Expert opinion on diagnosis and management of Severe Asthma in low and middle income countries (LMIC) with focus on India.

OBJECTIVE: In this document, 9 Indian experts have evaluated the factors specific to LMICs when it came to Severe Asthma (SA) diagnosis, evaluation, biologic selection, non-biologic treatment options, and follow-up. DATA SOURCES: A search was performed using 50 keywords, focusing on the Indian/LMICs perspective, in PubMed, Cochrane Library, and Google Scholar. The key areas of the search were focused on diagnosis, phenoendotyping, non-biological therapies, selecting a biologic, assessment of treatment response, and management of exacerbation. STUDY SELECTIONS: The initial search revealed 1826 articles, from these case reports, observational studies, cohort studies, non-English language papers, etc., were excluded and we short-listed 20 articles for each area. Five relevant articles were selected by the experts for review. RESULTS: In LMICs, SA patients may be referred to the specialist for evaluation a little late for Phenoendotyping of SA. While biologic therapy is now a standard of care, pulmonologists in LMICs may not have access to all the investigations to phenoendotype SA patients like fractional exhaled nitric oxide (FeNO), skin prick test (SPT), etc., but phenotyping of SA patients can also be done with simple blood investigations, eosinophil count and serum immunoglobulin E (IgE). Choosing a biologic in the overlapping phenotype of SA and ACO patients is also a challenge in the LMICs. CONCLUSIONS: Given the limitations of LMIC, it is important to select the right patient and explain the potential benefits of biological therapy. Non-biologic add-on therapies can be attempted in a resource-limited setting where biological therapy is not available/feasible for patients.

Identification of Common Dysregulated Genes in COVID-19 and Hypersensitivity Pneumonitis: A Systems Biology and Machine Learning Approach.

A comprehensive knowledge on systems biology of severe acute respiratory syndrome coronavirus 2 is crucial for differential diagnosis of COVID-19. Interestingly, the radiological and pathological features of COVID-19 mimic that of hypersensitivity pneumonitis (HP), another pulmonary fibrotic phenotype. This motivated us to explore the overlapping pathophysiology of COVID-19 and HP, if any, and using a systems biology approach. Two datasets were obtained from the Gene Expression Omnibus database (GSE147507 and GSE150910) and common differentially expressed genes (DEGs) for both diseases identified. Fourteen common DEGs, significantly altered in both diseases, were found to be implicated in complement activation and growth factor activity. A total of five microRNAs (hsa-miR-1-3p, hsa-miR-20a-5p, hsa-miR-107, hsa-miR-16-5p, and hsa-miR-34b-5p) and five transcription factors (KLF6, ZBTB7A, ELF1, NFIL3, and ZBT33) exhibited highest interaction with these common genes. Next, C3, CFB, MMP-9, and IL1A were identified as common hub genes for both COVID-19 and HP. Finally, these top-ranked genes (hub genes) were evaluated using random forest classifier to discriminate between the disease and control group (coronavirus disease 2019 [COVID-19] vs. controls, and HP vs. controls). This supervised machine learning approach demonstrated 100% and 87.6% accuracy in differentiating COVID-19 from controls, and HP from controls, respectively. These findings provide new molecular leads that inform COVID-19 and HP diagnostics and therapeutics research and innovation.

NMR metabolomic and microarray-based transcriptomic data integration identifies unique molecular signatures of hypersensitivity pneumonitis.

Hypersensitivity pneumonitis (HP) is an immune-mediated granulomatous interstitial lung disease (ILD) that results from repeated inhalation of certain antigens. Despite major advances in research, pathophysiology of the disease remains poorly understood. The present study combines metabolomic and transcriptomic data to determine alterations in HP subjects as compared with healthy controls. Metabolic signatures were identified in serum, exhaled breath condensate (EBC) and bronchoalveolar lavage fluid (BALF) of HP patients using proton nuclear magnetic resonance (NMR) metabolomics. The expression of three metabolites, i.e., lactate, pyruvate, and proline, was found to be significantly altered in all three biofluids. The potential of differential diagnosis based on these three metabolites was investigated by including a group of patients with sarcoidosis, which is another type of granulomatous ILD. In addition, differentially expressed transcriptomic fingerprints in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. The transcriptomics analysis of these microarray-based data revealed 59 genes to be significantly dysregulated in patients with HP. Over representation analysis of the metabolites and genes of interest was performed using IMPaLA (Integrated Molecular Pathway Level Analysis) version 12. Integrated analysis of serum metabolite signatures and blood gene expression suggests dysregulation of PI3K-AKT signaling and TCA cycle pathways in these patients. This preliminary study is a step towards better understanding of the pathogenesis of HP by identification of differentially expressed metabolites and transcriptomic fingerprints. These molecular signatures may be explored as diagnostic markers for differentiating HP from other lung diseases.

Role of doxycycline to resolve different types of non-malignant lung and pleural pathology: The results of a pilot observation.

BACKGROUND: Lung lesions may develop from tissue reactions to known or unknown stimuli and present with different morphological descriptions. The pathogenesis may be induced and maintained by different bioactive substances, of which, the upregulation matrix metalloproteinases (MMPs) play a vital role. Inhibition of the MMPs, therefore, may be a prospective mode of therapy for such lesions. MATERIALS AND METHODS: A number of patients with lung lesions of different morphologies and presentations were treated empirically with long-term oral doxycycline (100 mg BID) upon exclusion of malignancy and infection in an open, single-arm, prospective, observational pilot study. The effect of the treatment was recorded on serial x-rays/computed tomography (CT) scans and the impact of treatment was measured with a visual analog scale (VAS) or a Likert-like scale. Furthermore, six independent pulmonologists' opinion (expressed on a '0' to '100' scale) were pooled with regard to the significance and the expectedness of such a change. RESULTS: Twenty-six patients (mean age 49.33 years and male: female ratio = 10:3) with different types of pulmonary parenchymal/pleural lesions were treated with long-term oral doxycycline for a mean duration of 386.88 days related to the available radiological comparison. They showed a mean improvement of 3.99 on the Likert-like scale and 78% on the VAS scale. The mean significance of the change was 83.33%, with a mean expectedness of 18% as per the pooled opinion of the pulmonologists. INFERENCE: The significant and unexpected resolution of different tissue lesions from long-term doxycycline appears to be a novel observation. This needs proper scientific validation.