Genomic biomarkers in chronic beryllium disease and sarcoidosis

Infectious and non-infectious precipitants of sarcoidosis

Sarcoidosis is a chronic inflammatory disease that can affect any organ in the body. Its exact cause remains unknown, but it is believed to result from a combination of genetic and environmental factors. Some potential causes of sarcoidosis include genetics, environmental triggers, immune system dysfunction, the gut microbiome, sex, and race/ethnicity. Genetic mutations are associated with protection against disease progression or an increased susceptibility to more severe disease, while exposure to certain chemicals, bacteria, viruses, or allergens can trigger the formation of immune cell congregations (granulomas) in different organs. Dysfunction of the immune system, including autoimmune reactions, may also contribute. The gut microbiome and factors such as being female or having African American, Scandinavian, Irish, or Puerto Rican heritage are additional contributors to disease outcome. Recent research has suggested that certain drugs, such as anti-Programmed Death-1 (PD-1) and antibiotics such as tuberculosis (TB) drugs, may raise the risk of developing sarcoidosis. Hormone levels, particularly higher levels of estrogen and progesterone in women, have also been linked to an increased likelihood of sarcoidosis. The diagnosis of sarcoidosis involves a comprehensive assessment that includes medical history, physical examination, laboratory tests, and imaging studies. While there is no cure for sarcoidosis, the symptoms can often be effectively managed through various treatment options. Treatment may involve the use of medications, surgical interventions, or lifestyle changes. These disparate factors suggests that sarcoidosis has multiple positive and negative exacerbants on disease severity, some of which can be ameliorated and others which cannot.

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.

From Karl Wurm and Guy Scadding's staging to 18F-FDG PET/CT scan phenotyping and far beyond: perspective in the evading history of phenotyping in sarcoidosis

Sarcoidosis is an inflammatory granulomatous disease of unknown etiology involving any organ or tissue along with any combination of active sites, even the most silent ones clinically. The unpredictable nature of the sites involved in sarcoidosis dictates the highly variable natural history of the disease and the necessity to cluster cases at diagnosis based on clinical and/or imaging common characteristics in an attempt to classify patients based on their more homogeneous phenotypes, possibly with similar clinical behavior, prognosis, outcome, and therefore with therapeutic requirements. In the course of the disease's history, this attempt relates to the availability of a means of detection of the sites involved, from the Karl Wurm and Guy Scadding's chest x-ray staging through the ACCESS, the WASOG Sarcoidosis Organ Assessment Instruments, and the GenPhenReSa study to the 18F-FDG PET/CT scan phenotyping and far beyond to new technologies and/or the current "omics." The hybrid molecular imaging of the 18F-FDG PET/CT scan, by unveiling the glucose metabolism of inflammatory cells, can identify high sensitivity inflammatory active granulomas, the hallmark of sarcoidosis-even in clinically and physiologically silent sites-and, as recently shown, is successful in identifying an unexpected ordered stratification into four phenotypes: (I) hilar-mediastinal nodal, (II) lungs and hilar-mediastinal nodal, (III) an extended nodal supraclavicular, thoracic, abdominal, inguinal, and (IV) all the above in addition to systemic organs and tissues, which is therefore the ideal phenotyping instrument. During the "omics era," studies could provide significant, distinct, and exclusive insights into sarcoidosis phenotypes linking clinical, laboratory, imaging, and histologic characteristics with molecular signatures. In this context, the personalization of treatment for sarcoidosis patients might have reached its goal.

Sarcoidosis: Epidemiology and clinical insights

Sarcoidosis is characterized by noncaseating granulomas which form in almost any part of the body, primarily in the lungs and/or thoracic lymph nodes. Environmental exposures in genetically susceptible individuals are believed to cause sarcoidosis. There is variation in incidence and prevalence by region and race. Males and females are almost equally affected, although disease peaks at a later age in females than in males. The heterogeneity of presentation and disease course can make diagnosis and treatment challenging. Diagnosis is suggestive in a patient if one or more of the following is present: radiologic signs of sarcoidosis, evidence of systemic involvement, histologically confirmed noncaseating granulomas, sarcoidosis signs in bronchoalveolar lavage fluid (BALF), and low probability or exclusion of other causes of granulomatous inflammation. No sensitive or specific biomarkers for diagnosis and prognosis exist, but there are several that can be used to support clinical decisions, such as serum angiotensin-converting enzyme levels, human leukocyte antigen types, and CD4 Vα2.3+ T cells in BALF. Corticosteroids remain the mainstay of treatment for symptomatic patients with severely affected or declining organ function. Sarcoidosis is associated with a range of adverse long-term outcomes and complications, and with great variation in prognosis between populations. New data and technologies have moved sarcoidosis research forward, increasing our understanding of the disease. However, there is still much left to be discovered. The pervading challenge is how to account for patient variability. Future studies should focus on how to optimize current tools and develop new approaches so that treatment and follow-up can be targeted to individuals with more precision.

Multiomic Signatures of Chronic Beryllium Disease Bronchoalveolar Lavage Cells Relate to T-Cell Function and Innate Immunity

Chronic beryllium disease (CBD) is a Th1 granulomatous lung disease preceded by sensitization to beryllium (BeS). We profiled the methylome, transcriptome, and selected proteins in the lung to identify molecular signatures and networks associated with BeS and CBD. BAL cell DNA and RNA were profiled using microarrays from CBD (n = 30), BeS (n = 30), and control subjects (n = 12). BAL fluid proteins were measured using Olink Immune Response Panel proteins from CBD (n = 22) and BeS (n = 22) subjects. Linear models identified features associated with CBD, adjusting for covariation and batch effects. Multiomic integration methods identified correlated features between datasets. We identified 1,546 differentially expressed genes in CBD versus control subjects and 204 in CBD versus BeS. Of the 101 shared transcripts, 24 have significant cis relationships between gene expression and DNA methylation, assessed using expression quantitative trait methylation analysis, including genes not previously identified in CBD. A multiomic model of top DNA methylation and gene expression features demonstrated that the first component separated CBD from other samples and the second component separated control subjects from remaining samples. The top features on component one were enriched for T-lymphocyte function, and the top features on component two were enriched for innate immune signaling. We identified six differentially abundant proteins in CBD versus BeS, with two (SIT1 and SH2D1A) selected as important RNA features in the multiomic model. Our integrated analysis of DNA methylation, gene expression, and proteins in the lung identified multiomic signatures of CBD that differentiated it from BeS and control subjects.