Candidate Gene Sequencing of SLC11A2 and TMPRSS6 in a Family with Severe Anaemia: Common SNPs, Rare Haplotypes, No Causative Mutation

Contains fulltext : 110476.pdf (publisher's version ) (Open Access)BACKGROUND: Iron-refractory iron deficiency anaemia (IRIDA) is a rare disorder which was linked to mutations in two genes (SLC11A2 and TMPRSS6). Common polymorphisms within these genes were associated with serum iron levels. We identified a family of Serbian origin with asymptomatic non-consanguineous parents with three of four children presenting with IRIDA not responding to oral but to intravenous iron supplementation. After excluding all known causes responsible for iron deficiency anaemia we searched for mutations in SLC11A2 and TMPRSS6 that could explain the severe anaemia in these children. METHODOLOGY/RESULTS: We sequenced the exons and exon-intron boundaries of SLC11A2 and TMPRSS6 in all six family members. Thereby, we found seven known and fairly common SNPs, but no new mutation. We then genotyped these seven SNPs in the population-based SAPHIR study (n = 1,726) and performed genetic association analysis on iron and ferritin levels. Only two SNPs, which were top-hits from recent GWAS on iron and ferritin, exhibited an effect on iron and ferritin levels in SAPHIR. Six SAPHIR participants carrying the same TMPRSS6 genotypes and haplotype-pairs as one anaemic son showed lower ferritin and iron levels than the average. One individual exhibiting the joint SLC11A2/TMPRSS6 profile of the anaemic son had iron and ferritin levels lying below the 5(th) percentile of the population's iron and ferritin level distribution. We then checked the genotype constellations in the Nijmegen Biomedical Study (n = 1,832), but the profile of the anaemic son did not occur in this population. CONCLUSIONS: We cannot exclude a gene-gene interaction between SLC11A2 and TMPRSS6, but we can also not confirm it. As in this case candidate gene sequencing did not reveal causative rare mutations, the samples will be subjected to whole exome sequencing

The association of mammographic density with ductal carcinoma in situ of the breast: the Multiethnic Cohort

INTRODUCTION: It is well established that women with high mammographic density are at greater risk for breast cancer than are women with low breast density. However, little research has been done on mammographic density and ductal carcinoma in situ (DCIS) of the breast, which is thought to be a precursor lesion to some invasive breast cancers. METHOD: We conducted a nested case-control study within the Multiethnic Cohort, and compared the mammographic densities of 482 patients with invasive breast cancer and 119 with breast DCIS cases versus those of 667 cancer-free control subjects. A reader blinded to disease status performed computer-assisted density assessment. For women with more than one mammogram, mean density values were computed. Polytomous logistic regression models were used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for two measurements of mammographic density: percentage density and dense area. RESULTS: Mammographic density was associated with invasive breast cancer and breast DCIS. For the highest category of percentage breast density (≥50%) as compared with the lowest (<10%), the OR was 3.58 (95% CI 2.26–5.66) for invasive breast cancer and 2.86 (1.38–5.94) for breast DCIS. Similarly, for the highest category of dense area (≥45 cm(2)) as compared with the lowest (<15 cm(2)), the OR was 2.92 (95% CI 2.01–4.25) for invasive breast cancer and 2.59 (1.39–4.82) for breast DCIS. Trend tests were significant for invasive breast cancer (P for trend < 0.0001) and breast DCIS (P for trend < 0.001) for both percentage density and dense area. CONCLUSION: The similar strength of association for mammographic density with breast DCIS and invasive breast cancer supports the hypothesis that both diseases may have a common etiology

Not all farming environments protect against the development of asthma and wheeze in children

BACKGROUND: In recent years, studies have shown a protective effect of being raised in a farm environment on the development of hay fever and atopic sensitization. Inconsistent data on the relation of farming to asthma and wheeze have raised some doubt about a true protective effect. OBJECTIVE: We sought to study the differential effects of farm-associated exposures on specific asthma-related health outcomes. METHODS: The cross-sectional Prevention of Allergy Risk Factors for Sensitization in Children Related to Farming and Anthroposophic Lifestyle study included 8263 school-age children from rural areas in 5 European countries. Information on farm-related exposures and health outcomes was obtained by using questionnaires. In subsamples allergen-specific IgE and RNA expression of CD14 and Toll-like receptor genes were measured, and dust from children's mattresses was evaluated for microbial components. RESULTS: Inverse relations with a diagnosis of asthma were found for pig keeping (odds ratio [OR], 0.57; 95% CI, 0.38-0.86), farm milk consumption (OR, 0.77; 95% CI, 0.60-0.99), frequent stay in animal sheds (OR, 0.71; 95% CI, 0.54-0.95), child's involvement in haying (OR, 0.56; 95% CI, 0.38-0.81), and use of silage (OR, 0.55; 95% CI, 0.31-0.98; for nonatopic asthma) and in Germany for agriculture (OR, 0.34; 95% CI, 0.22-0.53). Protective factors were related with higher expression levels of genes of the innate immunity. Potential risk factors for asthma and wheeze were also identified in the farm milieu. Levels of endotoxin and extracellular polysaccharides were related to the health outcomes independently of the farm exposures. CONCLUSIONS: The protective effect of being raised in a farm environment was ascribed to distinct exposures. CLINICAL IMPLICATIONS: The development of atopic sensitization and atopic and nonatopic asthma is most likely determined by different environmental factors, possibly reflecting distinct pathomechanisms

Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry.

BACKGROUND: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown. OBJECTIVES: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies. METHODS: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis. RESULTS: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. CONCLUSIONS: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363)

SNP genotyping results in SAPHIR (n = 1,726) and NBS (n = 1,832).

<p> <u>Notes:</u></p><p>The coding of genotypes is indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035015#pone-0035015-t002" target="_blank">Table 2</a>. SAPHIR data are indicated on the left side of the slash, and NBS data are given on the right side of the slash.</p><p>MAF… minor allele frequency.</p><p>CR… call rate (genotyping success rate).</p><p>HWE… p-value for test for Hardy-Weinberg-Equilibrium.</p

Genetic association between iron and ferritin levels and haplotype-pairs in <i>SLC11A2</i> and <i>TMPRSS6</i> in SAPHIR.

<p><u>Notes</u>: For the haplotype pairs in <i>SLC11A2</i> and <i>TMPRSS6</i>, the regression was performed on all haplotype pairs in one model with the most probable haplotype pair as the reference. For the combined analysis, each line reflects the results from a regression model of this particular haplotype-pair versus all other pairs on the traits (each model sex- and age-adjusted). The p-values are based on the log-transformed model; the β-estimates are based on the model on the original scale.</p