PubMed Health⌕ Search

PubMed · 11199104

Mucus hypersecretion in chronic obstructive pulmonary disease.

Abstract

Most patients with chronic obstructive pulmonary disease (COPD) exhibit characteristics of airway mucus hypersecretion, namely sputum production, increased luminal mucus, submucosal gland hypertrophy and goblet cell hyperplasia. The clinical consequences of hypersecretion are impaired gas exchange and compromised mucociliary clearance, which encourages bacterial colonization and associated exacerbations. However, the extent of the contribution of mucus to pathophysiology of COPD is controversial. Early epidemiological studies found little evidence for the involvement of mucus in the age-related decline in lung function and mortality associated with COPD and concluded that chronic airflow obstruction and mucus hypersecretion were independent processes. Later studies found positive associations between phlegm production and decline in lung function, hospitalization and death. Thus, although not diagnostic for the condition, mucus hypersecretion contributes to morbidity and mortality in certain groups of patients with COPD. This suggests that it is important to develop drugs that inhibit mucus hypersecretion in these patients. Unfortunately, ambiguity in clinical studies of mucoactive drugs means that mucolytics are not recommended in clinical management. Future research should determine whether there is an intrinsic abnormality in mucus in COPD, which will determine development of appropriate inhibitors, which in turn can be used in 'proof of concept' and in treatment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D F Rogers. 2001. Mucus hypersecretion in chronic obstructive pulmonary disease.. https://doi.org/10.1002/0470868678.ch5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗