For Chagas disease, the most serious infectious disease in the Americas, effective disease control depends on elimination of vectors through spraying with insecticides. Molecular genetic research can help vector control programs by identifying and characterizing vector populations and then developing effective intervention strategies.Triatoma infestans (Hemiptera: Reduviidae), the main vector of Chagas disease in Bolivia, was investigated using a hierarchical sampling strategy. A total of 230 adults and nymphs from 23 localities throughout the department of Chuquisaca in Southern Bolivia were analyzed at ten microsatellite loci. Population structure, estimated using analysis of molecular variance (AMOVA) to estimate FST  and RST (stepwise mutation model), was significant between western and eastern regions within Chuquisaca and between insects collected in domestic and peri-domestic habitats. Genetic differentiation at three different hierarchical geographic levels was significant, even in the case of adjacent households within a single locality . On the largest geographic scale, among five communities up to 100 km apart, RST = 0.12 and FST = 0.06. Cluster analysis combined with assignment tests identified five clusters within the five communities.The population genetic structure of RST and FST, supports the hypothesis of poor dispersal ability and/or reduced migration of T. infestans. The high degree of genetic structure at small geographic scales, inferences from cluster analysis and assignment tests, and demographic data suggest reinfesting vectors are coming from nearby and from recrudescence (hatching of eggs that were laid before insecticide spraying). Suggestions for using these results in vector control strategies are made.Some houses are colonized by insects from several genetic clusters after spraying, whereas other households are colonized predominately by insects from a single cluster. Significant population structure, measured by both  Trypanosoma cruzi. Chagas is prevalent throughout Central and South America, and it remains a chief concern in Bolivia. A movement that began in 1991 called the Southern Cone Initiative has been successful in reducing the incidence of Chagas disease in the Southern Cone countries of Argentina, Brazil, Chile, and Uruguay; but due to socio-economic and other factors, incidence remains high in Bolivia. The most important mode of transmission of T. cruzi to humans and other mammals is through feces of triatomine bugs. Thus, disease control and transmission prevention focus on elimination of triatomine vectors, and more specifically in Bolivia, it focuses on the elimination of Triatoma infestans. This study focuses on T. infestans in the Department of Chuquisaca, Bolivia. Ten highly variable microsatellite markers were used to analyze the population structure of insects collected in different towns. Statistical analyses show that T. infestans are highly structured, which means that they colonize on a small geographic scale. The results also suggest little active dispersal. These findings should be implemented during control efforts so that insecticide spraying focuses on geographic areas of colonization and re-colonization.Chagas disease is a protozoan infection caused by the parasite  Trypanosoma cruzi is transmitted by hematophagous insects of the sub-family Triatominae. Triatoma infestans is the major vector in the Andean highlands where the disease is endemic and has infected humans for over 9000 years T. infestans is exclusively domestic or peri-domestic, thus eradication of the vector in these regions, followed by vigilance against re-infestation, has proven largely successful in reducing transmission of T. cruzi and thus the prevalence of Chagas disease T. infestans in towns and homesteads is confounded by the possible re-infestation from surrounding sylvatic areas.Chagas disease is a parasitic disease in which the pathogenic agent, T. infestansMolecular genetic research can help vector control programs by identifying and characterizing genetically distinct vector populations and then developing effective intervention strategies T. infestans.To reduce transmission of Chagas disease, estimates of population differentiation are crucial to understand vector dispersal, sources of reinfestation, and gene flow; this genetic information is an important tool for effective management of insect control programs. Here we aimed to investigate the population genetic structure and inferred the source of colonization of vectors in the department of Chuquisaca, Bolivia using ten highly polymorphic microsatellite markers. The geographic region has high levels of human infection and house infestation and is located in a region thought to be the evolutionary origin of Insects were collected from 23 localities including both peri-urban (inhabited areas in the immediate vicinity of a city) and rural sites (less than 2000 inhabitants) in the provinces of Oropeza, Zudañez, Azurduy, Yamparaez, Tomina, Belisario Boeto and Hernando Siles within the Department of Chuquisaca, in the Bolivian highlands ranging from 1079 to 3020 meters above the sea level . This arT. infestans included in the present study were a mixture of nymphs and adults, collected from inside as well as the immediate vicinity of homes. Collections were made in the months of the Southern hemisphere summer 2002, spring 2005 and fall 2005. Forty-four insects came from a single corral in the community of Jackota in the province of Zudañez, 78 insects were collected in the community of Zurima in the province of Oropeza, and 37 were collected in Sucre the capital and main city of Chuquisaca located in the province of Oropeza. The remaining 71 insects came from collections in 20 localities throughout Chuquisaca. All insects included in the study were identified as T. infestans using taxonomic keys Specimens of DNA was extracted from three legs or 25 mg of tissue obtained from the posterior part of the abdomen of a given specimen using the Qiagen DNeasy DNA extraction kit . Care was taken to avoid sampling from the mid-abdomen as the stomach may inhibit the PCR reaction CAA ATA TTT TGT GTG TGT GTG -3”, TiD09* ‘5 –GGT CTT GGA TTT TAT GGG TTA TTT T -3’, and TiF03* ‘5 –CAC ACA AAC ACT TAA ACA CAC ACA A -3’) were modified so that the PCR product size did not overlap with other products of the same size range and fluorescence label. Our PCR reactions used the Qiagen Multiplex PCR kit . Template DNA (50–100 ng), primers and molecular biology grade water were added to the 2× multiplex PCR master mix to a final volume of 25 µL. The concentration of each primer was adjusted to permit good readings of the fluorescent peaks, modified by the addition of a fluorescence label and produced PCR products with the number of base pairs as follows: TiA02, 0.138, HEX, 173–225; TiC02, 0.138, HEX, 157–211; TiC08*, 0.276, 6-FAM, 110–144; TiC09, 0.552, NED, 125–159; TiD09*, 0.276, NED, 294–342; TiE02, 0.138, HEX, 147–167; TiE12, 0.276, HEX, 303–321; TiF03*, 0.276, 6-FAM, 215–269; TiF11, 0.138, NED, 256–280; and TiG03, 0.552, HEX, 200–250. The amplification protocol consisted of an initial step of 15 min at 95°C to activate the DNA polymerase and denature the template DNA, followed by 30 cycles of 30 sec at 94°C, 90 sec at 55°C, 60 sec at 72°C, and a final extension step of 10 min at 72°C. All reactions were carried out in a Techne TC-512 thermocycler . PCR products were diluted 1/10 in distilled water then analyzed on an ABI Prism 3100 genetic analyzer using a ROX labeled size standard. Genotypes were read using GeneMapperTM version 4.0 software . The multiple PCR products were analyzed on an ABI Prism 3100 genetic analyzer using a ROX labeled size standard. Genotypes were read using GeneMapperTM version 4.0 .We used ten previously published microsatellite markers: TiA02, TiC02, TiC08, TiC09, TiD09, TiE02, TiE12, TiF03, TiF11 and TiG03 We investigated population genetic structure at both ecological and geographic levels . EcologiST  and RST  Genetic population structure was investigated using hierarchical analysis of molecular variance (AMOVA) FST, (FST/1−FST) onto the natural log of geographic distance. In addition, a Mantel test To test for isolation by distance we performed a regression analysis of Slatkin's Nm) based on private alleles RST for the sample sizes used in our study. The results were adjusted for diploid data, M = 2Nm. Based on the assignment test (see below) results from the software Structure Nm).Pairwise multilocus estimates of the effective number of migrants  among groups of insects within households from Zurima (12 households), Serrano (4) and Ingre (1) was determined using the software Relatedness version 5.0 The average relatedness ; both measures are also significant among the five communities <100 Km apart  and among houses in Zurima . We also observed significant differentiation between domestic and peri-domestic populations within the community of Zurima .There was significant genetic differentiation among populations based on analyzed . BetweenRST and FST among communities . The dendogram based on Nei's genetic distances showed a cluster comprising populations from Zurima, El Chaco and Sucre differentiated from a sister cluster with the Jackota population . These tmunities  support habitats .Five clusters were identified among the 5 communities . When asAt the household level, five genetic clusters were identified from the seven households . Insectsr<0 (P<0.05) indicating significant outcrossing. For seven houses r>0 (P<0.05). A value of r≈0.25  was obtained for four households, and although the relatedness was similar in these households, the composition of the insect collection varied (1 site only adults 1 site only nymphs and 2 sites a mix of adults and nymphs). For the sites with the highest relatedness values , in 2 houses a single adult and 2–4 nymphs were collected and for one household only nymphs were collected.Relatedness of insects in nine out of seventeen houses was not significantly different from 0 . From thNm, among towns <40 Km apart was higher (2.03) compared with those among more distant communities (1.42) and among houses within the town of Zurima (0.99). The Mantel test of isolation by distance revealed a non-significant correlation between Slatkin's linearized FST and Nm vs. the natural log of geographic distance . Non-significant results were also observed when applying the Mantel test for a correlation between Nei's genetic distances and geographic distances among populations , and altitude . The Mantel tests had low power because of the small samples within many of the communities.The estimates of the effective number of migrants per generation, T. cruzi infection Our study region is an ecologically diverse but geographically small valley–mountain environment in the department of Chuquisaca in Southern Bolivia. This region has high levels of house infestation and vector and human T. infestans from Bolivia have found geographical variation in patterns of population structure in this vector; therefore we examined distinct ecological and geographic hierarchical groups ranging from a single goat corral to comparing western and eastern regions of Chuquisaca.Previous studies on population genetics and morphometry of ST and RST showed differentiation even within a community. Previous study in the same area using a mitochondrial cyt b gene T. infestans from highlands (>1800 m) and lowlands (<500 m). We examined insects from eastern and western Chuquisaca that significantly differ in altitude, both groups are >2000 m, and we detected significant differentiation at this ecological level.Genetic analysis over twenty-three localities throughout the department of Chuquisaca have revealed moderate but highly significant levels of genetic variation among populations. Both FST values were larger than FST, suggesting polymorphism is high and rates of migration are low ST) indicate lower differentiation because they do not distinguish among shared alleles in different populations that are not identical by descent. Similar values of RST and FST are only to be expected when mutation rates are negligible in comparison to migration and drift. When the SMM contributes to population differentiation, RST values should be larger than FST values ST>FST suggesting that mutation contributes to differences at this geographic level. However, there is no such pattern for pairwaise RST and FST among households suggesting that mutation does not contributes much to differentiation at this level.In our study, RST>FST, T. infestans has a low capacity for active dispersal T. infestans in Chuquisaca. In our study, the results of the assignment of individuals to genetic clusters  were assigned to one cluster. The relatedness of insects in Z-6 was significantly greater than 0 Click here for additional data file.


UMOD gene (GeneID: 7369) on chromosome 16p12 . This gene encodes uromodulin , the most abundant protein in mammalian urine. The variant also associates significantly with serum creatinine concentration (SCr) in Icelandic subjects  but not in a smaller set of healthy Dutch controls . Our findings validate the association between the UMOD variant and both CKD and SCr recently discovered in a large GWAS. In the Icelandic dataset, we demonstrate that the effect on SCr increases substantially with both age (P = 3.0×10−17) and number of comorbid diseases (P = 0.008). The association with CKD is also stronger in the older age groups. These results suggest that the UMOD variant may influence the adaptation of the kidney to age-related risk factors of kidney disease such as hypertension and diabetes. The variant also associates with serum urea (P = 1.0×10−6), uric acid (P = 0.0064), and suggestively with gout. In contrast to CKD, the UMOD variant confers protection against kidney stones when studied in 3,617 Icelandic and Dutch kidney stone cases and 43,201 controls .Chronic kidney disease (CKD) is a worldwide public health problem that is associated with substantial morbidity and mortality. To search for sequence variants that associate with CKD, we conducted a genome-wide association study (GWAS) that included a total of 3,203 Icelandic cases and 38,782 controls. We observed an association between CKD and a variant with 80% population frequency, rs4293393-T, positioned next to the  UMOD gene that affects the risk of CKD. Here, we replicate the association between UMOD and CKD in an independent analysis. We also demonstrate for the first time an interaction between the UMOD variant and age that suggests that this variant may adversely affect the aging kidney and its adaptation to age-related risk factors of kidney disease, such as hypertension and diabetes. Furthermore, we show that the UMOD variant that affects risk of CKD also provides protection against kidney stone disease.Chronic kidney disease (CKD) is a common condition that is associated with substantial morbidity and mortality and has been recognized as a major public health problem worldwide. Common causes of CKD include hypertension, diabetes, and inflammatory disorders. Previous studies have shown a significant genetic contribution to kidney disease and a recent genome-wide association study yielded a variant in the  Chronic kidney disease (CKD) is a common disorder that can progress to kidney failure and is associated with an increased risk of cardiovascular disease and mortality UMOD, SHROOM3 (GeneID: 57619) and GATM-SPATA5L1 (GeneIDs: 2628 and 79029)), with eGFRcys at two loci (CST3-CST9 (GeneIDs: 1471 and 128822) and STC1 (GeneID: 6781)) and with CKD at one locus (UMOD) Previous studies have suggested a genetic contribution to the risk of kidney disease. Heritability estimates of serum creatinine (SCr) and estimated glomerular filtration rate based on SCr (eGFRcrea), both common measures of kidney function, have been reported as 29% and 33%, respectively UMOD locus that associates with both CKD and SCr at a genome wide-significant (GWS) level, providing an independent replication of the result by Köttgen et al With the aim of discovering sequence variants that associate with kidney function, we conducted a GWAS in a total of 3,203 Icelanders with CKD and 38,782 controls and in 24,635 Icelandic and 1,819 Dutch subjects with SCr information. We found a sequence variant at the A GWAS of 2.5 million SNPs, either directly genotyped (Illumina HumanHap300 or HumanHapCNV370 bead chips) or imputed based on the HapMap CEU samples P<5×10−8) association to increased risk of CKD and elevated SCr. For both phenotypes, this signal is tagged by rs4293393-T . Our findings provide an independent replication of the recently reported results by Köttgen et al UMOD region on chromosome 16p12 are shown in The GWAS on CKD and SCr both yielded several SNPs in high linkage disequilbrium (LD) on chromosome 16p12 with GWS (293393-T . For CKDervation . The com.7×10−20 . To follP = 0.39) .For further assessment, we tested rs4293393 in 1,819 Dutch subjects with SCr information. These were healthy population-based controls see . Interes = 0.39) . SignifiUMOD, encoding uromodulin, also known as the Tamm-Horsfall protein . The same perfect correlation between rs4293393 and rs13335818 was observed in a set of 3,364 Icelanders . Both rs4293393 and rs13335818 are also in perfect correlation with rs12917707 . In contrast, a strong interaction was observed between the UMOD variant and age in the Icelandic sample set (P = 3.0×1017). On average, SCr increased by an additional 0.09 µmol/L per year per allele of rs4293393-T (95% CI = 0.07–0.11). In order to visualize this interaction, we stratified our Icelandic samples based on age and sex and tested for association within each stratum . Similar results were obtained for type 2 DM  and MI  as well as the number of comorbid conditions  as well as in those with type 2 DM compared to those without (P = 0.063). In other words, the age-related increase in SCr levels appears to be greater in rs4293393-T carriers that have either HTN or type 2 DM than in carriers who do not have these risk factors. However, an age effect was still observed after accounting for these age-related risk factors. Furthermore, we also observed a significantly higher rate of SCr increase with age and rs4293393-T allele count stratifying on the number of comorbid conditions present (P = 0.0080)  .P = 0.014), but not with the other diseases tested. These data demonstrate that the effect of rs4293393-T on kidney function is not mediated through increased risk of these comorbid conditions, but rather suggest that the variant may affect the vulnerability of the kidney to these risk factors.To determine whether rs4293393-T influenced kidney function by directly affecting known risk factors, we tested the association of rs4293393-T in well powered Icelandic case-control sets of HTN, MI, stroke, and type 2 DM . A weak UMOD-associated increase in SCr but also the effect of age-related comorbid conditions, may explain why no association was observed between rs4293393-T and kidney function in the Dutch sample set of healthy population-based subjects with much lower SCr values and of much less variability than observed in the Icelandic samples .Urea is another small solute that is commonly used to assess renal function together with SCr. The correlation between SCr and serum urea in our data was 58%. We tested for association between rs4293393-T and serum urea in an Icelandic sample set that had urea measurements performed at the Laboratory in Mjodd  and found significant association with increased serum urea concentration  and observed significant association . We then tested for association with gout in a set of 377 Icelandic cases and 39,916 controls (see P = 0.097). These data contrast the work of Köttgen et al In humans, rare mutations in the P = 0.0053). In an attempt to replicate this observation, we genotyped rs4293393 in two additional sample sets of European ancestry, one from Iceland  and the other from the Netherlands  (P = 0.0089). There was no correlation between the effect size and year of birth of the kidney stone patients  . The effpatients .SHROOM3 and GATM-SPATA5L1) or eGFRcys (STC1 and CST3-CST9). We tested these variants in our Icelandic datasets, including a small sample set with cystatin C measurements  and the association with cystatin C replicated for the CST3-CST9 SNP but not the STC1 SNP . It should be noted, however, that the Icelandic cystatin C sample set is very small (N = 284) and possibly underpowered to replicate the reported association with the STC1 SNP. The STC1 SNP did show association with SCr in our dataset (P = 1.6·10−6) as was observed in the analysis by Köttgen et al SHROOM3 and GATM-SPATA5L1 SNPs showed suggestive association with CKD in the analysis by Köttgen et al UMOD variant, we did not observe an interaction between the variants at these other loci and age. Furthermore, none of the SNPs did associate with kidney stones in Iceland (data not shown). Finally, Köttgen et al JAG1 (GeneID: 182); we did not replicate this finding in our SCr scan , P = 0.17).Köttgen et al urements . The assUMOD that associate with increased risk of CKD and elevated SCr but confer protection against kidney stones. We also demonstrate an interaction between these variants and both age and comorbid conditions that are related to decline in kidney function. Our observations indicate that UMOD is important for maintaining kidney function with advancing age and exposure to risk factors that are associated with aging, such as HTN, type 2 DM and cardiovascular disease.In summary, we describe sequence variants next to and in 2 as having CKD. All individuals with acute kidney injury and those who had eGFR<60 ml/min/1.73 m2 of less than 3 months duration were excluded from the CKD sample set. The study included CKD patients that had donated blood through various genetic programs at deCODE genetics.Landspitali University Hospital (LUH) is a regional hospital for the greater Reykjavík area and a tertiary referral center for the entire Icelandic nation. The population of Iceland is comprised of 330,000 inhabitants of whom approximately 200,000 reside in the greater Reykjavik area. The nation's only nephrology clinic is located at LUH and all laboratory tests for the primary care clinics of the greater Reykjavik area are performed in the hospital's laboratories. We obtained results of all SCr measurements performed during the period 2003 to 2008 from the computerized database of the Clinical Laboratories at LUH and used the SCr values to identify those with chronic kidney disease (CKD) based on calculation of the estimated glomerular filtration rate (eGFR) by the original 4-variable Modification of Diet in Renal Disease (MDRD) study equation. We classified those with eGFR<60 ml/min/1.73 mBiochemical measurements including SCr, serum urea, serum uric acid and serum cystatin C were available from two laboratories, the Laboratory in Mjodd, Reykjavik, Iceland, a private outpatient laboratory, and the Clinical Biochemistry Laboratory of LUH, serving both inpatients and outpatients. The main referral center for the Laboratory in Mjodd is a multispecialty medical clinic in Reykjavik (Laeknasetrid). The laboratory tests were done in the years 1997–2008 at the Laboratory in Mjodd and in the years 2003–2008 at LUH. The Icelandic SCr measurements came from both laboratories, the Laboratory in Mjodd  and LUH . At the LUH, the same enzymatic method was used for measurement of SCr during the study period , whereas at the Laboratory in Mjodd, SCr measurements were performed by modified kinetic Jaffe rection assays until May 2007 when an enzymatic method was introduced.The Icelandic kidney stone cases consisted of patients with confirmed radiopaque kidney stones from the Icelandic Kidney Stone Disease Registry at LUH. The study included kidney stone patients that had donated blood through various genetic programs at deCODE genetics.The coronary artery disease The Icelandic controls used in the case-control genome-wide association studies (GWAS) and replication studies were selected among individuals who had participated in the various genetic programs at deCODE genetics; tremor, preeclampsia, endometriosis, psoriasis, type 2 DM, Alzheimer's disease, osteoarthritis, schizophrenia, peripheral artery disease, abdominal aortic aneurysm, chronic obstructive pulmonary disease, stroke, osteoporosis, coronary artery disease, HTN, asthma, Parkinson's disease, sleep apnea, age-related macular degeneration, polycystic ovary syndrome, rheumatoid arthritis, lung cancer, longevity, benign prostatic hyperplasia, enuresis, migraine, glaucoma, attention deficit hyperactivity disorder, prostate cancer, infectious diseases, anxiety, expression studies, autism, dyslexia, melanoma, colorectal cancer, deep vein thrombosis, restless leg syndrome, studies on addiction and population controls. Individuals who reported a history of the trait being tested (e.g. CKD) were excluded from the control set. Some of the controls participated in more than one genetic program in which case their genotypes are only included once.The study was approved by the Icelandic Data Protection Authority and the National Bioethics Committee. All patients signed informed consent and donated blood samples. Personal identities of the patients and biological samples were encrypted by a third party system provided by the Icelandic Data Protection Authority.All samples with SCr measurements came from the Nijmegen Biomedical Study. The details of this study have been reported previously The Dutch patients with kidney stones were recruited from two sources: The outpatient clinics of the RUNMC and The Nijmegen Biomedical Study. All patients who present to the outpatient clinics of the RUNMC are invited to participate in a study on the effects of genes and lifestyle on the development of urological diseases. In case of consent, the patients fill out a questionnaire on lifestyle and donate a blood sample for DNA isolation. The controls for the analysis of kidney stone disease were also taken from the biobank of the Nijmegen Biomedical Study. All patients and controls were of self-reported European descent and were fully informed about the goals and the procedures of these studies. The study protocols for the recruitment of patients from outpatient clinics and the recruitment of participants to the Nijmegen Biomedical Study were approved by the RUNMC Institutional Review Board. All study subjects gave written informed consent.All Icelandic case and control samples were assayed with the Illumina HumanHap300 or HumanHapCNV370 bead chips , containing 317,503 and 370,404 haplotype tagging SNPs derived from phase I of the International HapMap project, respectively. Only SNPs present on both chips were included in the analysis and SNPs were excluded if they had: (a) yield lower than 95% in cases or controls; (b) minor allele frequency less than 1% in the population; or (c) showed significant deviation from Hardy-Weinberg equilibrium in the controls (P<0.001). Any samples with a call rate below 98% were excluded from the analysis. The final analysis included 302,379 SNPs.The genome-wide association scan was based on expected allele counts obtained with the IMPUTE software Single SNP genotyping of all samples was carried out at deCODE genetics in Reykjavik, Iceland, applying the same platform to all populations studied. All single SNP genotyping was carried out using the Centaurus (Nanogen) platform P values and odds ratios (ORs) for each individual allele, assuming a multiplicative model for risk, i.e. that the risk of the two alleles carried by a person multiplies P values are given after adjustment for the relatedness of the subjects. When estimating genotype specific OR, genotype frequencies in the population were estimated assuming Hardy-Weinberg equilibrium.For case-control association analysis, e.g. for CKD and kidney stones, we utilized a standard likelihood ratio statistic, implemented in the NEMO software Results from multiple case-control groups were combined using a Mantel-Haenszel model For the quantitative trait association analysis, e.g. for SCr and cystatin C, we utilized a robust linear regression based on an M estimator P values shown were obtained from fitting the appropriate model without an interaction effect.Interaction effects were tested by assuming all main effects and lower order interaction effects were present under the null model but not the interaction effect, resulting in a one degree of freedom model. For example, when testing for an interaction effect on SCr between age and the rs4293393-T allele count, the null model included as covariates age, sex and the rs4293393-T allele count. The alternative model included all these covariates as well as the product of the interaction of age and the rs4293393-T allele counts. Similarly, when testing for the interaction between age, the number of comorbid conditions and the rs4293393-T allele count, the null model included as covariates age, sex, rs4293393-T allele count, the product of interaction of age and rs4293393-T allele count, the product of interaction of the number of comorbid conditions and rs4293393-T allele count, and the product of interaction of age and the number of comorbid conditions and the alternative model added the product of interaction of age, the rs4293393-T allele count and the number of comorbid conditions. In the instances when an interaction effect was estimated, the main effect estimates and Some of the individuals in the Icelandic patient and control groups are related to each other, causing the chi-square test statistic to have a mean >1 and median >0.675. We estimated the inflation factor for the genome-wide association by calculating the average of the 302,379 chi-square statistics, which was a method of genomic control Figure S1QQ plot of 2.5 million SNPs in the genome-wide association scans for chronic kidney disease. The black dots represent the observed P values and the blue ‘x’s represent the P values scaled down by an inflation factor estimated using genomic control (1.15). The diagonal red line represents where the dots are expected to fall under the null hypothesis of no association. The horizontal green line represents the threshold for genome-wide significance.(0.69 MB TIF)Click here for additional data file.Figure S2QQ plot of 2.5 million SNPs in the genome-wide association scans for serum creatinine. The black dots represent the observed P values and the blue ‘x’s represent the P values scaled down by an inflation factor estimated using genomic control (1.22). The diagonal red line represents where the dots are expected to fall under the null hypothesis of no association. The horizontal green line represents the threshold for genome-wide significance.(0.69 MB TIF)Click here for additional data file.Figure S3The observed distribution of SCr measurements in Iceland and the Netherlands. The measurements from Iceland come from the Laboratory in Mjodd and the Clinical Biochemistry Laboratory of Landspitali University Hospital (LUH). The red line denotes the population median and the two dashed blue lines the 5% and 95% quantiles. Measurements above 200 are lumped together for visualization purposes. The unit of measurement is µmol/L.(0.05 MB EPS)Click here for additional data file.Table S1−5) with CKD outside the UMOD region on chromosome 16p12.Strongest SNP associations (P<2·10(0.05 MB DOC)Click here for additional data file.Table S2−6) with SCr outside the UMOD region on chromosome 16p12.Strongest SNP associations (P<10(0.10 MB DOC)Click here for additional data file.Table S3Association of rs4293393-T with risk factors of kidney function decline in Icelandic case-control groups.(0.03 MB DOC)Click here for additional data file.Table S4Results of age-specific association for rs4293393-T and kidney stones using year of birth (YOB) as a proxy for age at onset.(0.03 MB DOC)Click here for additional data file.


Osteoarthritis (OA) is a debilitating chronic multijoint disease of global proportions. OA presence and severity is usually documented by x-ray imaging but whole body imaging is impractical due to radiation exposure, time and cost. Systemic (serum or urine) biomarkers offer a potential alternative method of quantifying total body burden of disease but no OA-related biomarker has ever been stringently qualified to determine the feasibility of this approach. The goal of this study was to evaluate the ability of three OA-related biomarkers to predict various forms or subspecies of OA and total body burden of disease.sHA), cartilage oligomeric matrix protein (sCOMP), and urinary C-telopeptide of type II collagen (uCTX2), were measured by ELISA. sHA, sCOMP and uCTX2 correlated positively with total osteophyte burden in models accounting for demographics : R2 = 0.60, R2 = 0.47, R2 = 0.51 ; sCOMP correlated negatively with total joint space narrowing burden: R2 = 0.69 (p<10−6). Biomarkers and demographics predicted 35–38% of variance in total burden of OA . Joint size did not determine the contribution to the systemic biomarker concentration. Biomarker correlation with disease in the lumbar spine resembled that in the rest of the skeleton.Female participants (461) with clinical hand OA underwent radiography of hands, hips, knees and lumbar spine; x-rays were comprehensively scored for OA features of osteophyte and joint space narrowing. Three OA-related biomarkers, serum hyaluronan (We have suspected that the correlation of systemic biomarkers with disease has been hampered by the inability to fully phenotype the burden of OA in a patient. These results confirm the hypothesis, revealed upon adequate patient phenotyping, that systemic joint tissue concentrations of several biomarkers can be quantitative indicators of specific subspecies of OA and of total body burden of disease. Osteoarthritis (OA) is a debilitating chronic multijoint disease with major global impact The inference of the relevant relationships between serum or urine biomarker concentrations and disease of specific joints is complicated by the fact that many or most OA patients have disease in multiple joints. Moreover, OA is a complex disease increasingly understood to be a mixture of distinct clinical and genetic entities Burden of disease, Investigational, Prognostic, Efficacy of intervention, and Diagnostic biomarkers). The categories to which each of these biomarkers corresponds for structural features of OA are as follows: HA–categories D, B, P, E Qualification is the evidentiary process of linking a biomarker with biological processes and clinical end points In this work we describe which radiological features are associated with each systemic biomarker and the extent to which these biomarkers reflect burden of disease of the various forms and features of OA. Here we have qualified these three biomarkers as strong quantitative traits of various features of OA. Uncoupling these features or processes of OA provided insights into the origin of these biomarkers. We observed that the canonical radiographic features of OA, namely osteophyte and joint space narrowing, correlated with one another, often in a complex manner with regard to their effects on a biomarker. We further observed that small joints did not necessarily contribute in a small way to serum biomarker concentrations. Moreover, with respect to the impact on biomarkers, the lumbar spine radiographic features behaved similarly to radiographic features in other joints, arguing for a similar disease process ongoing in the lumbar spine as in the rest of the skeleton. Finally, systemic biomarkers were more powerful predictors of multijoint disease than of disease in any single joint when added to a statistical model containing risk factor data . A notable exception was the knee in which biomarkers doubled the power of these factors alone for predicting radiographic features of OA.2), respectively. Just over two-thirds of the participants were evaluated through the Duke site (n = 322) and the remainder at the UNC site (n = 139).A total of 461 female participants were studied. Their mean (SD) age was 67 (10) years old. Their mean (SD) weight, height and body mass index were 74 (17) kg, 161 (7) cm, and 28 (6)_ and proximal (PIP) interphalangeal finger joints, metacarpophalangeal (MCP or knuckle) hand joints, carpometacarpal (CMC or base of thumb) joints, knees, hips and lumbar spine. OSTn and JSNn, respectively. We also summed the total number of joints with OA based on KL scoring (KL>1), designating this sum KLn. Among participants, OSTn and JSNn had a correlation coefficient of r = 0.66, which was very highly significant (p<10−6).For each participant, we summed the total number of joint faces with OST >0 and the total number of joints with JSN >0, designating these sums sHA) and urinary C-telopeptide fragment of type II collagen (uCTX2), were strongly positively correlated with the number of affected joint systems based on KL scoring, with their concentrations having significantly positive coefficients in a linear model with KLn as a predictor (sCOMP) showed a statistically significant (p = 0.024) concentration decrease as KLn increased. Note that these models were fit with the following risk factors: sHA; age for sCOMP; and for uCTX2 no other covariate was used. These covariates were tested based on their significance in previous work The OA-related biomarkers, serum hyaluronan (redictor . Quite sOSTn and JSNn (−6). In contrast, although sHA and uCTX2 showed a strong positive dose-dependent relationship with total JSN burden before adjustment for OST  for each joint site and radiographic feature (independent variables) in a mixed-effects model with all 14 distinct radiographic features (OST and JSN for each of 7 joint systems) included as predictors. This “saturated” model was not optimally predictive (we developed an optimized model below), but it did allow us to effectively decouple the correlations among the independent variables that would otherwise confound our estimation of the relationships between them and the biomarkers, and to evaluate the contributions of each in its natural context. In uCTX2. There are some notable differences between these results and those shown in the uCTX2 panel of uCTX2 for the CMC joint. It shows that when CMC joint faces affected by OST are measured alone they indicate the likelihood of joint faces affected by JSN, which in turn is predictive of increased levels of uCTX2. If both CMC JSN and OST are measured, greater OST involvement predicts a lower uCTX2 concentration.Due to the interrelationship of JSN and OST, (illustrated below for the CMC joint and an issue for most other joints as well), misleading conclusions can be drawn by examining given radiographic features in isolation from the rest. 17 = 131,072 models to evaluate, which we did exhaustively. We used the Akaike Information Criterion (AIC) uCTX2, distinct from the other two biomarkers, had no significant relationship to age, height or weight after accounting for the radiographic features of OA. IP JSN was negatively predictive of sCOMP concentrations. In addition, we computed the Akaike importance weights to examine the relative importance of each joint system apart from their role in the single optimal model . Since each of these terms can be either included or excluded in any given model, there are 2al model . The Aka6 = 64 linear regression models with interactions among predictors excluded for each of the 2×6 outcomes of interest. The outcome variables used were the percent maximum osteophyte burden and percent maximum joint-space narrowing burden, defined as the sum of the radiographic osteophyte or joint-space narrowing scores, respectively, over all joints within each of the six joint systems, or over all joints in total, divided by the maximum of the relevant sum  and biomarkers, are most useful for predicting the OA burden in each of the joint systems studied. For this purpose we used exhaustive testing of all 2sHA, sCOMP and uCTX2 are quantitative traits of the radiographic feature of OST while sCOMP is a negative indicator of joint space narrowing affected joint faces in the body. This paper may serve some in the relevant medical communities as an introduction to some novel methods for assessing biomarkers in combination, joint systems in combination, and OA features in combination. For instance, evaluated in combination using multimodel inference with linear mixed-effects regression, it becomes evident that sCOMP adds little information for predicting total OST burden when sHA and uCTX2 are available, although sCOMP is a marker of osteophytes.We have suspected for some time that the correlation of systemic biomarkers with disease has been hampered by the inability to fully phenotype the burden of OA in a patient, and in a sense, giving systemic biomarkers a ‘bad name’. The results presented here illustrate the strong correlation of total body burden of disease and several biomarkers, revealed upon adequate patient phenotyping. These results clearly show that all three biomarkers, et. al. identified three components that reflected radiographic OA at different joint sites. The components were comprised of 1) structural markers of cartilage  and bone turnover  associated with hip radiographic OA; 2) a marker of inflammation (serum hsCRP) associated with knee OA, high Western Ontario and McMaster Universities (WOMAC) scores and BMI; and 3) markers of cartilage turnover  associated with radiographic OA of the hands and lumbar spine as well as age. PCA by Davis et. al. has also identified several factors based on biomarkers that correlated with OA features: 1) a factor representing osteophytes that overlapped, as expected, with a factor that correlated with Kellgren Lawrence score; 2) a separate factor that correlated with subchondral bone mineral density; and 3) a factor that correlated with joint space width that overlapped with biomarkers associated with both osteophytes and bone mineral density et alet alThere have been several previous studies that used OA-related biomarkers in combination to quantify and characterize the OA process These studies support our finding that different biomarkers report on different aspects of joint pathology. Specifically, different biomarkers may reflect different molecular pathobiologic mechanisms and different joint contributions to the systemic biomarker concentrations. The interpretation of biomarker levels may be further complicated by the complex biology represented by a biomarker. For instance, decreased levels may reflect reduced matrix degradation, decreased synthesis, or impaired release from the tissue or origin The biomarker uCTX2 was independent of age while sHA and sCOMP increased with age; uCTX2 was also independent of height and weight, as was sCOMP while sHA increased with weight and decreased with height. The biomarkers increased the variance explained over simple demographic factors for predicting total body burden of disease. The different proportions of variance explained by biomarkers for the different joint systems may be related to genetic variation or to measurement variation, i.e. adequacy of phenotyping. In this regard, it is interesting that biomarkers added most to the prediction of knee OA, for which a standardized radiographic phenotyping procedure was used. This specialized x-ray may more accurately represent the disease. Thus, the biomarkers may not be so marginal but rather, true correlations of systemic biomarkers with disease burden may be higher, and only demonstrable with better gold standards for measuring the disease.sHA and uCTX2, however, there were cases where the difference among OST and JSN were significant and may have real consequences for the use of these biomarkers. One example of the potential complex interrelationship of OST and JSN was demonstrated by the increase of uCTX2 with increasing number of CMC joint faces affected by JSN. When the number of joint faces affected by JSN was held constant, however, uCTX2 declined with increasing number of CMC joint faces affected by OST. Another point, convincingly demonstrated herein by the large contribution of CMC-OST to both sHA and uCTX2 concentrations, is that joint size does not necessarily determine the contribution of a joint to the systemic biomarker concentration. One could truly say, based on these data that the CMC “sticks out like a sore thumb”. We do not know why the CMC joint, despite its small size, disproportionally impacted the concentration of all three systemic biomarkers. It is possible that the stage of disease accounted for this phenomenon. For instance, relative to other joints, increasing JSN in the CMC represented increasingly active matrix turnover while increasing CMC osteophyte severity, an anabolic phenomenon, represented waning activity with respect to biomarker production. Another possible explanation relates to relative turnover and clearance of the biomarkers from the CMC joint. Strong evidence exists for differences in joint tissue turnover for different joint systems, best exemplified by differences between knees and ankles The coefficients of a linear model estimate the average contribution per affected joint to the serum concentration of a biomarker. OST and JSN had very similar coefficients for sCOMP, and CMC on uCTX2  study and evaluated at two sites (Duke University and the University of North Carolina at Chapel Hill) Weight and height were measured. Participants underwent radiographic evaluation of both hands, hips, knees and lumbar spine as previously reported sHA using a commercially available kit , and sCOMP using an in-house enzyme-linked immunosorbent assay (ELISA) as previously described sHA and sCOMP were 10 ng/ml and 120 ng/ml, and intra-and inter-assay coefficients of variation (CVs) were <5% and <7% for sHA, and 2.1% and 14.2% for sCOMP. Urinary CTX2  was measured per the manufacturer's instructions by competitive ELISA to detect the degradation product, C-terminal telopeptides of type II collagen (uCTX2) in urine samples. The concentration of creatinine was measured using a commercially available colorimetric kit , and uCTX2 values were corrected for urine creatinine concentration (mmol/L). The minimum detectable concentration of uCTX2 is reported as 0.20 ng/ml by the manufacturer. Intra-and inter-assay CVs were 5.9% and 9.9%, respectively. There is strong evidence for association with OA at specific joint sites for each of these biomarkers Blood for sera, and an aliquot of unspun urine, were collected and stored at −80°C for biomarker analyses. Serum samples were analyzed in duplicate for sHA, sCOMP and uCTX2), we fit linear mixed-effects models using family as the random factor. These models allowed us to account for the possibility that there was an inheritable component of constitutive levels of expression for these proteins given that most subjects had one or more relatives in the database. Model estimation was done using Maximum Likelihood (rather than Restricted Maximum Likelihood) to permit comparisons among models using Akaike's Information Criterion (AIC). AIC provides a score that represents the balance between model fit and the number of parameters (model complexity) 2 values reported for mixed-effects models are computed using Nagelkerke's generalized R2C is a normalizing constant, and summing over all first-order linear models All statistical analyses were performed using S-Plus 7.0 (Insightful Corp). For each of the three proteins  by radiographic osteoarthritis status.(0.03 MB DOC)Click here for additional data file.Table S2Scoring system for radiographic features of osteoarthritis.(0.04 MB DOC)Click here for additional data file.Table S3Predictors and their coefficients comprising the minimum-AIC models for percentage of maximum osteoarthritis burden.(0.06 MB DOC)Click here for additional data file.Figure S1Age-related prevalence of OA based on radiographic features. Prevalence  of OA of affected joint systems based on any osteophyte , or any joint space narrowing  by decade of age. The error bars show exact binomial 95% confidence intervals. The numbers of subjects with OA of affected joint systems based on the Kellgren Lawrence grade >1 grade  by decade of age. The joint subtypes evaluated included the interphalangeal (IP) finger joints , metacarpophalangeal (MCP or knuckle) hand joints, carpometacarpal (CMC or base of thumb) joint, lumbar spine, hip, and knee joints.(0.25 MB PDF)Click here for additional data file.Figure S2Biomarkers and number of affected joint systems based on traditional Kellgren Lawrence (KL) scoring. Mean log sHA (top), log sCOMP (middle), and log uCTX2 (bottom), by number of affected joint systems with OA based on Kellgren Lawrence (KL) grade >1. These models were fit with the following risk factors: age, log height, log weight for sHA; age for sCOMP; and for uCTX2 no other covariate was used. The respective R2 values, giving the proportion of the among-KL grade variation explained by the linear regression are shown. Accompanying p-values are 3.6×10−7, 0.024, and 3.6×10−5.(0.66 MB TIF)Click here for additional data file.Figure S3Biomarkers and total burden of OA based on radiographic features (unadjusted). Point estimates are plotted for mean serum log sHA (top), log sCOMP (middle), and log uCTX2 (bottom) by number of joint faces affected by any osteophyte , and number of joint faces OA affected by any joint space narrowing . These unadjusted data are a companion to the adjusted data shown in (3.00 MB TIF)Click here for additional data file.Figure S4Examples of biomarker concentrations and radiographic features of OA in specific joint systems (adjusted). Point estimates, 95% confidence intervals, and mixed-model regression lines for mean serum log sHA (top), log sCOMP (middle), and log uCTX2 (bottom) by number of joint faces affected by any osteophyte , and number of joint faces affected by any joint space narrowing . Each panel represents an analysis that starts with the optimal mixed-effects model for each biomarker as developed in the text, and supplements it with the predictor of interest if it is not already in the optimal model. The line in each plot represents the prediction under the supplemented mixed-effects model. R2 values are indicated giving the among-radiography class explained variation. Corresponding p-values are (left to right by row): 1.9×10−4, 0.039, 0.11, 3.3×10−4, 1.3×10−3, 0.18.(1.98 MB TIF)Click here for additional data file.Figure S5Linear coefficients of biomarker associations with radiographic features of OA (unadjusted). In this example, features are fit independently, so that correlations among features are not accounted for; the relationships between features and biomarkers in this analysis are complicated by these correlations (compare with (1.71 MB TIF)Click here for additional data file.Figure S6Example of complex interaction of OST and JSN on a biomarker concentration. Mean log uCTX2 concentrations varied positively with JSN but negatively with OST. For the depicted component of the mixed-effects model, R2 = 0.977, p = 5.4×10−4.(1.05 MB TIF)Click here for additional data file.


The pathology displayed by the rdd chick resembles that observed in some of the more severe forms of human retinitis pigmentosa.In our previous paper we undertook proteomic analysis of the normal developing chick retina to identify proteins that were differentially expressed during retinal development. In the present paper we use the same proteomic approach to analyze the development and onset of degeneration in the retinal dysplasia and degeneration (rdd and wild-type (wt) chick retina.Two-dimensional gel electrophoresis (pH 4–7), gel image analysis, and mass spectrometry were used to profile the developing and degenerating retina of the rdd retina between embryonic day 12 (E12) and post-hatch day 1 (P1). Secernin 1 displayed the most significant variation in expression between rdd and wt retina; this may be due to differential phosphorylation in the rdd retina. Secernin 1 has dipeptidase activity and has been demonstrated to play a role in exocytosis; it has been shown to be overexpressed in certain types of cancer and has also been suggested as a potential neurotoxicologically relevant target. Its role in the retina and in particular its differential expression in the degenerate rdd retina remains unknown and will require further investigation. Other proteins that were differentially expressed in the rdd retina included valosin-containing protein, β-synuclein, stathmin 1, nucleoside diphosphate kinase, histidine triad nucleotide-binding protein, and 40S ribosomal protein S12. These proteins are reported to be involved in several cellular processes, including the ubiquitin proteasome pathway, neuroprotection, metastatic suppression, transcriptional and translational regulation, and regulation of microtubule dynamics.Several proteins were identified by mass spectrometry that displayed differential expression between normal and rdd retina and represents a unique data set that has revealed several proteins that are differentially expressed during retinal degeneration in the rdd chick. Secernin 1 showed the most significant differences in expression during this degeneration period. Further investigation of the proteins identified may provide insight into the complex events underlying retinal degeneration in this animal model.This proteomic study is the first such investigation of the  Retinitis pigmentosa (RP) is a major cause of blindness affecting approximately one in every 4,000 individuals . RP consEven though the apparent genetic causes of many forms of RP have been elucidated, the underlying mechanisms by which most of these mutations manifest themselves remain elusive. This therefore warrants analysis of the disease mechanisms at the level of the effector molecules, proteins. Consequently, knowledge of protein expression and posttranslational modification at the outset of disease will enable a better understanding of the causal pathology, and this knowledge may enable earlier diagnosis and direct the development of new therapeutics .Investigation of human retinal degeneration is often hampered from the slow progression of the disease and the significant difficulties in obtaining tissues that characterize the true progression of the disease process. Given these difficulties animal models of human retinal pathologies have been central to the study of retinogenesis and subsequent degeneration. Animal models offer the possibility to study pathology in vivo; this has provided new insights into human diseases that can be used to direct diagnosis and treatment. In particular, the chick visual system has long been recognized as one of the most valuable tools to study neural development and more recently, retinal pathologies. Several studies have already been undertaken examining the normal retinal proteome in the chicken –7.rd), a model for one form of Leber congenital amaurosis [rdd) [The completion of the chicken genome sequencing project identified one billion base pairs of DNA (about one-third as many as humans) and 20,000–30,000 genes (similar to the human genome) . The conmaurosis –14, blinmaurosis , a delaymaurosis ,16, and is [rdd) ,18.rdd chicken was first detected in 1979 by Randall and McLachlan [rdd chick resembles that observed in some of the more severe forms of human RP. Genetic analysis of the rdd chicken has revealed a null mutation in the Mpdz/Mupp1 gene (data not shown). In human, Mpdz/Mupp1 is known to interact with CRB1, mutations in which cause recessively inherited human RP and Leber congenital amaurosis (LCA) [The cLachlan ; affectecLachlan ,19. The is (LCA) ,21. Thisrdd retina during the early phase of degeneration, a period that corresponds to the major phases of neurogenesis and synaptogenesis in the wt retina. Histology, two-dimensional gel profiling, image analysis, and mass spectrometry (MS) were used to characterize the retinal architecture and to detect protein expression changes underlying retinal development and degeneration in rdd versus wt chick.Normal retinal development is characterized by a wave of neurogenesis and synaptogenesis initiating in the central retina, with a lag as it migrates to the peripheral retina. The specific ages that were chosen for this study permitted us to examine Gallus gallus domesticus; Roslin Institute, Edinburgh, UK), fertilized white leghorn eggs, rdd chickens, and fertilized rdd eggs (Roslin Institute) were maintained by standard feeding and husbandry procedures under a Home Office License. E12 and E13 chicks were killed by chilling in −80 °C for 15 min; E17 and E19 chicks were killed by decapitation; and post-hatch day 1 (P1) chicks were killed by cervical dislocation. The eyes were dissected and retinas for proteomic analysis were carefully peeled free of RPE. Retinas were washed in chilled PBS  to remove any extraneous RPE, snap frozen in liquid nitrogen within 10 min of death, and subsequently stored at −80 °C. All animal procedures were performed in compliance with the UK Animals (Scientific Procedures) Act 1986.White leghorn chickens (Resected eyes were perforated and immersion fixed in buffered 4% (w/v) paraformaldehyde , dehydrated sequentially in 70% , 95% , 100% (2×30 min) industrial grade methylated spirits followed by immersion in 100% ethanol (2×30 min) and 100% xylene . Retinal sections were stained with hematoxylin and eosin.rdd chicks were analyzed at E12, E13, E17, E19, and P1. The retinas from each chick were pooled, and retinal protein was collected and processed as previously described [A total of three wt and escribed . The retescribed . BrieflyTM DALTsix vertical system (Amersham Biosciences) with a MultiTemp™ III thermostatic circulator . Proteins were resolved by 2D PAGE under 600 V, 400 mA for 30 min; followed by 600 V, 400 mA, 17 W/gel for 4 h. Gels were then fixed, stained with Coomassie brilliant blue G-colloidal (Sigma-Aldrich), and destained using standard procedures. Gels were stored in 25% (v/v) ammonium sulfate  solution at 4 °C.IPG strips were equilibrated for 15 min in 10 ml equilibration buffer  containing 100 mg DTT. Following this, the IPG strips were added to 10 ml of fresh equilibration buffer containing 250 mg iodoacetamide (Sigma-Aldrich) for a further 15 min. SDS–PAGE was performed using an Ettanrdd (n=3 independent biologic replicates) retinas from E12, 13, 17, 19, and P1 were imported into five separate experiments, spots were detected and matched, and volumes normalized within triplicate (n=3) gel sets to create an average gel for each age and phenotype. The following parameters for protein spot detection were used: minimum spot radius of 16 , minimum spot intensity (volume above base level), and split factor 7 . Manual verification and editing of spot matching were performed, and only spots matched in at least two-thirds of the gel images were considered for further analysis. Gels from each experiment were processed using the normalization mode . To normalize spot volume, the volume of a spot is divided by the total volume of the entire image  and is calculated using the following formula: (the OPD of a spot) × (the area occupied by a spot). Subsequently spots were filtered to find significant differences within certain detection limits; only those filtered spots exceeding an intensity threshold of 1.5 to twofold increase or decrease between wt and rdd were subjected to further analysis. The threshold regulation factor for the significance level was set at p≤0.05; spots regulated more than the factor required for significance were further considered as candidate spots and subsequently subjected to manual verification. The two average gels (one wt and one rdd) from each of the five separate experiments were imported into one new experiment. Any significant differences in spot intensities that were detected in the previous individual experiments were analyzed at all other ages in the new experiment. A Student t-test and Analysis of Variance (ANOVA) were used to analyze differences in spot values, and spots that showed significant variation between wt and rdd retina were identified by MS.Gels were scanned as 16-bit grayscale tiff image files with a GS-800 calibrated densitometer , and the protein spot intensities were quantified using Progenesis PG220 version 2006 build 2417.2 . Gels from wt (n=3 independent biologic replicates) and Calibration of the mass spectrometer was first performed using Angiotensin I - 1296.6853, adrenocorticotropin (ACTH)  2093.087Peptide peak lists were generated with MASCOT Wizard and submitted to Mascot (version 2.1) for peptide mass fingerprint database searching of the National Center for Biotechnology Information (NCBI) database. The Mowse scoring algorithm  was usedGallus gallus” as the taxonomy, “Protein Prospector” was used to match small-molecular-weight proteins. The MS-Fit tool in Protein Prospector offers the option of refining the search parameters by entering the pI of the protein based on its position on the 2D gel and refining the taxonomy to Gallus gallus. After scanning the possible matches generated in Protein Prospector, the matched peptides were cross-referenced with the raw spectra. When the matched peptides were the most intense peaks in the spectra, there was a higher possibility that the match was correct. Settings used in Protein Prospector were: Database Search: NCBInr.2006.02.16; MW of Protein: All; Protein pI: 4–7; Min No of peptides required to match: 4; Taxonomy: Gallus gallus; Report MOWSE scores: PFactor 0.4; Instrument: MALDI TOF; Peptide masses are: monoisotopic; Mass Tolerance: ±50 ppm; Digest: Trypsin; Max no of missed cleavages: 1; Cys modified by: acrylamide.Small-molecular-weight proteins are often difficult to definitively identify because they generate only a small number of short peptide fragments after tryptic digestion. Since Mascot does not have an option to search “rdd retina with age-matched wt chick retina. This data aided the interpretation of 2D profiles and observed protein expression changes. Histology revealed that the characteristic developing laminated retinal architecture was comparable at E13  and outer nuclear layer (ONL) displayed a degree of disorganization in which the characteristic demarcation between these two layers had started to deform; this in turn led to folding in the ONL and INL  suggested that the observed interspot pI distances were compatible with differential phosphorylation states of secernin 1; however, this was not established definitively and would require further in depth MS analysis. Secernin 1 spots were present at all ages in the wt and rdd retina; however, there were significant differences between the expression of “isoform a” and “isoform b”  and secernin 2 (located on chromosome 7). The human genome contains three secernin genes, and there are at least two splice variants of secernin 2. Therefore different genes and splice variants may account for the four secernin 1 proteins identified in chick retina.Secernin 1 is a cystolic 50-kDa protein reported to regulate exocytosis in permeabilized mast cells ; howeverrdd retina, and as such, it is not possible to attribute any linkage between secernin 1 and Mupp1. Nevertheless, the data presented in this study clearly show a real alteration in secernin 1 expression in the degenerating retina that warrants further study. Preliminary analysis using the only secernin 1 antisera currently available detected numerous anomalous bands following western blot analysis, and while it did detect a cell population in the retina, these observations cannot be relied upon because of potential cross-reaction of antisera with unknown antigens expressed in retinal tissue.Given the widespread expression of secernin 1 in many tissues, secernin 1 is likely to have other functions beyond the regulation of mast cell exocytosis. Indeed, secernin 1 expression was upregulated in gastric cancer and colon cancer where it was implicated in cell growth , and it rdd chick resembles that observed in some of the more severe forms of human RP. It is now known that the rdd chicken carries a null mutation in the Mpdz/Mupp1 gene . At present there are no reports in the literature of a direct association between mutations in Mpdz/Mupp1 and rdd in humans; there is, however, evidence to show that Mpdz/Mupp1is directly associated with Crumbs homolog 1 (Crb1). Crb1 is localized to the subapical region adjacent to the adherens junction complex at the outer limiting membrane in the retina, and loss of Crb1 function leads to either recessively inherited RP or LCA in humans [The pathology displayed by the n humans . This anrdd chick. These proteins are involved in signaling, nucleoside biosynthesis, regulation of transcription, and protein synthesis. This study has generated novel data as some of these proteins have not previously been implicated in retinal degeneration. In particular this is the first report to demonstrate modulation of secernin 1 in relation to a retinal pathology. Consequently, the rdd chick would appear to be an important new animal model for the study of human RP.In summary, this investigation has successfully identified and provided novel data on several proteins differentially expressed during retinogenesis and with the onset of degeneration in the


There are around 400 internationally recognized dog breeds in the world today, with a remarkable diversity in size, shape, color and behavior. Breeds are considered to be uniform groups with similar physical characteristics, shaped by selection rooted in human preferences. This has led to a large genetic difference between breeds and a large extent of linkage disequilibrium within breeds. These characteristics are important for association mapping of candidate genes for diseases and therefore make dogs ideal models for gene mapping of human disorders. However, genetic uniformity within breeds may not always be the case. We studied patterns of genetic diversity within 164 poodles and compared it to 133 dogs from eight other breeds.Our analyses revealed strong population structure within poodles, with differences among some poodle groups as pronounced as those among other well-recognized breeds. Pedigree analysis going three generations back in time confirmed that subgroups within poodles result from assortative mating imposed by breed standards as well as breeder preferences. Matings have not taken place at random or within traditionally identified size classes in poodles. Instead, a novel set of five poodle groups was identified, defined by combinations of size and color, which is not officially recognized by the kennel clubs. Patterns of genetic diversity in other breeds suggest that assortative mating leading to fragmentation may be a common feature within many dog breeds.The genetic structure observed in poodles is the result of local mating patterns, implying that breed fragmentation may be different in different countries. Such pronounced structuring within dog breeds can increase the power of association mapping studies, but also represents a serious problem if ignored.In dog breeding, individuals are selected on the basis of morphology, behaviour, working or show purposes, as well as geographic population structure. The same processes which have historically created dog breeds are still ongoing, and create further subdivision within current dog breeds. The behavioral and morphological diversity present in modern purebred dogs is remarkable. For at least 4,000–5,000 years, dogs have existed in a variety of sizes and shapes, but the majority of the currently recognized 400 breeds have only existed since around 1850 or later, when dog shows became popular, Kennel Clubs were founded and Stud Books were established . In paraThe isolation and small effective population size of modern breeds has resulted in widespread inbreeding and the expression of a large number of genetic diseases. Many of these are also among the most frequently occurring diseases in humans, such as cancer, heart problems, deafness, blindness and joint diseases ,9,12-14.Association mapping of phenotypic traits or diseases is a population-based approach (instead of pedigree-based) and depends highly on the level of LD , which iOne example of this breed fragmentation is the case of the poodle. According to the World Canine Organization, FCI  , poodlesE = 0.64, HO = 0.56), and positive inbreeding coefficient (FIS = 0.133). This heterozygote deficit was consistently found in 20 of 27 markers, indicating that the deviation from Hardy-Weinberg equilibrium affects the whole genome rather than being a local, locus-specific phenomenon such as selection or genotyping problems . Heterozygote deficit could indicate a preferential mating with relatives within poodles, or the presence of population structure within the breed (Wahlund effect) [The study of 164 Swedish poodles revealed an average of 4.4 alleles per locus, a significant (p < 0.05) heterozygote deficit across all 27 markers (H effect) .ST [ST values in the range of 0.184 to 0.234 , which suggests very limited genetic exchange between them. On the other hand, the differentiation between the three smaller size classes  is much lower, with FST values at least 6.8 times smaller than the smallest value observed between any of them and standard poodles. Among them, miniature and toy poodles were indistinguishable (FST not significantly different from 0)  for different numbers of K, and by using the statistic ΔK [lnP(D) among successive K values. The latter approach indicated that the optimal number of groups was K = 2. However, lnP(D) continued to increase with increasing K until K = 5, and thereafter lnP(D) decreased. At K = 2, standard poodles appeared separated from the smaller poodles , and they remained separated with increasing K values. Larger K values failed to separate the other sizes . However, the differentiation among the four groups of smaller poodles, defined on the basis of color and size, is about four times larger than the largest difference observed between the size classes for the three small-sized poodles, reaching FST values of up to 0.120 , as compared to the much lower values for the size classes . For instance, the pairwise FST value between standard poodles and black and brown medium sized poodles was 0.265, larger than 40% of the comparisons between the five groups of poodles and the other 8 breeds included in this study. For example, the FST value between giant schnauzer and small white and silver poodles is just 0.170. A neighbor-joining analysis , shows standard poodles at the end of a long branch, indicating genetic uniqueness and separation from all other poodles.These five groups of poodles were also genetically compared to 133 dogs from eight other breeds Table . StandarOne possible explanation for the large difference between standard and small sized poodles could be a past bottleneck that reduced the diversity in the first compared to the latter. This bottleneck could translate into a strong differentiation by founder effects and drift. However, this does not seem to be the main reason for their uniqueness, because their allelic richness was similar to that observed in other groups of poodles after correcting for differences in sample size Table .In order to compare the uniqueness of the five poodle groups in relation to the differentiation between recognized dog breeds we conducted assignment tests. If breeds (and groups within poodles) are well differentiated, most of the individuals should be correctly assigned to their nominal group. In a first test we divided all poodles into two groups , and compared them to dogs from 8 other breeds. Only 2 out of 297 dogs were incorrectly assigned and none of these mis-assignments corresponded to poodles. This indicates that established dog breeds are well-isolated populations with unique microsatellite allele frequencies , anIS due to population fragmentation , FIS values as large as those in poodles  or significant heterozygote deficits . Since genetic diversity and inbreeding coefficient are not related to population size, the large differences in diversity and positive inbreeding coefficient in some breeds could be indicative of some degree of genetic fragmentation within these breeds, similar to that observed in poodles.Poodles could represent a unique case among dogs if they developed from the mixing of separate dog lineages. If this was the case, we would expect to find a larger genetic diversity in poodles than that observed in other breeds and, more importantly, a larger inbreeding coefficient F effect; ). When cAbout 400 dog breeds exist today. All of these breeds are characterized by unique morphology, characteristic behavior and often also by a suite of genetic diseases. The morphological differences between breeds are so large that they easily exceed the differentiation between all species in the Family Canidae ,27, and Poodles represent a breed encompassing apparent phenotypic diversity, which translates in the identification of four classes with regard to body size. However, our analyses revealed that this is not the only reason for the poodle intra-breed structure. Only a limited set of coat colors are accepted in purebred poodles, as for most modern breeds, and coats are required to be uniform ,19. SincHistorically, standard poodles were used as working dogs, while the three smaller poodle classes were mostly bred for company. Standard poodles have been bred separately from the smaller sizes for more than 100 years in Europe . In the US, however, some degree of interbreeding among the recognized poodle size classes occurs. This could result in different patterns of differentiation in American poodles. Our results show a strong differentiation between standard poodles and the smaller sizes Figure , Table 2The long isolation between standard poodles and the smaller sizes in Europe has resulted in a differentiation as pronounced as that between well defined breeds. Founder effects and subsequent bottlenecks during the history of breeds, together with highly selective breeding practices ,11, haveThe four groups within the small sized poodles show lower levels of differentiation, below the values commonly observed between the other breeds or between them and standard poodles Figure , Table 2The partitioning of genetic diversity into discrete classes (breeds) has made the dog an exceptional model for the study of the association between genotype and phenotype and for the identification of genes involved in phenotypic and behavioral traits or diseases ,14,15,20Assortative mating is leading to the creation of strong population structure within dog breeds and has contributed to the extreme plasticity of dogs and domestic animals under selection. Domestic animals are invaluable models to understand evolution, and in the same way that they were a major source of inspiration for Darwin , they coBuccal swabs were taken from 297 purebred dogs registered by the Swedish Kennel Club (SKK) . Of thesGenomic DNA was extracted from 400 μl of the buffer containing the cytology brush with the sample, by digestion with 0.3 mg of proteinase K. The samples were then incubated over-night at 37°C and DNA was extracted using a modified phenol/chloroform protocol .2, 0.025x Q solution, 0.45U HotStarTaq and 2 μl DNA template. The PCR profile included an initial denaturation step at 95°C for 15 minutes followed by 10 touchdown cycles , followed by 20 additional cycles , and a final extension step at 72°C for 10 min in a PTC-0225 DNA Engine Tetrad (Bio-Rad). PCR products were pooled in seven different pools and genotyped on a MegaBACE 1000™ instrument (Amersham Biosciences). Genotypes were identified using the software Genetic Profiler v2.2 (Amersham Biosciences).Twenty-eight biparentally inherited autosomal microsatellites, distributed across the canine genome, were typed for all dogs: Ren94K11 (mapped to canine chromosome CFA12), C17.402 (CFA17), Ren239K24 (CFA29), C18.460 (CFA18), Ren274F18 (CFA19), Ren181K04 (CFA23), C11.873 (CFA11), Ren73F08 (CFA10), Ren112I02 (CFA01), C02.894 (CFA02), Ren204K13 (CFA08), Ren160J02 (CFA04), Ren106I06 (CFA24) , FH3109 For each microsatellite marker and breed, a test for deviation from Hardy-Weinberg equilibrium frequencies was performed using GENEPOP on the Web 3.1c . A locusE and HO) [IS values for each locus and across loci were calculated using FSTAT 2.9.3.2 [We used the program Microsatellite Toolkit 3.1  to calcu and HO)  for each and HO)  to compa 2.9.3.2 .ST values [S) with the program POPULATIONS [ST values were compared to gene flow estimates based on three generations of pedigree information using a Mantel test [We quantified the degree of differentiation between breeds in GENETIX 4.05  by calcuT values . SignifiT values  standardULATIONS . Statisttel test . The pedAn additional estimate of the degree of differentiation between breeds  was the proportion of self-assignment. The assignment program Doh  was useda priori as belonging to one breed or another. We used the software STRUCTURE 2.1 [All the breed comparisons listed above involve groups of individuals defined TURE 2.1 ,50 withoCV designed and coordinated the study. SB and MN carried out all laboratory work. SB, FH and CV performed statistical analyses and prepared the manuscript. All authors read and approved the final manuscript.