Results showed that this formalized design process is more feasible concerning numbers of cells required. Furthermore, as a proof of principle, an Escherichia coli consortium that performs XOR function, a typical complex computing operation, was designed. The construction and characterization of logic operators is independent of “wiring” and provides predictive information for fine-tuning. This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.The concept of microbial consortia is of great attractiveness in synthetic biology. Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules. To manage such problem, here we propose a formalized design process: (i) determine the basic logic units  based on mathematical and biological considerations; (ii) establish rules to search and distribute simplest logic design; (iii) assemble assigned basic logic units in each logic operating cell; and (iv) fine-tune the circuiting interface between logic operators. We  Microbial consortia refer to a group of multiple interacting microbial populations to perform certain functions. They are ubiquitous in nature et al. engineered cells to carry a modular NOR gate with interchangeable input promoters and output signals, each acting as a logic-computing operator, wired by orthogonal quorum sensing molecules et al., however, constructed 16 different logic-computing operators, each encoded within a single yeast cell, dealing with one/two inputs either from environment or from another “operator”, and generating an output. This allows circuit design to be more feasible; it enables a circuit that requires fewer computing layers (thus fewer wiring molecules) Synthetic gene circuits are often designed to perform Boolean logic function, either for application et al.) and wiring efficiency . Standardized logic operators allow synthetic biologists to predict system performance confidently, without taking into account too many other factors. However, more computing layers and wiring molecules are required. As a result, information processing will be slowed down and will inevitably exhaust limited “chemical wires”  In fact, there is a trade-off between standardization of logic modules  In every logic operator , tandem layers should be limited to no more than three; more necessary tandem layer should be distributed into different cells. This is because previous study revealed that in in silico analyzed Boolean logic functions with two to four inputs and one output, and compared our method with two pre-existing methods, named as Standard NOR/NAND Combining together the four rules above, we E. coli consortium consisting of two types of logic operating cells . The circuit design of two logic operators is presented in In formulation of two-input one-output functions, we found that they could all be implemented in a single cell without violation of above rules, except for XOR gate and EQUALS gate, where two logic operators  are needed see . XOR andel al.T7ptag) can be rescued by supD tRNA, and as an output, genes of interest under T7 promoter would be transcribed only when functional T7 polymerase presents, which means T7ptag and supD  must both exist  consensus -35 and -10 hexamers of promoter could readily implement such repressor (th exist . We haveepressor . In the USC and DSC were constructed by assembling those three logic units in cells . The traT7ptag rescued by supD tRNA) as in USC, only by swapping input promoters and output signals according to system requirements. If necessary, such an AND gate module can still be adopted in other cells in more complex microbial consortia. For the transfer function of DSC, note that AHL has a repressing effect, which means the engineered promoter  would be at “ON” state when there is no existence of AHL. As a result, only when no AHL is in system and at least one of the other two signals  presents, output of DSC would be “ON”. In our initial experiment, strength of PBAD seemed too low compared with PSal to generate significant output contrast. Therefore, we utilized a stronger PBAD promoter by modifying its Aral1 site lux_rep is always on; therefore output of DSC is “ON” when either arabinose or salicylate (or both of them) exists. As a result, DSC behave as an OR gate [DSC has one more input, namely AHL, compared to USC. It is composed of an OR gate tandem connected with a downstream AND gate . Due to lux_rep together work as a transcription-inhibitory chemical wire for cell-cell signaling [in silico design synthetic ribosome binding site (RBS) sequences with requested relative translation strength AAAGAGGAGAAA, numbered BBa_B0034 in luxI in USC generated excessive AHL to repress Plux_rep in DSC [After characterizing USC and DSC, we need to circuit them together according to our design . Throughignaling . Fine-tuluxI compared with BBa_B0034 -, (90±20)- and (110±20)-fold, respectively [After getting the reference, we relied on our model simulation and RBS Calculator to get desired XOR function. With all other model parameters determined previously see , we fit Ba_B0034 . With thBa_B0034 . Among 3ectively . This reATTAAAGTTGAGAAA. The adapted USC still exhibited its property as AND gate, as shown in lux_rep response to AHL is shorter compared with that of cell growth . All plasmids in our experiments are shown in All experiments were performed using Strains were grown in LB liquid medium or on agar plates (1.5% agar), supplemented by antibiotics, ampicillin(50 µg/ml), kanamycin(10 µg/ml), and/or tetracycline(10 µg/ml). Inducers used were arabinose, salicylate and 3OC6-HSL. Reagents were purchased from Sigma-Aldrich. All restriction enzymes were from New England Biolabs, unless otherwise indicated. Primers were chemically synthesized at BGI Sequencing.luxI was conducted using online RBS calculator v1.1: Reverse Engineer RBSs .Strength prediction of RBS sequences prefixing Cells harboring plasmids were separately incubated overnight (no less than 15 h) in 3 ml of LB broth medium  in tubes without the presence of inducers. The cultures were then diluted 20-fold into 200 µl fresh LB broth medium with appropriate inducers in each well of a 96-well plate (2 ml in volume of each well) and incubated for additional 12 h before final pelleting and resuspension in 200 µl PBS  solution for micro-plate reader, or 10-fold dilution in PBS for cytometry analysis.All samples were measured florescence intensity using an exciting light of 470 nm and emitting light of 509 nm. Final florescence data were normalized by an OD600 value. Each sample contains 100 µl PBS solution in a well of micro-plate. All data were obtained using TECAN infinite M200.E. coli DH5α cells harboring no plasmid was subtracted before reporting the resulted fluorescence values. Data were obtained using BD LSR II Special Order System.All data contained at least 30,000 events. Mean value of the fluorescence distributions were calculated, and auto-fluorescence value of After chemical inducement, cultures of mixed cells were diluted 100,000-fold and plated on agar plates applied with appropriate antibiotics and salicylate. Then the plates were incubated under 37°C for 15 h, and colonies on plates were counted. Surface of agar plate was divided into 9 grids, of which 5 were selected for counting See . USCs coFor the search of simplest logic, we used breadth-first search (BFS) method to exhaust possible combinations of certain logic units, thus generating the simplest one. Further, Greedy Algorithm was applied to divide the units into different cells.All simulations were performed using MATLAB Version 7.13.0.564(R2011b) (Mathworks), and so were the experimental results in Figure S1In silico analysis of different approaches in multicellular logic circuits with 4 inputs and 1 output. Due to the limitation of computation capacity, we cannot exhaust all 4-input functions. So we just calculated functions which can be implemented with no more than 7 cells and 4 chemical wires. Again, our approach outweighed others in the number of computing operators, and remained comparative chemical wires. (A). Number of permissible 4-input 1-output Boolean functions versus the number of cells required for their implementation. (B). Number of permissible 4-input 1-output Boolean functions versus the number of chemical wires required for their implementation.(TIF)Click here for additional data file.Figure S2Simplest logic of all sixteen 2-input, 1-output logic gates expressed as combination of basic logic units . The simplest logic was established using computer-aided design with our program according to the four rules in main text. All gate functions can be implemented in a single cell, except for XOR and EQUALS gates.(TIF)Click here for additional data file.Figure S3Biological implementation of genetic AND, OR and NOT gates. AND gate (A): an amber mutated T7 polymerase rescued by supD tRNA. OR gate (B): two promoters with the same downstream gene. NOT gate (C): engineered quorum-sensing repressor.(TIF)Click here for additional data file.Figure S4Working mechanism of a quorum-sensing repressor. To convert a quorum-sensing transcriptional activator, such as LuxR, to a repressor, we positioned lux box, the DNA binding site of LuxR, between  consensus -35 and -10 hexamers of promoter, so that the binding of LuxR to lux box would repress the accessibility of RNA polymerase to promoter. Similar designs can be applied to RhlR and other LuxR-family transcriptional activators. (A). Quorum-sensing transcriptional repressor, without AHL. (B). Quorum-sensing transcriptional repressor, with AHL in the presence.(TIF)Click here for additional data file.Figure S5Dose response and time course of the modified repressive lux and rhl promoters. Coding sequence of fast-degradation GFP  was exploited as the reporter and then induced by gradient concentrations of 3OC6HSL or C4HSL, and afterwards normalized florescence was measured. (A). Dose response of Plux_rep with different inducing time duration. The data sets are for 10−5, 10−6, 10−7, 10−8, 10−9, 10−10, 10−11, 10−12 and 10−13 M 3OC6HSL. Error bars are calculated as mean ± s. d. (B). Time course of Plux_rep, with 10−5 M 3OC6HSL inducing. Error bars are calculated as mean ± s. d. Plux_rep has a quite fast response to 3OC6HSL, so that co-cultured USC and DSC were capable of transmitting signals within a short time. (C). Dose response of modified rhl repressive promoter. The data sets are for 10−6, 5×10−7, 3.5×10−7, 10−7, 5×10−8, 2×10−8, 10−8, 10−9, and 10−10 M C4HSL. Error bars are calculated as mean ± s. d. (D). Time course of modified rhl repressive promoter, with 10−6 M C4HSL inducing. Error bars are calculated as mean ± s. d. Lines in all subfigures are for guiding eyes.(TIF)Click here for additional data file.Figure S6Transfer function of USC, when AHL levels are measured as the output. USC was first induced with different concentrations of inducers, and filtrate from induced USC was used to culture cells bearing Plux_rep with gfp downstream (filtrate was blended 1∶3 in volume with fresh Luria–Bertani broth). Afterwards, corresponding florescence was measured through a micro-plate reader. The florescence has a negative correlation with AHL concentration expressed by USC: the higher AHL concentration, the more Plux_rep is repressed, and thus the less florescence in cells. (A). Experimental results. The data are for 10−1, 10−2, 10−3, 10−4, 10−5, 10−6, 10−7 and 10−8 M arabinose, and 10−3, 10−4, 10−5, 10−6, 10−7, 10−8, 10−9 and 10−10 M salicylate. (B). Corresponding simulation prediction.(TIF)Click here for additional data file.Figure S7BAD (A), adapted stronger PBAD version (B) and PSal (C).Transfer function of P Cells bearing each promoter with gfp downstream were induced, and florescence was measured through a micro-plate reader. The data are for 10−1, 10−2, 10−3, 10−4, 10−5, 10−6, 10−7, 10−8 and 10−9 M arabinose, and 10−3, 10−4, 10−5, 10−6, 10−7, 10−8, 10−9, 10−10 and 10−11 M salicylate. As shown in the figures (A) and (B), the adapted PBAD is stronger, compared with the original. The lines are for guiding eyes.(TIF)Click here for additional data file.Figure S8Model prediction for fine-tuning of circuiting interface. By adjusting luxI translation strength, different phase diagrams have been obtained. (A). Phase diagram for the original RBS, of which the sequence is AAAGAGGAGAAA. The other nine subfigures show phase diagrams with reduced translation strength in simulation. From (B) to (J), translation strengths are attenuated by 2, 5, 10, 20, 50, 100, 200, 500, and 1000 folds, respectively. And their corresponding signal-background ratios (the ratios of protein expression level between “ON” state and “OFF” state) are approximate 6, 9, 10, 8, 3, 2, 1, 0.9 and 0.8 in simulation. So we predict with modeling results that approximate 10-fold attenuated translation strength of RBS prefixing luxI would improve the circuiting.(TIF)Click here for additional data file.Figure S9Transfer function of adapted USC with the RBS prefixing luxIATTAAAGTTGAGAAA. changed to  AHL levels are measured as the output. Experiment protocols are the same as described in the legend of −1, 10−2, 10−3, 10−4, 10−5, 10−6, 10−7 and 10−8 M arabinose, and 10−3, 10−4, 10−5, 10−6, 10−7, 10−8, 10−9 and 10−10 M salicylate. (B). Corresponding simulation prediction.(TIF)Click here for additional data file.Figure S10XOR gate operates robustly under population fluctuation in simulation. In simulation, we changed the population proportion of USC and DSC, and found that within a quite large range (from USC:DSC = 1∶20 to USC:DSC = 1∶1), the system could always exhibit a high signal-background ratio. (A). USC:DSC = 1∶20, (B). USC:DSC = 1∶10, (C). USC:DSC = 1∶5, (D). USC:DSC = 1∶2, and (E). USC:DSC = 1∶1.(TIF)Click here for additional data file.Figure S11Plasmid constructions of USC and DSC. (A). Construction of USC for XOR function. The plasmid bearing RFP was used for flow cytometry assay. (B). Construction of DSC for XOR function. (C). Construction of USC used to measure its transfer functions. (D). Constructions used to measure AHL expression of USC.(TIF)Click here for additional data file.Figure S12Plating and counting. When counting population proportions Click here for additional data file.Table S1Truth tables for AND, OR and NOT gates.(PDF)Click here for additional data file.Table S2Parameters and their corresponding values used in simulation.(PDF)Click here for additional data file.Table S3Primers designed for mutagenesis of Ribosome Binding Site sequence prefixing luxI.(PDF)Click here for additional data file.Text S1Model for logic circuits, supplementary materials and methods.(PDF)Click here for additional data file.


Neuroendocrine tumors (NETs) are found throughout the body and are important as they give rise to distinct clinical syndromes. Glycosaminoglycans, in proteoglycan (PG) form or as free chains, play vital roles in every step of tumor progression. Analyzing tumor samples with different degrees of histological differentiation we determined the existence of important alterations in chondroitin sulfate (CS) chains. Analysis of the transcription of the genes responsible for the production of CS showed a decline in the expression of some genes in poorly differentiated compared to well-differentiated tumors. Using anti-CS antibodies, normal stroma was always negative whereas tumoral stroma always showed a positive staining, more intense in the highest grade carcinomas, while tumor cells were negative. Moreover, certain specific cell surface PGs experienced a drastic decrease in expression depending on tumor differentiation. Syndecan 2 levels were very low or undetectable in healthy tissues, increasing significantly in well-differentiated tumors, and decreasing in poorly differentiated NETs, and its expression levels showed a positive correlation with patient survival. Glypican 5 appeared overexpressed in high-grade tumors with epithelial differentiation, and not in those that displayed a neuroendocrine phenotype. In contrast, normal neuroendocrine cells were positive for glypican 1, displaying intense staining in cytoplasm and membrane. Low-grade NETs had increased expression of this PG, but this reduced as tumor grade increased, its expression correlating positively with patient survival. Whilst elevated glypican 1 expression has been documented in different tumors, the downregulation in high-grade tumors observed in this work suggests that this proteoglycan could be involved in cancer development in a more complex and context-dependent manner than previously thought. The neuroendocrine tumors (NETs) comprise a heterogeneous group of neoplasms, which express typical characteristics of cells of the neuroendocrine system, which in itself constitutes a diffuse structure that includes several cell types, which arise in tissues derived from the embryonic neural crest, specific zones of neuroectoderm and endoderm, and have many histological similarities to neural cells, such as secretory granules, similar cellular antigens and the fact they contain the potential markers chromogranin A, synaptophysin, and neurone-specific enolase. They produce bioactive substances such as peptide hormones and biogenic amines that serve a transmitter function .The incidence of NETs is two cases per 100,000 people, and they account for 0.5% of all malignancies. NETs can arise in many different parts of the body including the gastrointestinal (GI) tract, lung, pancreas, thymus, adrenal glands, ovary, and thyroid. However, approximately two-thirds of NETs are found in the GI tract, and approximately one-quarter occur in the lung, thus the two locations account for about 90% of such tumors . AlthougSome characteristics of NETs are specific to where they are located. Nonetheless, in general and according to WHO guidelines, these neoplasms are divided into well-differentiated and poorly differentiated. Well-differentiated NETs have a characteristic organoid arrangement of the tumor cells, and in general are either low or intermediate grade with mitotic activity and Ki67 proliferative index of under 20%. In contrast, poorly differentiated NETs display a more sheetlike or diffuse architecture, higher mitotic activity and are considered high-grade in all cases . The higVarious studies have addressed the analysis of the differential gene expression profile in NETs and have identified alterations affecting a variable number of transcripts, ranging from around a hundred to a thousand depending on the study design and experimental analysis –9. AmongN-acetylglucosamine (GlcNAc) and hexuronic acid residues while in CS GlcNAc is substituted by N-acetylgalactosamine   chains. GAGs are linear anionic polysaccharides made up of repeating disaccharides containing acetylated amino sugar moieties and acid, mainly uronic. Differences in the type of monosaccharide in the repeating unit, as well as their sulfation, result in the various types of GAGs. Heparan sulfate (HS) and chondroitin sulfate (CS) are the two most widespread forms of sulfated GAGs, being present in all cell types and tissues at the extracellular and cellular levels. HS consists of repeating disaccharide units of ) Figure .Heparan sulfate and CS synthesis occurs mainly in the Golgi apparatus and is initiated by the formation of a tetrasaccharide linkage on a cognate serine residue of the PG core, synthesized by stepwise addition of xylose, two galactose units, and a glucuronic acid (GlcA) residue. The addition of this residue is the crucial factor in determining which GAG will be synthesized; while the incorporation of GalNAc initiates CS elongation, addition of GlcNAc results in HS formation . SubsequChondroitin sulfate and HS chains are able to interact selectively with many different types of ligands, including soluble factors, membrane proteins and the extracellular matrix (ECM), lipids, and even microorganisms. HSPGs and CSPGs are mainly associated with the cell surface and the ECM and a variety of both normal and pathological functions have been ascribed to them, including cell adhesion and migration, organization of the ECM, regulation of proliferation, differentiation and morphogenesis, cytoskeleton organization, tissue repair, inflammation, vascularization, and cancer metastasis , 13.Proteoglycans may play diverse roles in cancer and act as either inhibitors or promoters of tumor progression depending on the type and stage of the cancer. They have been found to be involved in several aspects of tumor biology including cell proliferation, adhesion and migration, inflammation, and angiogenesis –15. The Unlike other neoplastic transformations, there is little information concerning alterations of PGs in NETs. Some studies report changes associated with malignant transformation, such as the increased expression of lumican in carcinoid compared to neuroendocrine cell carcinomas, suggesting that this PG may play a role in the slow growth of these tumors . The oveIn this paper, we investigate the expression of PGs in NETs of different malignancy grade. By means of differential transcription studies we identify genes whose expression is significantly altered between tumors of low and high-grade. Subsequently, we analyze the alterations in the products of these genes, PGs or GAGs as appropriate, using immunohistochemistry, as well as their correlation with cell differentiation, grade and stage of tumor, and patient survival. Given the large number of genes involved in the biosynthesis of these complex molecules, we focus the analysis on two specific families that have been shown to be strongly involved in tumor transformation in other neoplastic processes: HSPG core proteins and enzyme encoding CS chains. The aim of the work was to detect genes whose expression may be related to tumor aggressiveness and which might be able to predict the behavior of NETs. These genes could, in the future, be useful in developing new chemical biology approaches to retarding tumor progression by modulating the deregulated biosynthetic pathways.The following materials were purchased from the manufacturers indicated: RNeasy Kit and RNase-Free DNase from Qiagen ; High-Capacity cDNA Reverse Transcription Kit and PowerSYBR Green PCR Master Mix from Applied Biosystems ; GenElute PCR clean-up kit and 3,3′-diaminobenzidine from Sigma-Aldrich ; and EnVision™ G|2 Doublestain System and EnVision FLEX Target Retrieval Solution of High pH from Dako . All other chemicals were obtained from commercial sources and were of analytical grade.The following antibodies were used in this study: rabbit anti-syndecan 2 (M-140) was from Santa Cruz Biotechnology, Inc. ; rabbit anti-glypican 1 polyclonal antibody from Thermo Scientific ; rabbit anti-glypican 5 monoclonal antibody from Novus Biologicals ; monoclonal anti-CS clone CS-56, purchased from Sigma-Aldrich ; and anti-mouse (sc-2020) and anti-rabbit (sc-2004) secondary antibodies, from Santa Cruz Biotechnology .Thirty-nine patients with gastroenteropancreatic (GEP) and pulmonary neuroendocrine tumors (L-NETs) were recruited at the Biobank of the Hospital Universitario Central of Asturias between February 2000 and October 2009. Institutional review board approval in relation to guidelines on ethical procedures, and patient informed consent for research on the samples were sought and given. The diagnosis of NETs was based on morphological criteria according to the updated World Health Organization grading classification (2010). The characteristics of the patients studied  and the clinicopathological features of their tumors  were recorded. Tissues obtained from biopsies were fixed in 10% formaldehyde and paraffin embedded then cut in 4 μm thicknesses, mounted on treated slides, and stained with Hematoxylin–Eosin (H&E). The neuroendocrine phenotype was confirmed by chromogranin A and synaptophysin immunohistochemical staining. Normal tissue present in each tissue section was used as a reference.Representative tumor regions were identified in each sample and selected to make a tissue microarray containing three tissue cores from each of 31 GEP and 8 L-NETs. After 5 min at 60°C the blocks were cut into 4 μm sections in preparation for immunohistochemical techniques.To obtain the RNA, fragments of frozen tissue of between 20 and 30 mg in weight were used. Samples were homogenized using a PT 2100 Polytron , and RNA was isolated using the RNeasy kit, following the manufacturer’s specifications. To ensure removal of residual contaminating DNA, samples were subjected to treatment with RNase-free DNase during the purification process itself. The concentration of RNA obtained was determined spectrophotometrically by measuring absorbance using a Picodrop Microliter UV/Vis spectrophotometer . The samples were divided into aliquots of 10 μl and used for reverse transcription reactions or stored at −20°C until further use.cDNA synthesis was carried out using the High-Capacity cDNA Transcription Kit following the manufacturer’s specifications. The reactions were performed using an iCycler iQ PCR Thermal Cycler , using 2 μg of RNA as starting material. The reaction products were cleaned using the PCR Clean-Up GenElute kit following the manufacturer’s instructions. Finally, the aliquots containing the cDNA were diluted 1:20 with water and used for qRT-PCR assays or stored at −20°C until use.Tm was above 77°C. The theoretical Tm for each amplicon was determined using the program BiomathIn all cases, specific oligonucleotides were designed on different exons or exon junctions, using the program Primer 3SDC1 (Gene ID 6382) forward 5′ CTCAGGTGCAGGTGCTTTG 3′, reverse 5′ CTGCGTGTCCTTCCAAGTG 3′; SDC2 (Gene ID 6383) forward 5′ GATGACGATGACTACGCTTCTG 3′, reverse 5′ TGGAAGTGGTCGAGATGTTG 3′; SDC3 (Gene ID 9672) forward 5′ CTCCTTTCCCGATGATGAAC 3′, reverse 5′ CGACTCCTGCTCGAAGTAGC 3′; SDC4 (Gene ID 6385) forward 5′ GGCAGGAATCTGATGACTTTG 3′, reverse 5′ TCTAGAGGCACCAAGGGATG 3′; GPC1 (Gene ID 2817) forward 5′ CATCGGGTGTGGAGAGTG 3′, reverse 5′ TGAGCGTGTCCCTGTTGTC 3′; GPC2 (Gene ID 221914) forward 5′ CTGGGACACGACCTGGAC 3′, reverse 5′ GCCATCCAGTCATCTGCATAC 3′; GPC3 (Gene ID 2719) forward 5′ CTGCTTCAGTCTGCAAGTATGG 3′, reverse 5′ GTGGAGTCAGGCTTGGGTAG 3′; GPC4 (Gene ID 2239) forward 5′ AGTGTGGTCAGCGAACAGTG 3′, reverse 5′ CAAACATATCATTCAGGGATTTCTC 3′; GPC5 (Gene ID 2262) forward 5′ GCCGCCCTGTAAGAACAC 3′, reverse 5′ TCATTCCATGCTTCTCTTTGC 3′; GPC6 (Gene ID 10082) forward 5′ CCAGGCATAAGAAATTTGACG 3′, reverse 5′ CATGTACAGCATGCCATAGGTC 3′; PRCAN (Gene ID 3339) forward 5′ TGGACACATTCGTACCTTTCTG 3′, reverse 5′ CACTGCCCAGGTCGTCTC 3′; AGRN (Gene ID 375790) forward 5′ ACTGTGTCTGCCCGATGC 3′, reverse 5′ GACACTCGTTGCCGTATGTG 3′; COL18A1 (Gene ID 80781) forward 5′ GTACAAGGGAGAGATTGGCTTTC 3′, reverse 5′ TTTCTCTCCTTTCAATCCGTTC 3′; XYLT1 (Gene ID 64131) forward 5′ ACTACCCCATCAGGACAAATGA 3′, reverse 5′ CTGCTTCCGAATGAACCTTG 3′; XYLT2 (Gene ID 64132) forward 5′ AGGGCCTGGTAGTGTGGAG 3′, reverse 5′ TGAACTGTCTGTGTCCTTGGAA 3′; FAM20B (Gene ID 9917) forward 5′ TCTGCAGAAGCACCGTCA 3′, reverse 5′ CAGCTGTGTCAATGATGTCCA 3′; B4GALT7 (Gene ID 11285) forward 5′ GCGAGGACGACGAGTTCTAC 3′, reverse 5′ CAGGTGGCGAAATGTCTTGTA 3′; B3GALT6 (Gene ID 126792) forward 5′ CACGTGGCCTTCGAGTTC 3′, reverse 5′ CCGAGAAGAAGCCCCAGTA 3′; B3GAT1 (Gene ID 27087) forward 5′ TGGTGAATGAGGGCAAGAA 3′, reverse 5′ CTTAGGAGTCGGCCTTGGA 3′; B3GAT2 (Gene ID 135152) forward 5′ GCTGACGACGACAACACCTA 3′, reverse 5′ CGGTGTACCAGCCAACAAC 3′; B3GAT3 (Gene ID 26229) forward 5′ GAAGAACGTGTTTCTCGCCTAC 3′, reverse 5′ CCTCAGATCCTTCTGCCGTA 3′; EXTL2 (Gene ID 2135) forward 5′ TGAACTGGAAACCAATGCAG 3′, reverse 5′ AGGAAATTGCTGCCAAACTG 3′; EXTL3 (Gene ID 2137) forward 5′ CTCCGCCATGACGAAATC 3′, reverse 5′ AGTTGGAGTTGTAGAGCCAGGA 3′; CSGALNACT1 (Gene ID 55790) forward 5′ GGAGACCCTGAACAATCCTG 3′, reverse 5′ GCCGTTTGAATTCGTGTTTG 3′; CSGALNACT2 (Gene ID 55454) forward 5′ GCCATTGTTTATGCCAACCA 3′, reverse 5′ ATCCACCAATGGTCAGGAAA 3′; CHSY1 (Gene ID 22856) forward 5′ GCCCAGAAATACCTGCAGAC 3′, reverse 5′ CACTACTGGAATTGGTACAGATG 3′; CHPF (Gene ID 79586) forward 5′ CTGGGTCGCTGCATTCTC 3′, reverse 5′ GGCACTTCGGAAATGAGG 3′; CHSY3 (Gene ID 337876) forward 5′ CGCCGACGACGATGTCTAC 3′, reverse 5′ CCAGTCCCAGCTTTCCAAG 3′; CHST11 (Gene ID 50515) forward 5′ CGCTGCTGGAAGTGATGA 3′, reverse 5′ CAGCAGATGTCCACACCAA 3′; CHST12 (Gene ID 55501) forward 5′ GTAGCCGACAAATCCTTCCA 3′, reverse 5′ ACCGGTTTACCTCTGACTTGAC 3′; CHST13 (Gene ID 166012) forward 5′ CCGGCATTTGGAAACAGA 3′, reverse 5′ TCCAGGTCATAGAGCTTCTGC 3′; CHST14 (Gene ID 113189) forward 5′ CCACTGCCTAATGTCACCAA 3′, reverse 5′ ATGACAGGCAGAAGCACAGA 3′; CHST15 (Gene ID 51363) forward 5′ GTGCCAGGAATAAAGTTCAACA 3′, reverse 5′ CACTGGATAAGTCCCGAGTGA 3′; CHST3 (Gene ID 9469) forward 5′ TGCACAGCCTGAAGATGAGA 3′, reverse 5′ CAGCTTGTCTGAGACCCTTGA 3′; CHST7 (Gene ID 56548) forward 5′ GATCCGGGTCAGTCACCA 3′, reverse 5′ GACAGATTGCCCCCACAG 3′; DSE (Gene ID 29940) forward 5′ GTCCAGAGGCACTTCAACATC 3′, reverse 5′ AGTCCGCAATAGCCACAGTC 3′; and UST (Gene ID 10090) forward 5′ ACCATGGACCACCTCCTAGTAA 3′, reverse 5′ GCTTCTCCGACAAGATTCTCA 3′.The primer sequences were: Tm corresponding to that previously calculated was verified. In those cases in which the amplifications were not adequate, new primer pairs were designed. Actin was included on each plate as a control gene to compare run variation and to normalize individual gene expression.At least four repetitions of each qRT-PCR reaction were carried out in a final volume of 10 μl, according to the manufacturer’s specifications, using 1 μl of the cDNA dilution as template, with 2 μl of primer pair mix  and 5 μl of SYBR Green mix, contained in 96 well microtiter plates. The plates were sealed with optical film and centrifuged at 2500 rpm for 5 min before being placed in a Real-Time ABI Prism Detection System device  using the following cycling conditions: 95°C for 10 min, 40 cycles of 95°C for 15 s followed by 60°C for 60 s. Following thermal cycling and data collection steps, amplimer products were analyzed using a melt curve program . For each amplification the presence of a single peak with a Ct (gene) = ΔCt  − ΔCt . Here, rER is the relative expression ratio of a gene-of-interest GOI (relative to a housekeeping gene HKG).To calculate the efficiency of amplification for each gene we used the program LinRegPCR2O2) and non-specific binding  were blocked and the sections were incubated overnight at 4°C with primary antibodies using a 1:100 dilution. Secondary antibodies were used at a 1:100 dilution. 3,3′-diaminobenzidine was used as a chromogen. Finally, samples were counterstained with hematoxylin, dehydrated and mounted in Entellan® . The sections were studied and photographed (20× and 40× objective) under a light microscope (Nikon – Eclipse 80i).Tissue sections were dewaxed as described in the previous section. Rehydrated sections were rinsed in phosphate buffered saline (PBS) containing 1% tween-20. Sections were heated in High pH EnVision FLEX Target Retrieval Solution at 65°C for 20 min and then incubated for 20 min at room temperature in the same solution. Endogenous peroxidase activity  taking into account two parameters: immunohistochemical signal intensity  and the percentage of positive cells (0–100). For statistical purposes, the tumors were divided into two groups taking the median score value for each marker as a cutoff point.U test and between multiple samples by the Kruskal–Wallis test. Correlations were assessed by Pearson’s correlation coefficient. p < 0.05 was accepted as significant.All analyses were performed using the Statistics for Windows program . Mean values were compared between two samples by the Mann–Whitney We investigated the differential expression of two specific families of genes involved in the biosynthesis of PGs in NETs: HSPG core proteins and CS biosynthesis enzymes. Firstly, we used qRT-PCR to perform a quantitative analysis of the differential transcription of the coding genes using eight samples of lung NETs. Poorly differentiated large cell NETs were analyzed, and compared to low-grade well-differentiated typical carcinoids. The samples corresponded to patients of both sexes, aged between 49 and 84 years.Determining the expression of the different proteins or saccharide chains of the GAGs analyzed was performed by immunohistochemistry using a tissue array in which tumor and healthy samples of 39 patients were included Table . The arrOnly 13 genes encode HSPG core proteins. Two gene families, syndecans and glypicans, account for most cell surface HSPGs. Respectively these families comprise 4 (SDC1–4) and 6 (GPC1–6) different proteins. The three remaining molecules are arranged in the ECM and include perlecan (PRCAN), agrin (AGRN), and collagen type 18 (COL18A1) .When we analyzed the levels of transcription of these genes in well-differentiated and poorly differentiated lung tumors by means of qRT-PCR, there were no significant differences in any of the ECM PGs Figure A. Howevep = 0.05, Kruskal–Wallis test); specifically between healthy and tumor tissues, either well- or poorly differentiated , although there was a reduced level of significance between the two tumor subgroups (p = 0.051)   . The cases presenting higher levels of this PG displayed better prognosis. In addition, we quantified the existence of correlations between syndecan 2 expression and tumor topography using the classical division into foregut, midgut, and hindgut (p = 0.85).Changes in the expression of syndecan 2 in NETs were evaluated by immunohistochemistry using tissue arrays with tumors of different origins, grades, and cell differentiation, as detailed above. Labeling with anti-syndecan 2 was very low or undetectable in normal tissues, increasing significantly in well-differentiated tumors, and decreasing in poorly differentiated NETs Figure . Semiqua) Figure A. When t hindgut , althougAnalysis of the expression levels of the different glypicans showed substantial differences between them, far greater than in the syndecans, with the differences reaching up to two orders of magnitude. The qRT-PCR studies performed on well- and poorly differentiated lung NETs showed significant differences for two of the six species, glypican 1 and 5 Figure . Transcrp < 0.0001); the differences being most marked when comparing high-grade tumors with normal tissue or well-differentiated tumors (p < 0.0001 in both cases), but they were also significant for the notable increase in expression when comparing normal tissue and low-grade tumors (p = 0.01) , reflecting the disappearance of the expression in tumors of grade 2 or higher  since expression increased in tumors up to stage 2, and then decreased progressively at higher stages . Furthermore, analysis of protein expression based on topography of the tumor showed no positive relationship to exist (p = 0.17).Analogously to syndecan 2, glypican 1 expression was analyzed by immunohistochemistry using tumor tissue arrays. Expression of glypican 1 could be determined in normal tissues, but its magnitude increased strongly in well-differentiated tumors. By contrast, the expression of this protein dramatically decreased in high-grade NETs, reaching undetectable levels Figure . Semiqua) Figure A. Further Figure B. The cos Figure C. Also, p = 0.001, Kruskal–Wallis test); specifically between well- and poorly differentiated tumors  , reflecting the increase in expression in tumors of grade 2 or higher  since expression increased in tumors of stages 3 and 4 , and analysis of protein expression based on the topography of the tumor showed no positive relationship exists (p = 0.11).The expression of glypican 5 was also analyzed at the protein level by immunohistochemistry. We were not able to detect positive results in the majority of normal tissues Figure , althoug) Figure A. Further Figure B, as wel4 Figure C. In conChondroitin sulfate is an anionic linear polysaccharide that is implicated in cancer through its involvement in many essential aspects of cell physiology. The synthesis of CS chains initially requires an array of glycosyltransferases (GTs), which generate a tetrasaccharide glycan linker on a cognate serine residue of the PG core. This linker is shared by CS and HS chains and its sequence is integrated by xylose–galactose–galactose–GlcA . CS chaiXYLT1 and XYLT2, responsible for the initial transfer of xylose residues, B4GALT7 and B3GALT6, which encode the enzymes responsible for the sequential addition of the two residues of galactose, and B3GAT1, B3GAT2, and B3GAT3, responsible for the transfer of the GlcA residue at the end of the linker  were also studied. Under-expression was detected in two, CSGALNACT1 and CHPF, in high-grade compared to well-differentiated NETs, and in both cases the deregulation was around threefold , along with the xylose kinase involved in the regulation of HS biosynthesis (FAM20B)  . We were(FAM20B) .N-acetylgalactosamine 4-sulfate 6-O-sulfotransferase (CHS15) ; sulfation at C4 of GalNAc, catalyzed by different isoenzymes with specificity for CS or DS chains ; addition of sulfate at C6 of GalNAc, also catalyzed by different isoenzymes (CHS3 and CHS7); and sulfation at C6 can also occur in pre-sulfated residues catalyzed by a ) Figure . The ana) Figure .p < 0.01) and healthy and poorly differentiated NETs (p < 0.0001), but not between high-grade and low-grade tumors (p = 0.46) to be determined , although these correlations did become significant when expression levels in normal tissues were also considered (p = 0.0002 in both cases)  or location of the tumor (p = 0.97).The combined action of all the above genes results in CS chains with specific patterns of sulfation. Changes in the expression of CS chains in NETs were evaluated by immunohistochemistry using tissue arrays, as indicated above, and monoclonal anti-CS antibodies. Antibodies applied to normal tissue sections showed faint stromal staining, which was also present in isolated intestinal epithelial cells. However, in well-differentiated NETs we found focal stromal staining, which was clearly enhanced in the cases of the highest grades Figure . Semiquad Figure A. In the Figures B,C. MoreUnlike other tumor types such as lung cancer or breast cancer, which are defined by their tissue of origin, NETs arise from a cell type, and may have originated in many parts of the body, although GI and lung tumors account for around 90% of all cases. Although some of the clinical and pathological features of these tumors are specific to the organ of origin, other attributes are shared by all these neoplasms and there are many similarities among NETs throughout the body . Taking Proteoglycans are critical in regulating the activity of many signaling pathways, as well as cell–microenvironment interactions. The development of cancer typically involves distinct stages, including transformation, invasion and metastasis, and as a result of their diverse functions, PGs and their GAG chains have been found to be involved in tumorigenesis in several cancers, including breast, lung, brain, pancreatic, and colorectal. Many studies suggest that PGs regulate multiple oncogenic pathways in tumor cells and promote critical tumor–microenvironment interactions, such that these molecules are potentially important tumor markers and therapeutic targets .N- and O-sulfotransferases, epimerases, sulfatases, kinases, or hydrolases, besides enzymes involved in the synthesis and transport of precursors . In the syndecans group, only eceptors .in vitro” neovascularization -linked to the cell membrane. Their expression is cell type and developmental-stage-specific and they are involved in fundamental biological processes including cell division, interactions with ECM, differentiation, and morphogenesis . At the In adults, GPC5 is primarily expressed in the brain, and it is also detected in fetal brain, lung, and liver. In well-differentiated NETs, GPC5 mRNA shows the highest level of transcription relative to the other glypican isoforms, but this transcription declines dramatically in poorly differentiated tumors. Surprisingly, analysis of the expression of GPC5 at the protein level, performed by immunohistochemistry, revealed that only a small proportion of normal tissues and well-differentiated tumors showed a positive reaction, whereas the protein was overexpressed by 70% of high-grade tumors. Furthermore, there were some phenotypic differences between poorly differentiated tumors expressing the protein, which displayed epithelial differentiation, and those that did not, which showed a neuroendocrine phenotype. Previous studies have reported low correlations between mRNA levels and protein expression in many genes, suggesting different mechanisms are responsible for the observed differences in the quantitative relation between transcriptome and translatome . In studWith regard to GPC1, immunohistochemical studies show strong expression levels in well-differentiated NETs, which decreases drastically and disappears altogether in poorly differentiated ones. As with SDC2 and GPC5, expression was not influenced by the location of the tumor, suggesting that this is another common feature shared by all neoplasms, wherever they are in the body. However, when transcription levels of the gene were analyzed, poorly differentiated tumors displayed mRNA levels of around 30% of those present in well-differentiated NETs. This discrepancy again suggests the existence of other, overlapping, levels of regulation, possibly at the level of translation; as such, the existence of translation-level regulation has been described in some genes involved in the biosynthesis of GAGs and PGs –49. GPC1GPC1 overexpression has been described in certain tumors, although with some peculiarities depending on the neoplasia. This isoform shows increased expression in gliomas, where it acts to enhance FGF signaling and mitogenesis ; in pancAlthough GPC3 is the species for which most expression alterations in tumors have been described, it does not appear to undergo alterations in NETs in relation to the degree of cell differentiation. Unlike isoforms 1 and 5, whose alterations involve increased protein expressions associated with carcinogenesis, variations in GPC3 expression levels vary greatly depending on the type of neoplasm , 41. DowXYLT1, responsible for the initial transfer of xylose residue, B3GAT1, and B3GAT3, responsible for the transfer of the first GlcA, CSGALNACT1, one of the genes that directs biosynthesis toward CS chains, and CHPF, involved in CS polymerization. Taken together, these data indicate a decrease in the synthesis of CS chains in poorly differentiated tumors. This conclusion is reinforced by the lack of alteration in the enzymes responsible for the initiation of the synthesis of HS EXTL2 and EXTL3, which acts in competition with CS biosynthesis. In addition, during synthesis of the linkage region, a transient phosphorylation of the xyl residue occurs, catalyzed by a xylose kinase (encoded by FAM20B), which is dephosphorylated before subsequent polymerization. If persistent xylose phosphorylation occurs, the transference of a GlcNAc residue, catalyzed by EXTL2, terminates HS chain elongation , but is also able to recognize other types of structures, including CS-A, -B, -C, and -E . Normal Finally, a result of note common to all sections analyzed in this work was the absence of significant correlations between the expression of these molecules and the anatomic location of the tumors, suggesting that the alterations described are common features of all NETs.Olivia García-Suárez carried out the histochemistry. Beatriz García performed the qPCR experiments and PCR data analyses. Iván Fernández-Vega contributed to sample and array preparation. Aurora Astudillo supervised sample preparation, the compilation of patient information as well as the histochemical analyses. Luis M. Quirós co-ordinated the study and drafted the manuscript. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


DRD4) influenced sport-specific sensation seeking, we analyzed five polymorphisms  in the promoter region of the gene in a cohort of skiers and snowboarders (n = 599) that represented a broad range of sensation seeking behaviours. We grouped subjects by genotype at each of the five loci and compared impulsive sensation seeking and domain-specific (skiing) sensation seeking between groups. There were no significant associations between genotype(s) and general or domain-specific sensation seeking in the skiers and snowboarders, suggesting that while DRD4 has previously been implicated in sensation seeking, the promoter variants investigated in this study do not contribute to sensation seeking in this athlete population.Sensation seeking is a personality trait that has been associated with disinhibited behaviours including substance use and gambling, but also with high-risk sport practices including skydiving, paragliding, and downhill skiing. Twin studies have shown that sensation seeking is moderately heritable, and candidate genes encoding components involved in dopaminergic transmission have been investigated as contributing to this type of behaviour. To determine whether variants in the regulatory regions of the dopamine-4-receptor gene ( Downhill sports such as skiing and snowboarding are popular high-risk pastimes that often involve high speeds, hazardous terrain, and uncertain weather conditions and thus carry a potential for severe injury DRD4, 11p.15.5) and its adjacent regulatory region have been associated with personality traits that share a common motivational tendency towards behavioural approach, including, novelty seeking, extraversion, and sensation seeking DRD4 are often investigated using the −521T/C SNP and a 48-bp repeat in exon 3, which are relatively heterogeneous in most populations. While there is support for the involvement of the DRD4 in approach-related traits, the results of these studies have been inconsistent Most genetic studies of sensation seeking traits have investigated genes encoding components of the dopaminergic pathway due to the purported role of dopamine in behavioural activation and instrumental learning DRD4 and high-risk social populations, such as substance abusers and alcoholics Despite the interest in DRD4 influence sensation seeking traits in athletes, a cross-sectional, single cohort design was employed in a sample of proficient skiers and snowboarders to test for associations between DRD4 genotypes and sensation seeking phenotypes established using: 1) Zuckerman's measure for impulsive sensation seeking (ImpSS) DRD4 and then the sensation seeking phenotypes between each category were compared in order to determine if any of the phenotypes were associated with a specific genotype (or genotypes). Inclusion of polymorphisms was limited to informative, common SNPs (heterozygosity >.20) located in the promoter region of the gene. The DRD4 promoter contains a high density of SNPs with low linkage disequilibrium To test the hypothesis that variants in n = 363) of the participants described in the current study were included in a previous study, their questionnaire data was used in both studies, but we analyzed different genetic variants n = 599, mean  = 27.12 years, SD  = 6.45) and were pre-screened for ability . The majority of subjects were either skiers (56%) or snowboarders (38%) while the remaining practiced both sports (5%) or were telemarkers (1%). To minimize confounding effects due to differing biogeographical background, we excluded the small number of subjects that did not self-report as being of European descent subjects from the analysis.Skiers and snowboarders recruited at a winter festival in Whistler, British Columbia, Canada completed two questionnaires (in English) and provided a buccal (cheek) cell swab for DNA preparation  ImpSS measure, scored true/false Participants completed the 10 item CSSQ-S, anchored on a Likert scale by 1 (strongly disagree) and 5 (strongly agree). Exemplar items from the CSSQ-S include “I like to ski/ride fast” and “I like to go down runs that I have never been down before” ng DNA/μl. The DNAs were genotyped at Génome Québec Innovation Centre at McGill University, Montréal, Quebec, Canada using the Sequenom iPLEX technique . The initial goal was to obtain data for five SNPs (DRD4 −1106T/C (dbSNP rs936460), −906T/C (dbSNP rs3758653), −809G/A (dbSNP rs936461), −616G/C (dbSNP rs747304), −291C/T (dbSNP rs916457)); however, we were unable to obtain data on −616G/C due to amplification problems. We also attempted to obtain −521T/C (dbSNP rs1800955) genotypes for those samples (n = 236) that had not previously been analysed Buccal cell DNA was isolated from cytobrushes  using an alcohol purification technique μL reaction contained 20 mM Tris-HCl pH 8.4, 50 mM KCl, 1.5 mM MgCl2, 0.2 mM dNTP, 0.2 μM of each primer, 5% DMSO, 0.5 U Taq polymerase , and 10–20 ng DNA template. PCR products were size separated by electrophoresis on 8% PAGE gels run in TBE buffer, stained with SYBR Safe DNA gel stain  and visualized using BIORAD Gel Doc EZ System . Alleles were characterized by “short”  versus “long”  amplification products.The 120-base pair tandem duplication was amplified using primers and polymerase chain reaction (PCR) reagents described by Seaman et al. vs. SS), −1106T/C (TT vs. TC vs. CC), −906T/C (TT vs. TC vs. CC), −809G/A (GG vs. GA vs. AA), −291C/T (CC vs. CT vs. TT)) and each of the dependent variables . As there is no evidence for a dominant or recessive mode of action for any of the SNPs examined, an additive model of inheritance (three factor levels) was applied. The 120-bp tandem duplication was analyzed using both additive and grouped models, as there is evidence that the L allele is dominant i.e., the five independent genotypes for three dependent variables) was applied to the data, so the threshold for significance was set at an alpha of .003 (.05/15)  was used to analyze main effects of the individual genotypes . The mean marker call rate for SNPs analyzed using Sequenom iPlex was 98±0.33%. Scores derived from the ZKPQ ImpSS and CSSQ-S scales were normally distributed, with no univariate outliers. Sex was entered in the initial analysis as a covariate because there were significant differences between the sexes in both domain-specific and general sensation seeking (CSSQ-S: t(576)  = 13.70, p<.001; ZKPQ ImpSS: t(494)  = 3.20, p = .001). Age was related to both CSSQ-S and ImpSS (CSSQ-S r(575)  = −.24, p<.001; ImpSS r(597)  = −.09, p = .04), but the relationship between ImpSS and age was too weak to be included as a covariate. Finally, ability was significantly correlated with CSSQ-S, r(570)  = .60, p<.001 and was included as a covariate for CSSQ-S analyses only.The genotype distributions at the five loci  were conn = 445 to 599. The mean values for the phenotypes (ImpSS and CSSQ-S) did not vary between the full sample (n = 599), and the sample that included genotypes for the 120-bp duplication . Univariate analyses comparing ImpSS (with sex as a covariate) between genotypes at each loci revealed no significant associations, the results for the impulsivity and sensation seeking scales when analyzed separately were also not significant (data not shown). Similarly, there were no significant associations  between domain-specific sensation seeking, CSSQ-S  and any of the polymorphisms tested (see p = .04), though not after consideration of multiple testing.The initial sample included 599 participants; however, not all DNAs were genotyped successfully for all five loci , so the final sample sizes for the genetic analysis ranged from sted see . The −29DRD4 promoter polymorphisms and two measures of sensation seeking were investigated in a sample of proficient skiers and snowboarders. There were no significant associations between either the CSSQ-S (the domain-specific sensation-seeking measure) scores or the ZKPQ ImpSS scores and genotype for any of the DRD4 promoter variants tested.The individual effects between five DRD4 has been implicated in numerous association studies of traits relating to behavioural approach and externalizing disorders The DRD4 promoter alleles and the phenotypes being investigated.Despite their location in the regulatory region of an important neurotransmitter receptor gene, there are few personality genetic studies that include the four SNPs that we present data for . The same four SNPs were investigated in association with schizophrenia in two Japanese populations DRD4 variants in a specialized cohort of athletes. One advantage of investigating genetic associations in cohorts that have distinctive, shared characteristics (e.g. gambling problems or athletic behaviours) is that domain-specific tools can be used to measure phenotypes DRD4 −521T/C variant and domain-specific sensation seeking in skiers and snowboarders, but there was no association with a broader measure of sensation seeking This is one of the first studies to investigate n = 599) was sufficient to achieve adequate power (∼.8) based on small effect sizes (ηp2 = .01 to .02) typical of SNP associations at an alpha of .01 p2<.005) suggesting that larger samples would probably find similarly non-significant results assuming comparable estimates of population parameters. Limited by power, we restricted our analysis to include highly heterogenic loci but acknowledge that it would be worthwhile to investigate the effects between all polymorphisms  in the 1.2 KB region of the DRD4 promoter.While there are benefits to studying homogeneous samples sharing distinctive characteristics , there are some limitations that should be considered before drawing broad conclusions from the results. In this study, the cohort was limited to: 1) experienced skiers and snowboarders (to ensure that respondents had sufficient ability to carry out the sport behaviours described in the CSSQ-S) and, 2) individuals of Northern European ancestry  failed to optimize and, two were deletion polymorphisms (−603del/T and −1217G/del describe in DRD4 promoter were tested for associations with sensation seeking in a sample of skiers and snowboarders. There were no significant associations between genotypes at these loci and sensation-seeking measures. These findings are the first to be reported in athletes and for the most part are in line with results from previous studies done in clinical and general populations, which reported no associations between approach-related traits and other DRD4 promoter variants other than the −521T/C.In summary, five polymorphisms in the


Candidatus Liberibacter asiaticus” (strain Guangxi-1). The 1,268,237-bp genome with a 36.5% G+C content comprises 1,141 open reading frames, 44 tRNAs, and 3 complete rRNAs in a circular chromosome.We report here the complete genome sequence of “ Candidatus Liberibacter.” The name of each species associated with HLB was based on its presumptive origin. “Candidatus Liberibacter asiaticus” is believed to have originated in Asia, “Ca. Liberibacter americanus” in the Americas, and “Ca. Liberibacter africanus” in South Africa (Ca. Liberibacter asiaticus” is the most widely distributed and responsible for the most significant economic losses to citrus production worldwide. The widely held assumption is that HLB originated in Asia (India or China). It was not until recently that HLB was introduced to the Western Hemisphere. HLB was reported in Brazil in 2004 and in the United States (Florida) in 2005  is the most devastating disease of citrus in the world . The dish Africa , 3. Amon in 2005 . Since tCa. Liberibacter asiaticus,” strain Guangxi-1 (GX-1). Genomic DNA was isolated from an Asian citrus psyllid (Diaphorina citri) collected from Guangxi, China. The complete genome sequence of the strain was obtained by using Illumina HiSeq 2000 with a 300-bp paired-end library, which achieved 50 to 80× coverage. De novo assembly was performed using the Velvet assembler version 1.1. Thirty-two contigs were identified via BLASTn analysis to belong to “Ca. Liberibacter asiaticus” sequences. The contigs’ alignment was aided with an available “Ca. Liberibacter asiaticus” genome of the psy66 strain (http://www.ncbi.nlm.nih.gov/genomes/static/Pipeline.html). The genome of strain GX-1 comprises 1,268,237 nucleotides, a G+C content of 36.5%, 1,141 predicted coding sequences, 44 tRNAs, 3 complete copies of rRNA genes , and 368 hypothetical genes. Comparative analyses showed that the overall distribution of predicted genes based on functional categories for the GX-1 genome was nearly identical to that for the previously sequenced “Ca. Liberibacter asiaticus” psy66 strain. However, in contrast to the reported “Ca. Liberibacter asiaticus” strain psy66 genome, the GX-1 genome was found to have two tandem prophage segments. The average G+C content in this prophage region is ~40%, a value that is significantly different from that for the core genome. The GX-1 prophage sequence has 96 to 99% similarity to the “Ca. Liberibacter asiaticus” strain UF506 (Ca. Liberibacter asiaticus” genome sequence permits comparative genome analysis and advances understanding of genomic evolution in “Ca. Liberibacter.”Here we report the complete genome sequence of a Chinese strain of “6 strain . The conCa. Liberibacter asiaticus” strain GX-1 has been deposited in the GenBank database under the accession number CP004005.The completed genome sequence of “


The discovery of discontiguous tRNA genes triggered studies dissecting the process of tRNA splicing. As a result, we have gained detailed mechanistic knowledge on enzymatic removal of tRNA introns catalyzed by endonuclease and ligase proteins. In addition to the elucidation of tRNA processing, these studies facilitated the discovery of additional functions of RNA ligases such as RNA repair and non-conventional mRNA splicing events. Recently, the identification of a new type of RNA ligases in bacteria, archaea, and humans closed a long-standing gap in the field of tRNA processing. This review summarizes past and recent findings in the field of tRNA splicing with a focus on RNA ligation as it preferentially occurs in archaea and humans. In addition to providing an integrated view of the types and phyletic distribution of RNA ligase proteins known to date, this survey also aims at highlighting known and potential accessory biological functions of RNA ligases.The online version of this article (doi:10.1007/s00018-012-0944-2) contains supplementary material, which is available to authorized users. Transfer RNAs (tRNA) are transcribed as precursor transcripts and are subjected to a series of posttranscriptional processing events before they are matured to fulfill their biological functions , 2. SequIn contrast to bacterial pre-tRNAs—which are disrupted by self-splicing group I introns , 13—archIn archaea, as many as 70% of genetically encoded pre-tRNAs can be disrupted by introns of 16–44 nucleotides in length, which are characterized by the presence of a highly conserved “bulge-helix-bulge” motif , 15. In Cyanidioschyzon merolae , α2 , (αβ)2 , and αβγδ  enzymes [Studies modifying the substrates used for in vitro processing reactions revealed that splice site recognition by archaeal tRNA splicing endonucleases relies on the presence of the bulge-helix-bulge motif . Archaea enzymes , 42–44.  enzymes . The monScSEN2 and ScSEN34, and two structural subunits, ScSEN15 and ScSEN54 [HsTSEN2, HsTSEN34, HsTSEN15, and HsTSEN54 [The eukaryal tRNA endonucleases are phylogenetically related to their archaeal counterparts . The yea ScSEN54 . All fouHsTSEN54 . AlthougHsTSEN54 , eukaryaHsTSEN54 . Apart fHsTSEN54 , little HsTSEN54 .Escherichia coli has been shown to catalyze ligation of tRNA exon halves [E. coli [Proteins catalyzing the final step of tRNA splicing—ligation of tRNA exon halves —have firn halves . Even ea[E. coli . These e[E. coli   acts both as phosphatase [cis diol and 5′-phosphate termini  subsequently joins these tRNA halves by 5′–3′ RNA ligation thus restores a functional tRNA [Several bacterial species have evolved to sacrifice individual members of their populations upon phage infection by activating various suicide response pathways . One of  tRNALys . It has sphatase  and polysphatase  thus connal tRNA .In addition to T4 Rnl1, another RNA ligase, T4 Rnl2, has been identified in the genome of bacteriophage T4 . T4 Rnl1ScTRL1) and plant  tRNA ligases—both identified by activity-guided chromatographic purification [cis diol termini generated by T4 Pnkp  featuring a stand-alone ligase protein with weak homology to yeast and plant RNA ligases [BfKinase/CPD or BfCLP1) do not reside on the ligase polypeptide  [HsCNP) [HsTRPT1) [HsCLP1, is an integral component of the tRNA endonuclease complex, suggesting the occurrence of coupled endonuclease and kinase reactions in humans [S. cerevisiae, where ligase and kinase activities both reside on ScTRL1. However, the relevance of the 5′–3′ RNA ligase mechanism for tRNA splicing in vertebrates is under debate since the 2′-phosphotransferase is not essential in mice [Although a 5′–3′ RNA ligase activity has been detected in biochemical experiments carried out with HeLa cell extracts , no homo ligases . In this HsCLP1) , cyclic  [HsCNP) –91, and HsTRPT1)  activitiHsTRPT1) –95 has tHsTRPT1) . In suppn humans , 88. In  in mice .To account for the substantial differences in sequence and domain organization, it has been suggested to group 5′–3′ RNA ligases in bacteriophages, yeast, plants, and animals into classes , 87; howPyrobaculum aerophilum and in humans [Escherichia coli as a 3′–5′ RNA ligase [Biochemical studies revealed that ligation of tRNA exon halves in vertebrates and archaea is mainly achieved by an alternative mechanism. A 3′–5′ RNA ligase activity resulting in incorporation of the precursor-derived 2′,3′-cyclic phosphate into the splice junction as a 3′,5′-phosphodiester was for the first time detected in HeLa cell extracts .3′–5′ RNA ligation appears to be the prevalent human tRNA splicing pathway , 56 and DDX1 observed in cancerous cell lines [The human tRNA ligase complex components seem to be constitutively and widely expressed in all human and mouse tissues analyzed so far, indicating that these proteins cooperate in a great variety of cellular contexts , 112. DDll lines , 120, itll lines –123, andll lines  are linkPublished information concerning the function of CGI-99 is scarce. Although the protein has been found to interact with human , 125 andThe identification of FAM98B as a human tRNA ligase complex component is the first piece of information published concerning its molecular function. Within the context of tRNA splicing, the exact role of FAM98B needs to be established. Since its RNAi-mediated depletion has almost no impact on RNA ligase activity in HeLa cell extracts , it is lXenopus laevis [X. laevis leads to severe developmental defects, it provided limited insights into mechanistic aspects of ASW function. ASW has been speculated to be specific for vertebrates as no homologous genes were detectable in Drosophila melanogaster or Caenorhabditis elegans [X. laevis.ASW has first been characterized in a genetic screen searching for genes differentially expressed in early neural specification in s laevis . Althoug elegans . NonetheTaken together, apart from HSPC117, which appears to be conserved among all domains of life, the additional components of the HSPC117 complex do not show a comparably broad taxonomic coverage. Orthologues of DDX1, FAM98B, and CGI-99 are spread more widely than ASW, which seems to be restricted to fewer species Fig. A.ligT, conserved not only in bacteria but also in euryarchaeota and crenarchaeota [E. coli catalyses an ATP-independent equilibrium reaction between 2′,3′-cyclic phosphate and 5′-hydroxyl termini and 2′,5′-phosphodiester bonds [Pyrococcus furiosus confirming the broad phyletic occurrence of LigT proteins [Another RNA ligase activity leading to the unusual 2′,5′-phosphodiester bond has been shown to act in extracts prepared from various bacterial species  where an RtcB/HSPC117 protein has been retained [B. floridae 5′–3′ RNA ligase BfRNL [RNA ligase proteins are detectable within all major lines of descent. Considering the different ligation mechanisms and classes of RNA ligase polypeptides, it seems, however, that several species abandoned one or the other mechanism of RNA ligation Figs. B, 6. Mosretained , 128 Below is the link to the electronic supplementary material.


Peroxisome proliferator-activated receptors are expressed in many tissues, including adipocytes, hepatocytes, muscles and endothelial cells; however, the affinity depends on the isoform of PPAR, and different distribution and expression profiles, which ultimately lead to different clinical outcomes. Because they play an important role in lipid and glucose homeostasis, they are called lipid and insulin sensors. Their actions are limited to specific tissue types and thus, reveal a characteristic influence on target cells. PPARα mainly influences fatty acid metabolism and its activation lowers lipid levels, while PPARγ is mostly involved in the regulation of the adipogenesis, energy balance, and lipid biosynthesis. PPARβ/δ participates in fatty acid oxidation, mostly in skeletal and cardiac muscles, but it also regulates blood glucose and cholesterol levels. Many natural and synthetic ligands influence the expression of these receptors. Synthetic ligands are widely used in the treatment of dyslipidemia (e.g. fibrates - PPARα activators) or in diabetes mellitus (e.g. thiazolidinediones - PPARγ agonists). New generation drugs - PPARα/γ dual agonists - reveal hypolipemic, hypotensive, antiatherogenic, anti-inflammatory and anticoagulant action while the overexpression of PPARβ/δ prevents the development of obesity and reduces lipid accumulation in cardiac cells, even during a high-fat diet. Precise data on the expression and function of natural PPAR agonists on glucose and lipid metabolism are still missing, mostly because the same ligand influences several receptors and a number of reports have provided conflicting results. To date, we know that PPARs have the capability to accommodate and bind a variety of natural and synthetic lipophilic acids, such as essential fatty acids, eicosanoids, phytanic acid and palmitoylethanolamide. A current understanding of the effects of PPARs, their molecular mechanisms and the role of these receptors in nutrition and therapeutic treatment are delineated in this paper. Peroxisome proliferator-activated receptors are ligand-activated transcription factors that regulate genes important in cell differentiation and various metabolic processes, especially lipid and glucose homeostasis. In molecular terms, PPARs represent a family of ligand-activated nuclear hormone receptors (NRs) belonging to the steroid receptor superfamily ,2 Figur. ExampleThe three-dimensional structure of PPARs consists of a DNA binding domain in the N-terminus and a ligand binding domain (LBD) in the C-terminus. After interaction with agonists, PPARs are translocated to the nucleus and heterodimerize with another nuclear receptor - the retinoid X receptor (RXR) Figure . The RXRThe function of PPARs is modified by a number of co-activators and corepressors, the presence of which can either stimulate or inhibit receptor function, respectively . LigandsThe family of peroxisome proliferation-activated receptors comprises three isoforms: PPARα, PPARβ/δ and PPARγ . These tPPARγ is expressed in white and brown adipose tissue, the large intestine and spleen. However, its expression is highest in adipocytes and it plays a key role in the regulation of adipogenesis, energy balance, and lipid biosynthesis ,16-18. TThe least known isoform is PPARβ/δ, which has not been so intensely studied as PPARα and PPARγ. PPARβ/δ is expressed ubiquitously in virtually all tissues; however, it is particularly abundant in the liver, intestine, kidney, abdominal adipose tissue, and skeletal muscle, all of which are involved in lipid metabolism. It participates in fatty acid oxidation, mainly in skeletal and cardiac muscles, regulates blood cholesterol concentrations and glucose levels ,13,19,20In conclusion, PPARα and PPARβ/δ mainly facilitate energy combustion, whereas PPARγ contributes to energy storage by enhancing adipogenesis .Many natural and synthetic agonists of PPARs are used in the treatment of glucose and lipid disorders. PPARs perform different activities, mainly via endogenous ligands produced in the metabolic pathways of fatty acids; and therefore, they are called lipid sensors. PPAR agonists have different properties and specificities for individual PPAR receptors, different absorption/distribution profiles, and distinctive gene expression profiles, which ultimately lead to different clinical outcomes ,17,22,234 stimulates PPARα, and prostaglandin PGJ2 activates PPARγ . Na. Na29].  models) . Thus, P models) ,33.The natural ligands of PPARα are omega-3 fatty acids. These acids contain three essential elements for optimal binding: a polar head group (a carboxylic group in the molecule of docosahexaenoic acid – DHA and eicosapentaenoic acid – EPA), a linker region (their long chains), and a hydrophobic tail . BecauseIn addition to PPAR activation, EPA and DHA have many beneficial health effects which are not characteristic for PPAR ligands. They reduce the risk of coronary heart disease, hypertension, primary heart attack, rheumatoid arthritis and play an important role in the development of attention-deficit/hyperactivity disorder . These bOmega-3 fatty acids also reveal an anti-inflammatory effect that results from the inhibition of their own oxidation caused by activated NF-κB in a PPARα-dependent pathway ,16. In aSynthetic PPARα ligands such as fibrates  decrease the triglyceride-rich lipoproteins in serum through an increase in the gene expression involved in fatty acid-β-oxidation and a decrease in apolipoprotein C-III gene expression ,29. TheyActive metabolites of fibrates, such as clofibric acid and fenofibric acid, are dual activators of PPARα and PPARγ, with about a 10-fold selectivity for PPARγ. The effect of bezafibrate, another compound from this group, is wider because it activates all 3 PPAR subtypes  at comparable doses to other fibrates . TherefoPPARβ/δ  plays a key role in lipid and cholesterol metabolism. It is implicated in fatty acid oxidation, improving lipid profiles and reducing adiposity, which in consequence prevents obesity development ,42. In aDecreased PPARβ/δ expression is also observed in cardiac muscle during the hyperglycemic state in diabetes mellitus ,44. ConvAlso beneficial is PPARβ/δ activation in adipocytes and skeletal muscles that results in fatty acid oxidation and utilization (studies in vitro) . Thus, PDual agonists of PPARβ/δ improve insulin sensitivity in both murine models and in humans. Thus, they might be considered a potential target in the treatment of obesity and obesity associated disorders. Only bezafibrate, a traditional PPARα agonist, seems to be a safe pan-agonist for all PPAR isotypes, but it has relatively low potency and low affinity for PPARβ/δ .2-terminal region composed of 30 amino acids. All PPARγ isoforms play an important role in adipocyte differentiation and glucose metabolism; however, their expression is different. The PPARγ1 isoform is expressed in nearly all cells, while PPARγ2 is limited mainly to adipose tissue. Nevertheless, PPARγ2 is a more potent transcription activator [PPARγ has attracted significant scientific and clinical interest because of its role in macronutrient metabolism. It is a target of the synthetic insulin sensitizers – thiazolidinediones - used in the treatment of type 2 diabetes mellitus. This receptor is abundantly expressed in adipose tissue where it plays a central role in adipogenesis and appears to be primarily involved in the regulation of lipid metabolism. Because the PPARγ gene has separate promoters and 5’exons, it results in three mRNAs: PPARγ1, PPARγ2, and PPARγ3. Proteins produced from PPARγ1 and PPARγ3 mRNAs are identical, whereas the product of PPARγ2 contains an additional NHctivator .Both forms of PPARγ1 and PPARγ2 are essential for the development of adipose tissue and the control of insulin sensitivity. However, PPARγ2 is the isoform regulated in response to nutrient intake and obesity ,10. The Adipose PPARγ protects non-adipose tissues against excessive lipid overload and maintains normal organ function . Activated PPARγ in adipocytes guarantees a balanced and adequate secretion of adipocytokines (adiponectin and leptin) that are mediators of insulin action in peripheral tissues. In consequence, the insulin sensitivity of the whole body is maintained . Apart fDespite controlling lipid metabolism, PPARγ also participates in the regulation of cancer development. Their agonists inhibit or promote cancer growth depending on cellular conditions and the stimulated signaling pathway (anti-proliferative and apoptotic) ,31. TheyThe selective PPARγ modulators are often called SPARMs by analogy to selective estrogen receptor modulators (SERMs). The distinct actions of SPARMs depend on the cellular context and on different receptor conformations, resulting in diverse gene interactions .Selected fatty acids are considered natural modulators of PPARγ; however, their connection with the receptor does not always lead to PPAR activation and target gene transcription. Activation of PPARγ by natural ligands such as PUFAs (mainly docosahexaenoic acid and eicosapentaenoic acid) results in a functional response in the tumor cells. Many studies have provided evidence that DHA inhibits the tumor development through activation of PPARγ (e.g. the growth of human lung cancer cells) . If DHA Apart from polyunsaturated fatty acids, phytanic acid is also a natural PPARγ agonist in the human diet that reveals a similar activity to omega-3 PUFA and increases glucose uptake and insulin sensitivity; however, with less capacity to differentiate adipocytes .PPARγ is a regulator of lipid and glucose metabolism and therefore its synthetic ligands such as glitazones – the derivatives of thiazolidinediones  - improve insulin and glucose parameters and increase whole body insulin sensitivity. Therefore, they are called insulin-sensitizing medications used in the treatment of diabetes . They inIn diabetes mellitus, long-term activation of PPARγ by thiazolidinediones not only reduces glycaemia and insulinemia but also attenuates vascular dysfunction . PPARγ iApart from these positive effects, activation of PPARγ by glitazones attenuates systemic inflammation ,4 and reγ modulators are currently in development , they also exhibit side effects, such as weight gain, edema, bone fractures, heart failure and increased risk of myocardial infarctions, which have limited the use of these drugs in diabetic patients with high lipid levels . Fortunas S26948  and INT1s S26948 ) and theα and γ receptor subtypes is very promising; however, further studies are needed.The synthesis of new drug generation - PPARα/γ dual agonists - connecting positive influences on both lipid and glucose metabolism has been recently developed as a response to the treatment challenge of co-existing type 2 diabetes mellitus with dyslipidemia. These double agonists not only have anti-diabetic capacity but also reduce arteriosclerosis development. They also exhibit anti-inflammatory and anticoagulant action, improve endothelial function, decrease plasma free fatty acids and lower blood pressure revealing beneficial effects on the vasculature. However, recent studies show that PPARα/γ dual agonists, similar to glitazones, exhibit the same side effects; for example, weight gain and edema ,79. NoneConsidering the wide range of actions on glucose, lipid metabolism and cell proliferation/apoptosis, PPARs and their modulators are suggested for the treatment of metabolic disorders such as hyperglycaemia, dyslipidemia and atherosclerosis. The prevention and treatment of both lipid and glucose profile disorders should consider the potency and affinity of selective PPARs and their potential cancerogenic influences. Therefore, natural compounds and their close derivatives are being targeted as future drugs against metabolic diseases. Even though early preclinical data are very promising, it is necessary to evaluate the clinical properties of new PPAR agonists and their influence on patient health.The author declares that he has no competing interests.


When choosing between immediate and temporally delayed goods, people sometimes decide disadvantageously. Here, we aim to provide process-level insight into differences between individually determined advantageous and disadvantageous choices. Participants played a computer game, deciding between two different rewards of varying size and distance by moving an agent towards the chosen reward. We calculated individual models of advantageous choices and characterized the decision process by analyzing mouse movements. The larger amount of participants’ choices was classified as advantageous and the disadvantageous choices were biased towards choosing sooner/smaller rewards. The deflection of mouse movements indicated more conflict in disadvantageous choices compared with advantageous choices when the utilities of the options differed clearly. Further process oriented analysis revealed that disadvantageous choices were biased by a tendency for choice-repetition and an undervaluation of the value information in favour of the delay information, making rather simple choices harder than could be expected from the properties of the decision situation. Human choices sometimes deviate from rationality standards as defined, for instance, by the economical rule of utility maximization Out of these considerations, we will present an approach to intertemporal choice based on participant-specific models of advantageous choices and the registration of within-trial choice action dynamics to compare the decision process leading to advantageous and disadvantageous choices. We designed a delay discounting computer game see  in whichTaken together, we expected the larger part of decisions to be advantageous. For the remaining disadvantageous choices, we anticipated a bias towards choosing the sooner/smaller option in concordance with previous research The study was performed in accordance with the guidelines of the Declaration of Helsinki and of the German Psychological Society. An ethical approval was not required since the study did not involve any risk or discomfort for the participants. All participants were informed about the purpose and the procedure of the study and gave written informed consent prior to the experiment. All data were analyzed anonymously.M = 2.45 Euro, SE = 0.05 Euro).In the experiment, 25 participants  played a choice computer game on a 17 inch screen running at a resolution of 1280×1024 pixels . Distances ranged from one to fourteen fields with the nearer option being at a distance of 1, 3, or 7 fields and the additional interval to the farer option being 1, 2, 4, or 7 fields. Reward values, smaller distance and interval to the larger distance were varied orthogonally with a random order of trials. For better understanding, we maintain in the following the standard description of the time dimension using “soon”, “late”, “delay”, and “interval”, although in our game time delay is represented by spatial distance.To investigate participants’ decision process in more detail by tracing mouse movements, we ensured that the two options were always placed in a way that the first click of a participant into a movement field decreased the distance to one option and increased it to the other option . Hence, every delay-value combination was dealt with almost 6 times. To evaluate participants’ performance, we determined the advantageous choice for each trial by determining which of the two options yielded the better cost/benefit ratio , while all following clicks needed an average time of 359 ms (SD = 84 ms). This difference between the first and following clicks, and a very low number of trials showing a change of mind after the first click  indicated that participants continued to pursue the option chosen with the first click. Based on participants’ data, we calculated hypothetical costs of each option for each participant, summing the time needed for the first click and the time for the remaining clicks needed to reach each option.On average, participants completed 351 trials  depending on the different reward ratios between the SS and the LL option F = 190.68, P<0,001, p2η = 0.89). Comparing modeled indifference points to real indifference points yielded a strong correlation . Across participants, 76.53% (SD = 5.5%) of real choices were classified as advantageous according to the model. Of the sooner/smaller choices, 63.3% (SD = 8.6%) were advantageous, while of later/larger choices 83.97% (SD = 3.47%) were advantageous, yielding a significant expected bias of disadvantageous choices being sooner/smaller choices of 20.67% .Participants’ indifference points showed significant discounting . Compared to the hypothetical gain of choosing only advantageous , participants lost 10 Cent (SE = 1.54 Cent) that could have been collected additionally in the given amount of time if participants had chosen advantageously (t(24) = 7.06, P<0.01). Second, calculating the hypothetical gain for choosing only SS or LL options revealed that overall such simple strategies would have led to less gain. Within the given overall time, however, a pure LL strategy would have yielded a much higher gain  than a pure SS strategy . This further indicates that within the paradigm, participants’ bias to the SS option lead to disadvantageous decision making.To further quantify participants’ performance in terms of money lost, we compared the real choice scenario with counterfactual scenarios where participants could have chosen a) only advantageously, b) only SS, and c) only LL. Notably, these counterfactual scenarios considered the resulting changes in trial durations and were always calculated to match the overall given time of 3×8 minutes. Hence, rewards for each scenario were calculated per unit time and extrapolated to overall given time of 3×8 minutes. First, we calculated the hypothetical amount of money that could have been collected within the given overall time if participants had only chosen advantageously  = 0.05, SE(r2) = 0.01, M(p) = 0.37, SE(p) = 0.06), indicating that planning effort did not play an influential role on choosing options.To validate our assumption that options’ values and the invested time were the main factors influencing participants’ decision, we aimed to exclude the possibility that choosing the LL choice simply included a higher effort in planning, which might be aversive, leading to a disadvantageous SS choice. We reasoned that due to its higher distance, the LL option had a higher probability of being placed diagonally to the avatar instead of being placed on a straight line. This could lead to a higher effort in planning the way to reach the LL option, since diagonal ways offer many different paths to be gone. To exclude this, we correlated the diagonality of each LL option  placed options) with the decision time of the first click, reasoning that increased decision difficulty due to planning effort should also increase decision time. However, the correlations showed no significant effects  or as yielding a clear utility difference .We expected mouse movement trajectories to yield further information about participants’ choice behavior, especially for disadvantageous choices. Hence, we analyzed mouse movement trajectories M = 110.2 trials, SE = 4.16 trials), clearly advantageous , slightly disadvantageous , clearly disadvantageous . To minimize the possibility of outliers influencing the averaged data especially for the conditions with fewer trials, we performed the following analysis on median movement deflection of each condition. An ANOVA with the factors utility difference (clear/slight) and choice (advantageous/disadvantageous) yielded no significant main effects , but the expected interaction  = 4.55, P<0.05, p2η = 0.16; see Together, this yielded 4 types of choices, with different numbers of trials per type: slightly advantageous . To derive comparable beta-weights in the next step, we normalized the range of each predictor to a range of −1 to 1. In the second step, we computed multiple regressions with these predictors  on the movement angle. This yielded 3 time-varying beta-weights (3 weights×33 time-slices) for each participant. Finally, in the third step, we computed grand averages of these 3 time-varying beta-weights yielding a time-varying strength of influence curve for each predictor in the 2 conditions  = 328 ms–680 ms); for the predictor value differences, beta-weights showed a significant influence between time-slices 7 to 33 (M(time) = 177 ms–832 ms); for the predictor previous choice, no significant influence was present.For advantageous choices see , left, tdistance to late option showed a similar significant influence in the direction of the SS option (positive angle) between time-slices 13 to 33 (M(time) = 349 ms–887 ms); for the predictor value differences, beta-weights showed a shortened significant influence between time-slices 18 to 33 (M(time) = 483 ms–832 ms); interestingly, for the predictor previous choice, beta-weights now showed a significant influence of the previous choice between time-slices 14 to 24 (M(time) = 376 ms–645 ms).For disadvantageous choices see , right, t-test based jackknifing procedure as has been used previously for mouse movement data distance to late option showed significant differences, with advantageous  choices indicating less weighing of this dimension than disadvantageous  choices (t(24) = 4.85, P<0.001). For value differences, this reversed significantly, with advantageous  choices indicating more weighing of this dimension than disadvantageous  choices (t(24) = 8.26, P<0.001). For previous choice, a significant increase from advantageous  choices to disadvantageous  choices (t(24) = 3.75, P<0.01) indicates that this dimension was only influential in the latter choices. From this time-continuous multiple regression analysis, we conclude that participants undervalued the value information in disadvantageous trials, in favour of the distance information and, additionally, were biased by their previous choice to a choice-repetition.To quantify the differences between the two conditions, we calculated the area under the curve for each beta-weight, yielding one value for each predictor and condition. To test the differences for statistical significance, we used a trial Nchoice, trialN+1choice, and advantageousness, indicating a significant three way interaction of these factors, F = 21.83, P<0.001. To check, if the repetition bias developed across the time of the experiment, we performed the same ANOVA adding the factor block. While we found again the expected three way interaction, F = 11.61, P<0.01, the only significant interaction with the factor block was a two way interaction with the number of SS/LL choices, F = 4.402, P<0.05, indicating a slight decrease in SS choices across the experiment. All other interactions with the factor block were not significant . Taken together, this supports our hypothesis that despite complete randomization of the options sequence and the placement of options, there was a significant constant repetition bias, but only for disadvantageous choices.To finally validate this choice-repetition bias, we performed a sequential analysis across choices, counting the occurrence of SS-SS, SS-LL, LL-LL, and LL-SS pairs of trials, separately for advantageous and disadvantageous trials. For disadvantageous trials, we found the expected choice repetition bias, with more frequent SS-SS (52.96%) pairs than LL-SS (42.43%) pairs, and more frequent LL-LL (57.57%) pairs than SS-LL (47.04%) pairs. This bias vanished for advantageous trials, with almost equally frequent SS-SS (26.96%) pairs and LL-SS (24.97%) pairs, and similarly, almost equally frequent LL-LL (75.03%) pairs and SS-LL (73.04%) pairs. This observation was supported by a repeated measures analysis of variance (ANOVA) with the factors This study suggests a participant-specific and process-oriented approach to human decision behavior in intertemporal choice tasks. Using a novel paradigm, we compared modeled advantageous decisions and real participants’ decisions. While the largest part of decisions was advantageous, the remaining disadvantageous choices showed a bias towards the sooner/smaller option in concordance with previous research The present study advances research of intertemporal choice in three ways. First, within this study, we modeled advantageous choices according to individual behavior. This offers a classification of advantageous and disadvantageous choices without strong prior assumptions. Therefore, the comparison of discounting parameters estimated on the basis of choices with a rather hypothetical model of utility Second, in the experiment, we observed decision processes continuously, opening a window on the weighing process to the final choice Third, the presented computer game increases ecological validity Click here for additional data file.


Gestational diabetes mellitus (GDM) is an important complication of pregnancy that poses significant threats to women and their offspring. Telomere length shortens as cellular damage increases and is associated with metabolic diseases. Telomere length in fetal leucocytes was determined in 82 infants of women with GDM (N = 82) and 65 normal pregnant women (N = 65). Women with preeclampsia (N = 45) and gestational hypertension (N = 23) were also studied. In the GDM group, telomere length was significantly shorter than normal pregnancy (P = 0.028), but there were no significant differences in fetal telomere length between preeclampsia and normal pregnancy (P = 0.841) and between gestational hypertension and normal pregnancy (P = 0.561). Regression analysis revealed that fetal telomere length was significantly associated with intrauterine exposure to GDM . Shortened telomere length may increase the risk of metabolic diseases in adulthood of GDM offspring. Telomeres contain noncoding hexameric tandem repeats ranging from a few to 15 kilobases in length that maintain chromosomal stability and genomic integrity Telomere length, a heritable human trait, has been considered a marker of cumulative cell damage Gestational diabetes mellitus (GDM) poses both short-term and long-term risks to the health of women and their offspring In the current investigation, we hypothesized that maternal GDM or preeclampsia, posed adverse conditions to the fetus that may induce fetal telomere attrition. To verify this hypothesis, we determined cord blood leukocyte telomere length in women with GDM, preeclampsia, gestational hypertension and normal pregnancy with a quantitative poly-chain reaction (PCR)-based assay.Women with GDM (N = 82), gestational hypertension (N = 15), preeclampsia (N = 45) and normal pregnancies (N = 65) were consecutively recruited to this cross-sectional survey. Pregnancy was diagnosed by positive human chorionic gonadotropin (hCG) test. Gestational age was calculated by ultrasound examination in the first trimester. GDM was diagnosed according to the criteria recommended by International Association of Diabetes and Pregnancy Study Groups (IADPSG) x2 = 1.563, P = 0.668).Maternal age was significantly different between the groups  . Women wExclusion criteria included multiple gestation, pre-gestational diabetes mellitus, chronic hypertension, infectious diseases recognized in pregnancy, premature rupture of membrane, active labor, polyhydramnios and concurrent medical complications. This cross-sectional study was approved by the Ethics Committee of Women's Hospital, School of Medicine, Zhejiang University and informed written consents obtained from all participants.Cord blood samples were collected in EDTA-treated tubes at delivery. Lymphocytes were isolated from cord blood and stored at −80C before extraction of DNA. Total DNA was isolated from lymphocytes using buffer ATL, proteinase K, and RNase A  followed by phenol–chloroform extraction and ethanol precipitation.Telomere length was measured using an established and validated quantitative PCR-based technique 5′-CGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGTT-3′; Tel-R: 5′-GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT-3′). A commercial kit was used according to the manufacturer's instructions to estimate the level of RNase P gene expression as an internal standard  using 1x primers and the TaqMan probe reagent, 1x TaqMan Genotyping Master mix and 3 ng of DNA. The cycling conditions for the telomere and RNase P assays were as follows: 95°C for 10 min, followed by 50 cycles of 95°C for 15 sec and 60°C for 1 min.The telomere reaction mixture contained 1x SYBR green TaqMan Gene Expression master mix , 1 ng of DNA, 300 nM Tel-F and Tel-R primers (Tel-F: 2>0.97). Any samples outside this range were diluted and rerun. For quality control, all samples were run in duplicate and duplicate values were checked for correlation. Samples showing a coefficient of variation (CV) >2% were excluded and rerun. To test the reproducibility of the assay, multivariate samples were randomly chosen and run again, and a high level of agreement was observed between the T/S ratios from the 2 runs .Along with the samples, each assay contained a calibrator sample (DNA from pooled samples). Dilution series (0.675-5 ng in two-fold dilutions) were assayed for both telomere and RNase P to establish the linear range. Good linearity was observed across this range  and compared by Student's t test or one-way ANOVA with LSD multiple comparisons. Regression was used to analyze the relationship of fetal telomere length with maternal age, gestational age at delivery, neonatal birth weight and fetal gender. Statistic package SPSS  was used for data analysis. A P<0.05 was considered to be statistically significant.t = 0.546, P = 0.587), GDM , gestational hypertension  or preeclampsia   . Thus da= 0.107) . The tel= 0.107) .2 = 0.019, P = 0.027 after adjustment for maternal age, gestational age at delivery, birth weight and fetal gender) but not maternal age, gestational age at delivery, birth weight or fetal gender. To evaluate the effect of maternal age on fetal telomere length, linear regression was performed between maternal age and fetal telomere length. Fetal telomere length was not significantly correlated with maternal age in normal pregnancy , GDM , gestational hypertension , preeclampsia , or all subjects .Multivariate regression analysis revealed that fetal telomere length was significantly associated with intrauterine exposure to GDM , fetal telomere shortening may be among the mechanisms linking maternal GDM with increased risks of metabolic diseases in offspring. However, our findings were different from those of Cross et al The intrauterine milieu of GDM exposes the fetus to oxidative stress and is possibly involved in fetal telomere attrition. Kamath et al On the other hand, our findings that telomere length did not differ in fetuses of women with preeclampsia or gestational hypertension from controls confirmed previous observations. Okuda et al A small number of studies looked at the effects of GDM or preeclampsia on maternal and placental telomere length. Harville et al Sample sizes were similar in our study and that of Cross et al Telomere shortening is caused by disturbances during cell division, and oxidative stress is the primary reason for telomere shortening. Conflicting results on oxidative stress status in fetuses born to preeclamptic women were described. Fetal oxidative stress status was reported to be increased In summary, shorter telomere length was demonstrated in infants born to GDM women, which indicates intrauterine exposure to GDM enhances fetal telomere attrition and shortened fetal telomere length may be among the mechanisms linking maternal GDM with increased risk of metabolic diseases in offspring. However, the role of shortened telomere length in the development of adult diseases needs further investigation.