Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5' to 3' degradation.

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3' ends in P-bodies in the early embryo, consistent with 5' to 3' degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

Sequential and additive expression of miR-9 precursors control timing of neurogenesis.

MicroRNAs (miRs) have an important role in tuning dynamic gene expression. However, the mechanism by which they are quantitatively controlled is unknown. We show that the amount of mature miR-9, a key regulator of neuronal development, increases during zebrafish neurogenesis in a sharp stepwise manner. We characterize the spatiotemporal profile of seven distinct microRNA primary transcripts (pri-mir)-9s that produce the same mature miR-9 and show that they are sequentially expressed during hindbrain neurogenesis. Expression of late-onset pri-mir-9-1 is added on to, rather than replacing, the expression of early onset pri-mir-9-4 and -9-5 in single cells. CRISPR/Cas9 mutation of the late-onset pri-mir-9-1 prevents the developmental increase of mature miR-9, reduces late neuronal differentiation and fails to downregulate Her6 at late stages. Mathematical modelling shows that an adaptive network containing Her6 is insensitive to linear increases in miR-9 but responds to stepwise increases of miR-9. We suggest that a sharp stepwise increase of mature miR-9 is created by sequential and additive temporal activation of distinct loci. This may be a strategy to overcome adaptation and facilitate a transition of Her6 to a new dynamic regime or steady state.

Chronic inflammatory arthritis drives systemic changes in circadian energy metabolism.

Chronic inflammation underpins many human diseases. Morbidity and mortality associated with chronic inflammation are often mediated through metabolic dysfunction. Inflammatory and metabolic processes vary through circadian time, suggesting an important temporal crosstalk between these systems. Using an established mouse model of rheumatoid arthritis, we show that chronic inflammatory arthritis results in rhythmic joint inflammation and drives major changes in muscle and liver energy metabolism and rhythmic gene expression. Transcriptional and phosphoproteomic analyses revealed alterations in lipid metabolism and mitochondrial function associated with increased EGFR-JAK-STAT3 signaling. Metabolomic analyses confirmed rhythmic metabolic rewiring with impaired ß-oxidation and lipid handling and revealed a pronounced shunt toward sphingolipid and ceramide accumulation. The arthritis-related production of ceramides was most pronounced during the day, which is the time of peak inflammation and increased reliance on fatty acid oxidation. Thus, our data demonstrate that localized joint inflammation drives a time-of-day­dependent build-up of bioactive lipid species driven by rhythmic inflammation and altered EGFR-STAT signaling.

SynBa: improved estimation of drug combination synergies with uncertainty quantification.

MOTIVATION: There exists a range of different quantification frameworks to estimate the synergistic effect of drug combinations. The diversity and disagreement in estimates make it challenging to determine which combinations from a large drug screening should be proceeded with. Furthermore, the lack of accurate uncertainty quantification for those estimates precludes the choice of optimal drug combinations based on the most favourable synergistic effect. RESULTS: In this work, we propose SynBa, a flexible Bayesian approach to estimate the uncertainty of the synergistic efficacy and potency of drug combinations, so that actionable decisions can be derived from the model outputs. The actionability is enabled by incorporating the Hill equation into SynBa, so that the parameters representing the potency and the efficacy can be preserved. Existing knowledge may be conveniently inserted due to the flexibility of the prior, as shown by the empirical Beta prior defined for the normalized maximal inhibition. Through experiments on large combination screenings and comparison against benchmark methods, we show that SynBa provides improved accuracy of dose-response predictions and better-calibrated uncertainty estimation for the parameters and the predictions. AVAILABILITY AND IMPLEMENTATION: The code for SynBa is available at https://github.com/HaotingZhang1/SynBa. The datasets are publicly available (DOI of DREAM: 10.7303/syn4231880; DOI of the NCI-ALMANAC subset: 10.5281/zenodo.4135059).

Component-specific clusters for diagnosis and prediction of allergic airway diseases.

BACKGROUND: Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes. OBJECTIVE: To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms. METHODS: In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models. RESULTS: Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04). CONCLUSION: Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.

Murine AGM single-cell profiling identifies a continuum of hemogenic endothelium differentiation marked by ACE.

In vitro generation and expansion of hematopoietic stem cells (HSCs) holds great promise for the treatment of any ailment that relies on bone marrow or blood transplantation. To achieve this, it is essential to resolve the molecular and cellular pathways that govern HSC formation in the embryo. HSCs first emerge in the aorta-gonad-mesonephros (AGM) region, where a rare subset of endothelial cells, hemogenic endothelium (HE), undergoes an endothelial-to-hematopoietic transition (EHT). Here, we present full-length single-cell RNA sequencing (scRNA-seq) of the EHT process with a focus on HE and dorsal aorta niche cells. By using Runx1b and Gfi1/1b transgenic reporter mouse models to isolate HE, we uncovered that the pre-HE to HE continuum is specifically marked by angiotensin-I converting enzyme (ACE) expression. We established that HE cells begin to enter the cell cycle near the time of EHT initiation when their morphology still resembles endothelial cells. We further demonstrated that RUNX1 AGM niche cells consist of vascular smooth muscle cells and PDGFRa+ mesenchymal cells and can functionally support hematopoiesis. Overall, our study provides new insights into HE differentiation toward HSC and the role of AGM RUNX1+ niche cells in this process. Our expansive scRNA-seq datasets represents a powerful resource to investigate these processes further.

Scalable inference of transcriptional kinetic parameters from MS2 time series data.

MOTIVATION: The MS2-MCP (MS2 coat protein) live imaging system allows for visualization of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. Computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A compound state Hidden Markov Model (cpHMM) was recently introduced to allow inference of promoter activity from MS2-MCP data. However, the computational time for inference scales exponentially with gene length and the cpHMM is therefore not currently practical for application to many eukaryotic genes. RESULTS: We present a scalable implementation of the cpHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters. AVAILABILITY AND IMPLEMENTATION: Python implementation is available at https://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HOX paralogs selectively convert binding of ubiquitous transcription factors into tissue-specific patterns of enhancer activation.

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

Glucocorticoids rapidly inhibit cell migration through a novel, non-transcriptional HDAC6 pathway.

Glucocorticoids (GCs) act through the glucocorticoid receptor (GR, also known as NR3C1) to regulate immunity, energy metabolism and tissue repair. Upon ligand binding, activated GR mediates cellular effects by regulating gene expression, but some GR effects can occur rapidly without new transcription. Here, we show that GCs rapidly inhibit cell migration, in response to both GR agonist and antagonist ligand binding. The inhibitory effect on migration is prevented by GR knockdown with siRNA, confirming GR specificity, but not by actinomycin D treatment, suggesting a non-transcriptional mechanism. We identified a rapid onset increase in microtubule polymerisation following GC treatment, identifying cytoskeletal stabilisation as the likely mechanism of action. HDAC6 overexpression, but not knockdown of αTAT1, rescued the GC effect, implicating HDAC6 as the GR effector. Consistent with this hypothesis, ligand-dependent cytoplasmic interaction between GR and HDAC6 was demonstrated by quantitative imaging. Taken together, we propose that activated GR inhibits HDAC6 function, and thereby increases the stability of the microtubule network to reduce cell motility. We therefore report a novel, non-transcriptional mechanism whereby GCs impair cell motility through inhibition of HDAC6 and rapid reorganization of the cell architecture.This article has an associated First Person interview with the first author of the paper.

In vivo bronchial epithelial interferon responses are augmented in asthma on day 4 following experimental rhinovirus infection.

Despite good evidence of impaired innate antiviral responses in asthma, trials of inhaled interferon-ß given during exacerbations showed only modest benefits in moderate/severe asthma. Using human experimental rhinovirus infection, we observe robust in vivo induction of bronchial epithelial interferon response genes 4 days after virus inoculation in 25 subjects with asthma but not 11 control subjects. This signature correlated with virus loads and lower respiratory symptoms. Our data indicate that the in vivo innate antiviral response is dysregulated in asthma and open up the potential that prophylactic rather than therapeutic interferon therapy may have greater clinical benefit.

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.

MOTIVATION: The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics. RESULTS: The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic. AVAILABILITY AND IMPLEMENTATION: GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Uncovering tissue-specific binding features from differential deep learning.

Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expression programmes. Recently, deep learning models have become state-of-the-art in various pattern analysis tasks, including applications in the field of genomics. We therefore investigate the application of convolutional neural network (CNN) models to the discovery of sequence features determining cooperative and differential TF binding across tissues. We analyse ChIP-seq data from MEIS, TFs which are broadly expressed across mouse branchial arches, and HOXA2, which is expressed in the second and more posterior branchial arches. By developing models predictive of MEIS differential binding in all three tissues, we are able to accurately predict HOXA2 co-binding sites. We evaluate transfer-like and multitask approaches to regularizing the high-dimensional classification task with a larger regression dataset, allowing for the creation of deeper and more accurate models. We test the performance of perturbation and gradient-based attribution methods in identifying the HOXA2 sites from differential MEIS data. Our results show that deep regularized models significantly outperform shallow CNNs as well as k-mer methods in the discovery of tissue-specific sites bound in vivo.

HiChIP-Peaks: a HiChIP peak calling algorithm.

MOTIVATION: HiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets. RESULTS: We develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data. AVAILABILITY AND IMPLEMENTATION: HiChIP-Peaks is freely available at https://github.com/ChenfuShi/HiChIP_peaks. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Classifying cells with Scasat, a single-cell ATAC-seq analysis tool.

ATAC-seq is a recently developed method to identify the areas of open chromatin in a cell. These regions usually correspond to active regulatory elements and their location profile is unique to a given cell type. When done at single-cell resolution, ATAC-seq provides an insight into the cell-to-cell variability that emerges from otherwise identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. This paper presents Scasat (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. Scasat treats the data as binary and applies statistical methods that are especially suitable for binary data. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. It is robust, flexible, interactive and easy to extend. Within Scasat we developed a novel differential accessibility analysis method based on information gain to identify the peaks that are unique to a cell. The results from Scasat showed that open chromatin locations corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.

OscoNet: inferring oscillatory gene networks.

BACKGROUND: Oscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes. RESULTS: We improve the optimisation scheme underlying Oscope and provide a well-calibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while requiring substantially less computation time than the extended nearest insertion approach. CONCLUSIONS: OscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method.

GrandPrix: scaling up the Bayesian GPLVM for single-cell data.

Motivation: The Gaussian Process Latent Variable Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has been used for pseudotime estimation with capture time information. However, current implementations are computationally intensive and will not scale up to modern droplet-based single-cell datasets which routinely profile many tens of thousands of cells. Results: We provide an efficient implementation which allows scaling up this approach to modern single-cell datasets. We also generalize the application of pseudotime inference to cases where there are other sources of variation such as branching dynamics. We apply our method on microarray, nCounter, RNA-seq, qPCR and droplet-based datasets from different organisms. The model converges an order of magnitude faster compared to existing methods whilst achieving similar levels of estimation accuracy. Further, we demonstrate the flexibility of our approach by extending the model to higher-dimensional latent spaces that can be used to simultaneously infer pseudotime and other structure such as branching. Thus, the model has the capability of producing meaningful biological insights about cell ordering as well as cell fate regulation. Availability and implementation: Software available at github.com/ManchesterBioinference/GrandPrix. Supplementary information: Supplementary data are available at Bioinformatics online.

Using functional genomics to advance the understanding of psoriatic arthritis.

Psoriatic arthritis (PsA) is a complex disease where susceptibility is determined by genetic and environmental risk factors. Clinically, PsA involves inflammation of the joints and the skin, and, if left untreated, results in irreversible joint damage. There is currently no cure and the few treatments available to alleviate symptoms do not work in all patients. Over the past decade, genome-wide association studies (GWAS) have uncovered a large number of disease-associated loci but translating these findings into functional mechanisms and novel targets for therapeutic use is not straightforward. Most variants have been predicted to affect primarily long-range regulatory regions such as enhancers. There is now compelling evidence to support the use of chromatin conformation analysis methods to discover novel genes that can be affected by disease-associated variants. Here, we will review the studies published in the field that have given us a novel understanding of gene regulation in the context of functional genomics and how this relates to the study of PsA and its underlying disease mechanism.

Genome-wide effect of pulmonary airway epithelial cell-specific Bmal1 deletion.

Pulmonary airway epithelial cells (AECs) form a critical interface between host and environment. We investigated the role of the circadian clock using mice bearing targeted deletion of the circadian gene brain and muscle ARNT-like 1 (Bmal1) in AECs. Pulmonary neutrophil infiltration, biomechanical function, and responses to influenza infection were all disrupted. A circadian time-series RNA sequencing study of laser-captured AECs revealed widespread disruption in genes of the core circadian clock and output pathways regulating cell metabolism (lipids and xenobiotics), extracellular matrix, and chemokine signaling, but strikingly also the gain of a novel rhythmic transcriptome in Bmal1-targeted cells. Many of the rhythmic components were replicated in primary AECs cultured in air-liquid interface, indicating significant cell autonomy for control of pulmonary circadian physiology. Finally, we found that metabolic cues dictate phasing of the pulmonary clock and circadian responses to immunologic challenges. Thus, the local circadian clock in AECs is vital in lung health by coordinating major cell processes such as metabolism and immunity.-Zhang, Z. Hunter, L., Wu, G., Maidstone, R., Mizoro, Y., Vonslow, R., Fife, M., Hopwood, T., Begley, N., Saer, B., Wang, P., Cunningham, P., Baxter, M., Durrington, H., Blaikley, J. F., Hussell, T., Rattray, M., Hogenesch, J. B., Gibbs, J., Ray, D. W., Loudon, A. S. I. Genome-wide effect of pulmonary airway epithelial cell-specific Bmal1 deletion.

Cytokine Responses to Rhinovirus and Development of Asthma, Allergic Sensitization, and Respiratory Infections during Childhood.

RATIONALE: Immunophenotypes of antiviral responses, and their relationship with asthma, allergy, and lower respiratory tract infections, are poorly understood. OBJECTIVES: We characterized multiple cytokine responses of peripheral blood mononuclear cells to rhinovirus stimulation, and their relationship with clinical outcomes. METHODS: In a population-based birth cohort, we measured 28 cytokines after stimulation with rhinovirus-16 in 307 children aged 11 years. We used machine learning to identify patterns of cytokine responses, and related these patterns to clinical outcomes, using longitudinal models. We also ascertained phytohemagglutinin-induced T-helper cell type 2 (Th2)-cytokine responses (PHA-Th2). MEASUREMENTS AND MAIN RESULTS: We identified six clusters of children based on their rhinovirus-16 responses, which were differentiated by the expression of four cytokine/chemokine groups: interferon-related (IFN), proinflammatory (Inflam), Th2-chemokine (Th2-chem), and regulatory (Reg). Clusters differed in their clinical characteristics. Children with an IFNmodInflamhighestTh2-chemhighestReghighest rhinovirus-16-induced pattern had a PHA-Th2low response, and a very low asthma risk (odds ratio [OR], 0.08; 95% confidence interval [CI], 0.01-0.81; P = 0.03). Two clusters had a high risk of asthma and allergic sensitization, but with different trajectories from infancy to adolescence. The IFNlowestInflamhighTh2-chemlowRegmod cluster exhibited a PHA-Th2lowest response and was associated with early-onset asthma and sensitization, and the highest risk of asthma exacerbations (OR, 1.37; 95% CI, 1.07-1.76; P = 0.014) and lower respiratory tract infection hospitalizations (OR, 2.40; 95% CI, 1.26-4.58; P = 0.008) throughout childhood. In contrast, the IFNhighestInflammodTh2-chemmodReghigh cluster with a rhinovirus-16-cytokine pattern was characterized by a PHA-Th2highest response, and a low prevalence of asthma/sensitization in infancy that increased sharply to become the highest among all clusters by adolescence (but with a low risk of asthma exacerbations). CONCLUSIONS: Early-onset troublesome asthma with early-life sensitization, later-onset milder allergic asthma, and disease protection are each associated with different patterns of rhinovirus-induced immune responses.

Evolution of IgE responses to multiple allergen components throughout childhood.

BACKGROUND: There is a paucity of information about longitudinal patterns of IgE responses to allergenic proteins (components) from multiple sources. OBJECTIVES: This study sought to investigate temporal patterns of component-specific IgE responses from infancy to adolescence, and their relationship with allergic diseases. METHODS: In a population-based birth cohort, we measured IgE to 112 components at 6 follow-ups during childhood. We used a Bayesian method to discover cross-sectional sensitization patterns and their longitudinal trajectories, and we related these patterns to asthma and rhinitis in adolescence. RESULTS: We identified 1 sensitization cluster at age 1, 3 at age 3, 4 at ages 5 and 8, 5 at age 11, and 6 at age 16 years. "Broad" cluster was the only cluster present at every follow-up, comprising components from multiple sources. "Dust mite" cluster formed at age 3 years and remained unchanged to adolescence. At age 3 years, a single-component "Grass" cluster emerged, which at age 5 years absorbed additional grass components and Fel d 1 to form the "Grass/cat" cluster. Two new clusters formed at age 11 years: "Cat" cluster and "PR-10/profilin" (which divided at age 16 years into "PR-10" and "Profilin"). The strongest contemporaneous associate of asthma at age 16 years was sensitization to dust mite cluster (odds ratio: 2.6; 95% CI: 1.2-6.1; P < .05), but the strongest early life predictor of subsequent asthma was sensitization to grass/cat cluster (odds ratio: 3.5; 95% CI: 1.6-7.4; P < .01). CONCLUSIONS: We describe the architecture of the evolution of IgE responses to multiple allergen components throughout childhood, which may facilitate development of better diagnostic and prognostic biomarkers for allergic diseases.