A detailed description of the aims and methods of the Bioconductor project, an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples. The Bioconductor project  is an inAmong the many challenges that arise for both statisticians and biologists are tasks of data acquisition, data management, data transformation, data modeling, combining different data sources, making use of evolving machine learning methods, and developing new modeling strategies suitable to CBB. We have emphasized transparency, reproducibility, and efficiency of development in our response to these challenges. Fundamental to all these tasks is the need for software; ideas alone cannot solve the substantial problems that arise.The primary motivations for an open-source computing environment for statistical genomics are transparency, pursuit of reproducibility and efficiency of development.a priori how sensitive the ultimate analyses are to variations or errors in the many steps in the pipeline. Credible work in this domain requires exposure of the entire process.High-throughput methodologies in CBB are extremely complex, and many steps are involved in the conversion of information from low-level information structures  to statistical databases of expression measures coupled with design and covariate data. It is not possible to say Experimental protocols in molecular biology are fully published lists of ingredients and algorithms for creating specific substances or processes. Accuracy of an experimental claim can be checked by complete obedience to the protocol. This standard should be adopted for algorithmic work in CBB. Portable source code should accompany each published analysis, coupled with the data on which the analysis is based.By development, we refer not only to the development of the specific computing resource but to the development of computing methods in CBB as a whole. Software and data resources in an open-source environment can be read by interested investigators, and can be modified and extended to achieve new functionalities. Novices can use the open sources as learning materials. This is particularly effective when good documentation protocols are established. The open-source approach thus aids in recruitment and training of future generations of scientists and software developers.The rest of this article is devoted to describing the computing science methodology underlying Bioconductor. The main sections detail design methods and specific coding and deployment approaches, describe specific unmet challenges and review limitations and future aims. We then consider a number of other open-source projects that provide software solutions for CBB and end with an example of how one might use Bioconductor software to analyze microarray data.The software development strategy we have adopted has several precedents. In the mid-1980s Richard Stallman started the Free Software Foundation and the GNU project  as an atOne of the key success factors of the Linux kernel is its modular design, which allows for independent and parallel development of code  in a virIn this section, we review seven topics important to establishment of a scientific open source software project and discuss them from a CBB point of view: language selection, infrastructure resources, design strategies and commitments, distributed development and recruitment of developers, reuse of exogenous resources, publication and licensure of code, and documentation.CBB poses a wide range of challenges, and any software development project will need to consider which specific aspects it will address. For the Bioconductor project we wanted to focus initially on bioinformatics problems. In particular we were interested in data management and analysis problems associated with DNA microarrays. This orientation necessitated a programming environment that had good numerical capabilities, flexible visualization capabilities, access to databases and a wide range of statistical and mathematical algorithms. Our collective experience with R suggested that its range of well-implemented statistical and visualization tools would decrease development and distribution time for robust software for CBB. We also note that R is gaining widespread usage within the CBB community independently of the Bioconductor Project. Many other bioinformatics projects and researchers have found R to be a good language and toolset with which to work. Examples include the Spot system , MAANOVAR is a high-level interpreted language in which one can easily and quickly prototype new computational methods. These methods may not run quickly in the interpreted implementation, and those that are successful and that get widely used will often need to be re-implemented to run faster. This is often a good compromise; we can explore lots of concepts easily and put more effort into those that are successful.The R environment includes a well established system for packaging together related software components and documentation. There is a great deal of support in the language for creating, testing, and distributing software in the form of 'packages'. Using a package system lets us develop different software modules and distribute them with clear notions of protocol compliance, test-based validation, version identification, and package interdependencies. The packaging system has been adopted by hundreds of developers around the world and lies at the heart of the Comprehensive R Archive Network, where several hundred independent but interoperable packages addressing a wide range of statistical analysis and visualization objectives may be downloaded as open source.The complexity of problems in CBB is often translated into a need for many different software tools to attack a single problem. Thus, many software packages are used for a single analysis. To secure reliable package interoperability, we have adopted a formal object-oriented programming discipline, as encoded in the 'S4' system of formal classes and methods . The BioAccess to data from on-line sources is an essential part of most CBB projects. R has a well developed and tested set of functions and packages that provide access to different databases and to web resources . There is also a package for dealing with XML , availabAmong the statistical and numerical algorithms provided by R are its random number generators and machine learning algorithms. These have been well tested and are known to be reliable. The Bioconductor Project has been able to adapt these to the requirements in CBB with minimal effort. It is also worth noting that a number of innovations and extensions based on work of researchers involved in the Bioconductor project have been flowing back to the authors of these packages.Among the strengths of R are its data and model visualization capabilities. Like many other areas of R these capabilities are still evolving. We have been able to quickly develop plots to render genes at their chromosomal locations, a heatmap function, along with many other graphical tools. There are clear needs to make many of these plots interactive so that users can query them and navigate through them and our future plans involve such developments.snow and rpvm simplify the development of portable interpreted code for computing on a Beowulf or similar computational cluster of workstations. These tools provide simple interfaces that allow for high-level experimentation in parallel computation by computing on functions and environments in concurrent R sessions on possibly heterogeneous machines. The snow package provides a higher level of abstraction that is independent of the communication technology such as the message-passing interface (MPI)  "HUMAN"> x$accession[1] "P09651"> unlist(x$get_keywords)[1] "Nuclear protein" "RNA-binding"[3] "Repeat" "Ribonucleoprotein"[5] "Methylation" "Transport"...RSPerl is not a Bioconductor-supported utility, and that installation of the BioPerl and RSPerl resources to allow interoperation can be complicated.The .PerlPackage command brings the BioPerl modules into scope. .Perl invokes the BioPerl get_sequence subroutine with arguments "swiss" and "ROA1_HUMAN". The resulting R object is a reference to a perl hash. RSPerl infrastructure permits interrogation of the hash via the $ operator. Note that Key differences between the Bioconductor and BioPerl projects concern scope, approaches to distribution, documentation and testing, and important details of object-oriented design.BioPerl is clearly slanted towards processing of sequence data and interfacing to sequence databases, with support for sequence visualization and queries for external annotation. Bioconductor is slanted towards statistical analysis of microarray experiments, with major concerns for array preprocessing, quality control, within- and between-array normalization, binding of covariate and design data to expression data, and downstream inference on biological and clinical questions. Bioconductor has packages devoted to diverse microarray manufacturing and analysis paradigms and to other high-throughput assays of interest in computational biology, including serial analysis of gene expression (SAGE), array comparative genomic hybridization (arrayCGH), and proteomic time-of-flight (SELDI-TOF) data. We say the projects are 'slanted' towards these concerns because it is clear that both projects ultimately aim to support general research activities in computational biology.perl -MCPAN -e shell. This process supports automated retrieval of requested packages and dependencies, but is not triggered by runtime events. Bioconductor has extended the CRAN distribution functionalities so that packages can be obtained and installed 'just in time', as required by a computational request. For both Perl and R, software modules and packages are structured collections of files, some of which are source code, some of which are documents about the code. The relationship between documentation and testing is somewhat tighter in Bioconductor than in BioPerl. Manual pages and vignettes in Bioconductor include executable code. Failure of the code in a man page or vignette is a quality-control event; experimentation with executable code in manual pages (through the example function of R) is useful for learning about software behavior. In Perl, tests occupy separate programs and are not typically integrated with documentation.BioPerl inherits the distribution paradigm supported by CPAN. Software modules can be acquired and installed interactively using, for example Both R and Perl are extensible computer languages. Thus it is possible to introduce software infrastructure supporting different approaches to object-oriented programming (OOP) in various ways in both languages.R's core developers have provided two distinct approaches to OOP in R. These approaches are named S3 and S4. In S3, any object can be assigned to a class (or sequence of classes) simply by setting the class name as the value of the object's class attribute. Class hierarchies are defined implicitly at the object level. Generic methods are defined as ordinary functions and class-specific methods are dispatched according to the class of the object being passed as an argument. In S4, formal definition of class structure is supported, and class hierarchy is explicitly defined in class definitions . Class iClass::Multimethod module can be used to allow multiple dispatch behavior of generic subroutines. The specific classes of objects identified in BioPerl are targeted at sequence data , location data , and an important class of objects called interface objects, which are classes whose names end in 'I'. These objects define what methods can be called on objects of specified classes, but do not implement any methods.OOP methodology in Perl has a substantial history and is extensively employed in BioPerl. The basic approach to OOP in Perl seems to resemble S3 more than S4, in that Perl's bless operation can associate any perl data instance with any class. The CPAN Other open bioinformatics projects have intentions and methods that are closely linked with those of Bioconductor.SJava interface lmFit from the limma package, which can assess differential expression between many different groups and conditions simultaneously. The function lmFit accepts a model matrix which describes the experimental design and produces an output object of class MArrayLM which stores the fitted model information for each gene. The fitted model object is further processed by the eBayes function to produce empirical Bayes test statistics for each gene, including moderated t-statistics, p-values and log-odds of differential expression. The log2-fold changes, average intensites and Holm-adjusted p-values are displayed for the top 10 genes < 0.05> esetSel <- eset There are 165 genes selected for further analysis. A heat map produced by the heatmap function from R allows us to visualize the differential action of these genes between the two groups of patients. Note how the different software modules can be integrated to provide a very rich data-analysis environment. Figure t-test. Many of these questions are normally addressed in terms of a hypergeometric distribution, but they can also be thought of as two-way or multi-way tables, and alternate statistical tests  can be applied to the resulting data.We can carry out many other tests, for example, whether genes encoded on a particular chromosome (or perhaps on a specific strand of a chromosome) are over-represented amongst those selected by moderated GOHyperG is found in the GOstats package. It carries out a hypergeometric test for an overabundance of genes in our selected list of genes for each term in the GO graph that is induced by these genes  annotation in conjunction with these data. We first identify the set of unique LocusLink identifiers among our selected Affymetrix probes. The function s Figure .p-value found was 1.1e-8 and it corresponds to the term, "MHC class II receptor activity". We see that six of the 12 genes with this GO annotation have been selected. Had we used a slightly less conservative gene selection method then the number of selected genes in this GO annotation would have been even higher.The smallest hgu95av2, with those for their array and the basic principles and code are unchanged.Reproducing the above results for any other species or chip for which an annotation package was available would require almost no changes to the code. The analyst need only substitute the references to the data package, Similarly, substitution of other algorithms or statistical tests is possible as the data analyst has access to the full and complete source code. All tools are modifiable at the source level to suit local requirements.We have detailed the approach to software development taken by the Bioconductor project. Bioconductor has been operational for about three years now and in that time it has become a prominent software project for CBB. We argue that the success of the project is due to many factors. These include the choice of R as the main development language, the adoption of standard practices of software design and a belief that the creation of software infrastructure is an important and essential component of a successful project of this size.The group dynamic has also been an important factor in the success of Bioconductor. A willingness to work together, to see that cooperation and coordination in software development yields substantial benefits for the developers and the users and encouraging others to join and contribute to the project are also major factors in our success.To date the project provides the following resources: an online repository for obtaining software, data and metadata, papers, and training materials; a development team that coordinates the discussion of software strategies and development; a user community that provides software testing, suggested improvements and self-help; more than 80 software packages, hundreds of metadata packages and a number of experimental data packages.At this point it is worth considering the future. While many of the packages we have developed have been aimed at particular problems, there have been others that were designed to support future developments. And that future seems very interesting. Many of the new problems we are encountering in CBB are not easily addressed by technology transfer, but rather require new statistical methods and software tools. We hope that we can encourage more statisticians to become involved in this area of research and to orient themselves and their research to the mixture of methodology and software development that is necessary in this field.In conclusion we would like to note that the Bioconductor Project has many developers, not all of whom are authors of this paper, and all have their own objectives and goals. The views presented here are not intended to be comprehensive nor prescriptive but rather to present our collective experiences and the authors' shared goals. In a very simplified version these can be summarized in the view that coordinated cooperative software development is the appropriate mechanism for fostering good research in CBB.


We report here the identification of a nonsense alternative transcript of the fumarylacetoacetate hydrolase  of the The del100 and del231 transcripts arise due to minor alternative splicing pathways and del100 is likely subjected to nonsense-mediated mRNA decay. However the remaining amount of transcript seems sufficient to produce a protein in different human tissues. This suggests that NMD has a broader role than simply eliminating aberrant transcripts and when coupled to alternative splicing, may act to modulate gene expression, by allowing the production of low amounts of protein. Caenorhabditis elegans [in silico analyses show that 35% of EST-suggested alternative transcripts contain PTCs [Cells have evolved surveillance mechanisms to ensure the fidelity of gene expression. One such mechanism, nonsense-mediated mRNA decay (NMD), was discovered about twenty years ago in yeast  and then elegans ,10. The  elegans ,10. This elegans ) is also elegans -14. It w elegans ,15,16. F elegans . The res elegans . This ne elegans . This coain PTCs . Alternaain PTCs , and in ain PTCs . Whetherfah coding gene located on chromosome 15 in the q23-q25 region [fah gene, including 7 nonsense mutations [per se. Interestingly, del100 has skipped exon 8 and as a consequence, the reading frame is shifted, with the appearance of several new PTCs. This transcript is therefore likely subjected to NMD, as suggested by a block of translation by cycloheximide. However, the amount of nonsense transcript which escapes NMD seems to be sufficient to produce a protein of 31-kDa, detected in several human tissues. This report suggests that NMD may allow for the production of low amounts of protein.Fumarylacetoacetate hydrolase  is the last enzyme of the tyrosine catabolic pathway. A deficiency in FAH causes hereditary tyrosinemia type I , the most severe disease of the pathway . This in5 region  spans ovutations -26. Whilfah gene at nucleotide position 786 and is frequent in the Finnish population [fah gene. The first one, del100, lacks exon 8 , which weakens the donor splice site of exon 9 . Becausefah gene, rather than resulting from the presence of the W262X mutation.Altogether, these data strongly argue in favor of del100 and del231 being minor alternative transcripts of the fah gene raised the question whether they resulted from errors of the splicing apparatus and were unproductive alternative transcripts or whether they could produce protein products with potential physiological roles. There is presently no reported indication for the existence of additional FAH isoforms. The DEL231 open reading frame is identical to FAH, except for the missing region encoded by the skipped exons 8 and 9 and corresponding to amino acids 203 to 280 . To verify if the nonsense del100 transcript was subjected to NMD, lymphoblastoid cells were treated with cycloheximide Figure  an inhibDel100 and del231 were originally identified while studying the impact of NMD on hereditary tyrosinemia type I. During the characterization of the effects of the W262X mutation on FAH mRNA metabolism , we detefah gene. However, it remained to see whether they were unproductive splice isoforms or whether they could code for protein isoforms. The del231 transcript retains an unchanged open reading frame when compared to FAH. The putative DEL231 protein would be similar to FAH except for the lower molecular weight (about 35-kDa), due to the missing region encoded by exons 8 and 9. We have been unable to detect a protein species of the size that could correspond to DEL231 using an antibody against full-length FAH. Whether this reflects the absence of such a protein or its presence in a very low amount undetectable with the presently available antibodies remains unknown. The latter explanation seems plausible since the del231 transcript, although not subjected to NMD, is much less abundant that the full-length FAH transcript or del100, as it is barely detected in W262X cells with the RT76 and RT025 primers . RNA from human normal liver was extracted using the RNAqueous kit (Ambion). 1 μg RNA was reverse transcribed using an oligo(dT) and Stratascript (Stratagene). FAH cDNA was amplified from exons 6 to 14 using the following primers: RT76 (5'-CGT GCC TCC TCT GTC GTG-3') and RT025 (5'-GGG AAT TCT GTC ACT GAA TGG CGG AC-3'). Sense primers were designed to specifically amplify del100 and del231. RT84 (5'-TGG AGC TGG AAA TGC ACG-3') spans the exon 7 to exon 9 junction, whereas RT85 (5'-TGG AGC TGG AAA TGG ACC-3') spans the exon 7 to exon 10 junction. Amplification of the alternative transcripts with RT84 or RT85 was performed using HotStart (Qiagen) and PCR conditions were optimized in order to minimize nonspecific hybridization of the primers. Moreover, for each amplification (the FAH transcripts and RAR), the kinetic of the reactions were performed and the number of cycles used for each PCR was in the exponential phase.Total RNA was extracted from 5·10Analysis of the cycloheximide treatment was done as previously described .An antiserum against the C-terminal part of the DEL100 protein was raised in mouse. The antigen is the C-terminal part (the last 67 amino acids) of the DEL100 protein and is different from FAH or the DEL231 protein. FAH cDNA was amplified from exon 9 to exon 14 using the primers hFAHsstermdel100 (5'-CGG GAT CCC TGC AGC ACG AGA CAT TCA GAA GTG G-3') and RT025 with Expand High Fidelity. The PCR product was inserted into pET30a (Novagen) at the BamHI and EcoRI sites, in order to express the reading frame of the C-terminal part of the DEL100 protein. The His-Tag fusion protein used for immunization was purified by affinity chromatography on a Ni-NTA column (Qiagen) in denaturing conditions with 6 M urea.Escherichia coli as previously described [The anti-hFAH monoclonal antibody was raised against the N-terminus of the protein. The 161 residue peptide was obtained by cutting the pET30a-FAH vector  with theescribed  and puriescribed .Cells were harvested and lysed in 1 × SDS sample buffer . Human tissues obtained at autopsy and stored at -70°C until used  were hom2HPO4, pH 8.2.In some experiments, the mouse antiserum against the DEL100 protein was adsorbed on the recombinant His-tag C-terminal protein blotted on nitrocellulose. After an overnight incubation at 4°C, the non-adsorbed fraction was removed and conserved for further characterization. The adsorbed antibody fraction (affinity purified) was eluted using 1 ml of glycine-HCl 0.1 M, pH 2.8 and the pH immediately neutralized by adding 100 μl of 1 M Kin vitro transcription-translation using the TNT coupled reticulocyte lysate system (Promega) according to the manufacturer's recommendations. 25 μl of the reaction were used for immunoprecipitation using the anti-Myc antibody as follows: the anti-Myc (1/100) was incubated 1 hour with protein A-sepharose beads (Sigma). The antibody was next immobilized on the beads using 20 mM dimethyl pimedilate (Sigma) in 0.2 M borate sodium (pH 9.0) for 30 min at room temperature. The reaction was stopped by washing the beads twice in 0.2 M ethanolamine and incubation in this solution for 2 hours at room temperature. The antigen (25 μl of the in vitro translated DEL100-Myc protein) was incubated with the beads for two hours at 4°C in a dilution buffer containing 10 mM Tris-HCl pH 8.0, 1 mM EDTA and 10% glycerol. The immunoprecipitated protein was eluted by adding 25 μl of SDS loading buffer . Samples were electrophoresed on SDS-15% polyacrylamide gels and proteins transferred to a nitrocellulose membrane. The anti-Myc was used at a dilution of 1/2,000 and the anti-DEL100 antiserum at a dilution of 1/1,000.The del100 cDNA was obtained by RT-PCR on total RNA extracted from W262X/W262X cells using ND1 (5' CCC AAG CTT CAG CAT GTC CTT CAT CCC GGT GG 3') and ND2 (5' TGC TCT AGA TTT ATT TGT CAC TGA ATG GCG G 3'). The amplification products were cloned into pDrive (Qiagen) and different clones were sequenced. One clone containing the del100 cDNA was used for further cloning. It was amplified using 5'del100-Eco (5' GGA ATT CCA GCA TGT CCT TCA TCC 3') and ND2. The amplified fragment was digested with EcoRI and XbaI and ligated into EcoRI-XbaI-digested pcDNA3-myc RANGAP, replacing the insert coding for RANGAP. pcDNA3-mycRANGAP was kindly provided by Dr M. J. Matunis  The construct was used for coupled CHX, cycloheximide; ESE, exonic splicing enhancer; FAH, fumarylacetoacetate hydrolase; HTI, hereditary tyrosinemia type I; Ig, immunoglobulin; NMD, nonsense-mediated mRNA decay; PCR, polymerase chain reaction; PTB, polypyrimidine tract binding protein; PTC, premature termination codon; RAR, retinoic acid receptor; RT, reverse transcription; RUST, regulated unproductive splicing and translation; TcR, T-cell receptor.ND carried out the experiments and wrote the manuscript. AM participated in the design of the study. JFBL participated in the cloning of Del100 into pcDNA3. AB raised the mAb directed against the N-terminal part of the FAH protein. RMT participated in the design, coordination of the study, and in the writing of the manuscript.


We therefore performed whole-cell patch clamp recordings from neurons in the caudal third of the pretectal nuclear complex in frontal brain slices obtained from 3 to 6 week old hooded rats and tried to classify pretectal neurons electrophysiologically.Neurons in the mammalian pretectum are involved in the control of various visual and oculomotor tasks. Because functionally independent pretectal cell populations show a wide variation of response types to visual stimulation in vitro. These cells had more positive resting potentials and higher input resistances than cells that were not spontaneously active. The maintained firing of spontaneously active pretectal cells was characterized by only small variances in interspike intervals and thus showed a regular temporal patterning. The firing rate was directly correlated to the membrane potential. Removing excitatory inputs by blockade of AMPA and/or NMDA receptors did not change the spontaneous activity. Simultaneous blockade of excitatory and inhibitory synaptic input by a substitution of extracellular calcium with cobalt neither changed the firing rate nor its temporal patterning. Each action potential was preceeded by a depolarizing inward current which was insensitive to calcium removal but which disappeared in the presence of tetrodotoxin.Pretectal neurons showed various response types to intracellular depolarizations, including bursting and regular firing behavior. One population of pretectal nuclear complex neurons could be particularly distinguished from others because they displayed spontaneous activity Our results indicate that a specific subpopulation of pretectal neurons is capable of generating maintained activity in the absence of any external synaptic input. This maintained activity depends on a sodium conductance and is independent from calcium currents. Neurons in the mammalian pretectal nuclear complex (PNC) are involved in the control of various oculomotor reflexes, like the pupillary light reflex and the optokinetic reflex (OKR). Pupil constriction is controlled by neurons in the olivary pretectal nucleus that project bilaterally to the Edinger-Westphal nucleus -7. Slow in vivo. Thus, neurons involved in the pupillary light reflex respond tonically to the overall retinal luminance . Becausin vitro. Furthermore, because the timing of postsynaptic spikes with respect to their presynaptic input might be of considerable functional importance for saccade-related neurons such cells should exhibit lower input resistance than neurons for which spike time precision is less important. Low input resistances allow faster depolarization of the postsynaptic membrane and, hence, less temporal variance or "jitter" between presynaptic and postsynaptic spikes will occur. However, spontaneously active PNC cells on average showed higher input resistances in our sample and we therefore do not think that they represent saccade-related PNC neurons.Neurons found in NOT and PPN include various functional cell populations. One of them has been associated with the generation of slow phase eye movements during OKR while others seem to transfer visual information linked to the execution of saccadic eye movements. Cells from these latter populations are all characterized by short duration, high frequency burst responses to fast image motions or rapid eye movements ,34-36,57in vivo when appropriately stimulated by low speed horizontal movements of whole field visual stimuli. In all mammals studied, neurons in the right PNC are excited by rightward stimulus motion and control eye movements to the right, while neurons in the left PNC are activated by leftward stimulus motion and control eye movements to the left [in vitro, it will be necessary to identify the postsynaptic targets of the spontaneously active PNC neurons.On the other hand, cells that control compensatory eye movements during OKN are characterized by tonic firing the left ,27-33. Tthe left . Howeverthe left ,59. Becain vitro. The spontaneous firing depends on a sodium conductance and is independent from afferent synaptic input. Although the postsynaptic target and, consequently, the functional role of the spontaneously active PNC cells remain to be determined it is reasonable to assume that these cells also show spontaneous activity in vivo. Therefore, one likely candidate to represent spontaneously active cells in vivo are PNC neurons that are involved in the generation of slow compensatory eye movements during optokinetic nystagmus. If this is true, spontaneous firing might help to maintain an activity balance between neurons in the right and in the left PNC and thus stabilize eye position in the absence of retinal image motion.We have been able to demonstrate a specific population of neurons in the PNC that is capable of generating spontaneous activity Guidelines for the Use of Animals in Neuroscience Research of the Society for Neuroscience. Animals were deeply anesthetized with halothane and a subcutaneous injection of ketamine (100 mg/kg body weight) and thiazine hydrochloride (1 mg/kg), and transcardially perfused with ice-cold artificial cerebro-spinal fluid (ACSF) containing (in mM), NaCl 123, KCl 2.5, NaH2PO4 1, NaHCO3 26, MgSO4 1.3, CaCl2 1.8, glucose 11, that was continuously gassed with 5% CO2 / 95% O2. After the brain had been removed from the skull, 350 μ m-thick coronal slices were cut on a vibratome in ice cold ACSF. Three to four single slices that included the caudal PNC were obtained from each experimental animal. Slices were kept in ACSF at 36°C for at least one hour to allow recovery from the slicing procedure. For recording, they were transferred to a submerged type recording chamber where they were superfused at 3 ml/min with ACSF at 34°C during patch clamp experiments.Acute brain slices were obtained from 3 to 6 week-old Long-Evans hooded rats of either sex that had been raised at the institute's own colony. All experimental procedures were in strict compliance with governmental regulations and in accordance with the 2 4, Na2ATP 4, Na3GTP 0.4, EGTA 0.5, to which 0.5% biocytin was added for morphological single cell reconstruction. Measured membrane potentials were corrected for the junction potential of -10 mV.Whole-cell recordings from neurons in the caudo-lateral PNC were performed under visual guidance using infrared differential interference videomicroscopy . For recPostsynaptic responses were evoked with a concentric bipolar stimulation electrode  placed in the optic tract (OT) at the lateral PNC border. Electrical stimuli delivered were 0.5 to 2 mA in amplitude and had a duration of 100 to 500 μs. The neuronal signals were amplified and filtered using an EPC9 amplifier , digitized at 20 kHz, and displayed, stored, and analyzed using PULSE/PULSEFIT software . Unless otherwise stated, postsynaptic current responses evoked by OT stimuli were averaged over three consecutive stimulus applications. All drug effects are given as mean values ± standard deviation, they were statistically tested for significance using the Student's t-test.All drugs used were obtained from Sigma-Aldrich  and were bath applied. A10-minute application time proved sufficient to achieve stable responses. Application of 20 μM 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX) was used to block AMPA receptors. Either 50 μM APV or 2 mM kynurenic acid were used to block NMDA receptors. Na currents were suppressed by application of 1 μM tetrodotoxin (TTX).At the end of each recording session, slices were immersion fixed in 4% parafomaldehyde in 0.1 M phosphate buffer, pH 7.4, at 4°C. After at least 24 h in fixative, slices were processed using standard histochemical techniques for visualization of biocytin with 3,3-diaminobenzidine . Morphological reconstruction of stained cells was done with the aid of a camera lucida.NP participated in the design of the study and executed all aspects including data collection, analysis and drafting the manuscript. MS initially designed the study, assisted with data collection and analysis, and edited the manuscript. Both authors read and approved the final manuscript.


The interferon (IFN)-induced, dsRNA-dependent serine/threonine protein kinase, PKR, plays a key regulatory role in the IFN-mediated anti-viral response by blocking translation in the infected cell by phosphorylating the alpha subunit of elongation factor 2 (eIF2). The human immunodeficiency virus type 1 (HIV-1) evades the anti-viral IFN response through the binding of one of its major transcriptional regulatory proteins, Tat, to PKR. HIV-1 Tat acts as a substrate homologue for the enzyme, competing with eIF2α, and inhibiting the translational block. It has been shown that during the interaction with PKR, Tat becomes phosphorylated at three residues: serine 62, threonine 64 and serine 68. We have investigated the effect of this phosphorylation on the function of Tat in viral transcription. HIV-1 Tat activates transcription elongation by first binding to TAR RNA, a stem-loop structure found at the 5' end of all viral transcripts. Our results showed faster, greater and stronger binding of Tat to TAR RNA after phosphorylation by PKR.In vitro phosphorylation experiments with a series of bacterial expression constructs carrying the wild-type tat gene or mutants of the gene with alanine substitutions at one, two, or all three of the serine/threonine PKR phosphorylation sites, showed that these were subject to different levels of phosphorylation by PKR and displayed distinct kinetic behaviour. These results also suggested a cooperative role for the phosphorylation of S68 in conjunction with S62 and T64. We examined the effect of phosphorylation on Tat-mediated transactivation of the HIV-1 LTR in vivo with a series of analogous mammalian expression constructs. Co-transfection experiments showed a gradual reduction in transactivation as the number of mutated phosphorylation sites increased, and a 4-fold decrease in LTR transactivation with the Tat triple mutant that could not be phosphorylated by PKR. Furthermore, the transfection data also suggested that the presence of S68 is necessary for optimal Tat-mediated transactivation.We have investigated the effect of phosphorylation on Tat-mediated transactivation. Our results showed faster, greater and stronger binding of Tat to TAR RNA after phosphorylation by PKR. These results support the hypothesis that phosphorylation of Tat may be important for its function in HIV-1 LTR transactivation. Furthermore, the presence of increasing levels of IFN in the serum of AIDS patients while viral replication continues and the disease progresses [Since its isolation in 1983 , human iin vitro , it has ogresses -7 indicaIn response to viral infection, IFN induces a number of genes including the dsRNA-dependent protein kinase R (PKR). PKR exerts its anti-viral activity by phosphorylating the alpha subunit of translation initiation factor 2 (eIF2α), which results in the shut-down of protein synthesis in the cell . The impHIV-1 Tat is a 14 kDa viral protein involved in the regulation of HIV-1 transcriptional elongation -26 and iWhile a number of reports have shown that PKR and Tat protein interact, and furthermore, that Tat is phosphorylated by PKR, none have yet addressed the issue of the functional consequences for the phosphorylation of the Tat protein. Here we examine the phosphorylation of Tat by PKR and its effect on TAR RNA binding and HIV-1 transcription, and show that the phosphorylation of Tat results in Tat protein binding more strongly to TAR RNA. Removal of the residues reported to be phosphorylated by PKR resulted in decreased Tat phosphorylation and a significant loss of Tat-mediated transcriptional activity.We first confirmed the capability of our PKR preparation immunoprecipitated from HeLa cells to phosphorylate synthetic Tat protein (aa 1–86) Figure , and we in vitro using PKR previously immunoprecipitated from HeLa cells. An electrophoretic mobility shift assay (EMSA) was performed to observe any difference in the binding of Tat-N and Tat-P to TAR RNA  and normal Tat (Tat-N) to bind to HIV-1 TAR RNA. Synthetic Tat protein (aa 1–86) was phosphorylated A Figure . It can As Tat-P appeared to bind more readily to TAR, we next investigated the differences in the binding efficiency of Tat-N and Tat-P with TAR RNA. EMSA were performed in the presence of increasing concentrations of NaCl (from 25–1000 mM). The progressive dissociation of the Tat-N-TAR RNA complex with increasing concentrations of salt in the buffer was observed , and the time required to reach half-maximal phosphorylation (K0.5)  Figure .max) for S62, T64 and T68 of 98.6%, 87.5% and 81.6% respectively compared to the wild type (Pmax = 82.8%) and K0.5 values of 10.9 min, 5.2 min and 0.8 min (wild-type = 5.5 min). This observation was also applicable to the Tat S62A.T64A mutant, which exhibited 87% phosphorylation . However, the percentage of phosphorylation at 15 minutes for the other double mutants and for the triple mutant decreased to 68% for Tat T64A.S68A, 48% for Tat S62A.S68A, and 56% for Tat S62A.T64A.S68A. These values also correlated well with the higher Pmax values  and K0.5 values  for each mutant, indicating slower, less efficient and non-specific phosphorylation.Phosphorylation of the single mutants was rapid and specific with maximal phosphorylation values .The absence of S62 in combination with S68 also had a marked effect on transactivation, reducing it 2.5-fold. On the other hand, the absence of S62 in combination with T64 reduced transactivation 1.8-fold. This suggests that the absence of S62 and T64 either singly or in combination is not as important for Tat-mediated transactivation as when these residues are absent in combination with S68, and may indicate a more important role for S68 in Tat transactivation. These data correlate with observations previously obtained in PKR phosphorylation experiments with these Tat mutants.HIV-1 inhibits the antiviral effects of IFN by the direct binding of its Tat protein to PKR . In the The binding of Tat and TAR RNA is a necessary step for Tat to mediate viral transcription elongation -35. In eHowever, the precise mechanism by which phosphorylated Tat accomplishes this remains to be elucidated. It may be that the phosphorylation of Tat changes its secondary structure. This may result in an increased net positive charge by either exposing basic amino acids or masking negative amino acids, and this increases the attraction to negatively charged RNA, as in the case of cAMP response element binding protein (CREB) phosphorylation by protein kinase A and glycogen synthase kinase-3 . On the in vivo with a series of mammalian expression constructs carrying the wild-type tat gene or mutants of the gene with alanine substitutions at one, two, or all three of the serine/threonine PKR phosphorylation sites. Firstly, we investigated the in vitro phosphorylation of Tat by PKR using Tat proteins expressed and purified from analogous bacterial expression constructs. These were subject to different levels of phosphorylation by PKR and displayed distinct kinetic behaviour. Nonlinear regression analysis of the proteins indicated that PKR could not phosphorylate S62 or T64 alone in the absence of S68. These results suggest a cooperative role for the phosphorylation of S68 in conjunction with S62 and T64, although the mechanism involved and the reason for cooperation require further investigation. Overall, a gradual reduction in phosphorylation was observed as the number of mutated phosphorylation sites increased, and any phosphorylation observed with the triple mutant was shown to be non-specific, thus confirming previous published results identifying S62, T64 and S68 as the only PKR phosphorylation sites [We examined the effect of phosphorylation on Tat-mediated transactivation of the HIV-1 LTR on sites . Howeverin vitro phosphorylation data and support the hypothesis that phosphorylation of Tat may be important for its function in HIV-1 LTR transactivation.Co-transfection experiments with the mammalian expression constructs showed a 4-fold decrease in LTR transactivation with the Tat triple mutant which could not be phosphorylated by PKR. A gradual reduction in transactivation was observed as the number of mutated phosphorylation sites increased – a 2-fold reduction with the removal of one site, and 2.5-fold with the removal of two sites. Furthermore, the transfection data also suggested that the presence of S68 is necessary for optimal Tat-mediated transactivation, since its absence in conjunction with one or both of the other residues yielded the lowest levels of transcription. These results were in agreement with the It is relevant to note that even in the absence of all three PKR phosphorylation sites the level of transcription was still 3-fold above baseline. This may imply that Tat can still transactivate in the absence of PKR phosphorylation, although at much reduced efficiency, and/or that the protein may be phosphorylated by other kinases at other sites, for example, PKC which phosphorylates Tat at S46 . AlternaThe mechanism by which the absence or presence of phosphorylation affects transactivation still requires further investigation. It could be that the introduction of an increasing number of mutations in the region 62–68 which lies next to the nuclear localization signal (aa 49–58) leads to conformational changes that prevent the protein from entering the nucleus. However, HIV-1 subtype C viruses which are rapidly expanding, carry mutations in Tat R57S and G63Q within and close to the basic domain, and yet exhibit increased transcriptional activity . On the Overall, these results suggest that the phosphorylation of Tat by PKR plays a key role in the ability of Tat to transactivate the HIV-1 LTR, allowing the virus to use the natural antiviral responses mediated by interferon to further its own replication. This may, in part, explain the observation of increasing IFN levels in patients with advanced AIDS. The gradual reduction in transactivation observed with the decreasing absence of phosphorylation residues suggest that the presence of all PKR phosphorylation sites within the protein may be required for the optimal function of Tat in transactivation, and that the absence of S68, especially when in combination with T64, has a greater negative impact on transactivation.in vitro transcription of TAR RNA after digestion with HinD III. A β-actin luciferase reporter gene plasmid was used as a transfection control to normalize transfection efficiency and was provided by Assoc. Prof. Nick Saunders, CICR, University of Queensland, Brisbane. The pHIV-LTR-CAT construct used in transfection experiments, the destination vector, pET-DEST42 , and the pET-DEST42-Tat86 construct were a gift from Dr. David Harrich, QIMR, Brisbane. The mammalian expression vector, pcDNA3.2-DEST was purchased from Invitrogen  and was used as the destination vector for the construction of the Tat86 wild-type and mutant constructs.The plasmid, pTZ18-TAR80 was a kind gift from Dr. E. Blair, and was used for Synthetic HIV-1 Tat(1–86) protein was a gift from Dr. E. Blair. The protein is a chemically synthesized, full-length HIV-1(Bru) Tat (amino acids 1–86). Histidine-tagged HIV-1 Tat86 was expressed in BL21(DE3)pLysS cells  and purified in the laboratory of Dr. David Harrich, QIMR, Brisbane. Histidine-tagged HIV-1 Tat86 phosphorylation mutants were prepared as described elsewhere in this method.PKR was prepared as described elsewhere in this method.tat gene at the three PKR phosphorylation sites: serine 62, threonine 64 and serine 68, by site-directed mutagenesis using complementary synthetic oligonucleotide primers  encoding the mutation of the residue, or residues, to alanine. The reaction for site-directed mutagenesis contained 32 μL distilled water, 5 μL Pfu I 10X reaction buffer , 100 ng pET-DEST42-Tat86, 5 μL 5' oligonucleotide primer at a concentration of 25 ng/μL, 1 μL 10 mM dNTP mix, and 3 Units Pfu I DNA polymerase . The reaction was subjected to PCR with the following cycling conditions: 95°C for 30 seconds, 18 cycles at 95°C for 30 seconds/55°C for 1 minute/68°C for 15 minutes, hold at 4°C. Electrocompetent JM109 cells were prepared in the laboratory and transformed with 2 μL of PCR reaction. Minipreps were prepared from selected ampicillin-resistant colonies and sequenced to confirm the mutation in the construct.Bacterial expression constructs were prepared using the prokaryotic expression vector, pET-DEST42-Tat86. Mutations were introduced in the tat genes from pET-DEST42-Tat86 wild type and mutants to the mammalian expression vector, pcDNA3.2-DEST, according to the protocol supplied by the manufacturer.Mammalian expression constructs were prepared using Gateway Cloning Technology  to transfer the mutated 600 was 0.6. The culture was inoculated with IPTG  to a final concentration of 200 μg/mL and incubation was continued for a further 2 hours. Cells were pelleted; the pellet was resuspended in 2 volumes of 6 M guanidine-HCl, pH 8.0 and incubated at room temperature overnight. The suspension was centrifuged at 14500 × g for 20 minutes, and the supernatant was centrifuged at 100 000 × g for 30 minutes. The supernatant was loaded onto a 1 mL equilibrated, packed resin . To equilibrate, the resin was washed twice with 10 mL of Milli-Q water and charged by incubating with 5 mL of 0.3 M CoCl2 at room temperature for 5 minutes. The resin was then washed extensively with water, and equilibrated in 6 M guanidine-HCl, pH 8.0. The HIS-tagged protein was allowed to bind to the resin by incubation on a rocking platform, at room temperature, for 1 hour. The resin was then sedimented at 700 × g for 2 minutes, and washed with 6 M guanidine-HCl, pH 8.0 for 5 minutes. The resin was sedimented as above and washed with 6 M guanidine-HCl, pH 6.0 for 5 minutes. The resin was loaded onto an empty column , and the wash allowed to flow through. The HIS-tagged protein was eluted with 4 mL of 6 M guanidine-HCl, pH 4.0, and collected in 500 μL fractions. Fractions were dialysed in 0.1 mM DTT in PBS, at room temperature, overnight, and then centrifuged at 14500 × g for 2 minutes. To identify fractions containing the HIS-tagged protein, 5–20 μL aliquots were analysed by 15% SDS-PAGE and stained with Coomassie blue. Fractions containing protein were assayed for protein concentration , and by Western blot against a 1:1000 dilution of monoclonal anti-poly HISTIDINE Clone HIS-1 antibody . Aliquots of fractions were stored at -80°C in 10 mM DTT in PBS.Competent BL21(DE3)pLysS cells  were transformed with 1 μL of pET-DEST42-His-Tat86 wild-type or mutants, and plated. A single ampicillin resistant colony was resuspended in 10 mL of LB broth/amp and incubated overnight at 37°C. This culture was added to 500 mL of LB broth/amp and incubated in an orbital shaker, at 37°C until the OD2 flasks were lysed in 1 mL of Buffer 1 , and centrifuged at 13500 × g for 30 minutes at 4°C. The supernatant was incubated in ice, for 30 minutes, with 2 μL of a 1:10 dilution of specific monoclonal antibody 71/10 , and then at 4°C overnight with 65 μL of protein G-sepharose , with continuous rotation. Protein G-sepharose-PKR was sedimented, washed three times with Buffer 1, and three times with DBGA . PKR was activated by incubating 120 μL of this suspension with 80 μL of DBGB (DBGA + 2.5 mM MnCl2), synthetic dsRNA  to a final concentration of 0.5 μg/mL, and 20 μL of 2 mg/mL ATP , at 30°C for 15 minutes.PKR was purified from HeLa cell extracts as described previously . Briefly32P-ATP . For measuring the extent of phosphorylation of the mutant Tat proteins, phosphorylation was stopped after 2, 5, 10, 15, 30, 45, and 60 minutes by the addition of protein loading buffer. Samples were analysed by 15% SDS-PAGE, and proteins were visualized by autoradiography, and scanning densitometry in a STORM 860 phosphorimager with ImageQuant® software .Phosphorylation reactions for Tat proteins contained 2 μg of HIV-1 Tat, unless otherwise indicated in the figure legend, 6 μL of activated PKR suspension, and DBGA to a final volume of 12 μL. Phosphorylation was performed at 30°C for 1 hour, unless otherwise stated, in the presence of 2 μCi of γ-in vitro transcription system  according to the protocol supplied with the kit. HIV-1 Tat was phosphorylated (Tat-P) with activated PKR for 1 hour, as described above, or in the absence of γ-32P-ATP (Tat-N). Tat-P and Tat-N were allowed to equilibrate at 30°C for 10 minutes in Binding Buffer , before incubating at 30°C for 10 minutes with 2.5 × 105 cpm of 32P-TAR RNA. The Tat-TAR RNA complexes were separated on a 5% acrylamide/0.25X TBE gel , for 3–4 hours, at 10 mA, and visualized by autoradiography.TAR RNA was synthesized from 0.8 μg of pTZ18TAR80 using a commercial 5 cells/mL. Each well was seeded with 2 mL of this cell suspension, and incubated at 37°C/5% CO2 for 24 hours or until the cell monolayer was 80–90% confluent. A solution of 625 μL of serum-free medium and 10 μg of total DNA  was mixed with 600 μL of serum-free medium containing 25 μL of Lipofectamine 2000 , and incubated at room temperature for 20 minutes. The cells were washed twice with serum-free medium, inoculated with the DNA-Lipofectamine mixture, and incubated at 37°C for 6 hours. The DNA solution was replaced with complete medium and the cells wee incubated as above for 24 hours. The cells were harvested and assayed for CAT activity using the CAT ELISA kit  according to the protocol supplied with the kit, for luciferase activity using the Luciferase Assay System  according to the supplied protocol, and for protein concentration .Transfections were performed in duplicate in 6-well plates. HeLa cells were diluted in Modified Eagle's Medium  supplemented with 10% foetal bovine serum , antibiotics and glutamine , to yield 5 × 10The author(s) declare that they have no competing interests.LEM was responsible for the experiments described and contributed to the drafting of the manuscript. TW performed the optimization experiments for the phosphorylation of Tat by PKR. DH participated in the design of the study, provided reagents and critically read the manuscript. NAJM conceived and coordinated the study, and contributed to the drafting of the manuscript.


Are evolutionary changes in gene expression determined mostly by natural selection or by random forces? It's been some 150 years since Charles Darwin proposed that organisms adapt to their environment through the process of natural selection, yet the debate still rages, particularly at the molecular level. Darwinian selection was challenged in 1983 by the Kimura neutral theory of molecular evolution, which argues that the majority of differences in DNA (nucleotide) and protein (amino acid) sequences within and between species have only minor or no selective effect and that these differences arise through mostly random processes. Mutations at the nucleotide level occur randomly and regularly. Some of them survive through generations, resulting in “fixed” evolutionary changes between species. Two potential mechanisms can lead to the fixation of a particular change: natural selection, which favors changes that convey a selective advantage, and stochastic (random) events, such as genetic drift .DNA mutations can lead to changes in gene expression levels, some of which may convey a selective advantage to an organism and therefore become fixed via natural selection. But since variation is produced at the genotype level, while selection is thought to operate largely at the phenotype level , it is reasonable to expect selection to be less apparent at the level of DNA sequence, and by extension, at the level of gene expression. Microarray technology has made it possible to systematically study expression levels of thousands of transcripts (the RNA copies of DNA that are translated into amino acid sequences) and to ask whether most changes of gene expression fixed during evolution between species result from selective or stochastic processes.To investigate this question, Philipp Khaitovich and colleagues analyzed the observed transcriptome differences among primate and mouse species as well as among various brain regions within a species. The team started out by analyzing the expression levels of some 12,000 genes in the prefrontal cortex of various primates, including humans. If evolutionary changes are caused by chance and not by natural selection, they will accumulate as a function of time rather than as a function of physical or behavioral changes in the organism. And that's what the authors found: the changes in gene expression among the species progressed linearly with time, suggesting that gene expression in primate brains evolved in large part from random processes introducing selectively neutral, or biologically insignificant, changes.According to neutral evolution theory, the same forces determine the rate of evolution both within and between species because similar random processes are at work on both levels. Consequently, genes that vary more within species should be more likely to vary between species. Comparing the expression levels of genes according to their variation within humans, the authors showed that genes with high variation among humans changed significantly faster between species than genes with low variation among humans. The authors also compared changes observed in genes to changes observed in pseudogenes  and found no significant difference between the two, suggesting again that most expression changes have no functional significance.While their analysis cannot exclude a role for natural selection, all the results are consistent with a neutral model of transcriptome evolution. This means that the majority of gene expression differences within and between species are not functional adaptations but selectively neutral and that we won't be able to explain species differences based on variation in gene expression in general.In addition to examining differences in gene expression in a particular tissue between species, the authors also discuss the evolution of different tissues within a species. The human brain is composed of regions that differ in function and histology (microscopic structure). Each of these regions acquired a functional or histological difference that separated it from its sister regions at some point in our evolutionary past. The authors show that the amount of change between regions correlates with tissue-divergence times estimated by other methods. If this finding applies for other tissues within and outside the brain, it could provide a method to reconstruct the evolution of tissues within a species.


Increasingly researchers are turning to the use of haplotype analysis as a tool in population studies, the investigation of linkage disequilibrium, and candidate gene analysis. When the phase of the data is unknown, computational methods, in particular those employing the Expectation-Maximisation (EM) algorithm, are frequently used for estimating the phase and frequency of the underlying haplotypes. These methods have proved very successful, predicting the phase-known frequencies from data for which the phase is unknown with a high degree of accuracy. Recently there has been much speculation as to the effect of unknown, or missing allelic data – a common phenomenon even with modern automated DNA analysis techniques – on the performance of EM-based methods. To this end an EM-based program, modified to accommodate missing data, has been developed, incorporating non-parametric bootstrapping for the calculation of accurate confidence intervals.Here we present the results of the analyses of various data sets in which randomly selected known alleles have been relabelled as missing. Remarkably, we find that the absence of up to 30% of the data in both biallelic and multiallelic data sets with moderate to strong levels of linkage disequilibrium can be tolerated. Additionally, the frequencies of haplotypes which predominate in the complete data analysis remain essentially the same after the addition of the random noise caused by missing data.These findings have important implications for the area of data gathering. It may be concluded that small levels of drop out in the data do not affect the overall accuracy of haplotype analysis perceptibly, and that, given recent findings on the effect of inaccurate data, ambiguous data points are best treated as unknown. Haplotype analysis has become a valuable tool for researchers in population genetics. In particular, the value attached to the prediction of the constituent haplotypes of a given sample and their frequency of occurrence is such that a variety of methods have been developed for this purpose. Many of these methods, however, depend on knowledge of the phase of the data supplied. In general, genotypic data from polymorphic loci are ascertained phase-unknown. Various methods for determining the gametic phase exist. With sufficient data from the genotyping of family members, definitive haplotypes may be inferred. However, in particular for late-onset disorders, these data may be difficult or even impossible to obtain. At the laboratory level, techniques such as chromosomal isolation or long-range PCR  may be uReliable computational techniques for the estimation of haplotype frequencies have been around for some time, and extensive studies of the accuracy of the EM-based methods have been carried out ,8, but uTwo sources of data were used for the principal part of this study. The first is real single nucleotide polymorphism (SNP) data; the second is multiallelic data generated via population generation software. Three additional sets of data containing 10%, 20% and 30% missing alleles respectively were generated from each of the two original sets. The process of generation is described in the Methods section. HFE was carried out on the eight data sets listed above. In each case 1,000 bootstrap iterations were performed for each HFE analysis and the 95% CIs about the point results were selected. For the sake of clarity the results from analyses of the 20% unknown alleles data sets have been omitted from the displayed graphs. Further tests were performed to investigate the effect of sample size upon the quality of the results. To this end two sets of progressively smaller data sets, with and without missing alleles, were generated from the SNP and multiallelic data sets, and HFE was carried out. The method of selecting these data is outlined in the Methods section.An additional data set, unrelated to those previously described, consisting of data from five SNP loci was generated for the purposes of performing tests on data with weak LD between the loci. A further data set with 10% missing alleles was generated from these additional data.D  [Figure D  ,11 givenhi and  are the haplotype frequencies derived from the phase-known and phase-unknown data respectively, and N is the number of possible haplotypes in the sample. As these data are from seven biallelic loci, N = 27 = 128 in this case. The results are displayed in Table D  as the percentage of unknown alleles in the sample increases. In each case it is the percentage increase relative to the complete data value that is measured. Three haplotypes absent from the phase-known data set appear in the results of the HFE analysis of the complete data. Their frequencies are 2.3 × 10-3, 1.4 × 10-3, and 1.1 × 10-3. Of the haplotypes present in the phase-known data, only one haplotype appears with a frequency less than these, the given frequency being 9.3 × 10-4. Figure D  is recorded. The subsequent percentage increases going from 10% to 20% and 20% to 30% unknown alleles are 22% and 16%, respectively, of the value of D  for the complete data. Figure where D , was measured. As the allele counts at each of the seven loci are 8, 2, 2, 9, 2, 5, and 2 respectively, the sum in Equation 1 is over the N = 5760 possible haplotypes in the sample. The results are displayed in Table D  as the percentage of unknown alleles in the sample increases is also recorded.Similar computations to those carried out for the SNP data sets were carried out for the four multiallelic data sets. Figure -6 as a result of the HFE analysis. 29 of these do not appear in the phase-known data, with the most common of these having a frequency of 2.187 × 10-3. 68 haplotypes in the phase-known data display a frequency greater than this. As with the SNP case, Figure D  with the 10% and 20% missing data cases to those of Table D  going from 20% to 30% unknown alleles comes to 40% of the value of D  for the complete data. In Figure 129 distinct haplotypes were estimated to have a frequency of greater than 10Similarly to the SNP case, Figures D  results for each were displayed in Table D  were those derived from the respective smaller samples . As may be expected, in all cases we see an increase in D  as we move from the complete data to the data sets with missing alleles. D  also is seen to increase as the sample size decreases. However, what is of note is the pattern involved. For the seven loci SNP case, the percentage increase in D  from complete to missing data itself increases monotonically as the sample size is reduced. A similar pattern is not observed in the multiallelic data.Investigations were made into the effect of the sample size on the performance of the HFE method when 10% of the data was missing. Three further data sets of sizes 300, 100 and 50 individuals were generated by random selection from the original seven loci SNP and multiallelic sets. From these data, six additional sets with 10% missing alleles were created. HFE was performed upon these additional data, and the D' [D  readings for this particular case. Here we see a large percentage increase of 60% in D  as we move from the complete data to 10% missing data.Fallin and Schork  illustraD'  was founThe results displayed here show the impact of the addition of increasing quantities of missing alleles on the quality of haplotype frequency estimates. Studying Figure D  and the RCI, used here to quantify the degradation in the quality of the results with increasing percentages of unknown alleles. Tables D  is an absolute measure of the performance of the algorithm, as the phase-known data are available for each data set and thus the exact sample haplotype frequencies are known. This discrepancy is to be expected; D  is a sum over all possible haplotypes and there exist only 128 (27) possible haplotypes for the seven loci SNP data, whereas the multiallelic data, as noted in the Results section, have 5760 possible haplotypes. Also, it is not surprising that haplotype frequencies estimated from the multiallelic data set are found to be less accurate than those estimated from SNPs, given the more complex nature of the data. The RCI is a relative measure, and illustrates not so much the accuracy of the algorithm, rather the effect of additional missing data. The results displayed in Tables D . Interestingly, the results for the multiallelic data set were achieved despite departure from Hardy-Weinberg equilibrium (HWE) at two of the seven loci (see Methods section). Although this technique relies on the assumption of HWE, Niu et al. [Although study of the illustrated graphs suggests that the impact of missing data is more pronounced with the more complex multiallelic data sets, Tables u et al.  have demnly 128 2 possibleD  observed when 10% of the seven loci SNP data is relabelled as missing does not change substantially as the size of the sample reduces. For the full sample of 536 individuals, the percentage jump in D  moving from the complete data to 10% missing data is approximately 35%. For the sample of size 300, this increase is 37%. Likewise for the samples of size 100 and 50, the increases are 41% and 47% respectively. However, for the multiallelic data, we see a contrasting trend. The percentage jump in D  decreases rather than increases with increasing missing data proportions. Inspection of Tables D  when moving from the complete data to 10% missing data for the full sample of 500 individuals is approximately 42%, whereas for the sample of size 300 this drops to 32%. The recorded increase for the sample of size 100, 5%, is even more striking. , and any conclusions drawn from analysis of this case would be highly suspect). Thus no definitive conclusions may be made as to the effect of missing data as the sample size is reduced, other that to say that the matching between the phase-known and phase-unknown frequencies deteriorates with falling sample size, as would be expected.The investigation into the effect of smaller sample sizes has produced some surprising results. Comparing Table D  for the complete data is comparable to that of the seven loci SNP data with 30% missing alleles. It should also be borne in mind that, as the weak LD data set features only five SNP loci, the sum for D  is over a mere 32 possible haplotypes, as compared to 128 for the seven loci SNP data, emphasising the fall-off in accuracy. Also of note is the similarity in the sample sizes -500 in the weak LD case, and 536 in the moderate to strong LD case. Moving to the 10% missing allele case, we witness a further 60% drop in accuracy, a considerably greater percentage that was observed for the medium to high LD data sets, a result which again calls into question the reliability of the method in the presence of weak LD.Table Here we show that the EM method, with the modifications to the implementation for complete data detailed here, can generate accurate estimates of haplotype frequencies even when large amounts of data are missing, in this case up to 30%. Moreover, using this method, the degree of accuracy can easily be estimated using conventional bootstrapping approaches. This is of considerable importance in the design of experiments, as it is therefore obvious that small levels of drop out in the data for whatever reason do not affect the overall accuracy of the approach perceptibly. Furthermore, considering the strongly deleterious effects of even small amounts of inaccurate data , this anD' was found to be ≥ 0.9 for all intervals but the third and fifth, where D' ≤ 0.25. As HWE is assumed for HFE, each locus was tested and found to be in HWE.The data used in this part of the study are derived from a genetic investigation of cystic fibrosis sufferers . The hapin silico. The number of distinct alleles at each locus ranged from two to nine. A trait marker was introduced between the 3rd and 4th loci for 10 of the 50 founders. The population was evolved for thirty generations as an isolated group with random mating. The birth rate per couple was binomially distributed, with a range of zero to ten offspring and a mean of 2.5. 500 individuals bearing the trait were randomly selected from the final generation for analysis. As with the SNP data, the level of LD across the interval was measured. D' was found to lie between 0.5 and 0.8 for all adjacent loci except between the second and third loci where D' = 1.0 and the fifth and sixth where D' = 0.24. A test for HWE [An initial population of fifty individuals with data from seven loci spaced 1 cM apart was generated  for HWE  was perfThe data sets of reduced size used in this analysis were generated from the original seven loci SNP and multiallelic data sets via a random sampling process. The process was identical for both. Initially 300 individuals were chosen from the original data. Following this, 100 individuals were chosen from the newly created set of size 300. Finally 50 individuals were chosen from the set of size 100. In each case the selection process was random and done without replacement. From each of these six smaller data sets, six additional sets of data with 10% missing alleles were generated by the process outlined below.in silico specifically for the testing of the performance of the HFE algorithm in low LD circumstances. D' was found to range between 0.117 and 0.014 for all adjacent loci. The data were also tested for HWE. The first locus was found to be marginally not in HWE (P = 0.0465), with excess homozygosity in evidence. All other loci were found to be in HWE.A population of 500 individuals with data from five SNP loci was generated The HFE algorithm assumes that the input data are phase-unknown, and thus no alteration was necessary to the sample data sets which were phase-known before input. Comparison tests on the phase-known data, and phase-unknown data generated from the phase-known data via a process of phase-randomisation have confirmed that no bias is introduced by the use of phase-known data (results not shown).Data sets containing unknown alleles were generated from the original data via the following procedure:1. Each individual is selected in turn.2. For each locus a random number between 0 and 100 is generated.3. If this random number falls below the desired percentage of unknowns, both of the individual's alleles at the locus in question are redefined as unknown. This ensures that all unknowns appear in homologous pairs.4. The process is repeated until all loci for all individuals are exhausted.Thus the desired percentage of unknown alleles is achieved globally, and the percentage of missing data at each locus may vary. Three additional sets of data were generated from each of the two original sets in this way, with 10%, 20% and 30% missing data respectively, giving eight data sets in all for the principal component of the study.For known gametic phase, HFE is a straightforward process of counting the constituent haplotypes in the sample. For the case where the gametic phase is unknown, maximum-likelihood haplotype frequencies are computed using the EM algorithm. The particular implementation used here for the finding of the haplotype frequencies is similar to that outlined by Excoffier and Slatkin . The opecj possible genotypes consistent with this phenotype whereMissing data in a sample necessitate alterations to the implementation for complete data of the EM-based algorithm. When all alleles in an individual are known, there exist sj is the number of heterozygous loci in phenotype j. However, when unknown alleles appear at a locus, the situation is considerably more complex. In this case each unknown allele may take on the identity of any of the alleles observed at that locus. We require that unknown alleles always appear in pairs – the amplification of one allele only would result in the appearance of a homozygote which may bias results. Thus if there are Ni distinct alleles (forms) observed at locus i in the entire sample, the number of possible complete phenotypes consistent with the observed phenotype is increased by a factor of Ni(Ni + 1)/2 by the presence of an unknown site. This factor is the number of ways of selecting two alleles from a pool of Ni distinct alleles when repetition is allowed. Thus the number of possible complete phenotypes given by phenotype j is given byand M is the number of loci in the sample andwhere Ni is the number of distinct alleles observed in the sample at locus i. For each possible complete phenotype i of the κj complete phenotypes possible for individual j, there exist ci possible genotypes, as given by Equation 2. Thus the number possible complete genotypes for phenotype j is given bywhere Pj of the jth phenotype, assuming random mating, is given by:Then, following , the proPi(hkhl)is the probability of the ith genotype made up of haplotypes k and l, andwhere pk and pl are the population frequencies of the kth and lth haplotypes.where tth step of the EM iterative process, the probability of resolving each phenotype into the different possible genotypes is given by:At the nj is the number of individuals with phenotype j, and n is the total number of individuals in the sample. Thus nj/n is the proportion of the total sample that has phenotype j, and Pj(hkhl)/Pj is the conditional probability of the particular genotype given the phenotype.where The haplotype frequencies are then computed using a form of gene-counting ,17 :N is the number of globally distinct haplotypes (the number of different possible haplotypes in the sample),  is the frequency of haplotype v, m is the number of distinct phenotypes in the sample, and εiv is equal to the number of times haplotype v appears in genotype i.where The technique of bootstrapping  was usedEDK carried out the main programming work, performed the tests and drafted the manuscript. FS designed the population generation tool and assisted in the programming effort. RM assisted in the drafting of the manuscript and provided the SNP data. All authors read and approved the final manuscript.


Prostate-specific membrane antigen (PSMA) is a well characterized prostate-specific tumor associated antigen. Its expression is elevated in prostate carcinoma, particularly in metastatic and recurrent lesions. These observations suggest that PSMA can be used as immune target to induce tumor cell-specific recognition by the host and, consequently tumor rejection. We utilized a DNA-based vaccine to specifically enhance PSMA expression. An immune modulator, such as CpG oligodeoxynucleotides which promote Th1-type immune responses was combined to increase the efficacy of tumor recognition and elimination.PSMA encoding full-length PSMA was constructed. C57BL/6 mice were immunized with endotoxin-free pCDNA3.1-PSMA alone or in combination with CpG oligodeoxynucleotides by intramuscular injection. After 4 immunizations, PSMA specific antibodies and cytotoxic T lymphocyte reactivity were measured. Immunized C57BL/6 mice were also challenged subcutaneously with B16 cells transfected with PSMA to evaluate suppression of tumor growth.A eukaryotic expression plasmid pCDNA3.1-Vaccine-specific cytotoxic T lymphocytes reactive with B16 cells expressing PSMA could be induced with this treatment schedule. Immune protection was observed in vaccinated mice as indicated by increased tumor growth in the control group (100%) compared with the groups vaccinated with DNA alone (66.7%) or DNA plus CpG oligodeoxynucleotides (50%) respectively. Average tumor volume was smaller in vaccinated groups and tumor-free survival time was prolonged by the vaccination.The current findings suggest that specific anti-tumor immune response can be induced by DNA vaccines expressing PSMA. In addition, the suppression of in vivo growth of tumor cells expressing PSMA was augmented by CpG oligodeoxynucleotides. This strategy may provide a new venue for the treatment of carcinoma of prostate after failure of standard therapy. Carcinoma of prostate is the most common cancer in males in America, ranking as the second most common leading cause of cancer-related deaths, just after carcinoma of the lung. In addition, the incidence and mortality of carcinoma of prostate are increasing in China. Although surgery and radiation therapy remain the primary choice for localized stage of carcinoma of prostate, there is no effective treatment for patients who develop recurrences or those who have metastatic disease at the time of diagnosis. Therefore, there is an urgent need for new types of treatment.Strategies that stimulate the ability of the immune system to recognize and destroy cancer cells via selective killing mechanisms have shown promise in the treatment of cancer. DNA vaccines offer several potential advantages for the immunotherapy of cancer. Proteins encoded by DNA vaccines are expressed in the cytoplasm and presented through the endogenous processing pathway associated with MHC Class I molecules, thereafter leading to the activation of CD8 + cytotoxic T lymphocytes (CTL) ,2, whichProstate specific membrane antigen (PSMA), a well-established prostate specific tumor associated antigen (TAA), is 100 kD type II transmembrane glucoprotein. It is predominantly expressed in the prostate gland, minimal levels of expression in brain tissue, jejunum and proximal kidney tubules ,5. Its emilieu [A number of strategies are under evaluation to enhance the potency of DNA vaccines, some of which involves broad stimulation of the immune system using immunomodulatory agents. Synthetic CpG oligodeoxynucleotides have immunological effects similar to those seen with bacterial DNA and represent promising vaccine adjuvants, which promote T helper1 (Th1)-type immune responses . Unmethymilieu , which fmilieu -17. ThesC57BL/6 mice (H-2b) were bred and kept under pathogen-free conditions. Male mice were used at 12 to 16 weeks of age. All animal experiments were performed in an approved protocol and in accordance with recommendations for the proper care and use of laboratory animals.The murine melanoma B16 cell was purchased from the Type Culture Collection of the Chinese Academy of Sciences and cultured in RPMI-1640 medium  supplemented with 10% (v/v) heat-inactivated fetal bovine serum , penicillin G (100 U/ml), and streptomycin (100 μg/ml).The COS-7 cell line was cultured in DMEM medium (Life Technologies) supplemented with 10% (v/v) heat-inactivated fetal bovine serum, sodium pyruvate (1 mM), penicillin (100 U/ml) and streptomycin (100 μg/ml).The monoclonal antibody 4A12 specific for an extra-cellular epitope of PSMA was previously described . The β-aGACGTTCCTGACGTT (CpG motifs were shown underlined) with the backbone phosphorothioate stabilized. CpG oligodeoxynucleotides were synthesized by Sangon , reconstituted in sterile pyrogen-free water and diluted in phosphate buffered saline for in vivo injections.CpG oligodeoxynucleotides 1826 chosen according to published data -21 had tPSMA encoding full-length PSMA was constructed by cloning the BamH I /Xho I fragment of pBluescipt-PSMA as described previously [The eukaryotic expression plasmid pCDNA3.1-eviously  into thePSMA or an empty (mock) vector by the mediation of liposome Tfx-20™  according to the manufacturer's instructions. Briefly, COS-7 cells were cultured in six-well tissue culture plates with a coverslip in each well and grown to 50–70% confluence. 1.5 μg plasmid DNA was mixed with 4.5 μl Tfx-20™ and diluted in 1000 μl serum-free DMEM medium before addition to cells. After 20 minutes of incubation, DNA-liposome complex was added to the cells and incubated for 6 hours at 5% CO2, 37°C. Complete DMEM medium containing 10% fetal bovine serum was added to the cells and incubated overnight, and then the medium was replaced with complete DMEM medium. After 2 days, the cells were fixed in cold acetone for 10 minutes at 4°C followed by extensive washing with phosphate buffered saline. The cells were incubated with anti-PSMA monoclonal antibody 4A12 for 1 hour at 37°C, subsequently incubated with FITC-labeled goat anti-mouse IgG (1:40) for 1 hour at 37°C. After thorough washing, the coverslips were mounted and observed with a fluorescence microscope.COS-7 cells were transfected with pCDNA3.1-PSMA or empty vector by the mediation of 6 μl liposome Tfx-20™ as above. After 2 days of culture, the cells were reseeded into a 10 cm-dish and cultured for other 2 days, complete RPMI-1640 medium containing 1000 μg/ml G418 (Life Technologies) was added to the culture. After 20 days of selection, all non-transfected cells died and discrete clones were visible in transfected cells. These clones were expanded in the presence of 400 μg/ml G418, positive cells expressing PSMA were identified as follows.The B16 murine melanoma cells were transfected with 2 μg of pCDNA3.1-2 (25 mM), 1 unit of Taq. The PCR reaction conditions included 5 minutes of initial denaturation at 94°C followed by 30 cycles of 30 seconds at 94°C, 1 minute at 62°C, 1 minute at 72°C and 10 minutes of final extension at 72°C. The 358 bp fragment was resolved on 2% agarose gel. GAPDH was also detected as internal reference.Total RNA was extracted from mock-transfected or transfected B16 cells using Trizol (Life Technologies) and dissolved in RNase free water. 2 μg of total RNA was transcribed into cDNA using AMV reverse transcriptase (Promega). Briefly, the total RNA was mixed with 1 μl oligo (dT) primers (0.1 μg/ μl), 4 μl RT Buffer (5×), 2 μl dNTPs (10 mM), 1 μl AMV reverse transcriptase and diethypyrocarbonate-treated water to a final volume of 20 μl. The cDNA synthesis was performed using the following PCR parameters: 37°C for 1 hour then 10 minutes at 95°C. Synthesized cDNA was used as template for PCR. The sequence of the primers used were 5'- CGAGGAGGG ATGGTG TT-3' (forward) and 5'-TGTTGTGGCTGCTTGAG-3' (reverse). PCR was carried out in a 10 μl aliquot containing 0.5 μl cDNA, 0.5 μl each primer (10 μM), 1 μl dNTPs (2 mM), 1 μl Taq buffer (10×), 0.8 μl MgClThe transfected and mock-transfected B16 cells were harvested and lysed with lysis buffer , 5 mM EDTA, 0.5% NP-40, 1 mM phenylmethylsulfonyl fluoride, 1 μg/mL aprotinin, 1 μg/mL leupeptin) for 30 min at 4°C, cell debris were removed by centrifugation. Cell lysates were heated at 100°C for 3 minutes, the samples were loaded on 6% SDS-PAGE for electrophoresis. After electrophoresis, the proteins were transferred to polyvinylidene difluoride membranes  using semi-humid transferring system . The polyvinylidene difluoride membranes were blocked with Tris-buffered solution containing 5% (w/v) non-fat milk for 1 hour at room temperature. For detection of protein, the polyvinylidene difluoride membranes were probed with anti-PSMA monoclonal antibody 4A12 and monoclonal antibody to β-actin (1:50) respectively for 1 hour at room temperature then overnight at 4°C, after then the membranes were incubated with horse anti-mouse IgG-HRP conjugate  for 1 hour at 37°C, ABC complex  for 1 hour at 37°C subsequently. The bands were visualized with 3,3'-diaminobenzidine substrate solution .PSMA and pCDNA3.1 were purified with EndoFree plasmid Maxi Kit . Three groups including 6 mice each were immunized: DNA vaccination group, CpG oligodeoxynucleotides and DNA vaccine co-administration group (hereafter referred to as CpG+DNA vaccination) and control group receiving empty plasmid. All mice were injected with 0.25% lidocaine in the quadriceps femoris muscle 3 days before vaccination in order to improve the uptaking of plasmids by muscles. The mice then received bilateral intramuscular injection with 50 μg of plasmid in the regenerating muscles. Mice in the DNA vaccination group were immunized with endotoxin-free pCDNA3.1-PSMA, mice in CpG+DNA vaccination group were further immunized with 25 μg of CpG oligodeoxynucleotides in the same location 3 days after DNA plasmid immunization, as control, mice were injected with pCDNA3.1 plasmid. All mice were boosted every 4 weeks for 3 times.Plasmids pCDNA3.1-Two weeks after the last immunization, the mice were bled and serum antibodies were measured by solid phase enzyme-linked immunosorbent assay (ELISA). Briefly, bacterially expressed fusion protein containing PSMA-derived fragment was coated on 96-well plates. The plates were blocked with 5% bovine serum albumin in phosphate buffered saline overnight at 4°C. The sera from C57BL/6 mice were serially diluted in phosphate buffered saline with 5% bovine serum albumin and then 100 μl of diluted serum was added into each well. The plates were incubated at 37°C for 1 hour, 100 μl of a 1:3000 dilution of goat anti-mouse IgG-HRP conjugate  was added into each well and incubated for 1 hour at 37°C, then 100 μl tetramethyl benzidine (TMB) chromagen/substrate solution  was added to each well. The plates were read and the absorbance at 450 nm (A450) was measured by microplate reader.7 lymphocytes (responders) were incubated in 6-well plates with 2 × 106 stimulator cells in the presence of 50 IU/ml recombinant murine IL-2 and 2 μg/ml ConA. After 6 days of culture, the re-stimulated cells were harvested and separated from the dead cells. Target cells (B16-PSMA) and re-stimulated lymphocytes (effector cells) were resuspended in phenol red-free RPMI-1640 medium supplemented with 5% new born calf serum, 5 × 103 target cells and various number of effector cells were added into individual flat-bottom wells in 96-well plates. The cells were incubated at 37°C overnight. 50 μl per well of supernatant was transferred to fresh 96-well plates. CTL reactivity was assessed by measuring lactate dehydrogenase (LDH) release using a Cytotox 96 assay kit (Promega). Controls were setup on each plate for spontaneous LDH release by target and effector cells. A parallel experiment using B16 cells transfected with pCDNA3.1 as target cells was also performed to test the specificity of lysis. All experiments were performed in triplicate. Percent lysis was calculated according to manufacturer's instructions.Two weeks after the last immunization, mice were sacrificed. Their spleens were removed and teased apart in serum-free RPMI-1640 media, the lymphocytes were collected and cultured in RPMI-1640 medium supplemented with 10% heat-inactivated fetal bovine serum, 50 IU/ml recombinant murine IL-2 and 2 μg/ml ConA for 2 days. B16 stimulator cells expressing PSMA (B16-PSMA) were prepared in complete RPMI-1640 medium containing 50 μg/ml mitomycin C for 1 hour at 37°C. Two × 105 cells were subcutaneously injected into the left lateral flank of mice. The time to the development of tumor was recorded. After tumors became detectable, their volume was measured two-dimensionally with a caliper along the longest axis (x) and the axis perpendicular to the longest axis (y) every second days. The volume of tumors was estimated by the following formula:C57BL/6 mice were divided into 3 groups and immunized as above. B16-PSMA cells were trypsinized and resuspended in phosphate buffered saline, 2 × 102Volume = π/6 × x × yAfter 26 days, when tumor reached 20 mm in their largest axis, the mice bearing tumors were sacrificed. Tumors were removed and weighed.P < 0.05 were considered significant.The data from ELISA and CTL assays are expressed as means ± SD and are representative of at least three different experiments. Comparisons between individual data points were made using ANOVA or student's t-test. In the tumor challenge experiment, the primary endpoint was time of tumor appearance. Tumor-free survival time was compared by the Kaplan-Meier method and log-rank statistic. -PSMA was introduced into COS7 cells. The cells were the incubated with anti-PSMA monoclonal antibody and goat anti-mouse IgG-FITC conjugate. The immunofluorescence assay demonstrated that the reactivity was present in the cytoplasm of COS7 cells transfected with pCDNA3.1-PSMA but not in mock-transfected cells, thus indicating that pCDNA3.1-PSMA could express protein in mammalian cells . However, titers were similar between the DNA vaccine and the CpG+DNA vaccination groups (P > 0.05), suggesting that CpG oligodeoxynucleotides did not augment the antigen-specific humoral immunity . More importantly, CTL reactivity was significantly enhanced in mice treated with CpG oligodeoxynucleotides compared with DNA vaccine alone at E:T ratios of 40:1, 20:1   Figure .PSMA or empty vector, and then challenged with B16-PSMA cells. Protection was observed in pCDNA3.1-PSMA vaccinated mice with decrease of tumor incidence. After 26 days, all mice in the control group developed tumors while 2 (2/6) and 3 tumor-free mice (3/6) were observed in the DNA and in the CpG + DNA vaccination groups respectively. Kaplan-Meier curves showed that the tumor-free survival interval was 19.67 ± 2.24 days in the DNA vaccination group, 22.33 ± 1.61 days in the CpG +DNA vaccination group and 13.17 ± 1.01 in the control group , so was the difference between CpG+DNA vaccination group and control group (P = 0.0016). Tumor-free survival time was longer in CpG+DNA vaccination group than DNA vaccination group, but the difference was of no statistical significance (P = 0.49). This observation may be associated with the small number in each group.C57BL/6 mice (n= 6/group) were vaccinated with pCDNA3.1-oup Fig. . The difPSMA vaccination, the individual tumors were consistently smaller than those in the control group and the analysis of tumor growth kinetics indicated that the tumor growth was significantly slower in the CpG +DNA vaccination group compared to the other two groups . Moreover, the difference between DNA vaccination group and CpG +DNA vaccination group was statistically significant .The volume of the tumors in the control group was consistently larger than in other two groups Fig.  and the Although treatments are available for organ-confined carcinoma of prostate, there is no effective approach to treat recurrent disease after androgen deprivation therapy fails. New approaches are required to treat this incurable disease. DNA vaccination enables maintenance of tumor antigen expression at the vaccination site and results in immune responses in the host, therefore, shedding light on the treatment of cancer. It has been reported that tumor growth is suppressed when tumor cells are implanted in mice previously immunized with DNA vaccines encoding tumor antigens -25.PSMA is a well-defined prostate-restricted tumor associated antigen whose expression is significantly elevated in carcinoma of prostate, especially in advanced stages. The expression of PSMA is down-regulated by androgen, after androgen deprivation therapy, its expression is strongly elevated,26-28, TThe purpose of our work was to delineate new ways to induce immune responses by DNA vaccination. In this study, all mice immunized with DNA vaccine expressing PSMA generated PSMA specific antibodies at a low level, which may result from the small amount of antigen expressed by plasmid in vivo. What is noteworthy is that all immunized mice developed CTL reactivity to B16-PSMA which led to suppression of tumor growth. In addition, although some tumors developed in some treated mice, they were consistently smaller in the control group. These findings suggest that DNA vaccines expressing PSMA could elicit immune response against tumor cells expressing the target molecule.Although DNA vaccines provide a convenient and effective approach to elicit cellular immunity, clinical outcomes have not been satisfactory, mainly because tumor-specific CTL elicited by the vaccines are insufficient to suppress cancer progression. CD8+ CTLs constitute one of the most important arms of the immune system, exhibiting the capacity of recognizing and destroying cancerous cells,33. A vaSynthetic CpG oligodeoxynucleotides represent a promising adjuvant. The predominant effect of CpG oligodeoxynucleotides exposure is the promotion of Th1-type immune responses. Professional antigen presenting uptake CpG oligodeoxynucleotides and become activated with increased expression of MHC and co-stimulatory molecules -38 that CpG oligodeoxynucleotides 1826 is a potent enhancer of Th1-type immune responses and may benefit anti-cancer therapy -43. We, Consistent with previous reports, this study suggests that CpG oligodeoxynucleotides enhance cellular immunity. The activity of CTL against PSMA expressing cells in the CpG +DNA vaccination group was significantly higher than in the DNA vaccination group. Furthermore, tumor challenge experiments demonstrated a potentiation of the suppressive effects on the growth of tumor cells expressing PSMA. These findings indicate that CpG oligodeoxynucleotides should be a powerful adjuvant in the context of DNA-based vaccination.In this study, we designed a DNA vaccine expressing prostate specific membrane antigen (PSMA) and utilized CpG oligodeoxynucleotides to promote Th1-type immune response. We discovered that the constructed vaccine generated anti-tumor reactivity against malignant cells expressing PSMA that was enhanced by CpG oligodeoxynucleotides co-administration. This strategy may provide a new venue for the treatment of carcinoma of prostate, particularly for recurrent disease after hormone therapy fails.None declared.R.J.Q participated in the design of the study and carried out plasmid DNA transfection, RT-PCR, immunofluorescence assay, DNA vaccination, lymphocyte stimulations, cytotoxicity assays, and completed the preparation of the manuscript. Z.L participated in the design of the study and carried out the construction of the expression plasmid, western blotting, and histological analysis and assisted in the preparation of the manuscript. C.Q carried out cell culture, L.H carried out RNA extraction, Z.L carried out ELISA. Z.H.G conceived of the study, participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript.


ABCCB and KCNJ11" on page 45, and the Supporting Information Tables S1 and S2.One of the variants associated with increased diabetes risk was incorrectly indicated throughout this article. The A1369S variant in the gene ABCCB should have been written S1369A. The alanine variant is associated with increased risk. This mistake affects Tables 2 and 4, the text of the article in the section entitled "The full text XML and HTML versions of the article, and the supporting Tables S1 and S2 have been corrected online.