A couple of years ago, a paper was published in a high-profile journal that challenged a long-established model of cell signaling. While researchers in the field mostly greeted the results with skepticism, some went into the lab to investigate the discrepancy. Many elements of this pathway, called the Wnt pathway, have been well characterized. The standard model of Wnt signaling holds that when the Wnt protein binds to its receptor, it initiates a labyrinthine signaling cascade that sends a protein called β-catenin into the cell's nucleus where, together with a protein complex, it initiates transcription. In the absence of this signal, β-catenin binds to an inactivating complex in the cytoplasm and is targeted for degradation. The paper that disputed this view suggested that β-catenin can effect gene expression without entering the nucleus and that it can activate the Wnt pathway while tethered to the cell membrane.Before that paper was published, Nicholas Tolwinski and Eric Wieschaus had shown that β-catenin, also known as Armadillo (Arm) in the fruitfly, is sent into the nucleus in response to Wnt signaling. Upon entering the nucleus, Arm interacts with a second protein complex to activate transcription. Now Tolwinski and Wieschaus have reexamined the function of Arm in the fruitfly and have demonstrated that the pathway “in fact does depend on the nuclear localization of β-catenin.” While their paper was in the final stages of acceptance, the dissenting paper was retracted, after it was learned that the results had been fabricated. Tolwinski and Wieschaus' findings confirm what had already been known about Arm's role in Wnt signaling and also fill in important details about how it works.Multicellular organisms rely on elaborate communication networks of signaling proteins and enzymes to exchange information between cells. The Wnt signaling pathway regulates the expression of a host of different genes during embryogenesis to control body patterning and cell differentiation in organisms from fruitflies to mammals. Miscommunications in this tightly regulated pathway contribute to a variety of developmental defects and cancers.In the developing fruitfly, Wnt signaling is normally restricted to the front of each larval segment, where it produces a smooth surface; the rear of the segments, where Wnt signaling is absent, is hairy. If Wnt signaling is overexpressed, it produces fruitfly larvae with only smooth segments; lack of Wnt signaling produces hairy segments. Using the smooth phenotype as a measure of Wnt signaling, Tolwinski and Wieschaus delved deeper into the role of Arm in this signaling process.These experiments are complicated because Arm functions not just in Wnt signaling, but also in cell adhesion. The trick is to make the endogenous Arm (the version encoded by the fly genome) defective for signaling, while leaving the cell adhesion functions fairly normal. Set against this “background,” an additional Arm protein is expressed that is tethered to the membrane; it still retains the protein domains required for signaling, but it's stuck on the inside of the membrane and can't move into the nucleus. In this background, any signaling in response to Wnt must mean that Arm can signal to the nucleus without actually having to get inside. Tolwinski and Wieschaus prove—again—that this is not the case. They do this by showing that the weak, medium, and strong endogenous Arm mutants—these classifications reflect the severity of the mutations' effects—have different effects on signaling in the presence of the membrane-tethered Arm.It's clear, they argue, that tethered Arm cannot signal on its own and must somehow be helping the weaker mutants signal. To further investigate how the tethered Arm activates the endogenous mutants, Tolwinski and Wieschaus developed two new and “cleaner” Arm mutants that impair Arm's signaling ability but have no effect on its cell adhesion function. The tethered Arm could not produce a completely smooth phenotype with these nonsignaling endogenous mutants. These experiments, the authors conclude, indicate that the tethered form of Arm produces its transcriptional effects by activating the endogenous Arm protein. Normal activation of the pathway liberates Arm proteins from the inactivating complex, which allows them to enter the nucleus and activate transcription. Tethered Arm appears to accomplish this by sequestering the inactivating complex at the cell membrane, preventing it from interfering with endogenous Arm.Even though Tolwinski and Wieschaus started these experiments based on what turned out to be fabricated results, their investigations produced valuable contributions. They not only reaffirm the standard model of Wnt signaling, but reveal important new insights into the workings of a major player in the pathway.


Anopheles mosquitoes may have important implications for malaria vector control especially considering the current trend for large scale pyrethroid-treated bednet programmes. Screening Anopheles gambiae populations for the kdr mutation has become one of the mainstays of programmes that monitor the development of insecticide resistance. The screening is commonly performed using a multiplex Polymerase Chain Reaction (PCR) which, since it is reliant on a single nucleotide polymorphism, can be unreliable. Here we present a reliable and potentially high throughput method for screening An. gambiae for the kdr mutation.A single base pair mutation in the sodium channel confers knock-down resistance to pyrethroids in many insect species. Its occurrence in kdr alleles in the homozygous and heterozygous states, and was optimized for use in low-tech developing world laboratories. Results from the HOLA were compared to results from the multiplex PCR for field and laboratory mosquito specimens to provide verification of the robustness and sensitivity of the technique.A Hot Ligation Oligonucleotide Assay (HOLA) was developed to detect both the East and West African kdr mutation, gives a bright blue colouration for a positive result whilst negative reactions remain colourless. The results are apparent within a few minutes of adding the final substrate and can be scored by eye. Heterozygotes are scored when a sample gives a positive reaction to the susceptible probe and the kdr probe. The technique uses only basic laboratory equipment and skills and can be carried out by anyone familiar with the Enzyme-linked immunosorbent assay (ELISA) technique. A comparison to the multiplex PCR method showed that the HOLA assay was more reliable, and scoring of the plates was less ambiguous.The HOLA assay, developed for detection of the kdr alleles in the homozygous and heterozygous states from fresh or dried material using several DNA extraction methods. It is more reliable than the traditional PCR method and may be more sensitive for the detection of heterozygotes. It is inexpensive, simple and relatively safe making it suitable for use in resource-poor countries.The method is capable of detecting both the East and West African  Anopheles gambiae, the major vector of malaria in sub-Saharan Africa, threatens to compromise the successful use of insecticide-treated materials [An. gambiae sensu stricto in West Africa [The successful trials of pyrethroid insecticide-treated nets for malaria control in various endemic settings has led to the Roll Back Malaria initiative adopting the approach as one of the cornerstones of its malaria control programmes -3. Howevaterials . Resistat Africa  and has t Africa . Whilst t Africa , there it Africa .kdr allele. This allele encodes a modified voltage-gated sodium channel that has reduced sensitivity to DDT and pyrethroids. Molecular studies identified a single point mutation in the kdr allele that causes an amino acid substitution in domain II of the protein [An. gambiae; the first causes a leucine to phenylalanine amino acid change and has been found in several West African countries [Anopheles mosquitoes is not yet fully understood. However, monitoring its frequency, as a rapid indicator of the development of resistance, should be an integral component of insecticide resistance management programmes.One allele commonly associated with resistance to permethrin is the knock-down resistance or  protein . Two difountries -11, whilountries ,12. The kdr mutations involves a multiplexed PCR technique. Single Nucleotide Polymorphism (SNP) detection is problematic with simple PCR approaches, requiring the use of highly toxic reagents [kdr resistance allele frequencies in An. gambiae, it is broadly applicable where target-site insensitivity is an important mechanism of resistance to insecticides and to chemotherapeutics.The most commonly used method for identifying the reagents  or prohikdr mutation), Kisumu , and Odumasi . Adult females were stored at -20°C before extraction. Field caught specimens were collected using resting catches from Asembo in western Kenya in May 2004, and by pyrethrum spray collections in Odumasi, Ghana in June 2003. Samples were dried over silica gel for later analysis.Specimens were obtained from laboratory colonies of RSP , 0.3 μM each primer (Qiagen), Taq DNA polymerase 0.034 U/μl (Qiagen 201203). Reaction conditions were 94°C for 4 min, 25 cycles of 94°C for 25 sec, 56°C for 20 sec, 72°C for 8 sec; and a final extension step of 72°C for 10 min  was set up for each oligo pair. Four reactions were set up for each PCR sample to test for the East and West resistant alleles and the susceptible allele . The reaction conditions were 95°C for 5 min, 25 cycles of 94°C for 1 min, 58°C for West African kdr detection or 60°C for East African kdr detection for 2 min; with a final hold at 4°C. Ligated products were kept at 4°C in the dark and used as soon as possible for SNP analysis.3 μl of PCR product from the above reaction was used in a hot ligation with Detector and Reporter oligonucleotides (MWG Biotech) Table . Aliquot96-well plates (VWR 402 200 402) were prepared using 100 μl of 5 μg/ml streptavidin (Sigma S4762) per well. The plate was left to dry overnight and then washed 4 times in 250 μl of 1 x PBS with 0.1% v/v Tween 20. Buffer was removed by tapping the plate upside down and 200 μl of blocking solution  added for 1 hour. Four more washes of 250 μl with PBS were carried out before plates were covered with a plastic seal and stored at 4°C for up to one week.20 μl of TNE  was added to the hot ligation reaction and then all 40 μl was placed in a well of the streptavidin plate and allowed to incubate at room temperature for 30 min in the dark. The ligation reaction was carefully removed with a multichannel pipette and the plate washed twice in 250 μl of freshly prepared wash buffer 1  and then twice in 250 μl of wash buffer 2 .40 μl of 75 mU/ml HSP-conjugated antifluorescein Ab (Roche 1 426 346) solution in 1% w/v BSA solution was placed in each well and incubated at room temperature for 30 min. The plate was then washed three times in 250 μl of wash buffer 2. All buffer traces were removed by tapping the plate upside down on a paper towel and 100 μl of room-temperature TMB solution (Roche BM Blue Pod Substrate 1 484 281) added. At least 5 min were allowed for the colour to develop before plates were scored. Plates were read at 680 nm in a Molecular Devices Versa Max plate reader to provide a quantitative method of scoring which could be compared to the visual method of scoring to check reliability.kdr allele. Heterozygotes are easily distinguishable. The protocol presented here for kdr detection is reliable and gives unambiguous results (Table An. gambiae from East Africa compared to the commonly used PCR multiplex approach. The genotype was unambiguously determined by the HOLA technique, whereas the PCR results were more difficult to interpret and often required a repeat reaction (Table kdr allele is rare in the Kenyan population [A schematic of the HOLA approach is given in Figure ts Table . Visual on Table . There won Table . It is bpulation  and so wThe HOLA method allows for over 40 samples to be screened on a single microtitre plate. As shown in Figure An. gambiae mosquitoes to be characterized for the East and West African kdr mutations. Homozygotes and heterozygotes can be easily distinguished using low cost equipment and a simple methodology which makes this technique suitable for use in resource-poor countries. In our hands the method is more reliable than the current multiplex PCR approach, less ambiguous and may be more sensitive for the detection of heterozygotes.The HOLA method allows fresh and stored DNA – Deoxyribonucleic acid.ELISA – Enzyme-linked immunosorbent assayHOLA – Heated oligonucleotide ligation assay.Kdr – Knock down resistance.PCR – Polymerase chain reaction.SNP – Single nucleotide polymorphism.kdr mutation and drafted the manuscript. HR conceived of the study and participated in its design. NPR carried out the multiplex PCR. PJM and EDW helped draft the manuscript. WCB developed the HOLA technique. MJD participated in the design of the study and substantially helped draft the manuscript.AL developed the HOLA method for the


The primate-specific Alu elements, which originated 65 million years ago, exist in over a million copies in the human genome. These elements have been involved in genome shuffling and various diseases not only through retrotransposition but also through large scale Alu-Alu mediated recombination. Only a few subfamilies of Alus are currently retropositionally active and show insertion/deletion polymorphisms with associated phenotypes. Retroposition occurs by means of RNA intermediates synthesised by a RNA polymerase III promoter residing in the A-Box and B-Box in these elements. Alus have also been shown to harbour a number of transcription factor binding sites, as well as hormone responsive elements. The distribution of Alus has been shown to be non-random in the human genome and these elements are increasingly being implicated in diverse functions such as transcription, translation, response to stress, nucleosome positioning and imprinting.We conducted a retrospective analysis of putative functional sites, such as the RNA pol III promoter elements, pol II regulatory elements like hormone responsive elements and ligand-activated receptor binding sites, in Alus of various evolutionary ages. We observe a progressive loss of the RNA pol III transcriptional potential with concomitant accumulation of RNA pol II regulatory sites. We also observe a significant over-representation of Alus harboring these sites in promoter regions of signaling and metabolism genes of chromosome 22, when compared to genes of information pathway components, structural and transport proteins. This difference is not so significant between functional categories in the intronic regions of the same genes.Our study clearly suggests that Alu elements, through retrotransposition, could distribute functional and regulatable promoter elements, which in the course of subsequent selection might be stabilized in the genome. Exaptation of regulatory elements in the preexisting genes through Alus could thus have contributed to evolution of novel regulatory networks in the primate genomes. With such a wide spectrum of regulatory sites present in Alus, it also becomes imperative to screen for variations in these sites in candidate genes, which are otherwise repeat-masked in studies pertaining to identification of predisposition markers. In the post genome sequence era, repetitive sequences, erstwhile considered junk and devoid of function, are increasingly being implicated in many cellular functions, genome organization and diseases -8. Alu rAlus have been shown to harbor a number of regulatory sites like hormone response element (HRE), and a couple of ligand activated transcription factor binding sites -24. ThesAlus originally demonstrated to have non uniform distribution on the chromosomes through banding studies ,34 have Identification and analysis of various permutations and combinations of these regulatory elements in otherwise conserved repetitive Alus are mostly excluded from genetic analysis. Since, Alus occupy a tenth of the human genome, it is imperative to identify those, which might assume function in the proper context. Our primary aim in this analysis is to find out if any bias exists in the distribution of transcriptional regulatory sites in Alus of various evolutionary ages and their distribution with respect to the functional classes of genes.As a first step toward examining the role of these regulatory sites, we mapped their most probable positions on Alus, using in house developed algorithms Figure . This waNearly all the analyzed regulatory sites for RNA polymerase II (RNA pol II) are distributed in the region between A- Box and B-Box with more clustering near the B-Box region Figure . There iin vitro as well as in vivo studies in the 'B' Box that 'G' and 'T' residues at the 1st and 3rd positions respectively are very critical for it's functioning [th position in this case is mutated to "T" in the older families. The Yb8 family that has been reported to be transcriptionally and retropositionally active amongst the younger subfamilies, retains the B'-Box element in a significant fraction. This suggests that even though retropositionally competent younger Alus are hypothesized to be transcriptionally active, only a minority retains consensus B'-Box. It is possible that the enhancing activity of the A Box is sufficient to drive transcription from the weaker B'- Box in the younger subfamilies. Our findings corroborates well with an earlier study in which presence of all subfamilies in the RNA polymerase III driven Alu transcript pool was reported [Majority of Alu retroposition has ceased at least 30 million years ago and only a few Alu subfamilies are still active ,17,41. Tctioning . Our anactioning  for the reported . Additioreported . Additioreported ,49, tranAlus have been demonstrated to exert effects at transcription, post-transcription as well as at the translation level. In an earlier study on complete chromosomes 21 and 22, we have demonstrated that the Alu elements are clustered in genes of signaling, metabolic and transport proteins and rarely present in the structural and information proteins . This clGene inversions, duplications and formation of pseudogenes have been extensively reported to be mediated both through retrotransposition as well as recombination of Alus. This, in many cases, has also been associated with aberrant gene expression. For instance, presence of AML sites in an Alu upstream of MPO gene, has been first demonstrated to be associated with Acute Myelocytic Leukemia . This isAnalysis of regulatory sites within Alus suggests that a polymorphic Alu has the potential to transpose and recombine which allows it to integrate at random sites in the genome. They also harbour potential regulatory sites, which could evolve to become accessory sites for RNA pol II transcription as revealed by their clustering in older subfamilies. Further, the Alu sequence due to acquisition of novel functions could form a part of the transcription repertoire involved in the regulation of the downstream /associated genes and create novel regulatory networks Figure . These rComparison of sequences in the regulatory regions of many homologous genes in human have shown accumulation of Alus, not only post divergence from non-human primates but also during primate evolution . PerhapsCurrently, Alus are repeat-masked in all studies pertaining to identification of predisposition markers in complex disorders. With such wide spectrum of nuclear receptors, which play a major role in maintaining normal physiological state and affect as diverse processes as development, reproduction, general metabolism, residing in Alus, it therefore becomes imperative to screen for variations in these sites. This might have important consequences in the candidate genes for those complex diseases that are triggered in response to hormonal imbalances as well as other environmental cues.126 polymorphic Alu sequences cited in literature ,40 were Information about the regulatory sites and their sequences was collected from various literature sources Table . CharactTwo different programs were written in order to locate the most probable biologically significant regions. A local alignment based program, Xalign, was implemented in C++, Red Hat 7.3 based Linux. This program finds the probable sites by aligning the consensus of regulatory site with the query sequence. Multiple queries with a size upto 600 nucleotides can be taken at a time. Another program, Promotif, was implemented in C++, Red Hat 7.3 based Linux, using the probabilistic modeling approach. It uses the position weight matrix, normalization of the positions with conservation index , and inter-nucleotide dependence in terms of transition matrix to find out the sites. Position weight matrices were generated using Gibbs Motif Sampler, for every site included in the program. The sequences for position weight matrix generation were carefully selected based on the sequence and length reported for each binding site. The final length for search was fixed at the lowest length observed. This provides element specific matrix with lesser chance for the selection on non-RE regions. For the sites analyzed, it had an in built transition matrix, position weight matrix and conservation index. Batch analysis of over a thousand Alu sequences can be performed with this program.Using the annotated sequences from literature as well as from NCBI web page, training set for the probabilistic model was created. Training was done for approximately 70% sequences and rest of the sequences were taken as test set. Details of the program along with the equations used are available on request.About 126 recently integrated Alus from younger subfamilies were searched in the human genome using BLASTn at NCBI server and regulatory sites were mapped in these regions using the programs discussed above.Alus in the promoter regions and intronic regions of functionally classified genes  of chromRS developed the algorithms and programs for identifying regulatory and significant regions, carried out the analysis of distribution of these sites in Alu subfamilies, association analysis and drafted the manuscript. DG was involved in chromosome 22 analyses. SKB participated in the design of the study. MM conceived of the study, participated in its design, analysis, coordination and manuscript preparation. All authors read and approved the final manuscript.The analysis over the promoter and intronic regions has been performed through the data given in the supplementary table file, supplementary table 3_ravishankar et al. Format: .xls. For human chromosome 22, the data contains the accession number, associated Alu family, the respective positions, functional class of the region and further details, for each associated regulatory element found within the Alu repeats in the 5' flanking promoter and intronic regions. The zipped file name is supplementary 1.zip. Details about programs used are on request for academic users.Click here for file


Melanoma patients vaccinated with tumor-associated antigens frequently develop measurable peptide-specific CD8+ T cell responses; however, such responses often do not confer clinical benefit. Understanding why vaccine-elicited responses are beneficial in some patients but not in others will be important to improve targeted cancer immunotherapies.We analyzed peptide-specific CD8+ T cell responses in detail, by generating and characterizing over 200 cytotoxic T lymphocyte clones derived from T cell responses to heteroclitic peptide vaccination, and compared these responses to endogenous anti-tumor T cell responses elicited naturally (a heteroclitic peptide is a modification of a native peptide sequence involving substitution of an amino acid at an anchor residue to enhance the immunogenicity of the peptide). We found that vaccine-elicited T cells are diverse in T cell receptor variable chain beta expression and exhibit a different recognition profile for heteroclitic versus native peptide. In particular, vaccine-elicited T cells respond to native peptide with predominantly low recognition efficiency—a measure of the sensitivity of a T cell to different cognate peptide concentrations for stimulation—and, as a result, are inefficient in tumor lysis. In contrast, endogenous tumor-associated-antigen-specific T cells show a predominantly high recognition efficiency for native peptide and efficiently lyse tumor targets.These results suggest that factors that shape the peptide-specific T cell repertoire after vaccination may be different from those that affect the endogenous response. Furthermore, our findings suggest that current heteroclitic peptide vaccination protocols drive expansion of peptide-specific T cells with a diverse range of recognition efficiencies, a significant proportion of which are unable to respond to melanoma cells. Therefore, it is critical that the recognition efficiency of vaccine-elicited T cells be measured, with the goal of advancing those modalities that elicit T cells with the greatest potential of tumor reactivity. State-of-the art analysis of patients' response to melanoma vaccines yields lessons about cancer vaccines and rationale vaccine design in general The immunotherapy of cancer holds promise in harnessing the host immune response to specifically target tumor cells without harming normal tissues. Strategies involve adoptive cellular therapy or active immune induction (commonly referred to as “cancer vaccination”). Cancer vaccines may consist of whole tumor cells or tumor lysates, but identification of tumor-associated antigens (TAAs) over the past decade has made possible the use of specific proteins or peptides as cancer vaccines. The anti-tumor potential of TAA-specific CD8+ T cells has been illustrated by the demonstrated capacity of adoptive T cell therapy to reduce tumor size . While eVarious reasons for the paradoxical coexistence of cancer cells and TAA-specific T cells within patients have been proposed ,19. One While isolated T cell clones with low RE have indeed been generated from melanoma patients following heteroclitic peptide vaccination, the proportion of vaccine-elicited T cell responses these cells represent in vivo is not clear. If predominantly high-RE, tumor-cytolytic T cells are generated, then a small fraction of low-RE T cells generated would be of little consequence. However, if predominantly low-RE T cells are generated, then this low proportion of high-RE T cells may be an important factor in the observed lack of clinical effectiveness of current cancer vaccination strategies. To address this important issue, we undertook a systematic examination of the complexity of T cell responses induced by heteroclitic peptide vaccination, and compared these responses to endogenous anti-tumor T cell responses which develop in some patients. Typically, responses to vaccination are examined following in vitro expansion from patient samples, which may alter the composition of cells and consequently not reveal the proportion of cells in vivo having sufficiently high RE to lyse tumor targets. Although staining with peptide–MHC tetramers provides a direct estimate for the number of TAA-specific T cells present in vivo, and intensity of tetramer staining has been employed as a parameter for isolation of high-RE, tumor-lytic T cells , staininTo analyze and compare T cell responses in melanoma patients on a single-cell level, we generated and examined a large number of cytotoxic T lymphocyte (CTL) clones derived from post-vaccination or endogenous anti-tumor T cell responses. Each clone was analyzed for T cell receptor (TCR) variable chain beta (VB) expression, RE, and ability to lyse melanoma targets. Importantly, these clones were generated directly ex vivo through tetramer-guided sorting, which minimizes the selection bias that could be introduced by prior in vitro expansion. Therefore, data from these clones could be taken to estimate the complexity of the responses in vivo.9/l or more, hemoglobin of 90 g/l or more, and total white blood cell count of 3.0 × 109/l or greater. Tests for human immunodeficiency virus, hepatitis C antibody (Ab), and hepatitis B surface antigen were required to be negative, and all patients were HLA-A2 antigen positive by a microcytotoxicity assay. All patients were required to comprehend and sign an informed consent form approved by the National Cancer Institute  and the Los Angeles County/University of Southern California Institutional Review Board. Analysis of the patient samples was approved by Stanford University's Institutional Review Board. Peripheral blood mononuclear cell (PBMC) samples were isolated from patients after vaccination with the heteroclitic peptides MART 26–35 (27L) (ELAGIGILTV) and gp100 209–217 (210M) (IMDQVPSFV) at the University of Southern California Norris Cancer Center . Clinical-grade peptides used were provided by the Cancer Therapy Evaluation Program of the NCI under an Investigational New Drug application BB 6123 held by the NCI. Immunizations (1 mg of each peptide emulsified with incomplete Freund's adjuvant) were administered every 2 wk for 8 wk, then every 4 wk for 12 wk, and then once 8 wk later. PBMC samples were collected 4 wk after the final immunization and stored at −130 °C. Samples were thawed the day before an experiment for overnight culture in CTL medium. The following morning, viable cells were isolated by ficoll density centrifugation, washed, and resuspended to the appropriate concentration in a solution of 90% Iscove's Modified Dulbecco's Medium (IMDM) and 10% fetal bovine serum (FBS).All patients had resected stage III or IV melanoma, as determined by the 1988 modified American Joint Commission on Cancer staging system. They were required to have a magnetic resonance imaging or computed tomographic scan of the head and computed tomographic scans of the chest, abdomen, and pelvis showing no indication of disease within 4 wk of therapy to verify that they were clinically free of melanoma. Eligibility criteria included age 18 y or older, creatinine of less than 180 μmol/l, bilirubin of less than 110 μmol/l, platelet count of 100 × 10For isolation and detection of peptide-specific T cells, patient PBMC samples were stained and analyzed by fluorescence-activated cell sorting (FACS) as previously described . BrieflyThe HLA-A*0201-positive melanoma lines Malme-3M and A375 and the T2 cell line were purchased fromATCC  and maintained according to instructions provided by theATCC. The HLA-A*0201-positive melanoma line mel526 was obtained from the Surgery Branch of the NCI. While Malme-3M and mel526 express both MART and gp100, A375 does not express MART or gp100 and served as a negative control. Expression (or lack thereof) of these antigens by each cell line was further confirmed by immunohistochemical staining. Cells were trypsinized using Trypsin/EDTA solution  before use. T2 cells were HLA-A2.1+ and were pulsed prior to assays with peptides indicated in the text.7/ml) in CTL medium.Effector cells, which include clones, cell line, and PBMC samples, were frozen and analyzed in batches. The cells were thawed the day before an experiment for overnight culture in CTL medium. The following morning, viable cells were isolated by ficoll density centrifugation, washed, and resuspended to the appropriate concentration  in 100% EtOH, 100 μl of target cells, 100 μl of effector cells, and 1 μl of CD107-APC Abs. The cells were mixed well using a multichannel pippetor. The plate was centrifuged at 300g for 1 min to pellet cells, then placed into an incubator at 37 °C for 4 h. After the incubation, the plates were centrifuged to 500g to pellet cells, and the supernatant was removed. Cell–cell conjugates were disrupted by washing the cells with PBS supplemented with 0.02% azide and 0.5 mM EDTA, and mixed vigorously, then stained with additional Abs.All assays were done at least twice, with duplicates for each condition. The effector to target (E:T) ratio used was generally 1:2, with 2 × 102 to allow for growth. Potential clones became visible around day 14 and were then transferred to 24-well plates containing 1 ml of CTL medium with 100 units/ml IL-2. Wells were selected based on cell confluency for expansion and further analysis. Clones confirmed to be tetramer-positive were expanded in T-25 flasks containing irradiated JY cells and fresh PBMCs in 25 ml of CTL medium containing PHA. IL-2 was added to a final concentration of 50 units/ml on day 1 and then every 2 d thereafter for 2 wk.CD8+ T cell clones were derived by FACSorting individual tetramer-positive cells from PBMC samples prepared for flow cytometry as described above. CD8+ tetramer-positive T cells were sorted under sterile conditions into 96-well plates, one cell per well, using a FACS Vantage (Becton Dickinson). Wells contained 100 μl of CTL IMDM, with 10% FBS, 2% human AB sera, and penicillin, streptomycin, and L-glutamine, supplemented with 100 units/ml IL-2. Sorted cells were expanded in vitro using standard protocols. Briefly, irradiated feeder cells (JY cells and fresh PBMCs) were added to wells containing the sorted T cells, and the 96-well plates were incubated at 37 °C with 7% CO51Chromium, washed, and resuspended to 105 cells/ml. One hundred microliters of target cells were incubated with 100 μl CTL clones at 10:1 E:T ratio for 4 h. Percent specific release of 51Chromium from target cells was calculated from 40-μl cell-free supernatants.Target cells were as described above under CD107 Mobilization Assays, and were labeled overnight with −7 M and decreasing by log steps to 10−13 M. T cell clones were incubated with T2 targets at 10:1 E:T ratios for 4 h, then chromium release was measured and percentage cytotoxicity calculated by standard methods. Prior to each cytotoxicity assay, clones underwent ficoll-hypaque centrifugation to remove dead feeder cells and were determined to be greater than 80% CD8+ tetramer-positive T cells by FACS. The E:T ratio was based upon live T and target cells. For each T cell clone, percent cytotoxicity was plotted against peptide concentration. The peptide concentration at which the curve crossed 40% cytotoxicity was defined as the RE of that clone [Chromium-labeled T2 targets were pulsed with a range of peptide concentrations, generally starting at 10at clone .g, and incubated 4 h at 37 °C. Percent specific release of 51Chromium was calculated from 40 μl of cell-free supernatant.Cells were isolated directly from PBMCs from patient 422 by FACS as described above. Cells were collected in microfuge tubes containing 1 ml of ice-cold 90% IMDM with 10% FBS. Collected cells were washed and resuspended to 83,300 cells/ml in 90% IMDM with 10% FBS. Targets were prepared as described above and resuspended to 8,300 cells/ml in 90% IMDM with 10% FBS. A total of 2,500 sorted cells (30 μl) and 250 target cells (30 μl) were transferred to a microcentrifuge tube , centrifuged 1 min at 200RNA was extracted from clones and tetramer-positive cells using TRIzol  and reverse-transcribed into cDNA using SuperScript II Reverse Transcriptase (Invitrogen). PCR was performed using 34 different 5′ primers that specifically amplify all functional TCR VB genes. Most of the 5′ primers used have been previously described . These pA standard software package  was used to provide descriptive statistical plots. Barcharts were provided with standard errors on them. Linear plots were provided with standard errors computed at each point. A linear regression (using least squares) of percent specific lysis on recognition efficiency is shown in To address the complexity of T cell responses against melanoma in vivo, patients with vaccine-induced or endogenous TAA-specific responses were selected. In recent cancer vaccine trials ,4,5, manPatient 422 had the largest detectable TAA-specific CD8+ T cell response (2.5% G209–2M-tetramer-positive) and thus sufficient numbers for examination of lytic function immediately following isolation. To test whether peptide-vaccine-induced T cell responses were functionally active directly ex vivo, T cells isolated by G209–2M-tetramer-guided cell sorting from patient 422 were tested for lysis of peptide-pulsed and melanoma target cells in microcytotoxic assays B. The diTo assess the functional status of the smaller TAA-specific CD8+ T cell responses in the other five patients—which were too small for direct cytotoxicity assays after sorting—we utilized a novel FACS assay for degranulation based on CD107 mobilization . All sixTo substantiate the generality of these findings, we analyzed four additional patients with vaccine-elicited responses. One subject responded to G209–2M only (patient 722), one to M26 only (patient 713), and two to both G209–2M and M26 (patients 721 and 735). Similar to the first four vaccine-elicited patients, these four additional patients  exhibited variable reactivity to melanoma targets, ranging from 13% to 49.6% .To confirm and further investigate the differences in tumor reactivity between endogenous and vaccine-elicited responses, we reasoned that analysis of a set of clonal CTL lines that represented the tetramer-positive population would provide an accurate estimate of the complexity of the TAA-specific T cell response in each patient. A large number of clonal CTL lines (more than 200) were generated by FACS of individual G209–2M- and M26-tetramer-positive cells directly from PBMC samples . Up to 8Peptide specificity and CD8 expression of each clone was confirmed by staining with G209–2M- and M26-tetramers and anti-CD8 monoclonal Ab (data not shown). To obtain an accurate reflection of the total T cell population detected with tetramer in each patient, we decided to rigorously examine at least one representative clone for each subpopulation expressing a different TCR VB . MultiplTo determine the effectiveness of tumor lysis by the different TAA-specific T cell clones that were propagated, clones were analyzed for their ability to lyse melanoma cell lines mel526 and Malme-3M. A375 cells served as a control for antigen-specific killing. In addition, each CTL clone was examined for antigen-specific lysis of T2 cells pulsed with high levels (1μg/ml) of G209–2M or M26 peptides. “Efficient lysis” in these experiments was defined as 40% or greater specific release of radiolabel from the target cells; 10% or less specific release was categorized as “low or no lysis,” and 10% to 40% was termed “intermediate lysis.” All but two of the CTL clones elicited from endogenous anti-tumor responses (from patients 132 and 461) exhibited “efficient lysis” of both the mel526 and Malme-3M melanoma cell lines . In contWe hypothesized that CTL clones that did not efficiently lyse melanoma targets may be incapable of recognizing the relatively low surface densities of native peptide present on tumor cells. CTL clones selected for analysis of tumor lysis were also assessed for RE for the native and heteroclitic peptides via a ten-log range of dilutions. This is illustrated with clones 132.1 and 476.105 A. There 10 value of the peptide concentration required for 40% specific lysis at an E:T ratio of 10:1. For clones 132.1 and 476.105, these scores were 11.1 and 8.3 for assays with G209n peptide  to isolate such cells. CTL clones were selected directly from patient PBMC samples without enrichment in culture to closely reflect the composition of the antigen-specific T cell response in vivo at the time of isolation.3-fold higher concentration of peptide for similar levels of lysis of targets compared to T cell clones that were tumor-lytic. Side-by-side comparison of endogenous responses and vaccine-induced responses suggests that low RE TAA-specific T cell responses may be preferentially driven by heteroclitic peptide vaccination. Thus, high doses of peptide and/or the higher levels of expression of heteroclitic peptide on APCs may induce and actively propagate predominantly T cells with RE too low for recognition of physiological levels of the native peptide present on tumor targets. These data suggest an inverse relationship between antigen density and the RE of T cells elicited. This would be an important consideration in design of future vaccine strategies.Our data revealed that T cell populations induced by vaccination were significantly different from endogenous responses: while some CTLs elicited by vaccination could kill melanoma targets, most were inefficient in tumor cell lysis. In contrast, nearly all clones from endogenous responses were efficient at melanoma cell lysis. This difference was related to RE for the native peptide. Clones that did not lyse tumor cells required up to 10Differential recognition of native and heteroclitic peptides by many T cells may also account for the induction of non-tumor-lytic clones by heteroclitic peptide vaccines, which has been suggested previously ,32. HoweAnother implication of this study is that the number of cells measured by current methods, including ELISPOT or staining with MHC tetramers, may not correlate directly with the RE or tumor reactivity of T cell responses to vaccination. For example, of the nine clones analyzed from patient 517, none were efficient in tumor cell lysis, yet these cells were detectable by MHC tetramer staining. T cells with low RE for native TAA do not efficiently lyse tumor, and therefore are unlikely to have an impact on clinical outcome. Furthermore, it may be possible that low-RE TAA-specific T cells may interfere with elicitation of high-RE T cells, either by direct competition for antigen on APC surface ,34 or doOur data support the notion that not only quantity, but quality, of the T cell response elicited by vaccination may be important for clinical efficacy. There are a number of strategies to increase the magnitude of T cell responses to peptide vaccines. These include using various adjuvants, such as incomplete Freund's adjuvant and immunomodulatory agents, such as IL-12 , GM-CSF In summary, we have demonstrated that vaccination with heteroclitic peptide at high concentrations may drive T cell responses of variable tumor-cytolytic potential in cancer patients—and that the ability to lyse tumor cells correlates with the T cell's RE for native peptides. This represents an important—but not sole—factor in explaining the lack of correlation between immunological and clinical responses after vaccination for cancer. Importantly, the situation is different in endogenous responses, in which cells are predominantly of high RE. This suggests that the manner in which T cells are elicited in vivo are different in these two settings and may underlie their differences in biology.Our immune system protects us against infectious diseases. It can also recognize and destroy early cancer cells before they form tumors. Researchers have been trying to find a way to boost the anti-cancer function of the immune system so that it can kill even established tumors. This is the idea behind developing vaccines for treating cancer—the vaccine alerts and boosts the patient's immune system and so helps to fight the cancer. The idea of enlisting the immune system against cancer has been around for a long time. There have been some spectacular successes, but it has proven difficult to find vaccines that work in more than just a few patients. And we don't yet understand why vaccines seem to work in some patients but not in others.Peter Lee and colleagues are trying to find out why some patients respond to vaccines and others don't by looking at the immune response in vaccinated patients. In this study, using state-of-the art technology, they examined patients who received different vaccines against the skin cancer melanoma. They concentrated on the so-called killer T cells (cytotoxic T cells), which directly attack and kill tumor cells, and analyzed them in great detail.Most cytotoxic T cells produced by patients after vaccination—including vaccination with so-called heteroclitic peptides that had been specifically designed to provoke a very strong immune response—did not kill tumor cells very well, but a few of them did. These results provide some explanation as to why cancer vaccines haven't been as successful as many had hoped, but also suggest that if it were possible to get more of the potent T cells or to expand the ones that are already produced with the current vaccines, there would be a stronger anti-tumor response.How to get effective cancer vaccines remains an open question. But at least technologies such as those used in this study now exist that allow researchers to analyze how the immune systems of different patients react to vaccination and hence can guide the development of better vaccines.http://www.cancerresearch.org/The Cancer Research Institute: http://www.cancerresearch.org/US Food and Drug Administration page on cancer vaccines: http://www.cancer.med.umich.edu/learn/cancervaccines.htmUniversity of Michigan page on cancer vaccines:


This study reinvestigated the recent finding that females – but not males – die in traffic accidents on Friday the 13th more often than on other Fridays . The current study used matched setting and injury accident data base that is more numerous than fatality data. If such an effect would be caused by impaired psychic and psychomotor functioning due to more frequent anxiety among women, it should also appear in injury crashes.We used the national Finnish road accident database for 1989–2002. To control seasonal variation, 21 Fridays the 13th were compared in a matched design to previous and following Fridays, excluding all holidays, on number of accidents, male/female responsibility for accidents, and the number of dead, injured and overall number of active participants  as a consequence of the accident.There were no significant differences in any examined aspect of road injury accidents among the three Fridays, either in females or males. Women were not overrepresented in crashes that occurred on Fridays 13th.There is no consistent evidence for females having more road traffic crashes on Fridays the 13th, based on deaths or road accident statistics. However, this does not imply a non-existent effect of superstition related anxiety on accident risk as no exposure-to-risk data are available. People who are anxious of "Black Friday" may stay home, or at least avoid driving a car. One widely spread superstition is that Friday the 13th brings bad luck. However, the few studies published on human behaviour and its consequences on that day show inconsistent results, whether they be on economic behaviour -6. A recThis interpretation is not without problems. First, although the author repeatedly refers to driving, it should be noted that he also included water and air traffic accidents. Secondly, as the author pointed out himself, his data included passengers killed in accidents, who typically have no control on the task. Impaired psychic and psychomotor functioning due to anxiety, which could indeed be more frequent in females due to their higher neuroticism rate , superstIn spite of Näyhä's fairly conservative conclusion, his results have been widely publicised as evidence that superstitious female drivers die on Fridays the 13th [We reinvestigated the case using the national Finnish road accident data base of injury accidents  for 1989There were 24 Fridays the 13th during the study period. However, three of them were excluded because two were Good Fridays and one followed a Thursday holiday. To control seasonal variation in traffic and weather-type, the remaining 21 Fridays the 13th were compared with the previous Fridays the 6th and the following Fridays the 20th on the number of accidents, male/female responsibility for accidents (police officer judgment), the number of dead, injured and overall number of active participants as a consequence of accident, separately for women and men. Active participants included drivers, bicyclists and pedestrians who actively controlled their motion in traffic and may get involved in crashes. Motor vehicle passengers were excluded. Nine holidays or otherwise unusual control Fridays were replaced by the mean values of the accident variables  from the previous and following years' closest Fridays. For example, Friday the 20th in June 1997 fell on Midsummer holiday eve, and was replaced by mean values gathered from Fridays June 14th, 1996 and June 12th, 1998. This was done to preserve size of the sample. To avoid violating parametric assumptions, the Friedman analysis of variance by ranks  was usedTables 2 = 3.534, df = 2, p = 0.171); in active participants, for females  or males ; in injured active participants, for females  or males ; and in dead active participants, for females  or males  among three Fridays. To test the Gender × Day interaction, we also computed female/male ratios of active participants for each Friday of each triplet, and applied the Friedman test to check whether this ratio is systematically higher on Fridays 13th, as expected from Näyhä's [2 = 0.400, df = 2, p = 0.819) indicating that, with respect to men, women are not overrepresented in crashes that occur on Fridays 13th. The odds for women being involved in an injury tend to be even somewhat smaller on Friday 13th. There was no such overrepresentation in injured  or dead  women among active participants.Comparisons of 21 triplets showed no significant difference in injury accidents .Finally, we similarly checked a possible Gender × Day effect in legally responsible crashes (responsibility drawn from police reports), computing female/male ratios of guilty participants for each day and triplet, but did not found any effect, or about the effect of superstition on those risks. For such conclusions, disaggregated individual level data is needed with detailed information of exposure to risk and respective accident outcome. People themselves adjust their exposure to risk at several levels, while making trip decisions, choosing transport mode, or selecting routes to the destination (see the "multiple sieve model" of accident output ). TherefWe conclude that, in the Finnish traffic accident statistics for 1989–2002, females have not incurred more injury  road traffic accidents on Fridays the 13th than expected, as a driver, bicyclist or pedestrian. We suggest that Näyhä's contradicting result on fatalities is due to different sampling, non-optimal setting and chance in a fairly small data. However, this does not imply a non-existent effect on accident risk as no exposure-to-risk data  are avaiThe authors declare that they have no competing interests.Both authors participated in each stage of research and manuscript preparation.The pre-publication history for this paper can be accessed here:


A single, fertilized egg divides into apparently identical daughter cells. As these twins divide again and again, differences emerge among their progeny, establishing segments that will distinguish back from front and head from tail in the growing embryo. Development of segments—and, later, distinct tissues—requires a carefully coordinated square dance of gene expression machinery. Proteins coded by special genes called transcription factors call out the steps by binding to DNA to block or encourage expression of specific genes. With to-the-minute timing, transcription factors call other genes into action to produce the proteins that will determine cell fate. Developing organisms express different transcription factors at specific times and locations to coordinate the changes that make some cells head, others tail; one, a neuron, another, muscle.Drosophila melanogaster. Misregulation of Hairy and related factors is associated with cancer and developmental defects across species. Mutations in the gene that codes Hairy lead to overexpression of genes involved in development, but it is not clear whether Hairy normally represses those genes directly, or through intermediaries. To find the answer, Susan Parkhurst and colleagues at the Fred Hutchinson Cancer Research Center in Seattle, Washington, set out to identify Hairy's direct targets.A transcription factor called Hairy is one of the first activated during segment development in the fruitfly Drosophila cells called Kc cells and in embryos collected at the peak of Hairy expression during Drosophila segmentation. Because they searched approximately half of the expected Drosophila genome, the researchers estimate that they identified roughly half of all Hairy target genes. The list included genes known to act during segmentation, as expected, as well as many others with roles in cell division, growth, and shape.The researchers found a total of 59 genes bound directly by the Hairy protein in cultured Drosophila C-terminal binding protein (dCtBP), and Drosophila Sir2 (dSir2)—may help to set the tempo and timing of Hairy's repression of genes.Of the 59 Hairy targets identified, only one appeared in both Kc cells and embryos. The lack of overlap may reflect a difference in developmental stage between the Kc cells, which are thought to be precursors to neurons, and the relatively undifferentiated embryos, and suggests that Hairy's role changes with context, such as the stage of development or tissue type. But Hairy doesn't act alone. Like most transcription factors, Hairy requires assistant proteins, called cofactors, to do its job. The availability of Hairy's known cofactors—Groucho, Groucho was thought to be Hairy's main assistant, but Parkhurst and colleagues found that only one of the identified Hairy target genes bound Groucho in Kc cells. The majority of Hairy targets overlapped with those of dCtBP, most often in combination with dSir2. All of the cofactors also bound to non-Hairy targets, suggesting that they assist other transcription factors as well.Drosophila genes has grown 2-fold to include almost all of the expected genome. Repetition of these experiments with the expanded gene set could identify most or all of Hairy's target genes.Next, Parkhurst's lab plans to explore the biological functions of the 59 Hairy target genes, to see how they help Hairy coordinate development. Meanwhile, the list of known The current results suggest that Hairy plays roles in segmentation, cell division, and tissue formation that evolve as an organism develops. Differences in cofactor involvement could help regulate Hairy's repression of genes. This paper demonstrates a powerful technique to explore how developing embryos keep gene expression in step.


Many arrhythmias are triggered by abnormal electrical activity at the ionic channel and cell level, and then evolve spatio-temporally within the heart. To understand arrhythmias better and to diagnose them more precisely by their ECG waveforms, a whole-heart model is required to explore the association between the massively parallel activities at the channel/cell level and the integrative electrophysiological phenomena at organ level.We have developed a method to build large-scale electrophysiological models by using extended cellular automata, and to run such models on a cluster of shared memory machines. We describe here the method, including the extension of a language-based cellular automaton to implement quantitative computing, the building of a whole-heart model with Visible Human Project data, the parallelization of the model on a cluster of shared memory computers with OpenMP and MPI hybrid programming, and a simulation algorithm that links cellular activity with the ECG.We demonstrate that electrical activities at channel, cell, and organ levels can be traced and captured conveniently in our extended cellular automaton system. Examples of some ECG waveforms simulated with a 2-D slice are given to support the ECG simulation algorithm. A performance evaluation of the 3-D model on a four-node cluster is also given.Quantitative multicellular modeling with extended cellular automata is a highly efficient and widely applicable method to weave experimental data at different levels into computational models. This process can be used to investigate complex and collective biological activities that can be described neither by their governing differentiation equations nor by discrete parallel computation. Transparent cluster computing is a convenient and effective method to make time-consuming simulation feasible. Arrhythmias, as a typical case, can be effectively simulated with the methods described. Arrhythmias, a significant direct cause of death in heart diseases, are emergent and evolvable events that come with little prior warnings and allow limited response time . AlthougTo overcome the inherent limitations of clinical investigation, computational modeling and simulation has been widely recognized as a valuable alternative approach. Traditionally, cardiac modeling has centered on ECG simulation. Using the finite element method (FEM), the virtual heart and whole chest are partitioned into numerous elements representing a group of cells. The ECG is then simulated, based on computing the body surface potential of each cardiac element -5. BasicTo study arrhythmias using a large-scale realistic electrophysiological model, two issues need to be effectively resolved: model building and operation. Though the widespread paradigm of modeling with C or C++ remains a workable choice, the huge number of cardiac cells in a realistic three-dimensional (3-D) whole-heart model and the numerous modifications of the model to simulate various pathological conditions, make more desirable efficient modeling based on transparent parallel computing. To build and run a model with parallel computing technologies, two strategies were separately developed in recent years. To provide transparent and parallel descriptions, cellular automata were used -11,50; fThere is compelling evidence that cardiomyocytes are not arranged in a uniformly connected continuum, as has often been assumed and simulated in the past. Along with nonlinear ionic channel electrical activity, discontinuous electrical propagation is another key feature of cardioelectrical activity . The forCellular automata were first introduced by John von Neumann and Stanislaw Ulam in the 1940s, and gradually used to solve a wide range of problems, including multicellular biological modeling in which a natural correspondence between each automaton cell and each biological cell is assumed -21. SimpCellular is a cellular automata system based on the language Cellang  = type, ......if-then statement on the value of type, the cell program is divided into several parts, each being executed by cells of the specific type. Second, type can be modified at runtime to simulate pathological changes. After a cell type is changed, its program and therefore its behavior also change.x, y, and z are the coordinates. Values of other fields can be defined or not in the data file. With a nested In a tissue or organ, aside from heterogeneity, cells often show anisotropy and inhomogeneity that cannot be conveniently described while processing the raw data. To describe these two properties needs a bit of pattern formation programming , an inteLet the layer number of SAN cells (at least one SAN cell is on the epicardium) be 0 and the layer number of AVN cells (at least one AVN cell is on the endocardium) be 50.• If the current cell is neither a ventricular nor an atrial cell, then: if it connects to a cell whose layer is 0, its layer is 0; if it connects to a cell whose layer is 50, its layer is 50.Min. The layer of this cell is Min+1.• If the current cell is a ventricular or an atrial cell, then: visit all its neighboring cells and find out their minimal layer number, A slice of the stratified 3-D model and of the stratified 2-D model is shown in Figure The slices of the digital cadaver do not provide any useful information on the distribution of terminal Purkinje fibers. Even if we know that most of them are located in the subendocardium, it is impractical to make a slice-by-slice manual description. Based on the stratified cardiac walls, this issue can be easily solved by using a lateral inhibition algorithm applied to cells on the ventricle subendocardium to generate a mesh-like Purkinje network. On completion of running the algorithm, some of the cell types are changed from ventricular cell to Purkinje cell.n-by-n cell communication among automaton cells of conduction fibers without physically building the fiber structure; n = 4 gives very satisfactory simulation results. Again, we propose that by using a pattern formation algorithm the physical construction of fiber structures is also feasible. In this scenario, a fiber is assembled by n successive automaton cells sharing the same, unique identity number, among which there is no conduction delay. Currently, with the 2-D model we find that even if letting each automaton cell stand for a discrete ventricular cell, the model works quite well in ECG simulation.The more difficult engineering issue is the fine structure of cardiac cells and conduction fibers. Simulation with the 2-D model demonstrates that, even if the HH action potential model of Purkinje cells produces a very fast upstroke, which means a quick transjunctional conduction, yet a one-cell by one-cell conduction can never give normal propagation profiles as observed in Durrer's experiment, due to a too long transjunctional delay . Neverth+ channels are slightly changed to produce action potential duration of human cardiac cells. Time constraints are the only reason for us to adopt early published, simpler action potential models. In the given simulations, all action potential models are solved using explicit Euler integration with a time step, Δt, of 0.01 ms [Each automaton cell is a computing unit for action potential and ECG simulation. The electrical activity of each automaton cell of a specific cell type is described by the corresponding HH type action potential model. Five action potential models are employed to simulate activities of different cardiac cells  -31. Sinc 0.01 ms . An asyn 0.01 ms . ElectriEach automaton cell in the 3-D model has 26 neighbors, linked by gap junctions. When a cell depolarizes, driven by the potential difference between it and its neighboring cells, transjunctional currents generate and propagate to neighbors through gap junctions. Simulations with the 2-D model show that the simple static gap junction model, in which the resistance of the gap junction is a constant and the transjunctional current follows Ohm's law, works quite well. The dynamic gap junction models, in which the resistance of gap junction changes in a nonlinear manner with membrane potential, will significantly increase computational time ,34 and cIstim, that the cell receives. The direction and speed of electrical propagation within the heart are jointly controlled by: (1) the stratification of the ventricular walls, (2) the speed of end-to-end and side-to-side conduction, (3) the ratio of end-to-end conduction speed to side-to-side conduction speed, and (4) the distribution of the trunk conduction bundle.Values of gap junction resistance between different cells and in different directions are initially set according to available experimental data, and then tuned via simulation according to Durrer's experimental observations . Excitatblood, with normal value 1.0 is defined in the cell program, and introduced as an extra parameter in action potential and in gap junction models. To multiply the conductivity of the gap junction by an abnormal value of blood  can simulate a lowered gap junction conduction speed. A small value for blood, when inserted into the equation computing Ca++ current, affects the generation of the action potential. Dynamically modified blood can naturally simulate progressive ischemia  which significantly influences action potential. Abnormality of transjunctional conduction constitutes the third class, and the fourth class is is comprised of anomalous dynamics of cellular electrical activity itself. Changes in the HH equations can simulate these aberrations. In most cases, pathological changes affect more than one aspect. For example, in addition to providing an abnormal cell environment, ischemia also results in changed cellular electrical activity and altered gap junction resistance. To simulate the effect of ischemia, a special field Combining these factors, a variety of arrhythmias can be simulated. For example, by partially or completely blocking the activity of cells, or the conduction of gap junctions at specific locations, various conduction blocks such as AV (atrium-ventricle) block, LBBB (left bundle branch block) and RBBB (right bundle branch block) can be conveniently simulated. Many rapid arrhythmias are triggered by successive ectopic beats. Such abnormal beats can be produced in the model by providing cells at specific locations with extra stimulation or by changing them from atrial/ventricular to SAN cells. For abnormal propagations such as long QT syndrome, caused by the extra long refractory period of M cells, we can adjust, either dynamically or statically, the layer number of cells at the middle layers of the ventricular walls. Complex spatiotemporal patterns of arrhythmias are formed through discrete cell-cell communication. By these means, arrhythmic electrical activities can be simulated in flexible ways, different from those models created by cable equations or other partial differential equations (PDE).Linking ECG simulation directly with cellular and channel electrical activity is crucial for the understanding and treatment of arrhythmias. Since each automaton cell in our model is a computing unit, a cell level ECG simulation algorithm is developed to compute each cell's field point potential. The algorithm, implemented as a backend function coded in C language, shows strengths, because such cell level simulation builds a link between the action potentials of cells and the ECG waveforms. The limitation here is that this heart model does not include the chest, so that the contribution of thoracic tissues to body surface potential is much simplified. The model reads the membrane potential of every automaton cell and computes its field potentials at the standard lead locations.Vm, is uniform on all parts of the cell membrane, except at gap junctions. (b) Gap junctions occupy the same area and are symmetrically distributed on each cell. (c) The distance from cell to field point is long enough so that the two solid angles subtended by the positive and negative sides of a cell membrane to a field point are equal. (d) Dielectric effects on field potential are neglected. σmedia is the average conductivity of the 1-D tissues between a source cell and a field point; σe the conductivity of the intercellular matrix; and σi the conductivity of the intracellular cytoplasm. (e) For any neighboring cells, j and k, σje = σke, σji = σki and φje = φke, φe is the potential in intercellular space. For convenience, we present the algorithm for the 2-D model here; the 3-D case is similar. The equation computing the field potential of an isolated cell at field point P is [Several assumptions are made in the computations on account of established physical laws. (a) Each (automaton) cell has a spherical shape. Thus, the transmembrane potential, int P is i φi - σe φe is the double layer strength of the cell membrane. When there is no propagation on the cell membrane, the field potential is zero. If the cell connects with eight adjacent cells, equation (1) becomes:σi φi - σji φji is the double layer strength of the gap junction between the cell and its jth neighbor. The transjunctional potential difference between the two cells then becomes:Here, σi - φji = φi - φe + φe - φji = φi - φe + φje - φji = Vm - Vjm     (3)φ1 to Ω8 are solid angles subtended by them; and S0 denotes the remaining part of the cell membrane  SB = SA, (b) the same and even number gap junctions distribute symmetrically on SB and SA, and (c) all gap junctions have an equal area, then it can be proved that:S1 to S8 are areas of the eight gap junctions; ΩA is the sum of areas of gap junctions on SA, and Σ KB is the sum of areas on SB. In this circumstance, the first term in equation (2) is zero, and only the second term makes a contribution to field potential. The equation now becomes:Here, Σ K1 = cos α5 = cos 90° = 0, we have:Considering all Sj (j = 1...8) to be equal, and cos α is a constant that affects only the baseline, but not the shape of the ECG waveform. The field potential at point P, generated by the whole heart, is the superposition of all cells' contributions:Here, n is the number of automaton cells, and φj(P) is computed with equation (5).Here, media R2 has to be much simplified, as described in equation (7), where σmedia_i is the conductivity of tissue i, and Ri is the length of tissue i on the line between the source cell and the field point P.Due to the time needed for the numerical solution of massive HH equations, the computing of σDepo = 0.4 mhos/m in depolarization, σRepo = 0.25 mhos/m in repolarization, σRest = 0.2 mhos/m in resting state, σInfarct = 0.1 mhos/m in acute infarcted area, and σIschemia = 0.16 mhos/m in ischemic area. We let σBlood = 0.6 and σChest = 0.1 be the average conductivity of tissues in chest [As noted above, both the boundary effect and order of dielectric distribution are neglected. The 2-D (3-D) boundary between two tissues is treated as a 1-D boundary, and the ordered dielectric distribution is treated as an unordered distribution. Because of the huge number of cardiac cells and various possible field points on body surface, epicardium, endocardium and cardiac cavities, it is impossible to deal with numerous situations of boundary conditions between cells and field points. The method we adopt here is to make a runtime traverse from the source cell to the field point and meanwhile check the conductivity of each tissue and determine the 1-D space it occupies. The scaled distance is 1.0 between two perpendicular-connected cells, and 1.414 between two oblique-connected cells. Although the electrical property of non-cardiac tissues is much simplified, the electrical property of cardiac cells is intensively considered. Cardiac cells under different conditions have different conductivity . It is σin chest ,41. We fFor the physically parallelized whole-heart electrophysiological model built with the extended cellular automata, several factors impair the simulation efficiency on a cluster of SMP.slaves located on different nodes. The time cost of communication, spent on the exchange of boundary sheets between neighboring slaves in each round, rises with the increase of slave number. An extreme case for a 128 × 128 × 128 model is that there are 128 nodes, and each slave deals with just one sheet, requiring every sheet to be exchanged in each round. The physical link between nodes also significantly affects performance. An additional limitation is that the overhead of fork/join operations in the OpenMP parallelized program degrades performance, although not very significantly. The OpenMP directive parallel can be inserted either before the endless while loop, or more simply, before the three for loops. If it is inserted before the for loops, the fork and join operations, which create and delete threads and allocate and collect memory for temporary variables in each thread, will be repeatedly executed in each round. The preferable way, which eliminates this unnecessary expenditure, is to insert the directive omp parallel before the while loop, and the directive omp for before the for loops. Between the omp parallel and the omp for,omp master is used to limit parallelism to only the cell program part. Finally, load balance becomes a limitation when a model has an irregular and/or heterogeneous structure. The heart, with four chambers and an uneven shape, is a typical case. Usually, on a cluster consisting of m identical nodes each containing n CPUs, a program is evenly dispatched to distribute the cell space across all nodes. We find that using this strategy, the 3-D heart model cannot reach the best load balance and performance because different nodes deal with different numbers of cardiac cells. Furthermore, even if each node contains the same number of cardiac cells, since different cells run different action potential models, the burden of computation remains unequal. Only a solid cubic model with homogeneous cells occupying the full cell space can ensure a best performance. Thus, if a model needs to run many simulations, an important issue is to find the best cell space partition.The first difficulty is the graphic display of the simulation. The main display window, as the critical resource in the parallelized program, can only be sequentially accessed by the cells. Thus, to display the updated state of all cells leads to a significant decrease of running speed. We mitigate this problem by displaying the cell state every 20 or even 50 steps, instead of every single step. The second problem is the overhead of communication among master and 4 slaves, are created at each run. The master and a slave are assigned to the head node, and each remaining slave is assigned to a node. Within each slave, four threads are created using the OpenMP parallel directive. Figure slaves-4 threads does not give the best performance. The 4 slaves-2 threads setting behaves better because the overall cost is lower. . However, when the whole cell space is occupied by ventricular cells, the 4 slaves-4 threads version provides the best performance, as predicted.Pilot runs with the 3-D model have been made on a 4-node SUN computer cluster. The head node has eight UltraSparc CPUs, and the other nodes each have four identical CPUs. Five processes, 1 Arrhythmias are a group of complex syndromes not well understood. Many difficult issues exist when investigating arrhythmias through computational modeling. We cover here only a few relating to model building and running.et al. [We adopt early published action potential models in the current 3-D model because they are simpler and more computationally affordable. In the first version of the Luo-Rudy model , there aet al.  in 1993 et al. . This stet al. . We arguet al. . To doubAlthough experimental evidence show that fiber orientation takes a role in arrhythmia generation ,47, to iAnother issue relates to ECG simulation. Although simulation results of the 3-D model have not been acquired thus far, the simulated ECGs with the 2-D model in different lead locations and under physiological and pathological conditions are impressive and qualitatively agree with recorded ECGs, supporting the validity of the algorithm. Theoretically, the boundary effect of dielectrics is not negligible for field potential computing; yet in practice simplifications are often inevitable. So far we find that our simplified treatment of boundary effect on field potential does not visibly affect ECG simulation. There may be two reasons for this. First, the conductivity of the tissues (not including the heart) in the chest may not be significantly different. Second, the boundaries among different tissues are so irregular that the net boundary effect may be effectively neutralized. On the other hand, we find that to let cardiac cells under different conditions  have different conductivity can improve the ECG quality, indicating that the precise description of the source may be more important than the precise description of the dielectric.Finally, we point out that, as in single cell models where adaptive time steps can greatly improve the running performance, this same strategy can produce the same results in multicellular models built with this cellular automaton . SimulatEffective simulation of arrhythmias needs a whole-heart model, differential description of electrical properties of cardiac cells, membrane equation based computation, association between cellular activities and ECG generation, flexible description of pathological conditions, and long running time. To comprehensively address these issues, we develop a method based on cellular automata and parallel computing technologies to build large-scale electrophysiological models with extended cellular automata, and run such models on clusters of shared memory machines. The dynamically traced and captured electrical activities at channel, cell, and organ levels can substantially help us understand abnormal cardioelectrical activities through simulation. Simulation results with the 2-D model support the validity of the ECG simulation algorithm. Transparent cluster computing is a convenient and effective solution to the excessive time consumption of computational intensive simulation.in silico research is to facilitate the discovery and evaluation of drugs. This helps to reduce the risk and cost of clinical trials, shorten the cycle of development, and remove randomness in candidate screening. A whole-heart electrophysiological model that links electrical activities at channel, cell, and organ levels can help achieve this result. The modeling method described in this paper shows the advantages of precisely linking cell and organ activities, exploiting the intrinsic parallelism in tissue/organ level biological activities. Besides modeling electrical activity, the method is also applicable to many other multicellular models in which quantitative description is required.In addition to reaching a mechanistic understanding of arrhythmias, an important goal of HZ develops the methods and built the initial 3-D model. AM provides data of conduction system distribution in ventricles. YS and GR help solve some technical issues. PD is responsible for the project.


A microarray technology suitable for analyzing the expression of microRNAs and of other small RNAs was used to determine the microRNA expression profile during mouse-brain development and observed a temporal wave of gene expression of sequential classes of microRNAs. MicroRNAs are a large new class of tiny regulatory RNAs found in nematodes, plants, insects and mammals. MicroRNAs are thought to act as post-transcriptional modulators of gene expression. In invertebrates microRNAs have been implicated as regulators of developmental timing, neuronal differentiation, cell proliferation, programmed cell death and fat metabolism. Little is known about the roles of microRNAs in mammals.We isolated 18-26 nucleotide RNAs from developing rat and monkey brains. From the sequences of these RNAs and the sequences of the rat and human genomes we determined which of these small RNAs are likely to have derived from stem-loop precursors typical of microRNAs. Next, we developed a microarray technology suitable for detecting microRNAs and printed a microRNA microarray representing 138 mammalian microRNAs corresponding to the sequences of the microRNAs we cloned as well as to other known microRNAs. We used this microarray to determine the profile of microRNAs expressed in the developing mouse brain. We observed a temporal wave of expression of microRNAs, suggesting that microRNAs play important roles in the development of the mammalian brain.We describe a microarray technology that can be used to analyze the expression of microRNAs and of other small RNAs. MicroRNA microarrays offer a new tool that should facilitate studies of the biological roles of microRNAs. We used this method to determine the microRNA expression profile during mouse brain development and observed a temporal wave of gene expression of sequential classes of microRNAs. Caenorhabditis elegans [lin-4 led to the surprising discovery that this gene encodes a 21-nucleotide non-coding RNA that regulates the translation of lin-14 mRNA through base-pairing with the lin-14 3' UTR [let-7, encodes another small non-coding RNA that is conserved in flies and mammals [MicroRNAs constitute a large class of small regulatory RNAs . Their m elegans . In hete mammals .C. elegans, plants, Drosophila melanogaster and mammals [lin-4 and let-7 genes, other microRNAs encode 21-25-nucleotide RNAs derived from longer transcripts that are predicted to form stem-loop structures. More than 200 microRNAs are encoded by the human genome [Biochemical and bioinformatic approaches have identified many genes that encode microRNAs in  mammals -18. Liken genome .C. elegans, lin-4 and let-7 act in developmental timing, and the microRNA lsy-6 controls neuronal asymmetry [Drosophila, the microRNAs bantam and mir-14 act in the regulation of cell growth and cell death [The biological roles of microRNAs are poorly understood. In symmetry . In Drosll death ,21. The ll death ,23.Mature microRNAs are excised from a stem-loop precursor that itself can be transcribed as part of a longer primary RNA (pri-miRNA) . The priThe animal microRNAs studied so far act by reducing the levels of proteins from genes that encode mRNAs with sites partially complementary to microRNAs in their 3' UTRs ,31. The Determining spatial and temporal patterns of microRNA expression should yield insight into the biological functions of microRNAs. As the number of microRNAs identified has increased rapidly, the need for a method that allows for the parallel detection of microRNA expression has become apparent. Recent studies used a dot-blot technique to study 44 mouse microRNAs and northern blotting analysis to study 119 microRNAs from mouse and human organs ,40.In this study we cloned microRNAs from rat and monkey brains, developed a microRNA labeling method and used a microarray to monitor expression of microRNAs during mouse brain development. We determined the temporal expression pattern of 138 microRNAs in the mouse brain and found that the levels of 66 microRNAs changed significantly during development. We identified sets of genes with similar expression patterns, including genes that peaked in expression at different stages of development. More generally, the microRNA microarray we have developed can be used to determine the expression of all known microRNAs simultaneously under any set of experimental conditions or constraints.Rattus norvegicus) and from the cerebral wall of a 114-day-old fetal rhesus monkey (Macaca mulatta)  corresponding to the 68 mammalian microRNAs we isolated from rat and monkey brains, to 70 mammalian microRNAs isolated by others from a variety of mouse tissues and mammalian cell lines, and to predicted microRNAs. In addition, we included a set of control probes as well as 19 probes corresponding to presumptive small RNAs that we and others identified but that do not satisfy all the criteria for a microRNA . Probes had a free amine group at the 5' terminus and were printed onto amine-binding glass slides and covalently linked to the glass surface. All probes were printed in quadruplicate .We developed a method for preparing microRNA samples for microarray analysis. Several methods for mRNA sample labeling for microarray analysis have been described -47, but We optimized the conditions for hybridization to our microarray. The small sizes of microRNAs leave little opportunity for oligonucleotide (array probe) design to achieve homogeneous probe-target melting temperatures. Differences in melting temperatures are expected to result in greater nonspecific binding if hybridizations are performed at low temperatures  and in less specific binding if hybridizations are performed at high temperatures . To assess this issue we included control probes with two internal mismatches on the microarray for a subset of the microRNA probes . We tested a range of hybridization temperatures, and, on the basis of the signal of microRNA probes versus control probes, we determined that a hybridization temperature of 50°C was a reasonable compromise between sensitivity and specificity (data not shown).Even at 50°C, specificity as assayed by comparing microarray spot signal intensities from matched and mismatched probes varied among the microRNAs assayed. As expected, specificity at 50°C was negatively correlated with calculated melting temperatures Figure . In all To determine the detection range of the microarray, we synthesized three artificial RNAs with the characteristics of microRNAs. These RNAs were phosphorylated RNA oligonucleotides of 20-23 bases; their sequences were chosen at random and were without any significant sequence similarity to known mammalian microRNAs . We titrated these RNAs into total mouse RNA samples, labeled them and hybridized them to a microarray that in addition to microRNA probes included probes corresponding to these three RNAs, called syn1, syn2 and syn3. Figure We isolated small RNAs from mice at five developmental stages: embryonic days 12.5 and 17.5 (E12.5 and E17.5), postnatal days 4 and 18 (P4 and P18) and 4-month-old adults. E12.5-E17.5 spans a period of major neuronal proliferation and migration in the mouse brain, in particular the birth and subsequent migration of most neurons in the ventricular zone epithelium . Betweenp < 0.001) and 66 (57%) changed more than twofold. Of the microRNAs we cloned from rat and monkey and for which probes against the corresponding mouse homologs were present on the microarray, we detected 97% robustly.We purified and size-selected RNA from whole mouse brains. For each sample, the products of four independent RNA amplifications based on two independent RNA ligations were hybridized to the array. A detailed description of our analysis of the microarray data is presented in Additional data file 5. Of the 138 microRNAs and 19 small RNAs represented by the probe set, 116 (74%) were expressed robustly (more than 75-fold over the level of background controls) at least at one time point. Of these, 83 (71%) changed significantly during the period surveyed  with sequences complementary to microRNAs. Each probe was modified with a free amino group linked to its 5' terminus through a 6-carbon spacer (IDT) and was printed onto amine-binding slides . Control probes contained two internal mismatches resulting in either C-to-G or T-to-A changes . Printing and hybridization were done using the protocols from the slide manufacturer with the following modifications: the oligonucleotide concentration for printing was 20 μM in 150 mM sodium phosphate pH 8.5, and hybridization was at 50°C for 6 h. Printing was done using a MicroGrid TAS II arrayer (BioRobotics) at 50% humidity.Whole brains from three to eight C57BL/6 mice were pooled. Starting with 250 μg of total RNA for each time point, 18-26-nucleotide RNA was purified on denaturing PAGE gels. The samples were divided, and the following cloning steps were done independently twice for each time point. 3' and 5' adaptor oligonucleotides were ligated to 18-26-nucleotide RNA followed by reverse transcription, essentially as described for microRNA cloning . BrieflyMicroarray slides were scanned using an arrayWoRx biochip reader (Applied Precision), and primary data were analyzed using the Digital Genome System suite (MolecularWare) and Spotfire DecisionSite (Spotfire). Cluster analysis was performed using the CLUSTER/TreeView software . For detNorthern blots were performed as described . Twenty The probes used were: EAM119 (miR-29b), EAM125 (miR-138), EAM224 (miR-17-5p), EAM234 (miR-199a), EAM131 (miR-92), EAM109 (miR-7), EAM150 (miR-9) and EAM103 (miR-124a).The following additional data files are available with the online version of this article. A file (Additional data file A file (Additional data file A file (Additional data file A file (Additional data file A file (Additional data file % are not in the current release of the miRNA registry but are deposited in NCBI and described elsewhere [And a file (Additional data file lsewhere ,15,18.An Excel file (Additional data file A file with details of rat microRNA precursorsClick here for additional data fileA file containing precursor sequences and secondary structure predictions for the novel microRNA miR-421Click here for additional data fileA file with details of other small RNAs cloned from rat and monkey brainsClick here for additional data fileA file showing rno-miR-138 brain specific expressionClick here for additional data fileA file with details of microarray design and data analysisClick here for additional data fileA file with a summary of the microRNA microarray dataClick here for additional data fileAn Excel file with the primary microarray data corresponding to the E12.5 time pointClick here for additional data fileAn Excel file with the primary microarray data corresponding to the E17.5 time pointClick here for additional data filean Excel file with the primary microarray data corresponding to the P4 time pointClick here for additional data fileAn Excel file with the primary microarray data corresponding to the P18 time pointClick here for additional data fileAn Excel file with the primary microarray data corresponding to the Adult time pointClick here for additional data file