Identification of novel mutations in X-linked retinitis pigmentosa families and implications for diagnostic testing

Contains fulltext : 69886.pdf (publisher's version ) (Open Access)PURPOSE: The goal of this study was to identify mutations in X-chromosomal genes associated with retinitis pigmentosa (RP) in patients from Germany, The Netherlands, Denmark, and Switzerland. METHODS: In addition to all coding exons of RP2, exons 1 through 15, 9a, ORF15, 15a and 15b of RPGR were screened for mutations. PCR products were amplified from genomic DNA extracted from blood samples and analyzed by direct sequencing. In one family with apparently dominant inheritance of RP, linkage analysis identified an interval on the X chromosome containing RPGR, and mutation screening revealed a pathogenic variant in this gene. Patients of this family were examined clinically and by X-inactivation studies. RESULTS: This study included 141 RP families with possible X-chromosomal inheritance. In total, we identified 46 families with pathogenic sequence alterations in RPGR and RP2, of which 17 mutations have not been described previously. Two of the novel mutations represent the most 3'-terminal pathogenic sequence variants in RPGR and RP2 reported to date. In exon ORF15 of RPGR, we found eight novel and 14 known mutations. All lead to a disruption of open reading frame. Of the families with suggested X-chromosomal inheritance, 35% showed mutations in ORF15. In addition, we found five novel mutations in other exons of RPGR and four in RP2. Deletions in ORF15 of RPGR were identified in three families in which female carriers showed variable manifestation of the phenotype. Furthermore, an ORF15 mutation was found in an RP patient who additionally carries a 6.4 kbp deletion downstream of the coding region of exon ORF15. We did not identify mutations in 39 sporadic male cases from Switzerland. CONCLUSIONS: RPGR mutations were confirmed to be the most frequent cause of RP in families with an X-chromosomal inheritance pattern. We propose a screening strategy to provide molecular diagnostics in these families

Scatter-plots demonstrating correlations for MSLT versus overall RT in good sleeper controls (left panel) and primary insomnia patients (right panel).

<p>RT = reaction time; MSLT = multiple sleep latency test.</p

Multiple Linear Regression Models predicting overall RT in primary insomnia patients.

<p>Note: RT = reaction time; BDI = Beck Depression Inventory-I; SAI = State Anxiety Inventory; MSLT = multiple sleep latency test.</p><p>Model 1: unadjusted;</p><p>Model 2: adjusted for gender, age and education years;</p><p>Model 3: adjusted for gender, age, education years, BDI and TAI;</p><p>Model 4: adjusted for gender, age, education years, BDI, TAI, sleep latency, total sleep time and sleep efficiency.</p

The flow chart of experimental process: (A, the four cue conditions; B, three target conditions; C, an example of the procedure).

<p>RT = reaction time.</p

PSG sleep data in GSCs, PIPs with EDS and PIPs without EDS (mean ± SD).

<p>Note: SOL = sleep onset latency; TIB = time in bed; TST = total sleep time; WASO = wake time after sleep onset; SE = sleep efficiency; REM = rapid eye movement Latency; MA index = microarousal index;</p>a<p>Kruskal-Wallis Test.</p>b<p>Tukey Test.</p>c<p>PIPs without EDS vs. GCSs.</p>d<p>PIPs without EDS vs. PIPs with EDS.</p

Descriptive data and daytime symptoms in GSCs, PIPs with EDS and PIPs without EDS.

<p>Female value is in %; other values are in mean ± SD;</p><p>Note: BMI = Body Mass Index; PSQI = Pittsburgh Sleep Quality Index; FSS = Flinders Fatigue Scale; BDI = Beck Depression Inventory-I; SAI = State Anxiety Inventory; TAI = Trait Anxiety Inventory.</p>a<p>Kruskal-Wallis Test.</p>b<p>Tukey Test;</p>c<p>PIPs without EDS vs. GCSs.</p>d<p>PIPs with EDS vs. GCSs.</p

Associations between nighttime eating and total caloric intake in college-aged students

Background: Obesity is a nationwide concern across all age groups including the college-aged population. Approximately 35% of college students are reported to be overweight or obese in America, as defined by a body mass index (BMI) over 25 kg/m2. Increased caloric intake has shown to be associated with an elevated BMI. Nighttime eating may be a contributing factor to increased total caloric intake, and it has been associated with higher total caloric intake and weight gain in shift workers and older adults. However, research has not examined whether an association exists between nighttime eating and total daily caloric intake in college-aged students. Nighttime eating has been identified by college students as a potential concern for weight gain, thus making this an important and novel investigation. Objective: The primary objective was to examine possible relationships between nighttime eating and total caloric intake amongst college-aged undergraduate students at the University of Rhode Island (URI) during three consecutive semesters. The secondary objectives were to investigate associations between nighttime eating and dietary quality and sleep patterns. The exploratory objectives were to consider associations between nighttime eating and metabolic syndrome (MetS) risk and body composition. The primary hypothesis was that nighttime eaters would have a higher caloric intake. Design and Methods: This cross-sectional data analysis was an add-on study to an ongoing secondary data analysis project that examines the relationship between diet and chronic disease risk in college-aged students, referred to as the Nutrition Assessment Secondary Data Analysis Project. Undergraduate students (n=173, 72.25% females; BMI=23.7kg/m2) completed the Nutrition Assessment Survey (NAS) to categorize nighttime eaters and assess quantitative sleep patterns. Statistically controlled for confounding variables included, gender and smoking status. The International Physical Activity Questionnaire (IPAQ) assessed activity levels. The Diet History Questionnaire (DHQ II), a web-based food frequency questionnaire, estimated total caloric intake. The DHQ II was also used to calculate the total and component scores of the Healthy Eating Index-2010 (HEI-2010), an indicator of dietary quality. Anthropometric and biochemical measures were taken to determine the students’ number of risk factors for MetS and body weight status. Results: In this population, caloric intake within 2 hours of sleep or after 10:00PM provided more accurate definitions of nighttime eating than in other populations. Caloric intake after 10:00PM and within 2 hours (p=.015, r2=.034) of sleep onset was related to higher caloric consumption (+235.56 - 543.07kcals), lower HEI-2010 total scores (-4.78 – 5.91), and more MetS risk factors. Conclusion: This analysis aimed to determine if nighttime eating was associated with differential total daily caloric intake, along with dietary quality, sleep patterns, MetS risk, and BMI status. This study identified previously uninvestigated information regarding the prevalence of nighttime eating, along with differences in several health-related variables between students who engage in nighttime eating and those who do not. Nighttime eating was associated with increased caloric intake and a poorer diet quality in college students

8 data: Timing and spatial distribution of loess in Xinjiang, NW China

<p>The data of Figure 8.</p> <p>Figure 8 Map showing the contours of the >63 μm particle component of topsoil in Xinjiang: (A) the Junggar Basin; (B) the Tarim Basin; and (C) the Ili Basin. Cross symbols represent our sampling sites, dots represent the city/county, and arrows represent the possible wind direction.</p