MAT) loci. MAT–encoded genes function to define sexes via two distinct paradigms: 1) by controlling transcription of components common to both sexes, or 2) by expressing specially encoded factors (pheromones and their receptors) that differ between mating types. The human fungal pathogen Cryptococcus neoformans has two mating types (a and α) that are specified by an extremely unusual MAT locus. The complex architecture of this locus makes it impossible to predict which paradigm governs mating type. To identify the mechanism by which the C. neoformans sexes are determined, we created strains in which the pheromone and pheromone receptor from one mating type (a) replaced the pheromone and pheromone receptor of the other (α). We discovered that these “αa” cells effectively adopt a new mating type (that of a cells); they sense and respond to α factor, they elicit a mating response from α cells, and they fuse with α cells. In addition, αa cells lose the α cell type-specific response to pheromone and do not form germ tubes, instead remaining spherical like a cells. Finally, we discovered that exogenous expression of the diploid/dikaryon-specific transcription factor Sxi2a could then promote complete sexual development in crosses between α and αa strains. These data reveal that cell identity in C. neoformans is controlled fully by three kinds of MAT–encoded proteins: pheromones, pheromone receptors, and homeodomain proteins. Our findings establish the mechanisms for maintenance of distinct cell types and subsequent developmental behaviors in this unusual human fungal pathogen.Cell type specification is a fundamental process that all cells must carry out to ensure appropriate behaviors in response to environmental stimuli. In fungi, cell identity is critical for defining “sexes” known as mating types and is controlled by components of mating type ( MAT) loci. MAT–encoded genes function to define sexes via two distinct paradigms: 1) by controlling transcription of components common to both sexes, or 2) by expressing specially encoded factors (pheromones and their receptors) that differ between mating types. The two mating types of Cryptococcus neoformans (a and α) are specified by an extremely unusual MAT locus. The unique architecture of this locus makes it impossible to predict which paradigm governs mating type. To identify the mechanism by which the C. neoformans sexes are determined, we created an α strain where the pheromones and pheromone receptor were replaced with the analogous genes from an a strain. We discovered that the resulting strain (αa) now behaves as if it is an a. It senses and responds to α cells, mates with α cells, and no longer exhibits other α-specific behaviors. Our data show that replacement of two and only two genes completely alters the sexual identity of α cells, establishing pheromones and their receptors as the determinants of sexual identity in C. neoformans.All organisms that undergo sexual reproduction carry out specific mechanisms to establish different sexes. In fungi, sexual identity is typically determined by components housed within specialized regions of chromosomes known as mating-type ( Saccharomyces cerevisiae.One of the most important processes that occurs in cells is the specification of cell type, and it is by this process that cells adopt the genetic state that governs their subsequent behaviors. One mechanism of cell type-specification is the expression of genes encoded only in a given cell type. For example, X and Y chromosomes determine male and female sexes because they encode different genes, such as SRY, a Y-specific protein whose expression establishes male identity S. cerevisiae the two haploid cell types, a and α, are distinguished from one another by the actions of specific transcription factors encoded at the mating-type (MAT) locus MATa encodes the homeodomain transcription factor a1, and MATα encodes α1 and α2, an α-domain protein and a homeodomain protein, respectively. The actions of a1, α1, and α2 govern control of haploid cell behavior through the differential expression of a-specific, and α-specific genes, including pheromone and pheromone receptor genes a cells secrete mating factor a pheromone (MFa), which binds to a receptor (Ste3) on the surface of α cells, and α cells secrete MFα pheromone, which is sensed by a receptor on the surface of a cells (Ste2). In response to the presence of the pheromone of a mating partner, cells undergo a cell cycle arrest and subsequent morphological changes to prepare for mating. After cell fusion, a1 and α2 act in concert to regulate haploid-specific genes, specifying the diploid a/α cell, thus completing a MAT-controlled regulatory circuit In Ustilago maydis. In U. maydis specific pheromones and receptors are expressed in different cell types; however, in contrast to S. cerevisiae, these genes are not under the transcriptional control of the homeodomain proteins of the traditional MAT locus MAT locus, and these alleles are sufficiently distinct from one another to confer cell type-specificity. In this case, haploid cells expressing distinct pheromones and receptors from the pheromone MAT locus sense and respond to partners of other mating types and fuse. Once compatible mating types have fused, two transcriptional regulators, bE and bW, which are encoded at the second MAT locus, regulate a transcriptional cascade that promotes further sexual development A similar cell-cell recognition process occurs in many other fungi, including the corn smut, Cryptococcus neoformans, the determinants of haploid cell identity are unknown. C. neoformans contains a single MAT locus that is over 100 kb in size and contains 23 genes, some of which have been found to be involved in sexual development and others that appear to be essential “housekeeping” genes MAT loci found in basidiomycete fungi like U. maydis, to the single MAT locus found in ascomycetes MAT locus function to specify haploid cell type. In the C. neoformans MAT locus, there are five genes in each mating type that represent the classic MAT components found in basidiomycete MAT loci. They include the homeodomain transcription factors SXI2a and SXI1α, the six pheromone genes MFa1-3 and MFα1-3, and the pheromone receptors STE3a and STE3α. The transcription factors, Sxi1α and Sxi2a, do not appear to play any role in haploid cells, including establishment of haploid cell identity. In contrast, Sxi1α and Sxi2a are both necessary and sufficient to specify the dikaryotic state following cell fusion and ensure that sexual development continues C. neoformans have been shown previously to be necessary for haploid cell behaviors, such as sensing and responding to a mating partner U. maydis), or whether the actions of other regulators are required to establish the a and α mating types (as in S. cerevisiae).In a related, clinically important human pathogen, C. neoformans, we carried out a “swapping” experiment in which components from the a mating-type (STE3a and MFa1) were relocated into a strain of the opposite mating type, α , and the effects of these modifications on mating and development were examined. The results presented here reveal that pheromones and their receptors are both necessary and sufficient to specify haploid cell identity. That is, strains that harbor a receptor and pheromone from the opposite mating type are capable of sensing and fusing with wild type cells of the same mating type. Furthermore, the fused cells are incapable of progressing through sexual development in the absence of both Sxi transcription factors; however, exogenous addition of SXI2a facilitates complete sexual development. These findings reveal that control of haploid cell identity in C. neoformans is mediated by MAT-encoded cell-cell communication components that are not mating type-specific in their downstream effects, and establishes a model for the maintenance of distinct cell types and developmental behaviors in this human fungal pathogen.To test the hypothesis that pheromones and pheromone receptors alone are sufficient to confer haploid cell identity in C. neoformans is controlled by pheromones and pheromone receptors, a strain where the pheromone and pheromone receptor genes from one mating type were replaced with those from the opposite mating type was constructed. Specifically, we created an altered α strain by functionally replacing the STE3α receptor gene and the three MFα pheromone genes of an α cell with the STE3a receptor gene and three copies of the MFa1 gene from an a cell (To test the possibility that haploid cell identity in n a cell .MFα gene had been deleted (α mfαΔ), were transformed with a construct to replace the endogenous copy of STE3α with STE3a  and Southern analysis  designed to integrate randomly into the genome  (lanes 3 and 4). However, when the MFa1 gene was introduced into the αmfαΔSTE3a,  strain (resulting in the αa strain), high levels of MFa transcript were detected, and this high level of expression occurred in all of the MFa transformants (lane 5 and data not shown). Because MFa1, in this case, is under the control of the MFα1 promoter , a likely explanation for our findings is that normal repression of MFa1 in haploid cells is disrupted. That is, the elements required for wild type levels of expression were likely not included in our construct, resulting in higher levels of MFa1 gene expression. Because pheromones expressed from a non-native promoter do not interfere with development and the mating response, the αa strain was used in our subsequent analyses While pheromone transcript was not detected in either haploid cell type , this result was not surprising because pheromone genes are transcribed at low levels in haploid cells grown on minimal medium To evaluate how the series of modified α strains interacted with mating partners under sexual development conditions, crosses using various test strains were carried out and assessed microscopically for the presence of fusants after 18 hours on V8 medium. The cells used in each cross were stained with either rhodamine (Alexa Fluor 594) or fluorescein (Alexa Fluor 488) prior to mixing with partner cells so that the original mating types could be discerned after cell fusion.a and α strains, fusants were identified as “dumbbell” shaped cells, consisting of one a cell (red) connected to one α cell (green) via a conjugation tube (a cells (red) and any modified α cells (green) (panels 2–4). Specifically, in crosses between a cells and αmfαΔ cells, the αmfαΔ cells responded to the presence of a cells by forming conjugation tubes; however, in the absence of α pheromone production, the frequency of fusion with a cells is predicted to be very low, and consistent with this expectation, we did not observe any fusants. Accordingly, in strains no longer harboring the STE3α receptor , the cells appear to neither form conjugation tubes (in the presence of a cells) nor fuse with a cells (panels 3 & 4). These results indicate that modified α strains, in which the STE3α receptor gene has been deleted, do not respond to or mate with a cells.In crosses between wild type ion tube , panel 1mfαΔ and αmfαΔ,STE3a (green) strains . It was not until a wild type α cell (red) was crossed with the altered αa strain (green) that fusants could be recovered at roughly wild type a x α levels (panel 8). Subsequent quantitative fusion assays confirmed that α x αa fusion levels are comparable to those of wild type a x α crosses (data not shown). This finding indicates that, as expected, pheromones and pheromone receptors are required for wild type fusion between a and α cells. It also reveals that the altered αa strain fuses only with wild type α cells, indicating that by simply replacing the α pheromones and receptors with those from a cells, cell identify was switched from α to a. Moreover, that change was sufficient to allow the altered αa cells to fuse with wild type α cells as though they were of opposite mating types.In parallel experiments, wild type α strains were crossed with the modified α strains. In a control cross, differentially labeled α cells (either red or green) were crossed and evaluated for the presence of fusants. As expected for wild type cells of the same mating type, α cells did not respond to one another or fuse (panel 5). Fusants were also not detected in crosses between wild type α cells (red) and the modified αa cells into α cells changes not only their ability to fuse but also the initial mating response. That is, αa strains do not produce germ tubes in response to wild type α cells, instead appearing to behave like wild type a cells  were crossed with either wild type a or α cells, none of the combinations resulted in wild type sexual development (panels 2–8). The a x αmfαΔ cross (panel 2) revealed a significant reduction in sexual development, and this result is consistent with previously published data a cross (panel 8). This limited, mutant filamentation was very close in appearance to crosses between strains containing deletions of SXI2a (panel 9). In crosses between wild type α strains and sxi2aΔ mutants, the strains fuse at wild type levels; however, only aberrant filaments are formed, and they do not progress through sexual development a must be present to specify the fused state and initiate either diploid-specific or dikaryon-specific developmental programs. Because the products of an α by αa cross contain only the Sxi1α developmental regulator (and not the Sxi2a regulator found only in a cells), such crosses would not be expected to progress through sexual development and would be expected to exhibit a sxi2aΔ cross phenotype. This is, in fact, the case. Conversely, if the Sxi proteins, pheromone, and pheromone receptors were necessary and sufficient to specify cellular identify (dikaryon vs. haploid), then simply supplying the SXI2a developmental regulator to an α by αa cross would result in complete sexual development.To assess the ability of the series of modified α strains to undergo sexual development, crosses were carried out and assessed for the production of filaments, basidia, and spores. In crosses between wild type he cross , panel 1a crosses were simply in need of SXI2a to continue through sexual development, we carried out crosses in which exogenous SXI2a was provided. Because strains containing both SXI1α and SXI2a are self-filamentous under sexual development conditions, we crossed the αa strain with an α sxi1αΔ strain carrying an integrated copy of SXI2a under the control of a constitutive promoter (GPD1). This cross resulted in the restoration of complete sexual development (panel 10). In addition, fluorescence and high-resolution microscopy show that dikaryotic filaments basidia, and spore chains were formed in αa x α + SXI2a crosses (a x α + SXI2a), no additional information from a cells is required for this process.To test the hypothesis that α x α crosses . Becausea/α diploids are self-filamentous on V8 medium, undergoing sexual development a strain to form diploids, crosses of αa by wild type a or α cells were carried out. αa cells fused to form mononucleate cells (presumed diploids) with wild type α cells, but not with wild type a cells, consistent with αa cells exhibiting a cell behavior. In this assay, αa cells  were mixed with either wild type α or a cells (URA5) and incubated on V8. After 24 hours at room temperature, the co-cultures were plated under double selection (−Ura + G418) at 37°C to select for mononucleate diploids a x αa crosses, indicating that no a/αa diploids were formed. In contrast, abundant colonies were recovered from the α x αa crosses, indicating that αa/α diploids were formed. The resulting αa/α strains were determined to be mononucleate (data not shown) and were evaluated for the ability to undergo sexual development by incubation on V8 at room temperature ; however, the addition of the SXI2a gene to the αa/α strain resulted in full sexual development on V8, similar to that of a wild type a/α diploid . Taken together, these results demonstrate that cell identity and sexual development are controlled entirely by three kinds of genes in C. neoformans: pheromones, pheromone receptors, and homeodomain proteins.This finding was further supported by the results of assays for diploid formation. Previous studies have shown that perature . As in αC. neoformans; however, this process is specific to α cells, which form filaments, basidia, and spores under severe nutrient limitation and desiccation a, wild type α, and αa strains. Strains were incubated on filament agar for 14 days at room temperature in the dark and evaluated for the production of filaments and spores. We observed that the αa strain undergoes α fruiting that is indistinguishable from wild type α cells , thus answering the question of how the MAT locus of C. neoformans controls cell type-specification.Sexual development is an essential part of the life cycle for most eukaryotic organisms and requires mechanisms to ensure that development takes place between compatible partners. In both ascomycete and basidiomycete fungi, mating type is specified by components of the S. cerevisiae, and myriad experiments have been carried out to understand the roles of two phylogenetically unrelated receptors STE2 (α factor receptor) and STE3 (a factor receptor). Through altered expression experiments it was found that mating type is controlled by the identity of the expressed surface receptor. Experiments where the receptor expression patterns were reversed in a and α cells led to changes in mating behaviors. For example, α cells that express only STE2 (and not STE3), can sense and respond to α pheromone and therefore mate as a cells and vice versa. Thus, in S. cerevisiae, both a and α cells contain the STE2 and STE3 genes (and both pheromone genes), and it is the actions of transcription factors that impart differential expression patterns that determine cell identity There is a long history of investigating pheromone/pheromone receptor function in fungi, particularly in U. maydis, in which different cell types encode distinct pheromones and pheromone receptors. Experiments in U. maydis where the pheromones and pheromone receptors have been “swapped” between mating types demonstrate that mis-expression of pheromones and pheromone receptors is sufficient to alter cell identity. In this case, the pheromone receptors have distinct binding specificities but have descended from a common ancestor S. commune and C. cinerea. Within these fungi, different mating types encode distinct alleles of pheromones and pheromone receptors; pheromone and pheromone receptors do not govern mating, and cell fusion occurs independently of pheromone signaling. Instead, pheromones and pheromone receptors control post-fusion sexual development This is in sharp contrast to other fungi, like C. neoformans, both a and α cells contain mating type-specific pheromone genes , but the regulation of these genes differs between mating types. In a cells, MFa pheromone is highly induced on V8 agar (or under conditions of nutrient limitation) in the absence of a mating partner. In addition to induction under nutrient limiting conditions, MFα pheromone expression is also activated by factors secreted by a cells (MFa pheromone) MFα genes is dramatically upregulated upon exposure to synthetic MFa pheromone In MFa1 gene in αa cells is driven by the MFα1 promoter. The MFa1 construct was originally engineered in this fashion to avoid any cell type-specific regulation that might be imposed upon an a-specific promoter in an α cell. However, in αa strains where MFa1 gene expression was driven by the MFa1 promoter, identical phenotypes and expression patterns were observed.In the experiments carried out here, expression of the STE3a cassette was transformed into a wild type α strain to replace the STE3α gene at its native locus. However, after multiple attempts, no transformants were recovered . Transformants were recovered only from strains in which the pheromone genes were deleted previously. These findings, while far from conclusive, intimate that cells expressing both STE3a and α pheromone may engage in autocrine signaling that affects cell growth.Attempts to test the roles of the pheromone receptors were not as straight forward, and initially, the S. cerevisiae, in which a strain that expresses both a pheromone and its cognate receptor undergoes G1 cell cycle arrest S. cerevisiae this arrest is transient; however, if a similar arrest is occurring in C. neoformans, the cells do not appear to recover. This is in contrast to U. maydis where cells that have been engineered to express both receptor alleles do not arrest, are fully viable, and are mating competent. It remains to be determined if and how autocrine signaling may be functioning in C. neoformans.A similar observation has been made in C. neoformans, a and α cells carry out distinct functions to initiate the mating process. For example, in response to nitrogen limitation, a cells secrete MFa pheromone, and α cells respond to the presence of the MFa pheromone by formation of germ tubes. This morphological response is specific to α cells, and only these cells (and not a cells) form germ tubes as an initial mating response a cells not only recognize and fuse with α cells (as would be expected for an a cell), but they also exhibit the morphology of an a cell, and do not produce germ tubes. This finding indicates that the morphological response to pheromone is mediated by the pheromone receptor  and is independent of other mating type-specific factors. Therefore, pheromones and pheromone receptors alone dictate both the detection of a mating partner and the initial morphological response of haploid cells in C. neoformans. Interestingly, however, changes in mating identity did not influence the α-specific process of α fruiting.In a, and that this process is attenuated in strains in which STE3α or the MFα genes have been deleted a or α cells.Monokaryotic (or α) fruiting is a process that is specific to α cells and was recently shown to be a form of sexual development C. neoformans and enhance our understanding of the myriad strategies by which fungi specify mating types and undergo sexual development.In summary, while the α fruiting process occurs despite the identity of pheromones and their receptors, these factors are wholly responsible for determining the initial mating response and haploid cell identity. The findings presented here establish a complete cell identity determination profile for mfαΔ, STE3a strain (CHY1900), the αmfαΔ (WSC17) strain was biolistically transformed with a fragment containing STE3a and a nourseothricin resistance (RNAT) cassette that was flanked by ∼1 kb of sequence from upstream and downstream of the STE3α open reading frame (ORF) STE3α locus, and putative deletion/insertion strains were confirmed by Southern blot analysis a strain (CHY1901), the αmfαΔ, STE3a strain was biolistically transformed with a randomly integrating fragment of DNA containing MFa1 (under the control of the MFα1 promoter) and a neomycin resistance (RNEO) cassette. Neomycin resistant transformants were screened for copy number of the insertion using Southern blot analysis a/α strain (CHY2049) was generated by crossing the 5-FOA resistant αa strain (CHY1517) by wild type α cells (JEC21) and selecting for fusants at 37°C on minimal medium containing neomycin. To generate the αa/αSXI2a strain (CHY2096), 5-FOA resistant αa/α cells were biolistically transformed with a randomly integrating fragment of DNA containing the SXI2a (under the control of the GPD1 promoter) and URA5 genes.To generate the αC. neoformans cells using a hot phenol extraction GPD1) probe was PCR-generated using CHO651 (CGTCGTTGAATCTACCGGTG) & CHO652 (CACCAGCAATGTAAGAGATG). All other probes were generated by PCR using the following oligonucleotides: CHO2030 (CCCCGACTATCCCTTTTGGAATCTCACTGC) & CHO2031 (GGCGAACAGTTCTTCGGGATATTGTGATACC) for STE3a, CHO2032 (GCACGACCTCAGCCTCGTCATTTTCAGCGG) & CHO2033 (CCGTATCCAGCAGCAATGATCGTCAGC) for STE3α, CHO2052 (GACTTACTCTTGCGTTATTGCTTAAAGTGGG) & CHO1917 (GAAAAGAGGTACGAGTAGAT) for MFa1, and CHO2053 (TTGTGTCATCGCCTAGACCCAACGTCCCC) & CHO2054 (CCATCTAAACAAGTCCCATACGCTTCGTTACC) for MFα1. Radiolabeled probes  were used in hybridization reactions as described previously RNA was prepared from a) and JEC21 (α), and crosses were photographed at 100X magnification 4)2SO4 at room temperature in the dark for 14 days. The periphery of test spots were photographed at 100X magnification.All strains used were of the serotype D background  and werea and α cells were grown to stationary phase in yeast extract peptone dextrose (YPD) liquid medium and were suspended in 1X phosphate buffered saline (PBS). Carboxylic acid, succinimidyl-ester Alexa Fluor 488 or 594 dyes  were coupled to a or α yeast cells in 100 mM potassium phosphate buffer pH 7.5 for 30 min at room temperature. After washing, differentially labeled cells were mixed in 1X PBS and spotted onto V8 agar. Spots were incubated at room temperature for 6 hours and were then resuspended into a mounting solution of 1X PBS containing 0.4 µg/ml calcofluor white MR2 (Sigma-Aldrich). For filament staining, crosses were incubated on a thin layer of V8 media on a microscope slide and incubated at room temperature for 24 hours. Cells were fixed with 2.5% gluteraldehyde/4% paraformaldehyde for 30 minutes. Cells were washed twice with 1X PBS containing 0.1% Triton X-100, once with 1X PBS, and incubated in 1X PBS containing 0.4 µg/mL calcofluor white to stain septa, and 1nM Sytox green (Invitrogen) to stain nuclei for 20 minutes. Cells were washed in 5% glycerol with 20% DABCO antifade (Sigma) prior to visualization.Fluorescent microscopy was carried out on a Zeiss Axioskop 2 fluorescent microscope fitted with an Axiocam MRM REV3 digital camera and corresponding AV4 software. Light microscopy was carried out using a Zeiss Axioplan microscope fitted with a 10X long working distance objective. Photographs were taken with a Nikon Coolpix 5400 camera mounted on the microscope. To label fusants,


The strain in the complex is further illustrated by the distorted tetrahedral angles of the P atoms, which range between 93.5 (1) and 122.2 (1)°. It is of interest to note that the N atom has to adopt an almost planar geometry with the two P atoms and the C atom attached to it [it is displaced by 0.093 (2) Å from the CP2 plane] in order to accommodate the steric bulk of the phenyl groups and the alkyl group of the ligand coordinated to the PtII centre. The molecules pack in horizontal rows across the bc plane. C—H⋯Cl hydrogen bonds stabilize the crystal packing.The Pt(II) atom in the title compound, [PtCl DOI: 10.1107/S160053680905301X/hy2248Isup2.hkl            Structure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF report            Additional supplementary materials:


An etching system with differential pumps and two parabolic focusing mirrors was constructed to perform the etching. The PDMS was found to be effectively etched by the SR irradiation under the XeF2 gas flow, and the etching process was area-selective and anisotropic. An extremely high etching rate of 40–50 µm (10 min)−1 was easily obtained at an XeF2 gas pressure of 0.2–0.4 torr. This suggests that SR etching using XeF2 gas provides a new microfabrication technology for thick PDMS membranes, which can open new applications such as the formation of three-dimensional microfluidic circuits.The synchrotron radiation (SR) stimulated etching of silicon elastomer polydimethylsiloxane (PDMS) using XeF This may be because PDMS has carbon as one of its major components.In the present work, we have constructed a new SR etching beamline consisting of differential pumping and an etching chamber with a parabolic mirror which focuses the SR beam to a pinhole which realises effective differential pumping. An extremely high speed of PDMS etching, 40–50 µm (10 min)2.2.1.2 (5 g) sealed in an SUS cylinder was purchased from Trichemical Laboratories and attached to the gas-supplying system directly. Gold-coated Cu gaskets and Ni gaskets (Ailin Vacuum) were used to connect the etching chamber, focusing mirror chamber and gas-supplying system. All parts of the end-station were made of stainless steel, except the pressure gauges which use inconel diaphragms.XeF2 gas flow.The PDMS substrates used in the experiments were fabricated from a 10:1 (weight ratio) mixture of PDMS Sylgard Silicone Elastomer 184W/C and Sylgard Curing Agent 184 (Dow Corning). The solution in a template was left in-vacuum for about 30 min to remove gas bubbles. Subsequently the gelled PDMS was cured at 313 K for 20 h, and then cooled to room temperature. The solidified PDMS was carefully peeled from the template and cut to a size of 7 mm × 14 mm. Etching was carried out by SR beam irradiation either directly or through a copper mesh with 400 wires per inch  on the PDMS substrate surface under the XeF2.2.−5 torr) of the focusing mirror chamber during the etching reaction where gas pressure in the etching chamber increased to about 1 torr. The vacuum pressures under usual operations were ∼10−10 torr at the FM1 chamber and ∼10−9, ∼10−8, ∼10−7 and ∼10−4 torr at DP1, DP2, DP3 and FMC, respectively.The SR etching of PDMS substrate was carried out at beamline 4A1 of the SR facility (UVSOR) at the Institute for Molecular Science . The beamline structure is schematically illustrated in Fig. 116 photons s−1 at 100 mA ring current. Precise estimation of the effective photon flux is, however, difficult owing to the reflectivity degradations of the FM1 and FM2 mirrors by contamination with residual hydrocarbon gases. Therefore the relative photon flux was roughly estimated in each experiment by measuring the photoemission current of the Pt plate set at the sample position.The distance between the FM2 mirror centre and the sample is about 300 mm. The SR beam spot size on the sample surface was about 0.5 mm in diameter. The etching chamber is connected to the focusing mirror chamber through an XY stage with bellows so that the aperture position can be adjusted to the focused SR beam position. The calculated photon flux was 4 × 102.3.2 gas strongly reacts on the wall of the container and this makes the pressure inside the reaction chamber unstable, XeF2 is usually supplied to the reaction chamber by pulse injection and evacuation . The reaction gas (100% XeF2) pressure was controlled in the range 0.1–0.5 torr in the etching chamber by a repeating open–close sequence of the valves PV1 and PV2 in Fig. 3The XeF4 is realised between the etching chamber and the FM2 mirror chamber when the 1 mm-diameter aperture was used. After etching, both the etching chamber and the FM2 mirror chamber are continuously pumped and purged with N2 gas flow.In this pressure range, a pressure difference of about 103.First, we have measured the etching characteristics for the direct SR beam irradiation of the PDMS sample surface without mask. The surface structure of the sample after etching was observed with an optical-microscopy, non-contact three-dimensional profile meter  and scanning electron microscope .2 gas pressure, 150 mA ring current (photoemission current of the Pt plate was ∼312 µA) and 10 min irradiation time. As discussed later in detail, the carbon contamination was easily induced, especially when the vacuum pumping of the etching chamber was not sufficient. To reduce the contamination, the etching chamber should be pumped for 2–3 h before etching. Carbon contamination was more enhanced by the higher beam intensity. The dependence of the etching rate and etched depth on the irradiation dose and that on the XeF2 gas pressure measured under the same conditions are shown in Figs. 5(a) and 5(b)2 as an etching gas, although some rugged structures are observed surrounding the etched hole which are probably due to the heating effect of the SR beam. Note that the PDMS was not etched by XeF2 gas without SR irradiation.Fig. 42 pressure of 0.4 torr and an etching time of 10 min. The depth of the etched hole was ∼40 µm. A similar result was obtained under the condition of 0.2 torr and ring current of 200 mA. After removal of the etching mask, each etched hole was found to have a size of 35 µm square at the bottom and 45 µm square at the top. The difference between the mask pattern and the etched pattern was explained by diffraction effects and an uncontrollable gap between the mask and the PDMS sample surface , at 363 K for 10 min. C 1s spectra of the PDMS samples after etching, before etching and the gold thin film only are shown in Figs. 7(a), 7(b) and 7(c)s region; one, at 284.7 eV, is assigned to C of PDMS  −1, is realised, and this result indicates that SR etching using XeF2 gas provides a new microfabrication technology for thick PDMS membrane. Sufficient vacuum pumping of residual gases in the etching chamber is important to reduce the deposition of the hydrogen-containing carbon material, which hinders the etching. This may be a unique point of PDMS etching in which carbon is one of the main components.A high-speed SR-etching beamline using XeF


The genetic changes that occur during the development of this carcinoma are poorly understood. It has been proposed that IGFIIR, TGFβ1 and TGFβRII act as a functional unit in the TGFβ growth inhibitory pathway, and that somatic loss-of-function mutations in any one of these genes could lead to disruption of the pathway and subsequent loss of cell cycle control. We have examined these 3 genes in 25 epithelial ovarian carcinomas using single-stranded conformational polymorphism analysis and DNA sequence analysis. A total of 3 somatic missense mutations were found in the TGFβRII gene, but none in IGFRII or TGFβ1. An association was found between TGFβRII mutations and histology, with 2 out of 3 clear cell carcinomas having TGFβRII mutations. This data supports other evidence from mutational analysis of the PTEN and β-catenin genes that there are distinct developmental pathways responsible for the progression of different epithelial ovarian cancer histologic subtypes. © 2001 Cancer Research Campaign


P<0.0001). The concentrations did not correlate with clinical stage and histopathological grade, but patients with increased human chorionic gonadotropin β levels had significantly shorter survival time than those with levels below the median . In multivariate analysis human chorionic gonadotropin β, tumour stage and grade were independent prognostic variables. The serum concentration of human chorionic gonadotropin β is an independent prognostic variable in renal cell carcinoma. The preoperative value of human chorionic gonadotropin β in serum may be used to identify patents with increased risk of progressive disease.The free β-subunit of human chorionic gonadotropin β is expressed in several nontrophoblastic tumours and this is usually associated with aggressive disease. Little is known about human chorionic gonadotropin β expression in renal cancer. We determined the pretreatment levels of human chorionic gonadotropin β in serum of patients with renal cell carcinoma, and studied whether elevated levels predicted the clinical outcome. Serum samples were collected before surgery from 177 patients with renal cell carcinoma and from 84 apparently healthy controls. Human chorionic gonadotropin β in serum was measured by a highly sensitive time-resolved immunofluorometric assay. The prognostic value of human chorionic gonadotropin β, and of usual clinical and pathological variables was analyzed by the Kaplan-Meier method, the log rank test and Cox multiple hazard regression. The serum concentrations of human chorionic gonadotropin β were increased in 23% of the renal cell carcinoma patients and they were significantly higher in patients with renal cell carcinoma than in controls  86, 185–189. DOI: 10.1038/sj/bjc/6600050www.bjcancer.comThe Cancer Research Campaign© 2002  The incidence of urological malignancies such as prostate cancer, bladder cancer and kidney cancer has been steadily increasing in the Western world during the past decades (Human chorionic gonadotropin (hCG) is a 38 kDa glycoprotein hormone consisting of two dissimilar subunits, α and β (hCGα and hCGβ) . ExpressData for this retrospective analysis was collected from 177 patients with RCC from whom pretreatment serum samples were available. The patients underwent radical nephrectomy at the University Hospital of Umeå, Sweden between 1983 and 1995. The study included 111 men and 66 women with a mean age of 65 years (range 25–85) . The independence of hCGβ as a predictor of survival was analyzed by multivariate analysis using the Cox proportional hazard model with serum hCGβ, nuclear grade and stage as input variables. The statistical endpoint was survival measured from the date of nephrectomy to date of death or date of last follow-up. Cases of death from unrelated causes were censored. All tests were two-sided and P-values below 0.05 were considered significant.Differences in serum hCGβ concentrations in patients with various stages and grades and controls were analyzed by the Mann-Whitney −1 (range 45–353 μmol l−1), and elevated levels occurred in 13%. The serum concentrations of hCGβ were not related to serum creatinine.The median concentration of creatinine in serum of RCC patients was 86 μmol l−1) in 23% of the patients with RCC (Table 2−1. The median concentration of hCGβ in serum was 1.2 pmol l−1 (range 0.2–18 pmol l−1), which was significantly higher (P<0.0001) than in controls . There was no difference in hCGβ levels between males and females, different age groups, different RCC types, aneuploid and diploid tumours, or tumours with and without venous invasion. Serum hCGβ concentrations were not either significantly correlated with tumour stage or grade  in univariate analysis. Patients with serum hCGβ concentrations above the median value (1.2 pmol l−1) had significantly shorter survival than those with lower levels (P=0.0029) . When serum hCGβ concentration was compared as quartiles there was no difference in disease specific survival between the two lowest quartiles or between quartiles 3 and 4 (P>0.6). In multivariate analysis using the Cox regression model with age, gender, serum hCGβ, nuclear grade and stage as input variables, stage, grade and serum hCGβ concentration were independently associated with the disease specific survival . Patients with serum hCGβ levels above the median value (1.2 pmol l−1) had significantly shorter survival than those with lower levels (P<0.0029). In multivariate analysis serum hCGβ, tumour stage and grade were independent prognostic variables. The fairly low frequency (23%) of elevated serum hCGβ suggest that this marker is expressed by only a part of the RCCs. However, because normal levels between 1.2 and 2 pmol l−1 were associated with adverse prognosis, it is possible that part of the hCGβ in these patients is derived from the tumour. A possible correlation with the response to treatment could not be evaluated in this study. The absence of a correlation with stage indicates that elevated levels are not only a result of tumour burden, but hCGβ expression rather characterizes a subgroup of tumours. The lack of significant correlation with established prognostic variables such as grade, stage and DNA-ploidy demonstrates the independent character of this marker.Expression of hCGβ has been observed in various tumours and cell lines on both at the protein and mRNA level (in vitro, suggesting paracrine or autocrine growth factor-like activity. Recently hCGβ has been shown to confer immortality features to cells by inhibiting apoptosis (HCGβ expression has been regarded as a characteristic of malignant transformation or dedifferentiation of cells, and its association with aggressive disease in transitional cell carcinoma of the bladder is well documented (The clinical utility of hCGβ in the management of RCC is limited by the infrequent expression. About one fourth of the patients had elevated levels and in half of these the elevation was substantial. However, a prognostic value was observed even with normal levels exceeding the median value, and this was the case also in patients with advanced disease. Thus it appears worthwhile to study whether hCGβ could be used as an aid in the selection of therapy or in monitoring of the disease after primary therapy. In conclusion, our results indicate that hCGβ in serum is a prognostic factor independent of stage and grade that can be used to identify a subgroup of patients with increased risk of aggressive disease.


The effect of irradiation of recipient mouse lung on tumour colony growth after intravenous injection of C3H mammary adenocarcinoma cells or aggregates was tested in C3H male mice irradiated with 14 MeV electrons to the whole or half of the thorax. Injections were made 3 h, 48 h, 3½ months or 9½ months after irradiation.The number and size of colonies were increased in irradiated areas of the lung at 48 h and at 3½ months after doses of 2000 rad. However, 9½ months after irradiation lungs were found to be atrophied and fibrotic and enumeration of tumour colonies was found to be impossible. An increase in colony growth after 250 rad was seen only at 9½ months after irradiation.


Histone deacetylase inhibitors (HDACi) comprise a family of chemotherapeutic agents used in the clinic to treat cutaneous T-cell lymphoma and tested for the therapy of other malignancies. Previous reports have shown that eIF2α phosphorylation is induced upon treatment with HDACi. However the kinase responsible for this phosphorylation or the biological significance of this finding is not yet established. Herein, we show that eIF2α phosphorylation is not attributed to a specific eIF2α kinase, but rather different eIF2α kinases contribute to its upregulation in response to the HDACi, vorinostat. More importantly our data indicate that eIF2α phosphorylation acts in a cytoprotective manner, whereas the eIF2α kinases PKR and GCN2 promote vorinostat-induced apoptosis. These results reveal a dual nature for eIF2α kinases with potential implications in the treatment with histone deacetylase inhibitors. Histone deacetylase inhibitors (HDACi) represent an emerging class of anticancer treatments. The most prominent one, suberoylanilide hydroxamic acid  was the first one to be approved by Food and Drug Administration (FDA) for the treatment of cutaneous T-cell lymphoma , followeHDACi act partially by inhibiting histone deacetylases, thus inducing the acetylation of histones. This modification renders chromatin in an active “open” state that allows accessibility to the transcriptional machinery . AlthougOne of the signaling pathways known to be activated by multiple stimuli is the one that converges on eIF2α phosphorylation . This moThe kinases that phosphorylate eIF2α belong to a family of four members and are activated by different kind of stimuli; the interferon-inducible protein kinase R (PKR) in response to dsRNA, the endoplasmic reticulum (ER)-resident kinase (PERK) by accumulation of unfolded proteins in the ER, the general amino acid nonderepressing kinase 2 (GCN2) in response to aminoacid starvation, and the heme-regulated inhibitor (HRI) by low levels of heme or iron . While iThe contribution of the eIF2α phosphorylation pathway in the response to HDACi has not been thoroughly explored. In the few examples present in the literature, it has been demonstrated that treatment of the eosinonophilic leukemic cell line Eol-1 with HDACi leads to differentiation of these cells to mature eosinophils. This transformation to a differentiated state was attributed to a downregulation of the oncogenic fusion protein Fip1-like1-platelet-derived growth factor receptor alpha (FIP1L1-PDGFRA), whose mRNA translation was blocked by an induction in eIF2α phosphorylation -16. In aIn this report we show that eIF2α phosphorylation is not only induced in response to vorinostat treatment but also protects against the cytotoxic effects of this agent. Furthermore, this modification is a combinatorial event, involving more than one of the eIF2α kinases. More interestingly, PKR and GCN2 were found to enhance the sensitivity of the cells to this HDACi, thus opposing the cytoprotective role of eIF2α phosphorylation.A/A), alongside their wildtype counterpart MEFs (eIF2αS/S)  Figure . Taken tNext we wished to determine which of the eIF2α kinases is responsible for mediating eIF2α phosphorylation in response to vorinostat. To this end, we treated MEFs deficient in each of the four eIF2α kinases together with their isogenic wildtype MEFs with the chemotherapeutic agent and examined eIF2α phosphorylation. Consistent with the previous findings, we detected an induction of eIF2α phosphorylation in all MEFs examined. However, even though the induction of eIF2α phosphorylation was lower in the knockouts (KO) of PERK, GCN2 and HRI compared to their isogenic wildtype cells (WT), it was not completely abolished in any of them, suggesting that vorinostat can activate more than one of the eIF2αkinases Figure . The redS/S and eIF2αΑ/Α MEFs with this drug and measured the cell death index by FACS analysis using propidium iodide (PI) staining. Our data show that eIF2αΑ/Α MEFs are more sensitive to this drug than eIF2αS/S MEFs, indicating that eIF2α phosphorylation protects against vorinostat-induced cell death , a structurally and functionally similar HDACi to vorinostat, and nicotinamide, a molecule that targets HDAC family members that are not inhibited by vorinostat or TSA. Similar to what we observed for vorinostat, these two compounds were more cytotoxic in the presence of GCN2 , and P<0.05 was considered statistically significant.All quantitative variables are presented as means ± S.E.M. We compared the differences of more than two groups using one-way ANOVA and the differences of two groups using two-tailed Student


There are at least 11 human 5–HT4 and three h5–HT7 receptor splice variants. This review describestheir discoveries, nomenclature and structures. The discovery that particular splice variants are tissue specific (or prominent) has highlighted their potential as future drug targets. In particular, this review examines the functional relevance of different 5–HT4 and 5–HT7 receptor splice variants. Examples are given to illustrate that splice variants have differential modulatory influences on signalling processes. Differences in agonist potency and efficacies and also differences in desensitisation rates to 5–HT occur with both 5–HT4 and 5–HT7 receptor splice variants. The known and candidate signalling systems that allow for splice variant specific responses include GPCR interacting proteins (GIPs) and GPCR receptor kinases (GRKs) which are examined.Finally, the relevance of 5–HT receptor splice variants to clinical medicine and to the pharmaceutical industry is discussed.G-protein-coupled receptors (GPCRs), which are encoded by >300 genes in the human genome, are by far the largest class of targets for modern drugs. These macromolecules display inherent adaptability of function, which is partly due to the production of different forms of the receptor protein. These are commonly called ‘isoforms’ or ‘splice variants’ denoting the molecular process of their production/assembly. Not all GPCRs are expressed as splice variants, but certain subclasses of 5–HT receptors are for example, the 5–HT G-protein-coupled receptors (GPCRs) are by far the largest class of targets for modern drugs. These macromolecules are encoded by >300 genes in the human genome. Once formed, they are transported and embedded in the cell-surface, where they take on their functions of detecting and responding to a diverse array of ligands. Numerous diseases and disorders have been linked to mutations and polymorphisms in GPCRs and in their natural states these receptors are the targets of an increasingly large number of therapeutic agents. It has been estimated that 50% of all modern drugs and almost one-quarter of the top 200 best-selling drugs in 2000 modulate GPCR activity (see  for revi1). Over 70% of multi-exon genes expressed in humans are alternatively spliced to form various splice variants and the proteins involved in cellular communication are common amongst examples . Selective antagonists for each receptor are now becoming available. The 5–HT3 receptor is a pentameric ion channel that appears to be located exclusively in neuronal tissue where it mediates fast depolarization. 5–HT4, 5–HT6 and 5–HT7 receptors all couple preferentially to Gs and promote cAMP formation, while the 5–ht5, receptor is able to couple to several signalling pathways including Gi/Go 4 receptog) [4(i) , h5–HT4(i) [4(h)  the only5–HT4(n)   is dominant in most tissues and the h5–HT4(a) is also common, so opportunities for drug discovery need to take advantage of other tissue-specific splice variants. Table 1 summarises the current state of knowledge. Focussing on the atrial tissue of the heart, it can be seen that it is relatively well endowed with splice variants compared to the kidney and bladder. Also, it expresses the (n) splice variant, as does the brain and oesophagus, but this is absent from the kidney, bladder, stomach, ileum and colon. The current state of knowledge shows the human small intestine and colon also express h5–HT4(d) and that this is not found in other tissues [The h5–HT tissues ,15,56. M4 receptor splice variant expression has been shown to change dramatically in cancerous tissue. Normal adrenal tissue expressed both 5–HT4(a) and 5–HT4(b) and rarely 5–HT4(d) splice variants while adrenocortical aldosterone producing adenomas increased their expression of 5–HT4(d) and no longer expressed 5–HT4(a) or 5–HT4(b) receptor splice variants [Interestingly, 5–HTvariants . These r7 receptor was first cloned from human tissues in 1993 [7 splice variants were then discovered and named, h5–HT7(a), h5–HT7(b) and h5–HT7(d) that differ in their C terminal tails [2B). It was found that the brain and spleen contained relatively small amounts of 5–HT7(d) mRNA. However, h5–HT7(d) was reported later to be predominantly expressed in the human small intestine and colon together with a certain amount of the h5–HT7(d+5) fragment [7 receptor  is summarised in Table 1.The 5–HTal tails  fragment . The kno4 and h5–HT7 receptors (except h5–HT4(h)) differ in the sequences of their intracellular (C-terminus) tails, but share an identical sequence up to Leu 358 for h5–HT4 and Leu 432 for h5–HT7 . This major portion contains the 7 transmembrane loops and the recognition site for 5–HT.All of the splice variants of both the h5–HT4 receptor splice variants have been found to directly influence their functional properties and this is most dramatically seen in their transduction of agonist responses. A notable example is renzapride, which is nearly 20 times more potent at the h5–HT4(d) than at the (g) splice variants in inducing cyclic AMP formation in COS cells. Another difference is that renzapride behaves as a full agonist at the h5–HT4(d), but is a partial agonist at the (g) variant [4 splice variants is that they can link to different G proteins. Experiments using human embryonic kidney (HEK) cells have shown that the potencies of 5–methoxytryptamine at 5–HT4(a) and 5–HT4(b) splice variants are different and that this is correlated to coupling to only Gs and to Gi/o plus Gs proteins, respectively [4(b) and 5–HT4(d) splice variants in rodent cardiac myocytes that do not naturally express 5–HT4 receptors demonstrated that the 5–HT4(d) receptor was more efficiently coupled to adenylyl cyclase [2+ current in rat myocytes expressing the 5–HT4(b) splice variant but not the 5–HT4(d) [s and Gi/o proteins.The C-terminal tails of 5–HT variant . One intectively . More re cyclase . In addi5–HT4(d)  providin7 receptors preferentially couple to adenylyl cyclase via Gsα [4 receptors. The screening of four agonists and a larger set of antagonists has so far failed to show any differences in binding affinities, potencies or efficacies at the three h5–HT7 splice variants [7 ligands (see [7(d) splice variant exists in a greater internalised state in the absence of agonist (5–carboxamidotryptamine) compared to the other two variants; h5–HT7(a) and h5–HT7(b). Interestingly, the 5–HT7 antagonist, SB-269970, induced a partial translocation of the h5–HT7(d) variant from cytoplasm to plasma membrane. Another noted difference was that the h5–HT7(d) variant was associated with a lesser efficacy at stimulating adenylyl cyclase. As a result, it was suggested that the C terminal tail of the h5–HT7(d) splice variant, which is the longest of the three human splice variants, may contain a motif that interacts with cellular transport systems to limit the amount destined for the plasma membrane [5–HT via Gsα  similar variants ,44. Howends . Post synaptic density (PSD) protein PSD-95, Discs large protein (the Drosophila homologue) and tight junction protein ZO-1. PDZ proteins are involved in scaffolding multi-protein complexes and have roles in protein trafficking (see [3). This example is based on results of proteomic studies on mouse brain where two 5–HT4 receptor splice variants with different PDZ ligands in the extremity of their C terminal tails were used as a bait ligand to isolate the interacting proteins [The most common GIPs interact with the C terminal tails of GPCRs; and PDZ domain containing proteins are the most abundant members of this class. PDZ domains were first recognised as sequence repeats contained in three separate proteins: ing  receptors indicating that the different tails containing PDZ ligands interact with specific sets of proteins [1B receptors to the plasma membrane [Several serotonin receptors contain PDZ ligands at the extremity of their C-terminal tails and have been the focus of investigation. GIPs containing PDZ domains specifically regulate receptors in the serotonin receptor family. The 5–HTlisation . Moreovereceptor . It is nproteins ,42. Thesmembrane . These f4 and one 5–HT7 receptor splice variants with canonical PDZ C–terminal extremity ligand binding sites . So far, there have been no reports on GIP interactions with any 5–HT4 or 5–HT7 receptor splice variants in human tissue. However, a recent study identified 13 GIPs (mainly PDZ domain proteins) that interact with either the mouse 5–HT4(a) or 5–HT4(e) receptors [4(a) receptor splice variant. One of these is sorting nexin (SNX27) which is enriched in the brain and is involved in escorting the 5–HT4(a) receptor splice variant to early endosomes for desensitisation. Another protein, NHERF appears to be involved in directing the 5–HT4(a) receptor to the microvilli region where the two proteins are co-localised with another protein called ezrin [4(b) splice variant does not contain a PDZ domain  and did not concentrate in the microvilli area. While the 5–HT4(e) receptor splice variant co-localised with CIPP which is a scaffolding protein and importantly this was not seen with the 5–HT4(b) receptor splice variant which does not contain a PDZ ligand [4 or 5–HT7 receptor splice variants contribute to the receptor localisation and also suggest that rates of receptor desensitisation may vary with the splice variant expressed. There are five 5–HTeceptors . Of thesed ezrin  that inted ezrin . The 5–HZ ligand . These e4). GRKs play a critical role in GPCR desensitisation. There are three GRK subfamilies: rhodopsin kinase containing GRK1 and 7; β adrenergic receptor kinase (GRK2 and 3); and GRK4 group . Both GRK2 and GRK5 can phosphorylate many GPCRs including 5–HT4 receptor splice variants [The diversity in C-terminus sequences probably also contributes to the well known pharmacological fact that 5–HT induces desensitisation (tachyphylaxis) at different rates and magnitudes in different tissues. This natural phenomenon functions to limit the biological response to endogenous substances, such as 5–HT by uncoupling the GPCR from its signal transduction pathway . Desvariants  and GRK24 receptors occurs in vivo in the rat. We found that 5 day infusions of 5–HT (75 μg/kg per hour) induced rightward shifts of the 5–4 receptor-mediated concentration-effect curves to 5–HT and the partial 5–HT4 agonist, SC 53116, in the oesophagus [4 receptor splice variants  in cellagonists . In additivation . 4 agonists ). The claims are mainly for treatments of the digestive tract, notably irritable bowel syndrome (IBS), but also for gastroesophageal reflux disease (GORD), delayed gastric emptying and chronic constipation. The main rationale for these applications is that 5–HT4 receptors are distributed along the length of the digestive tract, where their localisation on cholinergic nerves functions to enhance the release of acetylcholine. Localisation to smooth muscle cells also affects muscle tone. In the oesophagus, 5–HT4 receptors are thought to be located in the presynaptic nerve terminals of cholinergic interneurons and motor neurons [4 receptor activation was originally shown to enhance acetylcholine released in isolated preparations of the guinea-pig ileum [4 receptor function in the brain is to enhance the release of acetylcholine, but they also modulate the release of dopamine, GABA and 5–HT [4 agonists for treating cognitive disorders. Encouraging results have been obtained in rats, where the 5–HT4 agonist, RS 67333, improved associative memory and the 5–HT4 antagonist, RS 67532, decreased memory [4 agonist, mosepride, improved food intake [4 receptor agonists are splice variant-selective, however, there are patents covering the DNA of splice variants themselves. One suggests gene therapy using the nucleic acid sequence of the 5–HT4(h) splice variant for gastrointestinal diseases, while another the 5–HT4(d) splice variant for treating cardiac and bladder disorders. Reference to Table 1 shows there is potential for refining drug selectivity because the target organs for these conditions contain different combinations of splice variants. To date the digestive tract has received the greatest attention as an organ for treating various disorders using 5–HT4 agonists. Currently, these drugs are used to treat several types of motility disorders including gastro-oesophageal reflux disease (GORD). The most commonly used drugs were cisapride and metoclopramide, but cisapride has been virtually withdrawn (available on limited access) due to its ability to induce rare, but potentially fatal cardiac arrhythmias. Metoclopramide is a relatively old 5–HT4 agonist with significant affinity at other receptor types. The most selective 5–HT4 agonist to date is tegaserod, which is used to treat IBS. Consequently, the rational for using tegaserod for the treatment of IBS is outlined next.More than 50 patents have been lodged since 2000 covering potential clinical applications for 5–HT neurons , as well neurons . In the ig ileum -27. Pateand 5–HT , hence td memory . Their p3 antagonists, 5–HT4 agonists and SSRI antidepressants have been the subject of intense investigation for the treatment of IBS [4 agonist, tegaserod, has been shown to reduce abdominal pain and give a degree of relief from other symptoms in patients with constipation-predominant IBS [4 receptors are present in several discrete tissue locations in the human colon. These include the mucosa where the response to 5–HT released by enterotoxins induces Cl- secretion resulting in diarrhoea [4 receptor is also present in the circular smooth muscle cells of the human colon [4 agonists induce relaxation and inhibition of spontaneous contractions by activating adenylyl cyclase to increase intracellular levels of cAMP [4 receptors expressed by cholinergic neurones in the human colon oppose the effect of this inhibitory postsynaptic 5–HT4 population by enhancing acetylcholine release [4 agonists to facilitate colonic propulsion are partly achieved by a coordinated combination of circular muscle relaxation and longitudinal muscle contraction [4 receptor in the human colon is on sensory nerve endings, where its function may be to increase sensory perceptions arising from the abdomen leading to altered motility patterns. A recent clinical study lends some support to this hypothesis, which showed that IBS patients have significantly lower perception and defecation thresholds to rectal thermal and pressure stimuli compared to age and gender matched control subjects [4 splice variants . Although this quotation was intended to be humorous there is some truth which is illustrated by the symptoms of the irritable bowel syndrome (IBS), which is a common functional bowel disorder, associated with abdominal pain, sensations of bloating and altered bowel habit. Much research effort has focused on the serotonin-modifying drugs to overcome dysfunction caused by perceived imbalance in either the amount of 5–HT released in the intestine or the expression of its receptors. This is because of the wealth of evidence showing that 5–HT alters the rate at which contents travel down the digestive tract and also the rate at which fluid is absorbed . Consequt of IBS ,28. For nant IBS . 5–HT4 riarrhoea . The 5–Han colon . 5–HT4 a of cAMP ,54. Para release . The 5–H release . Consequtraction . Anothersubjects . The humTable 1, ,15,56). 7 receptors have also been identified in the circular muscle of the human colon, but as yet not in nerves [7 receptor splice variants are expressed in the human small intestine and colon including a certain amount of the h5–HT7(d+5) fragment [5–HTn nerves ,65. All fragment . 7 receptor agonists. The proposed applications are for the treatments of depression, anxiety and eating disorders, correction of circadian rhythm and migraine. As with the 5–HT4 ligands, none are suggested to be splice variant-selective. These applications are well-founded given the widespread distribution of the 5–HT7 receptor in the CNS and accumulating evidence for its functional effects. This includes the findings that some antidepressants, such as amitriptyline, have relatively high affinities at the 5–HT7 receptor  and the disease state of the tissue. Refinement of function is conferred by the C terminal tails of the splice variants which allow them to interact with different GIPs, GRKs and even different G proteins. To date, the established net outcome of splice variant-specific interactions within the cell determine factors such as the rate, duration and intensity of the response to 5–HT.The intricate differences in structural forms of 5–HT4(d or f)). Future drug discovery projects may also turn to the GIPs and GRKs in order to achieve tissue-specific effects.Although it has been shown that some splice variants have different sensitivities to ligands, it has only been demonstrated with a small number of agonists. It remains a considerable challenge to identify and develop splice variant-selective drugs (no antagonists have yet been identified). However, the pressure to do so may increase as a result of the recent voluntary restriction of tegaserod to Special Access use only. This was necessary as IBS patients have been shown to experience a higher chance of cardiovascular events, such as heart attack, stroke, or severe heart-related chest pain. This predictable problem could be avoided if it were possible to discover an agonist with reasonable selectivity for a splice variant(s) present in the intestines, but not in the heart (e.g. 5–HT


The arterial pulse is a viscous-fluid shock wave that is initiated by blood ejected from the heart. This wave travels away from the heart at a speed termed the pulse wave velocity (PWV). The PWV increases during the course of a number of diseases, and this increase is often attributed to arterial stiffness. As the pulse wave approaches a point in an artery, the pressure rises as does the pressure gradient. This pressure gradient increases the rate of blood flow ahead of the wave. The rate of blood flow ahead of the wave decreases with distance because the pressure gradient also decreases with distance ahead of the wave. Consequently, the amount of blood per unit length in a segment of an artery increases ahead of the wave, and this increase stretches the wall of the artery. As a result, the tension in the wall increases, and this results in an increase in the pressure of blood in the artery.An expression for the PWV is derived from an equation describing the flow-pressure coupling (FPC) for a pulse wave in an incompressible, viscous fluid in an elastic tube. The initial increase in force of the fluid in the tube is described by an increasing exponential function of time. The relationship between force gradient and fluid flow is approximated by an expression known to hold for a rigid tube.For large arteries, the PWV derived by this method agrees with the Korteweg-Moens equation for the PWV in a non-viscous fluid. For small arteries, the PWV is approximately proportional to the Korteweg-Moens velocity divided by the radius of the artery. The PWV in small arteries is also predicted to increase when the specific rate of increase in pressure as a function of time decreases. This rate decreases with increasing myocardial ischemia, suggesting an explanation for the observation that an increase in the PWV is a predictor of future myocardial infarction. The derivation of the equation for the PWV that has been used for more than fifty years is analyzed and shown to yield predictions that do not appear to be correct.Contrary to the theory used for more than fifty years to predict the PWV, it speeds up as arteries become smaller and smaller. Furthermore, an increase in the PWV in some cases may be due to decreasing force of myocardial contraction rather than arterial stiffness. Following the opening of the aortic valve in early systole, blood pressure in the aorta rises rapidly as does the velocity of blood flow. This increase in blood pressure and momentum travels the length of the aorta and is also passed on to blood in arteries that branch from the aorta . The phenomenon of rapidly increasing pressure and velocity spreading from the aortic root to distal arteries is termed the pulse wave.d, the assumptions that sea water is incompressible and that viscous forces are negligible lead to the predicted speed (gd)1/2, where g is the acceleration due to gravity /(iωρ) and writeWe define PQ(i3/2α) as J2(i3/2α)/[(i3/2α)2/8] and note that as R goes to zero the imaginary part of PQ(i3/2α) vanishes and |PQ(i3/2α)| goes to 1. Substitution now givesWe define I(i3/2α) is approximately R2/(8μ) when α2 ≪ 8 and is approximately 1/(iωρ) when α2 ≫ 8.Therefore, The shear force (per unit area) at the inner wall of the tube isμ [∂u/∂r|r = R = -[μ/(ρiω)]eiωti3/2(α/R)J-1(i3/2α)/J0(i3/2α), where i3/2(α/R)J-1(i3/2α) = dJ0(i3/2[ωρ/μ]1/2r)/dr|r = R. We note that -J-1(i3/2α) = J1(i3/2α), which is written as (i3/2α/2)PS(i3/2α). Substitution now gives the expression for the shear force (per unit area), [eiωtR/2]PS(i3/2α)/J0(i3/2α). This expression is further simplified to-πRμ. In Equations (37) and (38) of the article, the boundary conditions for the wall of the tube in contact with the fluid are described by:where ρ is the density of the tube, σ is Poisson's ratio and B is E/(1-σ2).where A1, C1, D1 and E1. Setting the determinant of coefficients equal to 0 gives the equation for c. A solution for c is easily found when σ = 0 and when the tube is tethered to surrounding structures so that E1 = 0. Combining Equations (27) and (28) givesWomersley interprets Equations (27)–(30) as a system of homogenous linear equations with variables Combining Equations (27) and (29) givesand dividing by Equation (31) givesJ2(i3/2α)/J0(i3/2α) for 1-F10(α) givesSubstituting -ω2Rhρ/(2ρ0) is small compared to Eh/(2ρ0R), we haveBecause c multiplied by ω as the coefficient in the exponential damping function of the wave as a function of distance. The exponential damping coefficient of time is therefore equal to the real part of c times the imaginary part of 1/c multiplied by ω. When α2 << 8, the real part of c is approximately c0α/4, and the imaginary part of 1/c is approximately 4/(c0α). Therefore, the technique of Womersley leads to the prediction that the coefficient for the damping with time is approximately equal to ω and that this coefficient is approximately independent of the radius in small arteries. This does not make sense because Equations (5) and (6) show that for α2 << 8 the damping coefficient is approximately 8μ/(ρR2), the expression used in the exponential pressure gradient model to describe damping of pulse waves in small arteries.Womersley interprets the imaginary part of 1/σ = 0 and E1 = 0 leads toThere is another solution for the PWV in the above equations from the paper of Womersley. Combining equations (27) and (30) for the case where c does not approach the Korteweg-Moens velocity for large values of R.which can not be correct because it predicts that The equations of Womersley do not contain an expression for the damping caused by shear force between the inner wall of the tube and the moving liquid. If the expression for shear force is added to the Womersley model, Equation (31) becomesand Equation (32) becomesω2ρ ≪ E/R2, the above equations are combined and approximated byWhen α2 << 8, {1-F10(α)}/(iω) is closely approximated by 1/K. Furthermore, replacing {1-F10(α)}/(iω) by 1/K and replacing iω by the constant a gives Equation (21). Therefore, it appears that the difference between Equation (21) and the corresponding expression derived by Womersely for c2 in the tethered, thin-walled tube model is due largely to his omission of the expression for damping due to friction between the wall of the tube and blood moving downstream in the pressure wave.When One possible source of errors in the analysis of this paper is the limited consideration of changes in arterial radius. Such changes are fully considered only when the Law of Laplace, Equation (15), is incorporated in an expression. For this reason, caution should be exercised when using the above results to interpret data from arteries in which there is a considerable change in the radius and the cross-sectional area during the cardiac cycle. In large elastic arteries, this change in radius may be 10% or more from the median value , and thiIn small arteries where pressure oscillations are of low magnitude, the above concern diminishes. In addition, as arteries become smaller and smaller, the flow becomes closely described by the equations for the rigid tube. Consequently, the damping function approaches the damping function calculated from Poiseuille's Equation. Therefore, concern for errors in the analysis is less for the expression giving the PWV in small arteries than it is for the expression giving the PWV in large arteries.E. This explanation is largely based on the Korteweg-Moens equation. An increase in the elastic modulus, E, or the relative thickness of the arterial wall, h/R, would increase the PWV. However, changes in other properties associated with arterial walls and surrounding tissue may also increase the PWV and may be interpreted as increasing stiffness. One source of arterial stiffness that is not considered in the above analysis is production of heat as the arterial wall is stretched [A plausible explanation for an increase in the PWV during the course of a disease is an increase in arterial stiffness leading to an increase in the parameter tretched .a decreases. The parameter a describes the rate of increase in pressure during the initial rise as the pulse wave approaches a point in an artery. This rise is determined by the rise in the ejection rate through the aortic valve in early systole. A number of heart disorders can affect this rate of rise. Examples include aortic stenosis, myocardial ischemia and certain conduction disorders. It appears plausible that, in certain diseases of the heart, attributing an increase in the PWV to arterial stiffness may not be the correct explanation. The link between an increase in the PWV and increased risk of myocardial infarction [The above results show that the PWV can increase in small arteries if the parameter farction  may be dThe author declares that they have no competing interests.


S. cerevisiae BZ 34. MET (5mM) gave a hypoxic enhancement ratio (ER) of 1·3. MEA  gave a dose-modifying factor (DMF) of 1·9 and 2·3 respectively for euoxic cells. However, the DMFs for hypoxic cells were 1·0 and 1·1 for 5 and 10mM concentrations of MEA. A combination of 5mM MEA and 5mM MET gave a DMF of 2·0 for euoxic cells and the ER remained at 1·3 for hypoxic cells. The “effective” oxygen enhancement ratios were 2·3 and 1·7 for the control and the sensitizer respectively. In the combination this value was equal to or even slightly less than 1. All DMF, ER and OER values were derived from D0 values of the survival curves. The values based on 10% survival are almost equal to those derived from D0 values. All the survival curves gave the same extrapolation number, showing that the chemicals individually or in combination were truly dose-modifying.This paper discusses the results of experiments using γ-rays and a hypoxic sensitizer metronidazole (MET) and also a well-known protector, mercaptoethylamine (MEA), individually and in combination, on the survival of the yeast These results indicate that protectors such as MEA could be preferentially protecting euoxic cells, and that combining such “oxic protectors” with a hypoxic sensitizer could result in protecting euoxic cells while the sensitization of hypoxic cells was not much reduced. The implications of our results for radiotherapy are discussed. It appears that the use of nontoxic oxic protectors may be a useful adjuvant in overcoming the hypoxic-cell problem in radiotherapy.


Detecting the patterns of DNA sequence variants across the human genome is a crucial step for unraveling the genetic basis of complex human diseases. The human HapMap constructed by single nucleotide polymorphisms (SNPs) provides efficient sequence variation information that can speed up the discovery of genes related to common diseases. In this article, we present a generalized linear model for identifying specific nucleotide variants that encode complex human diseases. A novel approach is derived to group haplotypes to form composite diplotypes, which largely reduces the model degrees of freedom for an association test and hence increases the power when multiple SNP markers are involved. An efficient two-stage estimation procedure based on the expectation-maximization (EM) algorithm is derived to estimate parameters. Non-genetic environmental or clinical risk factors can also be fitted into the model. Computer simulations show that our model has reasonable power and type I error rate with appropriate sample size. It is also suggested through simulations that a balanced design with approximately equal number of cases and controls should be preferred to maintain small estimation bias and reasonable testing power. To illustrate the utility, we apply the method to a genetic association study of large for gestational age (LGA) neonates. The model provides a powerful tool for elucidating the genetic basis of complex binary diseases. Single nucleotide polymorphisms (SNPs) are the most common genomic variations. Detecting the patterns of DNA sequence variants across the human genome, particularly the patterns of haplotypes, is a crucial step for unravelling the genetic basis of complex human diseases. With the growing density of SNP data produced by the human HapMap project ,2, assocχ2 test by comparing SNP allele frequencies between cases and controls )et al. , [12], [21] and [22]. Their haplotype frequencies are expressed asConsider two htSNPs within a haplotype block that cosegregate with the linkage disequilibrium                      	1r,2r denote the alleles of the two SNPs, respectively, and                     ∑r1=12∑r2=12pr1r2=1Once haplotype frequencies are estimated, allelic frequencies and LD can be obtained by solving Equation (1). where G ) denoted as                 	11/11,⋅⋅⋅,12/12,⋅⋅⋅22/22. The double heterozygotic genotype 12/12 contains two possible distinct diplotypes [11][22] and [12][21], and hence is phase ambiguous. The other eight genotypes are phase-known. Each diplotype contains two distinct haplotypes. Totally, there are 10 distinct phase-known diplotypes expressed as 1111,1112,⋅⋅⋅,2222 formed by two SNPs. Let Pr1r2r1r2 and Pr1r1/r2r2 denote the diplotype and genotype frequencies, respectively, and letnr1r1/r2r2 denote the number of observations of the above nine genotypes, where  jr = 1 or 2 . We use upper case P to denote the diplotype frequency and lower case p to denote the haplotype frequency. Assuming HWE, then ten diplotype frequencies can be calculated as a function of the corresponding haplotype frequencies, i.e.,Pr1r2r1r2=pr1r2pr1r2.A complete list of the genotype and diplotype configurations as well as their frequencies is given in Table  1.Random combination of the four maternal and paternal haplotypes forms nine observable genotypes  expressed as [11][11], [11]11¯ and 11¯11¯ (Table 1). Without loss of generality, we assume that a disease predisposing BTN containing haplotype [11] is associated with the disease phenotype. Such a distinct haplotype [11] is called the “risk” haplotype. Individuals carrying this specific haplotype may potentially have high or low risk to develop a disease with a risk level depending on the composition of the diplotype structure one carries on. All the other three haplotypes are called non-risk haplotypes. To distinguish the risk and non-risk haplotypes, we denote all the non-risk haplotypes asm SNPs considered, there could be 2m haplotype parameters need to be estimated for a full haplotype regression model and 2m-1(2m+1) parameters need to be estimated for a full diplotype model. When m is large, this could cause over-fitting problems. Moreover, large number of degree of freedom could decrease the power for an association test. With our formulation, there are always three composite diplotypes regardless of large number of SNPs. The regrouping method is biologically intuitive and statistically efficient. By formulating the composite diplotype, the additive and dominant effects of a risk haplotype can be estimated. Also, we could greatly reduce the number of parameters in the regression model. For example, when there are y denote a measured disease trait which takes two values, 1 or 0, corresponding to affected or control respectively. Let gX denote a matrix of numerical codes corresponding to the composite diplotype, g, including the intercept as the first column, and let e dXenote a matrix of measured clinical risk factors. Assuming that all these covariates influence the mean of the trait and not the scale, so that their effects can be summarized by a function of linear predictorsLet                    X =  and β =  Given a binary disease response, we can apply the logit model which corresponds to the natural logit link function with the formwhere α contain regression parameters for the intercept and the genetic effects of composite diplotypes on a disease trait; γ  contain the effects of clinical risk factors; logitπ=logπ1−π=η with the logistic distribution functionπ=hη=expη1+expη                                    x, the probability distribution of a disease status  Y = y for an individual i can be expressed as  A logistic regression model has been broadly applied to the modelling of binary data ,30. Giveiy takes value 1 or 0, gix0 is one for all i, the independent variablesgi x1  and gix2 are defined as  where  andxeij=j=1,⋅⋅⋅,p refers to the p clinical non-genetic covariates of interest.and variables                     α1 and α2 can be considered as the additive and dominant genetic effect of a risk haplotype  triggers a positive effect to increase a disease risk. The effect of BTNs is considered as pure additive, dominant or recessive if the ratio of the dominant over additive effect (α2 / α1) is 0, 1 or -1 respectively, and is considered as semi-dominant or over-dominant if the absolute value of this ratio is less than 1, or greater than 1 respectively. We call parameters contained in β =  quantitative parameters to distinguish them with the population parameters defined in Eq. (1).With the coding mechanism defined in (4) and (5), aplotype , α0  is We can further partition the logistic function defined in (3) into three distinct logistic regression functions corresponding to different composite diplotype groups as follows  for composite diplotype [11][11], and 					11¯, and                      for composite diplotype [11]                     11¯11¯.for composite diplotype   1.We define these three distinct logistic functions as the diplotype functions corresponding to different diplotypes illustrated in Table y) with the observed SNP genotypes (G). Our goal is to detect DNA sequence variants or BTNs underlying a disease trait. As shown in Table  1, most genotypes have one to one relationship with their diplotypes except the one with genotype denoted as 12/12. This double heterozygote can be partitioned into two possible diplotypes, [11][22] and [12][12] with relative frequencies ø and 1 - ø, respectively. Let p(ig | i G) denote the relative frequency for a diplotype ig consistent with the observed genotype Gi. The relative frequencies for all 10 possible diplotypes are given in Table  1. For individuals with known phase, ( p(ig | i G) takes value one. The individual contribution to the likelihood is given byThe logistic regression model links the interpatient variation in a disease trait  = π (iy | ix). For an individual with genotype 12/12, its likelihood contribution follows a mixture distribution with the formwhere                     where the mixture proportionϕ=p11p22p11p22+p12p12                                        i whose diplotype is [11][22], and                   π1yi|xi and π0yi|xi are the logistic regression functions defined in model (7) and (8), respectively.represents the relative frequency of subject Assuming independence among individuals, the joint prospective likelihood function can be expressed as                     et al. )The above model is developed by assuming that haplotype [11] is the risk haplotype. In reality, we have no prior information on which genetic component triggers a potential effect on a disease trait. We adopt the theoretical information criterion approach to select the risk haplotype. Among a pool of criteria, the Akaike's information criteria (AIC) has been widely used in a variety of fields for model selection . For a 2                s refers to the sth haplotype and sp refers to the number of parameters by taking the sth haplotype as the risk haplotype. The one which achieves the minimum AIC value is then subject to statistical test based on test (12). Significant BTNs are detected to be associated with a disease if a significant risk haplotype exists. When there are multiple haplotype blocks involved, corrections for multiple testing using false discovery rate (FDR) approach is required [11] can be calculated as odds1111=pyi=1|Xgi,Xeipyi=0|Xgi,Xei=expα0+α1+∑j=1pγjxeij                                    11¯11¯after controlling for other covariates can be computed as Thus the exponential of the parameters gives rise to a factorial contribution to the odds not only subject to clinical exposure but also to diplotype structure. Even though individuals are exposed to the same clinical condition, the chance to be affected varies depending on the diplotype structures they carry on. For example, the odds ratio of a disease for an individual carrying composite diplotype [11][11] and                 OR1111/11¯11¯=pyi=1| Xg2i=1,Xei/pyi=0|Xg2i=1,Xeipyi=1|Xg2i=−1,Xei/pyi=0|Xg2i=−1,Xei=exp2α1                                    α0 does not represent population prevalence for a case-control sample.Using delta method, the confidence interval of the odds ratio can be obtained . Note thK(K ≥ 3) htSNPs within a haplotype block constructed from a number of bi-allelic loci. Each of these K htSNPs contains two alleles denoted by                    Qrkkrk=1,2;k=1,⋅⋅⋅K, with allele frequencies denoted by prkkfor the k th htSNP. The coding form indicates that alleles with the same value of rk are located on the same chromosome.The idea of BTN mapping based on a two-SNP model can be extended to include an arbitrary number of SNPs whose sequence variants are associated with the disease variation. Consider K multilocus htSNPs. There are totally 2k possible haplotypes can be formed by the random combination of these K htSNPs. A general form of these haplotypes is expressed as                     Qr11Qr22⋅⋅⋅QrkK with corresponding haplotype frequencies denoted by                    pr1r2...rk. These K htSNPs form 3kobservable multilocus zygotic genotypes expressed as One of the key issues for the multi-SNPs model is to clearly formulate the haplotype and diplotype structures across the Qr11Qs11/Qr22Qs22/⋅⋅⋅/QrKKQsKK                                    with corresponding genotype frequency and observation expressed asPr1s1/r2s2/⋅⋅⋅/rKsK                       andnr1s1/r2s2/⋅⋅⋅/rKsK   2k−12k+1 distinct diplotypes expressed as respectively. The random combination of haplotypes derived from maternal and paternal parents generates Qr11Qr22⋅⋅⋅QrKK Qs11Qs22⋅⋅⋅QsKK                         with corresponding diplotype frequency expressed as Pr1r2⋅⋅⋅rKs1s2⋅⋅⋅sK =pr1r2⋅⋅⋅rKps1s2⋅⋅⋅sK                                    assuming HWE. The composite diplotype can be formulated in a similar way as illustrated in the 2-SNP model.Q11Q11/Q12Q22/Q13Q23                    could form two different diplotypes expressed as                      Q11Q12Q13 Q11Q22Q23 and Q11Q12Q23 Q11Q22Q13, while the genotype                    Q11Q21/Q12Q22/Q13Q23could form four different diplotypes. If we assume that                     Q11Q12Q13 is the risk haplotype, the three composite diplotypes can be formulated as                    Q11Q12Q13 Q11Q12Q13,Q11Q12Q13 Q11Q12Q13¯andQ11Q12Q13¯ Q11Q12Q13¯.As illustrated in the 2-SNP BTN model, the number of multilocus diplotype is generally greater than the number of genotypes when there are two or more heterozygotes present. For example, for a 3-SNP model, the genotype                     Pr1r2⋅⋅⋅rL,, can be decomposed into the following components: The multilocus haplotype frequency can be formulated as a function of allele frequencies and LD parameters of different orders . For exaPr1r2...rK=pr1pr2...prK												No LD+−1rK−1+rKpr1...prK−2DK−1K+...+−1r1+r2pr3...prKD12				Digenic LD+−1rK−2+rK−1+rKpr1...prK−3DK−2K−1K+...+−1r1+r+r3pr4...prKD123		Trigenic LD+...+−1r1+...+rKD1...K											K−genic LD                D 's are the linkage disequilibria of different orders among particular htSNPs.where The MLEs of quantitative parameters can be estimated by formulating the likelihood function similar to the 2-SNP model. The EM algorithm can be employed to estimate the MLEs of haplotype frequencies, and the quantitative parameters. The AIC-based model selection procedure can be adopted to select the risk hapltoype.n = 100,200 and 500), gene action mode , and sampling design on the precision of parameter estimations, type I error rates as well as the power to detect the association.We perform a series of Monte Carlo simulations to investigate the statistical behavior of the proposed BTN mapping approach. The simulation is designed to evaluate the model performance considering the effects of sample sizes  with yi = 1. For simplicity, we only consider one covariate in the model and it is simulated from a standard normal distribution. The given values for population and quantitative parameters are listed in Table 3. The data simulated with this distinct BTN structure are subject to statistical analysis.Assuming that one haplotype is distinct from the other ones, haplotype frequencies are calculated based on the given allele frequencies and LD parameter as listed in Table 3 and their square root of the mean squared errors (RMSEs) are given in the parenthesis. The proportion of cases for the simulated data is about 40-50% on average under the three gene action modes.In each simulation scenario, 1000 Monte Carlo repetitions are performed. For each Monte Carlo sample, the EM algorithm is used to obtain the MLE's of the haplotype frequencies, allele frequencies and LD parameter as well as the quantitative parameters which include the genetic and non-genetic covariates effects. The MLEs for all parameters are listed in Table 3), which infers the consistency of the parameter estimation.As expected, the true association can only be detected with the hypothesized risk haplotype, and all the parameters can be accurately estimated only under the correct haplotype distinction. Overall, our model provides reasonable parameter estimation under different simulation scenarios. All population parameters including the allele frequencies and LD parameter can be well estimated with high precision. The precision depends only on sample size and is not affected by gene action modes. Large sample size always leads to low bias and high precision  results in poor parameter estimation and the precision is dramatically improved when sample size is increased from 100 to 200. Second, as expected, the additive effect can be better estimated than the dominant effect in all simulations. For instance, the RMSE for additive effect is 30% smaller than the dominant effect under the dominant model with sample size 200. Third, the nongenetic covariate effect is not sensitive to the gene action mode, whereas the genetic parameters act differently under different gene action modes.With the estimated haplotype frequencies, we carry out the second stage estimation to estimate the quantitative parameters. As can be seen from Table 2 at the 0.05 nominal level with sample sizes ranging from 100 to 500. It can be seen that the estimated type I error rates are not appreciably different from the nominal level 0.05. For power analysis, we consider three disease models: additive, dominant, and recessive as given in Table  3. The testing power is defined as the percentage of simulations in which the true association is detected. For each simulation case, we run 1000 replicates. The results show that the power of an association test statistic depends on a number of parameters, such as the sample size and the gene action modes. As expected, the testing power increases as the sample size increases. For example, assuming additive model, the power increases from 64% to 93.3% when the sample size increases from 100 to 200. Simulation results also show that the test power is sensitive to gene action mode for small sample size (n = 100). When sample size increases to 200, we observe dramatic power improvement and the difference among different gene action modes is no longer remarkable.Type I error evaluation is summarized in Table 0 by holding other parameters unchanged as given in Table  3. The value of intercept determines the proportion of cases in a sample. Fig. (3) plots the effects of case proportions on the type I error rate with sample size 100 and 200. It can be seen that the type I error rates are inflated when the proportion of cases is away from 50%. As long as the case proportion is kept within the 30%-70% range, the false positive rate can be appropriately controlled. Figs. (1) and (2) plot the effect of case proportions on the testing power and the absolute averaged bias of parameter estimation under three disease models out of 1000 simulations. Clearly, the testing power and estimation biases are affected by case proportions. The power is greatly reduced and the parameter estimation is severely biased when the case proportion is far away from 50% for small sample size (say 100). As sample size increases to 200, the power is significantly increased and the bias is dramatically reduced. Higher sample size (500) leads to more dramatic improvement (data not shown). However, to achieve desired power and small bias, we still need to maintain a balanced case-control sample. When sample size is small (say less than 100), maintaining such a balance is even more crucial.Our association test is conducted based on a prospective likelihood setting assuming a random sample from a population. To evaluate the effect of sampling design on parameter estimation and testing power, we simulate samples through changing the intercept α4. In general, we observe similar trends for both population and quantitative parameters as in the 2-SNP model. As compared to the 2-SNP model, a slightly higher testing power is observed compared to the 2-SNP model with 100 sample size, especially under the dominant gene action mode. When sample size increases to 200 or 500, the difference is not remarkable. Similar results are observed for case proportion effect as in the 2-SNP model and hence is omitted.To test the performance of multi-SNP model, a simulation study assuming 3 htSNPs in a haplotype block is performed. The simulation design is similar to the 2-SNP model. The results are summarized in Table We apply our model to a genetic association study of LGA neonates. LGA may lead to complications for both newborns and mothers. Studies showed that LGA is associated with increased risk of infant mortality , and mayq23. There are total 6 positions showing polymorphisms, three at the intron 1 region for SNPs 633938761, 633938806, and 633938845, one at exon 3 region for SNP 633938988, one at intron 3 region for SNP 633939053 and one at exon 4 region for SNP 633939147. We use Haploview software to construct the haploype block [TC] with odds ratio            ORTC¯TC¯/TCTC=3.3582 and ORTCTC¯/TCTC=1.6711by holding constant for other risk factors.The MLEs of the quantitative parameters and their standard errors in the parenthesis are listed in Table We also calculated the odds ratio of developing LGA for individuals giving birth to different sex of baby. For example, the risk for women carrying the same composite diplotypes to develop LGA would be 1.73 times higher if they deliver baby boy compared to those who deliver baby girl. The risk to develop LGA for a 40-year old mother would be 1.7 times higher than a 25-year old mother after adjusting for other covariates effects. Holding constant for other covariates, the risk to develop LGA will be increased by 1.54 times for every 22 pounds weight gain.The study of common diseases can be broadly divided into two categories: family-based linkage studies across the entire genome, and population-based association studies of individual candidate genes . While aIn this article, we make an attempt to study genetic association by utilizing the abundant sequence variation information developed by the HapMap project. The model, called BTN mapping, is derived on the basis of multilocus haplotype analysis using a finite number of htSNPs within a haplotype block assuming that the candidate gene is not imprinted. Both simulation studies and a real example show that our BTN mapping approach can detect haplotype association underlying a disease trait with high power and, hence displays a number of merits.First, our approach can characterize the association of DNA sequence variants predisposing to a disease. Traditional disease mapping approaches such as binary trait locus (BTL) mapping, attempt to identify loci called BTLs that are linked with known markers -24. The Second, our approach is likelihood-based and is computationally fast. Regular model selection criteria such as the AIC criterion can be applied to determine the risk haplotype structures on a disease trait. The developed model also allows for nongenetic covariates effect which might provide potential information in genetic association study. It has been shown that risk of complex diseases such as cancers may be determined by both genetic and environmental factors . IncorpoThird, the proposed regrouping approach could potentially increase testing power by reducing the degree of freedom for an association test. In general, there are two ways to increase the power of an association test: developing appropriate statistical forms for an association test or reduce the degrees of freedom . A commoFinally, the model is robust and flexible to different genetic and experimental settings. The results from simulation studies indicate that the association between haplotype and disease phenotype can be well detected under different gene action modes with modest sample size. It is worthy to note that the proportion of cases shows a great impact on testing power and parameter estimation by various simulations. Results indicate that a nearly balanced sampling design provides optimal power and low estimation biases, especially for small sample size. Therefore, a balanced case-control design should always be preferred when recruiting samples in a case-control study.The effect of allele frequencies on testing power and parameter estimation is also investigated (data not shown). Our results indicate that testing power is not affected by allele frequency as long as the proportion of individuals carry double risk haplotypes is not terribly small. However, we do observe inflated variances for the genetic parameters when sample size or allele frequency is small, which might be due to multicollinearity among the genetic covariates given the categorical nature of variables . One posOur model is developed based on tag SNPs selected within a haplotype block. The purpose of using htSNPs is to control the number of SNPs used to construct a haplotype, which in turn, minimizes the computation burden for haplotype construction using EM algorithm. We may lose potential information when the efficiency of tag SNP selection is low. With more and more studies focused on tag SNP selection, more robust tagging approaches will improve our mapping efficiency. It should also be noted that the assumption by using tag SNPs can be relaxed in which all SNPs identified within a haplotype block can be used for analysis, especially when the SNPs size within a block is limited. However, corrections for multiple testings are necessary when the number of tested blocks are large.et al. [Our mapping approach considers binary disease traits and is a generalization of the model proposed by Liu et al.  which co