Explore the vast range of titles available.

Background Sarcoidosis is a heterogeneous granulomatous disease with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential biomarkers. Methods Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used. DNA methylation was profiled on Illumina HumanMethylationEPIC arrays, mRNA by RNA-sequencing, and miRNAs by small RNA-sequencing. Linear models were fit to test for effect of sarcoidosis diagnosis and progression phenotype, adjusting for age, sex, smoking, and principal components of the data. We built a supervised multi-omics model using a subset of features from each dataset. Results We identified 1,459 CpGs, 64 mRNAs, and five miRNAs associated with sarcoidosis versus controls and four mRNAs associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway, which is important in T cell and mTOR function. Novel immune related genes and miRNAs including LYST , RGS14 , SLFN12L , and hsa-miR-199b-5p, distinguished sarcoidosis from controls. Our integrated model also demonstrated differential expression/methylation of IL20RB , ABCC11 , SFSWAP , AGBL4 , miR-146a-3p, and miR-378b between non-progressive and progressive sarcoidosis. Conclusions Leveraging the DNA methylome, transcriptome, and miRNA-sequencing in sarcoidosis BAL cells, we detected widespread molecular changes associated with disease, many which are involved in immune response. These molecules may serve as diagnostic/prognostic biomarkers and/or drug targets, although future testing is required for confirmation.

Background Most phenotyping paradigms in sarcoidosis are based on expert opinion; however, no paradigm has been widely adopted because of the subjectivity in classification. We hypothesized that cluster analysis could be performed on common clinical variables to define more objective sarcoidosis phenotypes. Methods We performed a retrospective cohort study of 554 sarcoidosis cases to identify distinct phenotypes of sarcoidosis based on 29 clinical features. Model-based clustering was performed using the VarSelLCM R package and the Integrated Completed Likelihood (ICL) criteria were used to estimate number of clusters. To identify features associated with cluster membership, features were ranked based on variable importance scores from the VarSelLCM model, and additional univariate tests (Fisher’s exact test and one-way ANOVA) were performed using q-values correcting for multiple testing. The Wasfi severity score was also compared between clusters. Results Cluster analysis resulted in 6 sarcoidosis phenotypes. Salient characteristics for each cluster are as follows: Phenotype (1) supranormal lung function and majority Scadding stage 2/3; phenotype (2) supranormal lung function and majority Scadding stage 0/1; phenotype (3) normal lung function and split Scadding stages between 0/1 and 2/3; phenotype (4) obstructive lung function and majority Scadding stage 2/3; phenotype (5) restrictive lung function and majority Scadding stage 2/3; phenotype (6) mixed obstructive and restrictive lung function and mostly Scadding stage 4. Although there were differences in the percentages, all Scadding stages were encompassed by all of the phenotypes, except for phenotype 1, in which none were Scadding stage 4. Clusters 4, 5, 6 were significantly more likely to have ever been on immunosuppressive treatment and had higher Wasfi disease severity scores. Conclusions Cluster analysis produced 6 sarcoidosis phenotypes that demonstrated less severe and severe phenotypes. Phenotypes 1, 2, 3 have less lung function abnormalities, a lower percentage on immunosuppressive treatment and lower Wasfi severity scores. Phenotypes 4, 5, 6 were characterized by lung function abnormalities, more parenchymal abnormalities, an increased percentage on immunosuppressive treatment and higher Wasfi severity scores. These data support using cluster analysis as an objective and clinically useful way to phenotype sarcoidosis subjects and to empower clinicians to identify those with more severe disease versus those who have less severe disease, independent of Scadding stage.

Exposure to beryllium may lead to granuloma formation and fibrosis in those who develop chronic beryllium disease (CBD). Although disease presentation varies from mild to severe, little is known about CBD phenotypes. This study characterized CBD disease phenotypes using longitudinal measures of lung function. Using a case-only study of 207 CBD subjects, subject-specific trajectories over time were estimated from longitudinal pulmonary function and exercise-tolerance tests. To estimate linear combinations of the 30-year values that define underlying patterns of lung function, we conducted factor analysis. Cluster analysis was then performed on all the predicted lung function values at 30 years. These estimates were used to identify underlying features and subgroups of CBD. Two factors, or composite measures, explained nearly 70% of the co-variation among the tests; one factor represented pulmonary function in addition to oxygen consumption and workload during exercise, while the second factor represented exercise tests related to gas exchange. Factors were associated with granulomas on biopsy, exposure, steroid use and lung inflammation. Three clusters of patients (n = 53, n = 59 and, n = 95) were identified based on the collection of test values. Lower levels of each of the factor composite scores and cluster membership were associated with baseline characteristics of patients. Using factor analysis and cluster analysis, we identified disease phenotypes that were associated with baseline patient characteristics, suggesting that CBD is a heterogeneous disease with varying severity. These clinical tools may be used in future basic and clinical studies to help define the mechanisms and risk factors for disease severity.

ObjectivesHuman leukocyte antigen-DP beta 1 (HLA-DPB1) with a glutamic acid at the 69th position of the ß chain (E69) genotype and inhalational beryllium exposure individually contribute to risk of chronic beryllium disease (CBD) and beryllium sensitisation (BeS) in exposed individuals. This retrospective nested case–control study assessed the contribution of genetics and exposure in the development of BeS and CBD.MethodsWorkers with BeS (n=444), CBD (n=449) and beryllium-exposed controls (n=890) were enrolled from studies conducted at nuclear weapons and primary beryllium manufacturing facilities. Lifetime-average beryllium exposure estimates were based on workers’ job questionnaires and historical and industrial hygienist exposure estimates, blinded to genotype and case status. Genotyping was performed using sequence-specific primer-PCR. Logistic regression models were developed allowing for over-dispersion, adjusting for workforce, race, sex and ethnicity.ResultsHaving no E69 alleles was associated with lower odds of both CBD and BeS; every additional E69 allele increased odds for CBD and BeS. Increasing exposure was associated with lower odds of BeS. CBD was not associated with exposure as compared to controls, yet the per cent of individuals with CBD versus BeS increased with increasing exposure. No evidence of a gene-by-exposure interaction was found for CBD or BeS.ConclusionsRisk of CBD increases with E69 allele frequency and increasing exposure, although no gene by environment interaction was found. A decreased risk of BeS with increasing exposure and lack of exposure response in CBD cases may be due to the limitations of reconstructed exposure estimates. Although reducing exposure may not prevent BeS, it may reduce CBD and the associated health effects, especially in those carrying E69 alleles.

Previously we showed that alveolar macrophages (AMs) from patients with chronic beryllium disease (CBD) and beryllium sensitization (BeS) demonstrated significantly greater cell surface CD16 (encoded by the FCGR3A gene) than controls. We hypothesized that these differences were related to polymorphisms in the FCGR3A gene. This study was to determine the association between FCGR3A polymorphisms in CBD, BeS versus controls as well as clinical data, providing potential information about disease pathogenesis, risk, and activity. A total of 189 CBD/154 BeS/150 controls (92 Be-exposed non-diseased and 58 healthy controls) were included in this study. Sequence-specific primers polymerase chain reaction (PCR-SSP) was used to determine FCGR3A 158V/F polymorphisms. We found significantly higher frequencies of the 158V allele (OR: 1.60 (CI: 1.17–2.19), p = 0.004) and 158VV homozygotes (OR: 2.97 (CI: 1.48–5.97) p = 0.007) in CBD versus controls. No differences were found in the frequencies of FCGR3A alleles or genotypes between BeS versus controls and CBD versus BeS. Average changes in exercise testing maximum workload (Wlm), maximum oxygen consumption (VO2m), and diffusion capacity of carbon monoxide (DLCO) demonstrated greater decline over time in those CBD cases with the 158VV gene, modeled between 10 and 40 years from first beryllium exposure. The FCGR3A V158F polymorphism is associated with CBD compared to BeS and controls and may impact lung function in CBD.

PurposeExposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment. In a cohort of workers with Be exposure, we employed blood plasma extracellular vesicles (EVs) to discover novel biomarkers of exposure to Be.MethodsEVs were isolated from plasma using size-exclusion chromatography and subjected to mass spectrometry-based proteomics. A protein-based classifier was developed using LASSO regression and validated by ELISA.ResultsWe discovered a dual biomarker signature comprising zymogen granule protein 16B and putative protein FAM10A4 that differentiated between Be-exposed and -unexposed subjects. ELISA-based quantification of the biomarkers in an independent cohort of samples confirmed higher expression of the signature in the Be-exposed group, displaying high predictive accuracy (AUROC = 0.919). Furthermore, the biomarkers efficiently discriminated high- and low-exposure groups (AUROC = 0.749).ConclusionsThis is the first report of EV biomarkers associated with Be exposure and exposure levels. The biomarkers could be implemented in resource-limited settings for Be exposure assessment.

The blood beryllium lymphocyte proliferation test is used in medical surveillance to identify both beryllium sensitization and chronic beryllium disease. Approximately 50% of individuals with beryllium sensitization have chronic beryllium disease at the time of their initial clinical evaluation; however, the rate of progression from beryllium sensitization to chronic beryllium disease is unknown. We monitored a cohort of beryllium-sensitized patients at 2-year intervals, using bronchoalveolar lavage and repeated transbronchial lung biopsies to determine progression to chronic beryllium disease as evidenced by granulomatous inflammation in lung tissue. Fifty-five individuals with beryllium sensitization were monitored with a range of 2 to 5 clinical evaluations. Disease developed in 17 sensitized individuals (31%) within an average follow-up period of 3.8 years (range, 1.0-9.5 years). Thirty-eight of the 55 (69%) remained beryllium sensitized without disease after an average follow-up time of 4.8 years (range, 1.7-11.6 years). Progressors were more likely to have worked as machinists. We found no difference in average age, sex, race or ethnicity, smoking status, or beryllium exposure time between those who progressed to chronic beryllium disease and those who remained sensitized without disease. We conclude that beryllium sensitization is an adverse health effect in beryllium-exposed workers and merits medical follow-up.

Beryllium sensitization (BeS) and chronic beryllium disease (CBD) are determined by at least one genetic factor, a glutamic acid at position 69 (E69) of the HLA-DPB1 gene, and by exposure to beryllium. The relationship between exposure and the E69 genotype has not been well characterized. The study goal was to define the relationship between beryllium exposure and E69 for CBD and BeS. Workers (n = 386) from a U.S. nuclear weapons facility were enrolled into a case-control study (70 BeS, 61 CBD, and 255 control subjects). HLA-DPB1 genotypes were determined by sequence-specific primer-polymerase chain reaction. Beryllium exposures were reconstructed on the basis of worker interviews and historical exposure measurements. Any E69 carriage increased odds for CBD (odds ratio [OR], 7.61; 95% confidence interval [CI], 3.66-15.84) and each unit increase in lifetime weighted average exposure increased the odds for CBD (OR, 2.27; 95% CI, 1.26-4.09). Compared with E69-negative genotypes, a single E69-positive *02 allele increased the odds for BeS (OR, 12.01; 95% CI, 4.28-33.71) and CBD (OR, 3.46; 95% CI, 1.42-8.43). A single non-*02 E69 allele further increased the odds for BeS (OR, 29.54; 95% CI, 10.33-84.53) and CBD (OR, 11.97; 95% CI, 5.12-28.00) and two E69 allele copies conferred the highest odds for BeS (OR, 55.68; 95% CI, 14.80-209.40) and CBD (OR, 22.54; 95% CI, 7.00-72.62). E69 and beryllium exposure both contribute to the odds of CBD. The increased odds for CBD and BeS due to E69 appear to be differentially distributed by genotype, with non-*02 E69 carriers and E69 homozygotes at higher odds than those with *02 genotypes.

ObjectivesBeryllium sensitisation (BeS) and chronic beryllium disease (CBD) are caused by exposure to beryllium with susceptibility affected by at least one well-studied genetic host factor, a glutamic acid residue at position 69 (E69) of the HLA-DPβ chain (DPβE69). However, the nature of the relationship between exposure and carriage of the DPβE69 genotype has not been well studied. The goal of this study was to determine the relationship between DPβE69 and exposure in BeS and CBD.MethodsCurrent and former workers (n=181) from a US nuclear weapons production facility, the Y-12 National Security Complex (Oak Ridge, Tennessee, USA), were enrolled in a case–control study including 35 individuals with BeS and 19 with CBD. HLA-DPB1 genotypes were determined by PCR-SSP. Beryllium exposures were assessed through worker interviews and industrial hygiene assessment of work tasks.ResultsAfter removing the confounding effect of potential beryllium exposure at another facility, multivariate models showed a sixfold (OR 6.06, 95% CI 1.96 to 18.7) increased odds for BeS and CBD combined among DPβE69 carriers and a fourfold (OR 3.98, 95% CI 1.43 to 11.0) increased odds for those exposed over an assigned lifetime-weighted average exposure of 0.1 μg/m3. Those with both risk factors had higher increased odds (OR 24.1, 95% CI 4.77 to 122).ConclusionDPβE69 carriage and high exposure to beryllium appear to contribute individually to the development of BeS and CBD. Among workers at a beryllium-using facility, the magnitude of risk associated with either elevated beryllium exposure or carriage of DPβE69 alone appears to be similar.

OBJECTIVES: To describe relative hazards in sectors of the beryllium industry, risk factors of beryllium disease and sensitisation related to work process were sought in a beryllium manufacturing plant producing pure metal, oxide, alloys, and ceramics. METHODS: All 646 active employees were interviewed; beryllium sensitisation was ascertained with the beryllium lymphocyte proliferation blood test on 627 employees; clinical evaluation and bronchoscopy were offered to people with abnormal test results; and industrial hygiene measurements related to work processes taken in 1984-93 were reviewed. RESULTS: 59 employees (9.4%) had abnormal blood tests, 47 of whom underwent bronchoscopy. 24 new cases of beryllium disease were identified, resulting in a beryllium disease prevalence of 4.6%, including five known cases (29/632). Employees who had worked in ceramics had the highest prevalence of beryllium disease (9.0%). Employees in the pebble plant (producing beryllium metal) who had been employed after 1983 also had increased risk, with a prevalence of beryllium disease of 6.4%, compared with 1.3% of other workers hired in the same period, and a prevalence of abnormal blood tests of 19.2%. Logistic regression modelling confirmed these two risk factors for beryllium disease related to work processes and the dependence on time of the risk at the pebble plant. The pebble plant was not associated with the highest gravimetric industrial hygiene measurements available since 1984. CONCLUSION: Further characterisation of exposures in beryllium metal production may be important to understanding how beryllium exposures confer high contemporary risk of beryllium disease.