BackgroundSarcoidosis is a chronic multisystem disease of unknown etiology characterized by noncaseating granulomas that most often involves the lungs, but frequently has extrapulmonary manifestations, which might be difficult to treat in individual patients.ObjectiveTo review different disease manifestations, focusing on extrapulmonary organ systems, and to provide treatment options for refractory cases.Materials and methodsWe performed a literature search using Medline and Google Scholar for individual or combined keywords of “sarcoidosis, extrapulmonary, treatment, kidney, neurosarcoidosis, cardiovascular, gastrointestinal, transplantation, musculoskeletal, rheumatology, arthritis, and skin”. Peer-reviewed articles, including review articles, clinical trials, observational trials, and case reports that were published in English were included. References from retrieved articles were also manually searched for relevant articles.Results and conclusionIsolated involvement of a single organ or organ system is rare in sarcoidosis, and thus all patients must be thoroughly evaluated for additional disease manifestations. Cardiac sarcoidosis and neurosarcoidosis may be life-threatening. Clinicians need to assess patients comprehensively using clinical, laboratory, imaging, and histopathological data to recommend competently the best and least toxic treatment option for the individual patient.

Purpose: Low-dose CT screening can reduce lung cancer-related mortality. However, CT screening has an FDR of nearly 96%. We sought to assess whether urine samples can be a source for DNA methylation-based detection of non-small cell lung cancer (NSCLC).Experimental Design: This nested case-control study of subjects with suspicious nodules on CT imaging obtained plasma and urine samples preoperatively. Cases (n ¼ 74) had pathologic confirmation of NSCLC. Controls (n ¼ 27) had a noncancer diagnosis. We detected promoter methylation in plasma and urine samples using methylation on beads and quantitative methylation-specific realtime PCR for cancer-specific genes (CDO1, TAC1, HOXA7, HOXA9, SOX17, and ZFP42).Results: DNA methylation at cancer-specific loci was detected in both plasma and urine, and was more frequent in patients with cancer compared with controls for all six genes in plasma and in CDO1, TAC1, HOXA9, and SOX17 in urine. Univariate and multivariate logistic regression analysis showed that methylation detection in each one of six genes in plasma and CDO1, TAC1, HOXA9, and SOX17 in urine were significantly associated with the diagnosis of NSCLC, independent of age, race, and smoking pack-years. When methylation was detected for three or more genes in both plasma and urine, the sensitivity and specificity for lung cancer diagnosis were 73% and 92%, respectively.Conclusions: DNA methylation-based biomarkers in plasma and urine could be useful as an adjunct to CT screening to guide decision-making regarding further invasive procedures in patients with pulmonary nodules.

BackgroundDifferences in asthma severity may be related to inflammation in the airways. The lower airway microbiota has been associated with clinical features such as airway obstruction, symptom control, and response to corticosteroids.ObjectiveTo assess the relationship between local airway inflammation, severity of disease, and the lower airway microbiota in atopic asthmatics.MethodsA cohort of young adult, atopic asthmatics with intermittent or mild/moderate persistent symptoms (n = 13) were assessed via bronchoscopy, lavage, and spirometry. These individuals were compared to age matched non-asthmatic controls (n = 6) and to themselves after six weeks of treatment with fluticasone propionate (FP). Inflammation of the airways was assessed via a cytokine and chemokine panel. Lower airway microbiota composition was determined by metagenomic shotgun sequencing.ResultsUnsupervised clustering of cytokines and chemokines prior to treatment with FP identified two asthmatic phenotypes (AP), termed AP1 and AP2, with distinct bronchoalveolar lavage inflammatory profiles. AP2 was associated with more obstruction, compared to AP1. After treatment with FP reduced MIP-1β and TNF-α and increased IL-2 was observed. A module of highly correlated cytokines that include MIP-1β and TNF-α was identified that negatively correlated with pulmonary function. Independently, IL-2 was positively correlated with pulmonary function. The airway microbiome composition correlated with asthmatic phenotypes. AP2, prior to FP treatment, was enriched with Streptococcus pneumoniae. Unique associations between IL-2 or the cytokine module and the microbiota composition of the airways were observed in asthmatics subjects prior to treatment but not after or in controls.ConclusionThe underlying inflammation in atopic asthma is related to the composition of microbiota and is associated with severity of airway obstruction. Treatment with inhaled corticosteroids was associated with changes in the airway inflammatory response to microbiota.

BackgroundMicrobial longitudinal studies are powerful experimental designs utilized to classify diseases, determine prognosis, and analyze microbial systems dynamics. In longitudinal studies, only identifying differential features between two phenotypes does not provide sufficient information to determine whether a change in the relative abundance is short-term or continuous. Furthermore, sample collection in longitudinal studies suffers from all forms of variability such as a different number of subjects per phenotypic group, a different number of samples per subject, and samples not collected at consistent time points. These inconsistencies are common in studies that collect samples from human subjects.ResultsWe present MetaLonDA, an R package that is capable of identifying significant time intervals of differentially abundant microbial features. MetaLonDA is flexible such that it can perform differential abundance tests despite inconsistencies associated with sample collection. Extensive experiments on simulated datasets quantitatively demonstrate the effectiveness of MetaLonDA with significant improvement over alternative methods. We applied MetaLonDA to the DIABIMMUNE cohort (https://pubs.broadinstitute.org/diabimmune) substantiating significant early lifetime intervals of exposure to Bacteroides and Bifidobacterium in Finnish and Russian infants. Additionally, we established significant time intervals during which novel differentially relative abundant microbial genera may contribute to aberrant immunogenicity and development of autoimmune disease.ConclusionMetaLonDA is computationally efficient and can be run on desktop machines. The identified differentially abundant features and their time intervals have the potential to distinguish microbial biomarkers that may be used for microbial reconstitution through bacteriotherapy, probiotics, or antibiotics. Moreover, MetaLonDA can be applied to any longitudinal count data such as metagenomic sequencing, 16S rRNA gene sequencing, or RNAseq. MetaLonDA is publicly available on CRAN (https://CRAN.R-project.org/package=MetaLonDA).Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0402-y) contains supplementary material, which is available to authorized users.

Micro-RNAs (miRNAs) act as post-transcriptional regulators of gene expression. In sarcoidosis, aberrant miRNA expression may enhance immune responses mounted against an unknown antigenic agent. We tested whether a distinct miRNA signature functions as a diagnostic biomarker and explored its role as an immune modulator in sarcoidosis. Expression of miRNAs from peripheral blood mononuclear cells from subjects that met clinical and histopathologic criteria for sarcoidosis were compared to those from matched controls in the ACCESS study. Signature miRNAs were determined by miRNA microarray analysis and validated by quantitative reverse transcription PCR (RT-qPCR). Microarray analysis identified 54 differentially expressed feature mature human miRNAs between groups. Significant feature miRNAs that distinguish sarcoidosis from controls were selected by use of probabilistic models adjusted for clinical variables. Eight signature miRNAs were chosen to verify the diagnosis of sarcoidosis in a validation cohort and distinguished sarcoidosis from controls with a positive predictive value of 88%. We identified both novel and previously described genes and molecular pathways associated with sarcoidosis as targets of these signature miRNAs. Additionally, we demonstrate that signature miRNAs (hsa-miR-150-3p and hsa-miR-342-5p) are significantly associated with reduced lymphocytes and airflow limitations, known markers of poor prognosis. Together, these findings suggest that a circulating miRNA signature serves as a non-invasive biomarker that supports the diagnosis of sarcoidosis. Future studies will test the miRNA signature as a prognostication tool associated with poor clinical outcomes in sarcoidosis.