma and taken up by the liver we measured LPL activity in heart and adipose tissue before and 20 min after injection of heparin . Double staining with the ED2 antibody showed that the intensively LPL-positive cells were Kupffer cells. After 15 min the immunofluorescence had changed to a more punctuate pattern that still co-localized with Kupffer cells Figure . This inHeparin markedly changed the pattern of localization for the active lipase. The initial (two min after injection) staining along the sinusoids was much weaker than in sections from rats that had not received heparin. The staining was generally more intense at 15 min compared to at two min after injection of the lipase Figure . This isElectron microscopic autoradiography showed that when heparin was injected ten min after active LPL there was a strong reduction in the amount of LPL in the spaces of Disse and on endothelial cells, while the radioactivity found in hepatocytes and Kupffer cells remained (data not shown).Heparin had no marked effect on the distribution of the inactive LPL Figure . Most ofThis study shows that after injection of heparin, LPL activity and mass in liver increases several-fold, in concert with the hypothesis that heparin causes accelerated transport of LPL to the liver. In other parts of the body LPL is attached to HSPG ,2. Hepar125I-tyramine cellobiose and isolate the different cell types.Heparin markedly decreased the binding of LPL along the sinusoids. This presumably reflects that binding to HSPG was competed by heparin. Staining associated with Kupffer-like cells increased. This may be the same sites as those that bind inactive LPL. Another possibility is that the LPL-heparin complexes were recognized and taken up as such. There is evidence for binding and uptake of heparin by Kupffer cells . Most ofIt has been suggested that LPL and HL bind, at least in part, to the same sites in the liver ,28. It i125I-LPL to rats and found that about 10% of the lipase that had bound in the liver could be released [125I-LPL in a recirculating system for 10 min. After wash the perfusion was then continued in single pass mode with a heparin-containing medium. About 50% of the LPL that had bound in the liver reappeared in the perfusion medium within four min [125I-radioactivity in the liver observed in the present study.Earlier studies have shown that there are also heparin-sensitive sites that bind LPL in liver . Wallindreleased . Vilaró four min ,29. At lfour min ,31. Vilafour min . The enzfour min ,34. Immufour min ,35,36 anfour min ,25,35,37four min . The lipfour min ,40. The in vivo [Inactive LPL, as prepared here, was taken up in Kupffer cells. Most of the LPL in plasma is inactive ,10 and tin vivo  and on pin vivo . In thesin vivo ,42. Our 125I-labeled LPL in liver to that for asialofetuin, which is taken up by the galactose receptor [Before heparin, the ratio of LPL activity to LPL mass was about 0.2 mU/ng in liver and 0.3 mU/ng in blood Table . After hreceptor . The halSoleus muscle. Kuwajima et al perfused some of the rat hindlimb muscles (gastrocnemius, soleus and plantaris) with heparin in situ and observed a large release in fasted but not in fed rats [To explore the source of LPL released into plasma and taken up by the liver we measured LPL activity in adipose tissue and heart before and after injection of heparin. In fed rats there was a large decrease of LPL activity in adipose tissue, in accord with a previous study . From thfed rats . These dLPL in plasma is bound to lipoproteins and it has been suggested that the lipase serves as a ligand for binding and uptake of lipoproteins in the liver. Chevreuil et al injected doubly labelled chylomicrons to rats shortly after heparin . The res• In the liver, the active form of LPL initially binds to sinusoidal surfaces but then transfers to and is taken up mainly in hepatocytes• An inactive form of LPL, presumably monomers, was mainly taken up in Kupffer cells• Heparin retards the uptake of active LPL in liver, but there are heparin insensitive binding sites for LPL both on hepatocytes and on Kupffer cells• Release of LPL into blood by heparin results in accelerated transport of the lipase to the liver• The observation that rat liver contained substantial amounts of LPL, most of which was inactive, is in accord with the hypothesis that one route for turnover for endothelial LPL is transport to and degradation in the liver• The observations that most of the LPL in blood is inactive, that injected inactive bovine LPL located to Kupffer cells, and that the immunostaining for endogenous LPL was more intense over Kupffer cells than over hepatocytes suggest that a substantial fraction of the transport from extrahepatic tissues occurs with LPL that has lost its activity.• The main source of LPL released into plasma and taken up by the liver in fed rats is the adipose tissue, whereas in fasted rats the main sources are heart and skeletal muscles.125I-albumin and Cr51-labeled red blood cells [Male Sprague-Dawley rats (Moellegard Breeding centre, Denmark) weighing 180–220 g were used. They were kept on a standard pellet diet in a 12-hour light cycle. In order not to disturb blood circulation or the metabolic functions of the liver, we performed all experiments on unanaesthetized rats. They were killed through decapitation at the time of tissue removal. Injections were made in the tail vein. Mean liver weight for the rats was about 9 g. In some of the rats we dissected out all visible adipose tissue. The mean total weight was 14 g, including fibrous tissue removed with the subcutaneous adipose tissue. To correct values for LPL mass/activity in the liver we used an estimated figure of 3% for the amount of blood plasma remaining in the liver after exsanguination. This was based on earlier experiments with od cells ,46. The Vectashield mounting medium was from Vector Laboratories, Burlingame, CA. Tissue-Tec OCT compound was purchased from Sakura Finetek Europe BV, Zoeterwoude, The Netherlands. Microscope slides and cover slips were from Menzel – Gläser, Germany. Plasma, used in the preparation of catalytically inactive LPL, was taken from fasted rats with EDTA as anticoagulant. Heparin was obtained from Leo Pharma AB, Malmö, Sweden. The dose given was 500 IU/kg body weight.125I using the lactoperoxidase/glucose oxidase method [in vivo experiments and incubated this at different temperatures. On incubation at 37°C the LPL activity remained essentially stable for one hour. At higher temperatures the lipase became unstable. Based on these results we decided to use 45°C for gentle inactivation of LPL aimed to prevent aggregation of the enzyme. Chromatography on heparin-Sepharose of active and inactivated LPL showed, as expected [125I-labeled LPL was included in the preparation, to enable us to follow the distribution and metabolism of the injected material. Each rat received about 40 μg lipase protein, except in the electron microscopy studies where only a trace amount of the labeled lipase was injected.LPL was purified from bovine milk as previously described  and was e method . The labe method . To findexpected , that thAntibodies against bovine LPL were raised in a chicken (chicken no 225) and IgG were isolated from egg yolks as previously described . AntibodSmall pieces of liver were mounted in Tissue – Tec OCT and snap frozen in propane chilled with liquid nitrogen. The tissue pieces were then stored at -70°C until sectioning. Cryosections were fixed for 10 min in 4 % paraformaldehyde. After rinsing, the sections were blocked in 5 % goat serum for 10 min and then incubated overnight with the primary antibody. All these procedures were made at room temperature. Incubation with the secondary antibody was then for 30 min at 37°C. The sections were rinsed in 0.01 M phosphate 0.15 M NaCl at pH 7.4 and mounted in Vectashield medium (Vector laboratories, Burlingame, CA). The immunostained samples were analyzed by confocal laser scanning microscopy (Leica SP2 or Nikon Eclipse E 800). To avoid potential signal crossover the two fluorophores were sequentially scanned. Data were collected with sequential laser excitation to eliminate bleed through and with confocal parameters such as pinhole size set to minimize the thickness of the optical sections. The images were digitally optimized using the Adobe Photoshop software.125I-labeled LPL was injected to rats and 10 min later, the livers were perfused with 2% paraformaldehyde, 2.5% glutaraldehyde in 0.1 M phosphate buffer at pH 7.4 for 10 min. After fixation the livers were washed in 0.1 M phosphate buffer, cut in small pieces, dehydrated through graded acetone solutions and embedded in Spurr resin. Postfixation with 1% osmium tetroxide was not performed due to its known effect of fading latent images in autoradiography. Ultrathin sections, 70 nm thick, were collected over Formvar-carbonated copper grids. These sections were coated with a monolayer of Ilford L4 nuclear emulsion, diluted 1:4 with distilled water, by means of a tungsten wire loop following the "loop interference" technique. After an exposure of 8 months, the silver grains were revealed with Phenidon development. Sections were stained with uranyl acetate and lead citrate and examined in a Hitachi MT 800 electron microscope at 75 kV. Quantitative analysis was performed by counting the number of silver grains per area in each experimental condition. The area analyzed was about 25 × 104 μm2.For autoradiographic studies, 3H oleic acid-labeled triolein in egg yolk phospholipids. Hepatic lipase (HL) was inhibited by incubation of the samples on ice for two h with rabbit anti-HL IgG. In the HL assay, LPL is inactivated by 1 M NaCl. Both assays were run at 25°C for 30 min. All determinations were carried out in triplicate. The activities are expressed in mU/ml plasma. One mU corresponds to 1 nmol of fatty acid released / min. All determinations were carried out in triplicates.The activities of LPL and of hepatic lipase were determined as described . In the Plasma samples were stored frozen at -70°C before the analysis. Tissue samples were rinsed in cold 0.9 % NaCl, blotted dry, weighed and then immediately frozen in liquid nitrogen in 9 volumes of buffer at pH 8.2 containing per ml: 1 mg BSA, 10 mg Triton X-100, 1 mg SDS, 5 IE heparin, and protease inhibitor Complete Mini (Roche) 1 tablet / 50 ml buffer. They were stored at -70°C and later thawed and homogenized with a Polytron homogenizer . The homogenates were centrifuged for 15 min at 3000 rpm in a Beckman Microfuge and the supernatants were used for the assays. For assay of LPL in liver and post-heparin plasma, the activity of hepatic lipase was suppressed by incubating the extract with an excess of anti-HL immunoglobulins before assay.Detergent containing extraction buffers are needed to solubilize and stabilize active and inactive forms of LPL efficiently ,50, but LPL protein mass was measured by an ELISA, using chicken antibodies for capture and the monoclonal 5D2 antibody coupled to peroxidase for detection . Bovine LRP – low density lipoprotein receptor-related protein, LPL – lipoprotein lipase, HSPG – heparan sulphate proteoglycan, HL – hepatic lipase, ELISA – enzyme-linked immunoassay, BSA – bovine serum albumin, SDS – sodium dodecyl sulphate, VLDL – very low density lipoproteinLN carried out the immunolocalization studies, C L-I carried out the electron microscopic studies, SV participated in the design of the study and supervised the electron microscopy, JG carried out the studies on wash-out of LPL from tissues after heparin, TO conceived of the study, participated in its design and drafted the manuscript. GO participated in the design of the study and coordinated the work. All authors read and approved the final manuscript.


Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression.MYCN amplified samples. There were 6 independent non-contiguous amplicons (10.4–69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by NAG and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without MYCN amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype.We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the MYCN amplification.cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and  MYCN amplification status. Stage 1 disease is essentially curable, whereas patients with stage 4 disease, in particular those with MYCN amplification, remain largely incurable despite advances in cancer therapeutics [MYCN oncogene (found in 30% of NB) [Neuroblastoma (NB) is one of the most common pediatric solid tumors, and accounts for 7–10% of all childhood cancers. The prognosis of patients with NB varies according to the stage, age and apeutics -6. Based% of NB) ,9. OtherMYCN amplification) prognosis.Recently array-based CGH (A-CGH) on BAC and cDNA microarrays has been used to investigate the genomic alterations with high resolution -14. cDNAMYCN since it is the most commonly amplified gene in NB and correlates with the biological behavior of these tumors. Fig. MYCN amplification results from A-CGH and Quantitative-PCR (Q-PCR). We found that the slope of the fitting line was 0.35, and therefore an observed ratio of 2 by A-CGH corresponds to Q-PCR ratio of ~6. In order to identify the amplified regions, we initially selected genes with A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. Only two chromosomes (2p and 12q) showed amplifications by this criterion exclusively in the MYCN amplified samples. Focusing on 2p  in three tumor samples, whilst the smallest region was 27 Kb including an EST (clone: 241343), NCYM, and MYCN. We identified 9 previously reported co-amplified genes (HPCAL1, ODC1, NSE1, NAG, DDX1, NCYM, POMC, DNMT3A, ALK, MEIS1, TEM8) [NCOA1, ADCY3, PPP1CB, CGI-127, LBH, CAPN13, GalNac-T10, EHD3, XDH, SRD5A2, CGI-27, AMP18) and ESTs. Three of the cell lines (CHP134, IMR-5 and IMR-32) contained two amplicons in 2p13-15. The first (66.6–67.6 Mb) included previously reported amplified gene MEIS1, and the size of the second amplicon was 0.3 Mb (69.1–69.4 Mb), which was bounded by LOC200504 and TEM8. In addition to chromosome 2p, we identified another amplicon on 12q14-q15 in a single tumor (NB21); bounded by PRO2268 (68.9 Mb) and RAB3IP (69.9 Mb) containing one previously reported amplified gene (MDM2) [CPM, CPSF6, LYZ, GAS41, SNT-1, CCT2, VMD2L3, and RAB3IP) . Simultaneous gene expression profiling by using the same cDNA arrays for all samples showed that 47% of the amplified genes correlate with gene expression .Totally around 24,000 qualified array cDNA clones were applied for data analysis in 12 NB cell lines and 32 NB primary tumor samples  ,20-27, ae (MDM2)  as well IP) Fig. . We veriMYCN above   and compbove Fig . The undNPPA and PLOD, while the distal breakpoint is proximal of CDC2L1. The deletion detected by our method is bordered by KIAA0495 and CTNNBIP1, which is within the region reported. They are in a very good accordance. In addition, we also compared the 17q gain results for four NB cell lines (CHP134, IMR-5, SMS-KCNR, and SK-N-AS) with the results in literature by FISH and Q-PCR [In order to increase the sensitivity for detecting low copy number changes, we applied a probabilistic approach utilizing t-statistics and the local genomic sequence mapping information of each of the cDNA clones on our arrays. To validate our method, we re-analyzed the A-CGH data generated from the cell lines containing 1–5 copies of the X chromosome as described above, and we were able to detect a single copy loss and gain of X chromosome where the expected ratio was 0.5 and 1.5 respectively Fig. . In addind Q-PCR . Our resWe next analyzed the A-CGH data using this method to detect genome-wide alterations of DNA copy number in our NB samples. Using this t-statistics, we identified DNA copy number alterations that involved the majority of the chromosomes in both primary tumors and cell lines Fig. . We confMYCN amplification status, we partitioned the tumors into three subgroups (stage 1, stage 4 MYCN not amplified (4-) and amplified (4+)), and analyzed the frequency of genomic changes at each SW-locus (as defined in Methods) for each subgroup. Since cell lines may contain tissue culture related genomic alterations, we only used primary NB tumor samples for this analysis. The frequency of alterations for a given SW-locus was estimated using the average probability (P) value as described in the Methods. Fig. P associated with each SW-locus for all possible pair-wise comparisons among the three subgroups for gains or losses: Stage 1 vs. 4-, 4+ vs. 4- and 1 vs. 4+. As expected, since the majority of the loci show no change, they were plotted to values of co-ordinates around (0.5, 0.5) (shown in black). Regions altered in both classes with similar frequency are plotted close to the y = x diagonal, whilst off-diagonal points represented regions primarily altered in one or the other of the classes . Loci that plot around (0,0) reflect loci altered in both groups (termed common regions). The colored points were selected by using our criterion for "common" and "specific" SW-locus (see Methods). In summary, we found alterations that were common to all three subgroups, which included gain of 7q32, 17q21, 17q23-24 and loss of 3p21. We also found genomic imbalances that were specific for each of the subgroups and common regions of gain for stage 1 and 4- tumors. Of note there were no shared alterations of 4+ with 1 or 4- besides the regions common to all. A detailed description and map positions for all these recurrent regions are provided in the Table In order to identify the recurrent regions of genomic alterations that are specific to stage and around 0., 0.5 , mapped to 2p13.1, which has been shown to have elevated expression during tumor angiogenesis [TEM8 was amplified and over-expressed (data not shown) in several neuroblastoma cell lines. The significance of amplification of TEM8 in neuroblastoma cell lines but not endothelial cells raises an intriguing possibility that these tumor cells themselves contribute to the angiogenic process and requires further investigation. We also identified amplification of GAS41 (glioma amplified sequence) mapped to 12q14-q15 in one tumor sample. GAS41, a transcription factor ubiquitously expressed with the highest expression in human brain, was previously shown frequently amplified in human gliomas [ALK (anaplastic lymphoma kinase) receptor, an oncogene and reported highly expressed in neuroblastoma [MYCN amplified samples, and we have not found a single amplicon in MYCN single copy samples.In our study we found that unlike breast cancers  NBs do nogenesis . Indeed ogenesis . The gen gliomas . ALK  we found no evidence of amplifications in other chromosomal regions. This was in conflict with a study by Satito-Ohara et al. who found evidence of high level gains 9 NB cell lines and amplification as evidenced by a homogeneous staining region (HSR) in one line . This diIn this study, we have applied a t-statistics-based method to explore genomic alterations in cancer from data generated by A-CGH on a cDNA microarray platform. Our method efficiently dealt with the low sensitivity of cDNA microarrays to detect low copy changes. The microarrays we utilized contain 42,000 clones, containing around 24,000 unique UniGene clusters with an average coverage of one cluster every 125 Kb. Underestimation of DNA copy number ratios by cDNA A-CGH data made it difficult to detect low level of gains and losses using ratio threshold based approaches, which was addressed in our study and previous reports -16. The However, all these advantages are traded for a loss of resolution: genomic imbalances much smaller than the window-size cannot be detected and the boundaries of instable regions are blurred. Therefore we should choose the smallest window that has the desired level of statistical significance. The effective resolution can be obtained by analyzing the correlation of overlapping sliding windows. The integrated autocorrelation time is an estimator of the minimal distance for windows to be effectively uncorrelated  even wheall (100%) three subgroups of NBs, although such a region would be interesting since it may harbor specific genes that are uniquely responsible for NB tumorigenesis. We therefore focused on commonly altered regions where >70% of tumors showed changes in a given region, for our three different subgroups . Each DNA sample was analyzed in triplicate using the ABI PRISM 7000 Sequence Detector. For quantitative PCR, 10 ng of genomic DNA was used for SYBR green PCR assay. Serial dilutions of neuroblastoma cell line CHP134 DNA were used as templates for a standard curve, and the normal genomic DNA was used as a calibrator. The normalization was performed as described using ce genes .; June, 2002 Freeze). Throughout this publication, all genomic coordinates are given with the respect to this assembly. Finally the clusters were sorted according to their starting position of sequence on each individual chromosome.Fluorescence ratios were normalized for each microarray by setting the average log ratio for each subarray elements equal to zero . The data was quality-filtered by removing those clones with quality lower than 0.5 in more than 20% of all the samples . For thegc) obtained in this way is assigned to the center of the sliding window (referred to as the "SW-locus" throughout this manuscript). The t-test is valid in this instance because the observed distribution of Pgc for the loci in random order matches the expected theoretical distribution. The null-distribution used in t-test represents the unaltered part of genome. To identify the cDNA clones for the null-distribution, we start with the whole genome. The SW-loci corresponding to portions of the DNA that are amplified or deleted with a p-value smaller than 0.05 are removed recursively from the null dataset, until the null dataset is stable and there is no more amplified or deleted SW-locus in the null data set. Finally, the confidence of identified genomic alterations is visualized in a pseudo-color map in which color intensity represents the log of p-values (red for gain and green for loss).To identify the alterations of copy number along the genome, we compared the distribution of the ratios in a sliding window of 20 clones in genomic order with a "null distribution" using a t-test. The p-value for genomic change  <P> = 0.5. Therefore, for those cases where we are sure of a genomic imbalance Pgc is close to 0 (for example p < 0.01), whereas for the samples in which there are no changes Pgc = 0.5. According to the formula: P = ((Nno change × 0.5) + (Nwith change × 0))/Ntotal = (1 - fwith change) × 0.5, the lower the P the higher the frequency of a genomic change in that SW-locus. Thus the P can be used to determine the frequency of a given change e.g. P = 0.15 corresponds to a frequency of ~70% of the samples with that given change.The probabilistic approach above provides P-values for the presence of genomic alteration in a given sample. In order to estimate the frequency of a genomic alteration we can set a threshold ratio and identify how many samples have a ratio value outside this threshold in the same genomic region. The disadvantage of this method is that different ratio thresholds will give different frequencies. We therefore applied another approach to avoid the use of ratio thresholds. To determine the frequency of loss or gain that correlate with the stage or P < 0.15 in a specific subgroup as altered. This threshold corresponds to roughly a fraction of >70% of all tumor samples harboring that alteration in each subgroup. We define an altered SW-locus as common in all tumors, if the SW-locus passes the threshold for each of the three subgroups. A SW-locus is called differential in one subgroup with respect to another subgroup, if the frequency of genomic change is at least 3 times higher in one subgroup as compared to another. A SW-locus is defined as specific if the locus in one subgroup is differential with respect to each of the other subgroups; a SW-locus is defined as shared in two groups, if in both groups it is differential with respect to the third subgroup.We first define a SW-locus with NB: neuroblastomaA-CGH: array-comparative genomic hybridizationM-CGH: metaphase-comparative genomic hybridizationMYCN amplificationStage 1-: stage 1 without MYCN amplificationStage 4-: stage 4 without MYCN amplificationStage 4+: stage 4 with BAC: Bacterial artificial chromosomeQC carried out all experiments and participated in data analysis. SB performed array CGH data analysis and statistical analysis. QC and SB drafted the manuscript. JSW, CCW, NC and CS were involved in the microarray production and the manuscript edition. ALK and BG were involved in the data analysis and the manuscript edition. FW, FB, MS and DC provided the tumor samples and patient information and were also involved in the manuscript edition. JK principal investigator of the project, participated in its design and is the final editor of the manuscript. All authors read and approved the final manuscript.


Nevirapine and efavirenz are the most commonly prescribed of the class of antiretroviral drugs called non-nucleoside reverse transcriptase inhibitors (NNRTIs). Efavirenz has the advantage of once-daily dosing. In a recent study called the 2NN study (Lancet 363: 1253–1263), it appeared to be only marginally superior to nevirapine in terms of clinical success and virological suppression. Van Leth and colleagues have now shown that while nevirapine and efavirenz both raise high-density lipoprotein (HDL) cholesterol (the “good” type of cholesterol), the overall lipid profile is better with nevirapine than with efavirenz.“These data suggest that nevirapine may be preferable to efavirenz in HIV-infected adults with other cardiovascular risk factors,” says the study's academic editor, Andrew Carr of St. Vincent's Hospital in Darlinghurst, Australia. “However, perceived cardiovascular risk is only one factor that would affect the choice between these two drugs.”Van Leth and colleagues prospectively analyzed the lipids of patients enrolled in the 2NN study, a randomized, open-label efficacy study that included adults with HIV who had never been on antiretroviral drugs. All patients were given stavudine and lamivudine and were then randomized into three treatment groups: nevirapine, efavirenz, or both.For the lipid analysis, which was preplanned, the researchers included only the nevirapine and efavirenz groups (417 and 289 patients, respectively). This was because the 2NN study showed that simultaneous use of nevirapine and efavirenz should be avoided—the combination is associated with increased toxicity without increased efficacy. The increase in HDL cholesterol was significantly higher with nevirapine than with efavirenz. There was a decrease in the ratio of total cholesterol to HDL cholesterol with nevirapine and an increase with efavirenz.The study does not prove, however, that the rise in HDL cholesterol seen with NNRTIs  actually leads to a reduction in coronary heart disease. “There are no vascular functional data,” says Carr, “or clinical vascular endpoint data that confirm that the statistically significant lipid differences observed are clinically significant.”The study was funded by Boehringer Ingelheim, the manufacturer of nevirapine. The authors clearly state that the company had “a nonbinding input on issues of study design and analyses” but it had “no influence on reporting of the data or the decision to publish.”Despite its limitations, van Leth and colleagues' study “moves clinicians and patients away from ‘one-size-fits-all’ antiretroviral therapy,” says Carr. “It takes us further along the path of choice of antiretroviral therapy being individualized according to other patient comorbidities and risk factors, as well as therapy simplicity and side effects.”


Although current evidence suggests that the spatial T wave axis captures important information about ventricular repolarization abnormalities, there are only a few and discordant epidemiologic studies addressing the ability of the spatial T wave axis to predict coronary heart disease (CHD) occurrence.This prospective study analyzed data from 12,256 middle-aged African American and white men and women, from the Atherosclerosis Risk in Communities Study (ARIC). Following a standardized protocol, resting standard 12-lead, 10-second electrocardiograms were digitized and analyzed with the Marquette GE program. The median follow-up time was 12.1 years; incident coronary heart disease comprised fatal and non-fatal CHD events.The incidence rate of CHD was 4.26, 4.18, 4.28 and 5.62 per 1000 person-years respectively, across the spatial T wave axis quartiles. Among women for every 10 degrees increase in the spatial T wave axis deviation, there was an estimated increase in the risk of CHD of 1.16 (95% CI 1.04–1.28). After adjustment for age, height, weight, smoking, hypertension, diabetes, QRS axis and minor T wave abnormalities, this hazard rate ratio for women fell to 1.03 (0.92–1.14). The corresponding crude and adjusted hazard ratios for men were 1.05 (95% CI 0.96–1.15) and 0.95 (0.86–1.04) respectively.In conclusion, this prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow up. It is doubtful that spatial T wave axis deviation would add benefit in the prediction of CHD events above and beyond the current traditional risk factors. Ventricular repolarization abnormalities play an important role in the determination of arrhythmia and sudden cardiac death ,2 and maTheoretical and experimental studies suggest that ventricular repolarization occurs in a nonlinear and inhomogeneous fashion -10. As aClinical studies have shown that the T wave axis reflects changes associated with autonomic adaptive or maladaptive influences , systemiTwo prospective population-based reports on T wave axis deviation measured from standard 12-lead ECGs in older populations  study examined population-based samples of residents aged 45 to 64 years from 4 communities in North Carolina, Mississippi, Minnesota, and Maryland. From the initial sample of ARIC baseline participants (n = 15,792) we excluded those not having an ECG (n = 205), and because of small numbers, self-identified ethnicity other than white or African American (n = 48). Further, we excluded participants likely to present secondary repolarization abnormalities such as myocardial infarction (self-reported or ECG-diagnosed, n = 790), use of digitalis or anti-arrhytmic drugs (n = 115), or pathologies likely to alter the measurement or interpretation of the spatial T wave axis as identified by the Minnesota code (MC), such as major Q or QS waves (MC 1.1, 1.2), ST depression or elevation (MC 4.1 to 4.4 and MC 9.2), negative T waves (MC 5.1 or 5.2), WPW pattern (MC 6.6), ventricular conduction defects , (n = 2093). Participants who had ECG evidence or history of myocardial infarction, coronary bypass, or angioplasty) (n = 162) at baseline were excluded from analysis. Participants whose angina status was positive or unknown by the Rose questionnaire were not excluded because the questionnaire's validity, especially in women, has been questioned . The finDuring a fifteen-minute supine rest, trained and certified technicians positioned disposable Ag/AgCl electrodes and recorded a supine, resting 12-lead, 10-second electrocardiogram using a MAC Personal Cardiographer™ (Marquette Electronics, Inc., Jupiter, FL). Detailed procedures used for electrode placement and skin preparations are described in the Operation Manual . ECGs weQuality assurance procedures were designed specifically for ECG acquisition and processing . These pSpatial T-wave axis was calculated as previously described  from intThe repeatability of the spatial T wave axis deviation was determined using identical study procedures in a group of apparently healthy volunteers with demographic characteristics similar to the ARIC participants. The repeatability of the spatial T wave axis deviation was high with an intra-class correlation coefficient of 0.87 . The CorThe baseline examination was carried out in 1987–1989 and consisted of a home interview of all potential cohort members including items on cardiovascular risk factors, socioeconomic factors and family medical history. After obtaining informed consent, the clinic examination consisted of medical history interview, blood pressure and anthropometric measurements, venipuncture for blood samples and a 12-lead standard electrocardiogram. Anthropometrics were measured with participants wearing scrub suits and no shoes. Height, measured to the nearest centimeter and weight, measured to the nearest pound, were used to calculate body mass index (BMI). Waist-to-hip ratio was defined as the waist girth at the umbilicus (centimeters) divided by the maximum girth of the hips (centimeters). Smoking status was defined as "current smoker" if the person answered "yes" to both of the following questions: "Have you ever smoked cigarettes? and " Do you now smoke cigarettes?". Cholesterol and triglycerides were measured enzymatically. LDL-cholesterol was calculated. Hypertension was defined as having systolic blood pressure values equal or higher than 140 mmHg, or diastolic blood pressure values equal or higher than 90 mmHg or use of blood pressure lowering medication use in the past two weeks .Deaths and hospitalization events were ascertained by annual follow-up calls to the cohort members, review of vital records, and community surveillance of hospitalized and fatal events. CHD death was defined as lacking a probable non-CHD cause, and occurring in the context of a recent myocardial infarction, chest pain within 72 hours of death, or a history of CHD. Events were classified independently by two members of the Mortality and Morbidity Classification Committee and discrepancies were adjudicated by a third member. Descriptions of event ascertainment and classification have been published ,34. For We treated spatial T wave axis deviation as a continuous variable. For the purpose of comparison, however we categorized the spatial T wave axis deviation using thresholds employed in previously published reports: normal (<30 degrees), borderline (30 to 45 degrees), and abnormal deviation (>=45 degrees). Cox proportional hazards regression  was usedThe study population consisted of 12,256 participants, of which 7,143 (58.3%) were women and 3,050 (24.9%) were African American. The mean (SD) age of the study population at baseline was 53.8 (5.7) years. The cohort was followed up for a maximum of 14.1 years [mean (SD) 11.6 (2.3) years]. During this time 653 incident coronary heart disease events occurred. Of the total number of events, 250 coronary heart disease events occurred among women (cumulative incidence 3.5%) and 403 (cumulative incidence 7.9%) among men. The average annual incidence rate was 4.6 per 1000 person-years (95% CI, 4.2–4.9 per 1000 person-years).The distribution of the spatial T wave axis deviation at baseline was similar for men and women, with a mean (SD) of 23.9 10.7) degrees in men and 23.2 (11.6) degrees in women. Several traditional cardiovascular risk factors showed graded associations across the spatial T wave axis deviation quartiles, with similar patterns among both men and women  is presented in Table We found a small but significant inverse association between spatial T wave axis deviation and heart rate. From the first to the fourth T wave axis quartile the mean (SE) values for the heart rate were: 70.24 (0.30), 69.05 (0.23), 69.20 (0.23), 67.70 (0.23) beats per minute for women and 67.26 (0.28), 65.74 (0.28), 65.56 (0.28), 66.67 (0.28) beats per minute for men.Compared to those who remained free of disease, men and women who subsequently developed CHD tended to have higher mean age, blood pressure, glucose and atherogenic lipids at baseline. They also were more likely to be smokers, have hypertension, diabetes, a higher Cornell voltage, minor T wave abnormalities and a lower mean values for the QRS axis in frontal plane. The mean QT interval and the mean T wave axis in frontal plane were not significantly different between cases and non-cases. In contrast to men, women who developed CHD had higher mean values for body mass index, waist to hip ratio, and heart rate at baseline. Spatial T wave axis deviation was only slightly increased among cases compared to non-cases (25.2 degrees versus 23.2 degrees among women, and 24.4 degrees versus 23.9 degrees among men). Mean values for the QT interval, heart rate, or T wave axis in frontal plane were not statistically different.Overall, the cumulative incidence of CHD was almost uniform across the distribution of the spatial T wave axis deviation, with an increase in the last quartile. The incidence rate was 4.26, 4.18, 4.28 and 5.62 per 1000 person-years across quartiles of spatial T wave axis deviation respectively.As Table Cox regression-modeling of the unadjusted association between CHD and spatial T wave axis deviation among women showed that for every 10 degrees increase in the spatial T wave axis deviation, there was a 1.16 1.04 and 1.28)-fold increase in the risk of CHD  and 1.27 (0.79–2.04)-fold higher risk for developing CHD compared to the normal category. After adjusting for age, height, and weight and as well for smoking, hypertension, diabetes, QRS axis and T wave minor abnormalities, the association remained statistically non-significant. The prevalence of an abnormal spatial T wave axis deviation (larger than 45 degrees) in the ARIC population was extremely low (4.5%) so these risk estimates need to be interpreted with caution.Minor T wave axis abnormalities were only slightly associated with the risk of developing a coronary heart disease event, in both men and women. The hazard rate ratios for CHD in relation to minor T wave abnormalities were 1.96 (1.37–2.81) for women and 1.31 (0.92–1.88) for men. After adjustment for age, height, weight, smoking, hypertension, diabetes and QRS axis, these estimates became 1.25 (0.86–1.80) for women and 1.02 (0.71–1.47) for men.The measured (uncorrected for heart rate) QT interval was not associated with coronary events, in either gender group. Incidence rates for coronary events in the first and the last gender-specific QT interval quartiles were 8.04 and 8.00 per 1000 person-years among men and 3.8 and 4.5 per 1000 person-years among women. Adjustment for age and heart rate  did not change this lack of association.This prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow-up. The QT interval was not associated, and minor T wave abnormalities were only weakly associated, with the incidence of coronary events after adjustment for various covariates.Given the lack of association between spatial T wave axis and coronary events in this study, several issues must be considered. Potentially, incomplete ascertainment or misclassification of CHD events could bias the results and obscure an underlying association. The excellent quality control strategies employed by the ARIC study, the highly standardized protocol for data collection, independent case ascertainment through the cohort follow-up, and the epidemiologic surveillance system in place at each of the four ARIC study communities make the possibility of misclassification of coronary heart disease very unlikely.Inadequate statistical power to detect a meaningful association is not likely, due to the large sample size, long follow-up and relatively large number of events. The minimum effect size that the present study could have missed was computed by dichotomizing spatial T wave axis deviation, then contrasting "borderline" and "abnormal " deviation with the "normal" category (nQuery Advisor, version 4.0 ). Given Our results do not confirm the findings of the Rotterdam study  and the The discrepancy between our results and those reported in previous articles has several potential explanations, involving true variation between study populations  and different patterns of confounding by other cardiovascular risk factors.post hoc analyses under different scenarios.Compared to participants in the present study, those in studies that found a significant association between T wave axis deviation and CHD were older (Rotterdam study and CHS), and more likely to have a history of myocardial infarction, angina pectoris (Rotterdam study) or silent (ECG-diagnosed) myocardial infarction and hypertension at baseline (CHS study). These study populations also had more ECG evidence of ischemia and infarction as reflected by the presence of Q-waves and major ST-T abnormalities (Rotterdam study) or T wave inversion (CHS study) but this was not accounted for these confounders in the analysis. By contrast, the MRFIT study and the present ARIC study excluded these participants, preserving only those with minor T wave abnormalities, and found only weak or non-significant associations with CHD events. Studies reporting stronger associations also had shorter follow-up time. To explore the potential impact of these dissimilarities, we performed several In comparing individuals without CHD at baseline with prevalent CHD cases at baseline, the spatial T wave axis deviation was significantly different: 26.66 (16.81) and 44.88 (27.29) degrees respectively . The entire distribution of the spatial T wave axis deviation among those with CHD at baseline was shifted to the right of the distribution of participants without CHD at baseline. This difference persisted statistically significant even after adjustment for age and several standard cardiovascular risk factors  with adjusted mean values of 26.79 and 41.47 degrees respectively. However, among those without CHD at baseline in our study, there were various conditions or markers of clinical interest, some of established predictive value, such as ST segment depression, negative T waves or atrial fibrillation. In the clinical setting, the need for improved prediction and discriminatory ability of a new potentially useful marker is particularly useful in the absence of such markers. After excluding Minnesota codes representing secondary repolarization abnormalities, this difference was markedly attenuated, with mean spatial T wave axis deviation of 23.84 (12.36) for those free of CHD and 24.08 (15.34) degrees for those with CHD. This suggests that spatial T wave axis has no ability to discriminate between CHD cases and CHD-free individuals beyond and above secondary repolarization abnormalities. This might be one of possible explanations for the discrepant results between different cohort studies exploring T wave axis, as different studies have used different exclusion criteria.As an alternative to the exclusion of Minnesota codes reflecting secondary repolarization abnormalities, we performed a separate analysis this time including QRS duration as a covariate. The relationship of the spatial T wave axis deviation with incident CHD events followed the same pattern as in the restricted dataset, i.e. a slight increase in the incidence rates in the fourth quartile of the T wave axis, for each gender. The crude hazard rate ratios (95%CI) for incident CHD events for a 10 degree increase in the spatial T wave axis were 1.18 (1.13–1.23) for women and 1.14 (1.09–1.19) for men. After adjustment for the same covariates as in the restricted data set and for the QRS duration, the hazard rate ratios (95%CI) were 1.05 (0.99–1.16) and 1.04 (0.98–1.1).Ventricular repolarization abnormalities are closely related to the propensity for arrhythmia. Although none of the published population-based studies on T wave axis deviation attempted to ascertain arrhythmic events, it is plausible that the arrhythmic component of the CHD events was the driving force of the observed associations in some published studies. This suggestion is supported by the fact that in both, Rotterdam and CHS study, the association between T wave axis and coronary events was stronger for fatal than non-fatal events. Similarly, the spatial QRS-T angle was recently reported to be associated with fatal but not with non-fatal cardiac events . To expliii) similarly, we repeated the analysis without excluding those with negative T waves corresponding to MC 5.2, restricted to the age group greater than 55 years, restricted to participants with both hypertension and diabetes at baseline, restricted to CHD events occurring after a shorter follow-up (4–6 years). While the incidence rates were larger in absolute value in each T wave axis quartile, in relative terms no association with CHD events was detected.Experimental models to link the spatial T wave axis deviation with coronary evens are still sparse, but it can be speculated that an abnormal spatial T wave axis may possibly reflect disturbances in the repolarization process caused by subclinical myocardial disease, with or without an increased propensity for arrhythmic events, and thus an increased risk for fatal cardiac events. This is supported by the fact that subclinical disease is likely more prevalent in the older population of the CHS and Rotterdam study (which found an association between T wave axis and incident coronary events) than in the younger populations of the ARIC and MRFIT studies (both reporting a lack of association). Some indicators of subclinical disease such as the left ventricular hypertrophy (LVH) are clearly more prevalent in the Rotterdam than in the ARIC study. The presence of detected or undetected subclinical disease  may lead to ventricular electrical instability and a higher proportion of cardiac death related to primary arrhythmic events in a population. Thus, measured or unmeasured, subclinical disease may play a confounding role, and suggests one more explanation for the discrepancy of results between different studies.The ARIC, CHS and MRFIT study protocols all used the same definition of the spatial T wave axis deviation, while the Rotterdam study used the frontal plane T wave axis. There are no data available to explain the discrepancy of results solely on the basis of different measurement of the T wave axis.The comparison of spatial T wave axis deviation with its temporal counterpart, the QT interval raises an intriguing question. Incident coronary events were strongly associated with T wave axis in the cohort of older adults of the Rotterdam study, which examined a population of older adults in whom a prolonged QT interval was previously reported to be predictive. In the current work, neither spatial T wave axis deviation nor the QT interval (as measured or heart-rate corrected by different formulae) was associated with incident coronary events.In our study, a statistical interaction between QT interval and spatial T wave axis deviation was not detected within the range of the QT interval in this population (data not shown). It is important to mention that in the Rotterdam study the mean QT interval  was considerably larger than in the present study. From electrophysiological point of view, an abnormal ventricular repolarization can alter the QT interval, the T wave axis or both. It is therefore possible that a given magnitude of an altered spatial T wave axis in the presence of a QT interval within "the normal" ranges, may not be sufficient to elicit any causal association with incident coronary events.Our study found an inverse association between spatial T wave axis deviation and heart rate. Heart rate influences have been reported on the frontal plane T wave angle  and on tOur study confirms the results of other studies ,19 in fiThe present study has several strengths. The highly standardized data collection procedure and ECG protocol increase the internal validity of the findings. The ECG recording was performed by trained technicians using a standardized protocol for lead placement. The events ascertainment in the ARIC study is ensured by a highly standardized quality assurance protocol. The inclusion of apparently healthy participants in a population-based study, of African American and white men and women, the large sample size and the geographic distribution of the study population enhance generalizability.This study also has some limitations. The narrow age range precluded an extensive investigation of the role of age. The study also was limited by the lack of accurate measurements for characterizing the elliptical shape of the thoracic cavity. Constitutional and anthropometric variables such as the chest transverse diameter or chest shape have been associated with a shift of the T wave axis  or changVentricular replarization is a complex process, which cannot be fully captured by only a limited number of indexes such as QT interval and spatial T wave axis, exploring either the temporal aspects or the general direction of this process. Other indexes or more complex descriptors of the T wave loop have been described to be accurate markers of an altered repolarization . The spaIn conclusion, this prospective, population-based, bi-ethnic study of men and women free of coronary heart disease at baseline shows that spatial T wave axis deviation is not associated with incident coronary events during long-term follow up. It is doubtful that T wave axis deviation would add benefit in the prediction of CHD events above and beyond the current traditional risk factors. The QT interval was not associated with incident coronary events, and minor T wave abnormalities were only weakly associated among women.The author(s) declare that they have no competing interests.GV performed the overall study design, statistical analysis and manuscript preparation.PR designed the data collection protocol, participated in the interpretation of the ECG data, provided comments on the manuscript.RP participated in the quality control of the ECG data, participated in the interpretation of the findings and with comments on the manuscript.EAW participated in the interpretation of the findings and with comments on the manuscript, provided assistance in the manuscript preparation.LEC provided oversight of the statistical analysis, verified the appropriateness of statistical models, provided assistance in the manuscript preparation.ARF participated in the interpretation of the findings and with comments on the manuscript, provided assistance in the manuscript preparation.WDR collaborated in the study design, participated with comments on the manuscript.ZMZ managed the automated coding of ECGs and the quality assurance.RC participated with comments on the manuscript.GH collaborated in the design of the study, provided comments on the manuscript.The pre-publication history for this paper can be accessed here: