Genomic attributes of airway commensal bacteria and mucosa.

Microbial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins cognate management of mucosal immunity and pathogen resistance. We show here the initial results of systematic culture and whole-genome sequencing of the thoracic airway bacteria, identifying 52 novel species amongst 126 organisms that constitute 75% of commensals typically present in heathy individuals. Clinically relevant genes encode antimicrobial synthesis, adhesion and biofilm formation, immune modulation, iron utilisation, nitrous oxide (NO) metabolism and sphingolipid signalling. Using whole-genome content we identify dysbiotic features that may influence asthma and chronic obstructive pulmonary disease. We match isolate gene content to transcripts and metabolites expressed late in airway epithelial differentiation, identifying pathways to sustain host interactions with microbiota. Our results provide a systematic basis for decrypting interactions between commensals, pathogens, and mucosa in lung diseases of global significance.

Automated calibration for stability selection in penalised regression and graphical models.

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

Automated calibration of consensus weighted distance-based clustering approaches using sharp.

MOTIVATION: In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms. RESULTS: We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularized approaches. We propose a procedure for the calibration of the number of clusters (and regularization parameter) by maximizing the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximizing the sharp score compared to existing calibration scores and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes. AVAILABILITY AND IMPLEMENTATION: The R package sharp (version ≥1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.

Cardiometabolic and renal phenotypes and transitions in the United States population.

Cardiovascular and renal conditions have both shared and distinct determinants. In this study, we applied unsupervised clustering to multiple rounds of the National Health and Nutrition Examination Survey from 1988 to 2018, and identified 10 cardiometabolic and renal phenotypes. These included a 'low risk' phenotype; two groups with average risk factor levels but different heights; one group with low body-mass index and high levels of high-density lipoprotein cholesterol; five phenotypes with high levels of one or two related risk factors ('high heart rate', 'high cholesterol', 'high blood pressure', 'severe obesity' and 'severe hyperglycemia'); and one phenotype with low diastolic blood pressure (DBP) and low estimated glomerular filtration rate (eGFR). Prevalence of the 'high blood pressure' and 'high cholesterol' phenotypes decreased over time, contrasted by a rise in the 'severe obesity' and 'low DBP, low eGFR' phenotypes. The cardiometabolic and renal traits of the US population have shifted from phenotypes with high blood pressure and cholesterol toward poor kidney function, hyperglycemia and severe obesity.

Variational Bayes for high-dimensional proportional hazards models with applications within gene expression.

MOTIVATION: Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a posteriori estimates, or quantify the uncertainty at high (unscalable) computational expense. RESULTS: We bridge this gap and develop an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as sparse variational Bayes. Our method, based on a mean-field variational approximation, overcomes the high computational cost of Markov chain Monte Carlo, whilst retaining useful features, providing a posterior distribution for the parameters and offering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, and how the uncertainty quantification offered by our method can be used to provide an interpretable assessment of patient risk. AVAILABILITY AND IMPLEMENTATION: our method has been implemented as a freely available R package survival.svb (https://github.com/mkomod/survival.svb). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian optimization framework employing Gaussian process or machine learning emulator functions to calibrate a complex malaria transmission simulator. We demonstrate our approach by optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Our approach quickly outperforms previous calibrations, yielding an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability.

Atopic dermatitis or eczema? Consequences of ambiguity in disease name for biomedical literature mining.

BACKGROUND: Biomedical research increasingly relies on computational approaches to extract relevant information from large corpora of publications. OBJECTIVE: To investigate the consequence of the ambiguity between the use of terms "Eczema" and "Atopic Dermatitis" (AD) from the Information Retrieval perspective, and its impact on meta-analyses, systematic reviews and text mining. METHODS: Articles were retrieved by querying the PubMed using terms 'eczema' (D003876) and "dermatitis, atopic" (D004485). We used machine learning to investigate the differences between the contexts in which each term is used. We used a decision tree approach and trained model to predict if an article would be indexed with eczema or AD tags. We used text-mining tools to extract biological entities associated with eczema and AD, and investigated the discrepancy regarding the retrieval of key findings according to the terminology used. RESULTS: Atopic dermatitis query yielded more articles related to veterinary science, biochemistry, cellular and molecular biology; the eczema query linked to public health, infectious disease and respiratory system. Medical Subject Headings terms associated with "AD" or "Eczema" differed, with an agreement between the top 40 lists of 52%. The presence of terms related to cellular mechanisms, especially allergies and inflammation, characterized AD literature. The metabolites mentioned more frequently than expected in articles with AD tag differed from those indexed with eczema. Fewer enriched genes were retrieved when using eczema compared to AD query. CONCLUSIONS AND CLINICAL RELEVANCE: There is a considerable discrepancy when using text mining to extract bio-entities related to eczema or AD. Our results suggest that any systematic approach (particularly when looking for metabolites or genes related to the condition) should be performed using both terms jointly. We propose to use decision tree learning as a tool to spot and characterize ambiguity, and provide the source code for disambiguation at https://github.com/cfrainay/ResearchCodeBase.

Age groups that sustain resurging COVID-19 epidemics in the United States.

After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the United States and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20 to 49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one and that at least 65 of 100 COVID-19 infections originate from individuals aged 20 to 49 in the United States. Targeting interventions-including transmission-blocking vaccines-to adults aged 20 to 49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.

State-level tracking of COVID-19 in the United States.

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders.

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict "healthy" brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.

Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma.

BACKGROUND: Allergic sensitization is associated with severe asthma, but assessment of sensitization is not recommended by most guidelines. OBJECTIVE: We hypothesized that patterns of IgE responses to multiple allergenic proteins differ between sensitized participants with mild/moderate and severe asthma. METHODS: IgE to 112 allergenic molecules (components, c-sIgE) was measured using multiplex array among 509 adults and 140 school-age and 131 preschool children with asthma/wheeze from the Unbiased BIOmarkers for the PREDiction of respiratory diseases outcomes cohort, of whom 595 had severe disease. We applied clustering methods to identify co-occurrence patterns of components (component clusters) and patterns of sensitization among participants (sensitization clusters). Network analysis techniques explored the connectivity structure of c-sIgE, and differential network analysis looked for differences in c-sIgE interactions between severe and mild/moderate asthma. RESULTS: Four sensitization clusters were identified, but with no difference between disease severity groups. Similarly, component clusters were not associated with asthma severity. None of the c-sIgE were identified as associates of severe asthma. The key difference between school children and adults with mild/moderate compared with those with severe asthma was in the network of connections between c-sIgE. Participants with severe asthma had higher connectivity among components, but these connections were weaker. The mild/moderate network had fewer connections, but the connections were stronger. Connectivity between components with no structural homology tended to co-occur among participants with severe asthma. Results were independent from the different sample sizes of mild/moderate and severe groups. CONCLUSIONS: The patterns of interactions between IgE to multiple allergenic proteins are predictors of asthma severity among school children and adults with allergic asthma.

Inference of Multisite Phosphorylation Rate Constants and Their Modulation by Pathogenic Mutations.

Multisite protein phosphorylation plays a critical role in cell regulation [1-3]. It is widely appreciated that the functional capabilities of multisite phosphorylation depend on the order and kinetics of phosphorylation steps, but kinetic aspects of multisite phosphorylation remain poorly understood [4-6]. Here, we focus on what appears to be the simplest scenario, when a protein is phosphorylated on only two sites in a strict, well-defined order. This scenario describes the activation of ERK, a highly conserved cell-signaling enzyme. We use Bayesian parameter inference in a structurally identifiable kinetic model to dissect dual phosphorylation of ERK by MEK, a kinase that is mutated in a large number of human diseases [7-12]. Our results reveal how enzyme processivity and efficiencies of individual phosphorylation steps are altered by pathogenic mutations. The presented approach, which connects specific mutations to kinetic parameters of multisite phosphorylation mechanisms, provides a systematic framework for closing the gap between studies with purified enzymes and their effects in the living organism.

Blood Biomarkers of Sensitization and Asthma.

Biomarkers are essential to determine different phenotypes of childhood asthma, and for the prediction of response to treatments. In young preschool children with asthma, aeroallergen sensitization, and blood eosinophil count of 300/µL or greater may identify those who can benefit from the daily use of inhaled corticosteroids (ICS). We propose that every preschool child who is considered for ICS treatment should have these two features measured as a minimum before a decision is made on the commencement of long-term preventive treatment. In practice, IgE-mediated sensitization should be considered as a quantifiable variable, i.e., we should use the titer of sIgE antibodies or the size of skin prick test response. A number of other blood biomarkers may prove useful (e.g., allergen-specific IgG/IgE antibody ratios amongst sensitized individuals, component-resolved diagnostics which measures sIgE response to a large number of allergenic molecules, assessment of immune responses to viruses, level of serum CC16, etc.), but it remains unclear whether these can be translated into clinically useful tests. Going forward, a more integrated approach which takes into account multiple domains of asthma, from the pattern of symptoms and blood biomarkers to genetic risk and lung function measures, is needed if we are to move toward a stratified approach to asthma management.

An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling.

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.

Chronic TNFα-driven injury delays cell migration to villi in the intestinal epithelium.

The intestinal epithelium is a single layer of cells which provides the first line of defence of the intestinal mucosa to bacterial infection. Cohesion of this physical barrier is supported by renewal of epithelial stem cells, residing in invaginations called crypts, and by crypt cell migration onto protrusions called villi; dysregulation of such mechanisms may render the gut susceptible to chronic inflammation. The impact that excessive or misplaced epithelial cell death may have on villus cell migration is currently unknown. We integrated cell-tracking methods with computational models to determine how epithelial homeostasis is affected by acute and chronic TNFα-driven epithelial cell death. Parameter inference reveals that acute inflammatory cell death has a transient effect on epithelial cell dynamics, whereas cell death caused by chronic elevated TNFα causes a delay in the accumulation of labelled cells onto the villus compared to the control. Such a delay may be reproduced by using a cell-based model to simulate the dynamics of each cell in a crypt-villus geometry, showing that a prolonged increase in cell death slows the migration of cells from the crypt to the villus. This investigation highlights which injuries (acute or chronic) may be regenerated and which cause disruption of healthy epithelial homeostasis.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support.

Motivation: Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. Results: PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. Availability and implementation: https://github.com/MichaelPHStumpf/Peitho. Contact: m.stumpf@imperial.ac.uk or juliane.liepe@mpibpc.mpg.de.

Nanog Fluctuations in Embryonic Stem Cells Highlight the Problem of Measurement in Cell Biology.

A number of important pluripotency regulators, including the transcription factor Nanog, are observed to fluctuate stochastically in individual embryonic stem cells. By transiently priming cells for commitment to different lineages, these fluctuations are thought to be important to the maintenance of, and exit from, pluripotency. However, because temporal changes in intracellular protein abundances cannot be measured directly in live cells, fluctuations are typically assessed using genetically engineered reporter cell lines that produce a fluorescent signal as a proxy for protein expression. Here, using a combination of mathematical modeling and experiment, we show that there are unforeseen ways in which widely used reporter strategies can systematically disturb the dynamics they are intended to monitor, sometimes giving profoundly misleading results. In the case of Nanog, we show how genetic reporters can compromise the behavior of important pluripotency-sustaining positive feedback loops, and induce a bifurcation in the underlying dynamics that gives rise to heterogeneous Nanog expression patterns in reporter cell lines that are not representative of the wild-type. These findings help explain the range of published observations of Nanog variability and highlight the problem of measurement in live cells.

The nature and nurture of cell heterogeneity: accounting for macrophage gene-environment interactions with single-cell RNA-Seq.

BACKGROUND: Single-cell RNA-Seq can be a valuable and unbiased tool to dissect cellular heterogeneity, despite the transcriptome's limitations in describing higher functional phenotypes and protein events. Perhaps the most important shortfall with transcriptomic 'snapshots' of cell populations is that they risk being descriptive, only cataloging heterogeneity at one point in time, and without microenvironmental context. Studying the genetic ('nature') and environmental ('nurture') modifiers of heterogeneity, and how cell population dynamics unfold over time in response to these modifiers is key when studying highly plastic cells such as macrophages. RESULTS: We introduce the programmable Polaris™ microfluidic lab-on-chip for single-cell sequencing, which performs live-cell imaging while controlling for the culture microenvironment of each cell. Using gene-edited macrophages we demonstrate how previously unappreciated knockout effects of SAMHD1, such as an altered oxidative stress response, have a large paracrine signaling component. Furthermore, we demonstrate single-cell pathway enrichments for cell cycle arrest and APOBEC3G degradation, both associated with the oxidative stress response and altered proteostasis. Interestingly, SAMHD1 and APOBEC3G are both HIV-1 inhibitors ('restriction factors'), with no known co-regulation. CONCLUSION: As single-cell methods continue to mature, so will the ability to move beyond simple 'snapshots' of cell populations towards studying the determinants of population dynamics. By combining single-cell culture, live-cell imaging, and single-cell sequencing, we have demonstrated the ability to study cell phenotypes and microenvironmental influences. It's these microenvironmental components - ignored by standard single-cell workflows - that likely determine how macrophages, for example, react to inflammation and form treatment resistant HIV reservoirs.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures.

In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.