Biology subjects

Chung, K. F.

Publications and source records attributed to Chung, K. F..

8 recordsLinked to original sources

bioRxiv2026

Topology-Based Query Framework for Longitudinal Omics Trajectories

1 Abstract 1.1 Background Many longitudinal omics studies contain only a small number of repeated measurements collected before, during, or after an intervention. Existing approaches, including mixed-effects models and generalized additive models, estimate temporal effects but do not generally provide a discrete representation of trajectory topology that can be queried directly across experimental groups. 1.2 Methods We developed LongOmicsTraj, an open-source R package for topology-based representation and querying of short longitudinal omics trajectories. The framework encodes the direction of change between adjacent visits as up, down, or flat, with the ordered sequence defining an Ordinal Trajectory State (OTS). LongOmicsTraj operates downstream of trajectory estimation and can therefore be applied to empirical summaries or model-derived visit-level estimates, including those from linear mixed-effects models, generalized additive models, and polynomial regression, following a maSigPro-style time-course formulation [1]. OTS labels provide a common representation for topology-based querying, cross-group comparison, and evaluation of higherlevel representations such as trajectory clusters. We evaluated the framework using controlled simulations and bronchial biopsy transcriptomic data from the GLUCOLD corticosteroid intervention study (GEO accession GSE36221), measured at baseline, 6 months, and 30 months. The biological analysis compared continued inhaled corticosteroid (ICS) treatment, ICS withdrawal after 6 months, and placebo. 1.3 Results In simulations, LongOmicsTraj recovered predefined stable, monotonic, transient, rebound, and oscillatory trajectories with high accuracy when longitudinal signal was sufficiently clear, with performance declining under high-noise conditions and depending partly on the upstream estimator. In GLUCOLD, comparator-aware topology queries reduced 20,358 measured transcripts to 168 genes showing a corticosteroid response that was maintained during continued treatment, reversed following withdrawal, and was not reproduced under placebo. The selected genes included established corticosteroid-response genes and were enriched for immune-cell migration, chemotaxis, cell adhesion, and extracellular-matrix organisation. Topology-aware evaluation of FlexMix trajectory clusters additionally revealed substantial within-cluster temporal heterogeneity, with topology purities of approximately 46% to 60%. 1.4 Conclusions LongOmicsTraj provides a compact, directly queryable representation of temporal direction and order in short longitudinal omics studies. It complements existing longitudinal estimation and clustering methods by making trajectory structure explicit, enabling structured cross-group queries and quantification of temporal heterogeneity within trajectory clusters.

bioinformatics↗

bioRxiv2025

Genome dynamics across the radiation of a mega-diverse genus

Understanding the drivers of species diversity and rapid radiations is a major goal in evolutionary biology. Begonia is one of the most species-rich angiosperm genera with 2,164 species currently identified. This genus exhibits considerable variation in chromosome number and a wide range of genome sizes, allowing us to associate genome dynamics with divergence and speciation at a range of temporal scales. We investigate all main radiations within the Begoniaceae family using five previously published Begonia genomes and seven new genome assemblies. We show that Begonia species show more complex, repetitive and dynamic genomes overall than their close relative, the monotypic Hillebrandia sandwicensis. We identify families of repetitive elements that have recently expanded in species from two different highly speciose Southeast Asian sections and two large Neotropical radiations. Detailed characterisation of genomes from species belonging to two parallel radiations, one in Southeast Asia (Begonia section Coelocentrum) and the other in the Neotropics (Begonia section Gireoudia), revealed recent expansion in LTR retrotransposons (LTR-RTs) and satellite DNA, in contrast to more species-poor closely related clades. We further investigate variation in repetitive elements within species, finding that accessions from a population of the widespread Begonia heracleifolia with unusually large genomes show a markedly higher satellite repeat and Ty3/Gypsy LTR-RT content associated with the expansion of a few abundant repeat lineages. We find that accessions derived from this population show lower seed viability in crosses with other conspecific populations, and thus identify a direct link between expansions of repetitive DNA and the process of genetic isolation. These results show how genome dynamics may promote speciation in one of the most diverse flowering plant genera.

genomics↗

bioRxiv2024

Microbiome diversity, intra-mucosal bacteria and immune integration within normal and asthmatic airway mucosa

Asthma is characterized by reduced bronchial bacterial diversity and airway mucosal disruption. We examined spatial distributions of microbial sequences and host mucosal transcripts in bronchial biopsies from healthy controls and adult asthmatics. Bacteria were discovered by 16S ribosomal RNA staining in the lamina propria of all biopsies, with counts positively associated to lumenal bacterial diversity. Weighted correlation network analysis identified fifteen co-expression networks, including distinct programs of adaptive and innate immunity in differing spatial distributions. Stromal bacterial counts correlated significantly with eight of the network eigenvectors in directions compatible with beneficial relationships. The results suggest that dysbiosis may affect mucosal immunity through impaired interactions beneath the epithelial border. Intra-mucosal companion bacteria may be a potential substrate for selective management of immunity in a wide range of diseases. One-Sentence SummaryThe lung microbiome extends within the airway mucosa and associates spatially and functionally with immune networks.

microbiology↗

bioRxiv2022

Genomic and ecologic characteristics of the airway microbial-mucosal complex

SO_SCPLOWUMMARYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWPARAGRAPHC_SCPLOWLung diseases due to infection and dysbiosis affect hundreds of millions of people world-wide1-4. Microbial communities at the airway mucosal barrier are conserved and highly ordered5, reflecting symbiosis and co-evolution with human host factors6. Freed of selection to digest nutrients for the host, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the results of the first systematic culture and whole-genome sequencing of the principal airway bacterial species, identifying abundant novel organisms within the genera Streptococcus, Pauljensenia, Neisseria and Gemella. Bacterial genomes were enriched for genes encoding antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. RNA-targeting CRISPR elements in some taxa suggest the potential to prevent or treat specific viral infections. Homologues of human RO60 present in Neisseria spp. provide a possible respiratory primer for autoimmunity in systemic lupus erythematosus (SLE) and Sjogren syndrome. We interpret the structure and biogeography of airway microbial communities from clinical surveys in the context of whole-genome content, identifying features of airway dysbiosis that may presage breakdown of homeostasis during acute attacks of asthma and chronic obstructive pulmonary disease (COPD). We match the gene content of isolates to human transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways that can sustain host interactions with the microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosal immunity in lung diseases of global significance.

microbiology↗

bioRxiv2020

Pathophysiological regulation of lung function by the free fatty acid receptor FFA4

Increased prevalence of inflammatory airway diseases including asthma and chronic obstructive pulmonary disease (COPD) together with a significant number of patients being inadequately controlled by current frontline treatments means that there is a need to define novel therapeutic targets for these conditions1. Here we investigate a member of the G protein-coupled receptor (GPCR) family, FFA4, which responds to free circulating fatty acids, including dietary omega-3 fatty acids found in fish oils2–4. Although usually associated with metabolic responses linked with food intake, we show that FFA4 is expressed in the lung where it is coupled to Gq/11-signalling. Activation of FFA4 by drug-like agonists produced relaxation of murine airway smooth muscle mediated, at least in part, by the release of the prostaglandin PGE2 that subsequently acts on EP2 prostanoid receptors. In normal mice, activation of FFA4 resulted in a decrease in lung resistance. Importantly, in acute and chronic ozone models of pollution-mediated inflammation, and in house-dust mite and cigarette smoke-induced inflammatory disease, FFA4 agonists acted to reduce airway resistance, whilst this response was absent in mice lacking expression of FFA4. The expression profile of FFA4 in human lung was very similar to that observed in mice and the response to FFA4/FFA1 agonists similarly mediated human airway smooth muscle relaxation. Hence, our study provides evidence that pharmacological targeting of lung FFA4, and possibly combined activation of FFA4 and FFA1, has in vivo efficacy that might have therapeutic value in the treatment of bronchoconstriction associated with inflammatory airway diseases such as asthma and COPD.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2020

Novel Type 2-high gene clusters associated with corticosteroid sensitivity in COPD

RationaleSevere asthma and COPD share common pathophysiologic traits such as relative corticosteroid insensitivity. We recently published three transcriptome-associated clusters (TACs) using hierarchical analysis of the sputum transcriptome in asthmatics from the U-BIOPRED cohort with one Type 2-high signature (TAC1) and 2 Type 2-low signatures. ObjectiveWe examined whether gene-expression signatures obtained in asthma can be used to identify the subgroup of COPD patients with steroid sensitivity. MethodsUsing gene set variation analysis (GSVA), we examined the distribution and enrichment scores (ES) of the 3 TACs in the transcriptome of bronchial biopsies from 46 patients who participated in the GLUCOLD COPD study that received 30 months of treatment with inhaled corticosteroids (ICS) with and without an added long-acting {beta}-agonist (LABA). The identified signatures were then associated with longitudinal clinical variables after treatment. Measurements and main resultsBronchial biopsies in COPD patients at baseline showed a wide range of expression of the 3 TACs. After ICS{+/-}LABA treatment, the ES of TAC1 was significantly reduced at 30 months, but those of TAC2 and TAC3 were unaffected. A corticosteroid-sensitive TAC1 (sub)signature was developed from the TAC1 ICS-responsive genes. This signature consisted of mast cell-specific genes identified by single-cell RNA-seq and positively correlated with bronchial biopsy mast cell numbers following ICS{+/-}LABA. Baseline levels of gene transcription predicted change in FEV1 %predicted following 30-month ICS{+/-}LABA. ConclusionsSputum-derived transcriptomic signatures from an asthma cohort can be recapitulated in bronchial biopsies of COPD patients and identified airway wall mast cells as a predictor of those COPD patients who will benefit from inhaled corticosteroids.

bioinformatics↗

bioRxiv2019

A Topological Data Analysis Network Model of Asthma Based on Blood Gene Expression Profiles

Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNAs 3 UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

molecular biology↗

bioRxiv2018

Accounting for measurement error to assess the effect of air pollution on omic signals

Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking.\n\nIn this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the \"Oxford Street II Study\", a randomized crossover trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional MCMC simulative methods as well as integrated Laplace approximation.\n\nThe inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution (TRAP) measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).

epidemiology↗