scCODA is a Bayesian model for compositional single-cell data analysis.

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.

Determinants of SARS-CoV-2 receptor gene expression in upper and lower airways.

The recent outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has led to a worldwide pandemic. One week after initial symptoms develop, a subset of patients progresses to severe disease, with high mortality and limited treatment options. To design novel interventions aimed at preventing spread of the virus and reducing progression to severe disease, detailed knowledge of the cell types and regulating factors driving cellular entry is urgently needed. Here we assess the expression patterns in genes required for COVID-19 entry into cells and replication, and their regulation by genetic, epigenetic and environmental factors, throughout the respiratory tract using samples collected from the upper (nasal) and lower airways (bronchi). Matched samples from the upper and lower airways show a clear increased expression of these genes in the nose compared to the bronchi and parenchyma. Cellular deconvolution indicates a clear association of these genes with the proportion of secretory epithelial cells. Smoking status was found to increase the majority of COVID-19 related genes including ACE2 and TMPRSS2 but only in the lower airways, which was associated with a significant increase in the predicted proportion of goblet cells in bronchial samples of current smokers. Both acute and second hand smoke were found to increase ACE2 expression in the bronchus. Inhaled corticosteroids decrease ACE2 expression in the lower airways. No significant effect of genetics on ACE2 expression was observed, but a strong association of DNA- methylation with ACE2 and TMPRSS2- mRNA expression was identified in the bronchus.

The single-cell eQTLGen consortium.

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

IL-17C amplifies epithelial inflammation in human psoriasis and atopic eczema.

BACKGROUND: Key pathogenic events of psoriasis and atopic eczema (AE) are misguided immune reactions of the skin. IL-17C is an epithelial-derived cytokine, whose impact on skin inflammation is unclear. OBJECTIVE: We sought to characterize the role of IL-17C in human ISD. METHODS: IL-17C gene and protein expression was assessed by immunohistochemistry and transcriptome analysis. Primary human keratinocytes were stimulated and expression of cytokines chemokines was determined by qRT-PCR and luminex assay. Neutrophil migration towards supernatant of stimulated keratinocytes was assessed. IL-17C was depleted using a new IL-17C-specific antibody (MOR106) in murine models of psoriasis (IL-23 injection model) and AE (MC903 model) as well as in human skin biopsies of psoriasis and AE. Effects on cell influx (mouse models) and gene expression (human explant cultures) were determined. RESULTS: Expression of IL-17C mRNA and protein was elevated in various ISD. We demonstrate that IL-17C potentiates the expression of innate cytokines, antimicrobial peptides (IL-36G, S100A7 and HBD2) and chemokines (CXCL8, CXCL10, CCL5 and VEGF) and the autocrine induction of IL-17C in keratinocytes. Cell-free supernatant of keratinocytes stimulated with IL-17C was strongly chemotactic for neutrophils, thus demonstrating a critical role for IL-17C in immune cell recruitment. IL-17C depletion significantly reduced cell numbers of T cells, neutrophils and eosinophils in murine models of psoriasis and AE and led to a significant downregulation of inflammatory mediators in human skin biopsies of psoriasis and AE ex vivo. CONCLUSION: IL-17C amplifies epithelial inflammation in Th2 and Th17 dominated skin inflammation and represents a promising target for the treatment of ISD.

Common patterns of gene regulation associated with Cesarean section and the development of islet autoimmunity - indications of immune cell activation.

Birth by Cesarean section increases the risk of developing type 1 diabetes later in life. We aimed to elucidate common regulatory processes observed after Cesarean section and the development of islet autoimmunity, which precedes type 1 diabetes, by investigating the transcriptome of blood cells in the developing immune system. To investigate Cesarean section effects, we analyzed longitudinal gene expression profiles from peripheral blood mononuclear cells taken at several time points from children with increased familial and genetic risk for type 1 diabetes. For islet autoimmunity, we compared gene expression differences between children after initiation of islet autoimmunity and age-matched children who did not develop islet autoantibodies. Finally, we compared both results to identify common regulatory patterns. We identified the pentose phosphate pathway and pyrimidine metabolism - both involved in nucleotide synthesis and cell proliferation - to be differentially expressed in children born by Cesarean section and after islet autoimmunity. Comparison of global gene expression signatures showed that transcriptomic changes were systematically and significantly correlated between Cesarean section and islet autoimmunity. Moreover, signatures of both Cesarean section and islet autoimmunity correlated with transcriptional changes observed during activation of isolated CD4+ T lymphocytes. In conclusion, we identified shared molecular changes relating to immune cell activation in children born by Cesarean section and children who developed autoimmunity. Our results serve as a starting point for further investigations on how a type 1 diabetes risk factor impacts the young immune system at a molecular level.

Is the humoral immunity dispensable for the pathogenesis of psoriasis?

BACKGROUND: Imbalances of T-cell subsets are hallmarks of disease-specific inflammation in psoriasis. However, the relevance of B cells for psoriasis remains poorly investigated. OBJECTIVE: To analyse the role of B cells and immunoglobulins for the disease-specific immunology of psoriasis. METHODS: We characterized B-cell subsets and immunoglobulin levels in untreated psoriasis patients (n = 37) and compared them to healthy controls (n = 20) as well as to psoriasis patients under disease-controlling systemic treatment (n = 28). B-cell subsets were analysed following the flow cytometric gating strategy based on the surface markers CD24, CD38 and CD138. Moreover, immunofluorescence stainings were used to detect IgA in psoriatic skin. RESULTS: We found significantly increased levels of IgA in the serum of treatment-naïve psoriasis patients correlating with disease score. However, IgA was only observed in dermal vessels of skin sections. Concerning B-cell subsets, we only found a moderately positive correlation of CD138+ plasma cells with IgA levels and disease score in treatment-naïve psoriasis patients. Confirming our hypothesis that psoriasis can develop in the absence of functional humoral immunity, we investigated a patient who suffered concomitantly from both psoriasis and a hereditary common variable immune defect (CVID) characterized by a lack of B cells and immunoglobulins. We detected variants in three of the 13 described genes of CVID and a so far undescribed variant in the ligand of the TNFRSF13B receptor leading to disturbed B-cell maturation and antibody production. However, this patient showed typical psoriasis regarding clinical presentation, histology or T-cell infiltrate. Finally, in a group of psoriasis patients under systemic treatment, neither did IgA levels drop nor did plasma cells correlate with IgA levels and disease score. CONCLUSION: B-cell alterations might rather be an epiphenomenal finding in psoriasis with a clear dominance of T cells over shifts in B-cell subsets.

MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data.

Background: With the advent of the age of big data in bioinformatics, large volumes of data and high-performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts; however, its generic nature also enables the detection of microbial and viral transcripts. Findings: We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from six independent, controlled infection experiments of cell line models and compared them with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from more than 17,000 samples from more than 400 studies relevant to human disease using state-of-the-art high-performance computing systems. The resulting data from this large-scale re-analysis are made available in the presented MetaMap resource. Conclusions: Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation toward the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available.

Identification of a plasma miRNA biomarker signature for allergic asthma: A translational approach.

BACKGROUND: Asthma is a heterogeneous chronic disease with different phenotypes and treatment responses. Thus, there is a high clinical need for molecular disease biomarkers to aid in differentiating these distinct phenotypes. As MicroRNAs (miRNAs), that regulate gene expression at the post-transcriptional level, are altered in experimental and human asthma, circulating miRNAs are attractive candidates for the identification of novel biomarkers. This study aimed to identify plasmatic miRNA-based biomarkers of asthma, through a translational approach. METHODS: We prescreened miRNAs in plasma samples from two different murine models of experimental asthma (ovalbumin and house dust mite); miRNAs deregulated in both models were further tested in a human training cohort of 20 asthma patients and 9 healthy controls. Candidate miRNAs were then validated in a second, independent group of 26 asthma patients and 12 healthy controls. RESULTS: Ten miRNA ratios consisting of 13 miRNAs were differentially regulated in both murine models. Measuring these miRNAs in the training cohort identified a biomarker signature consisting of five miRNA ratios (7 miRNAs). This signature showed a good sensitivity and specificity in the test cohort with an area under the receiver operating characteristic curve (AUC) of 0.92. Correlation of miRNA ratios with clinical characteristics further revealed associations with FVC % predicted, and oral corticosteroid or antileukotriene use. CONCLUSION: Distinct plasma miRNAs are differentially regulated both in murine and in human allergic asthma and were associated with clinical characteristics of patients. Thus, we suggest that miRNA levels in plasma might have future potential to subphenotype patients with asthma.

Interleukin-4 and interferon-γ orchestrate an epithelial polarization in the airways.

Interferon-γ (IFN-γ) and interleukin-4 (IL-4) are key effector cytokines for the differentiation of T helper type 1 and 2 (Th1 and Th2) cells. Both cytokines induce fate-decisive transcription factors such as GATA3 and TBX21 that antagonize the polarized development of opposite phenotypes by direct regulation of each other's expression along with many other target genes. Although it is well established that mesenchymal cells directly respond to Th1 and Th2 cytokines, the nature of antagonistic differentiation programs in airway epithelial cells is only partially understood. In this study, primary normal human bronchial epithelial cells (NHBEs) were exposed to IL-4, IFN-γ, or both and genome-wide transcriptome analysis was performed. The study uncovers an antagonistic regulation pattern of IL-4 and IFN-γ in NHBEs, translating the Th1/Th2 antagonism directly in epithelial gene regulation. IL-4- and IFN-γ-induced transcription factor hubs form clusters, present in antagonistically and polarized gene regulation networks. Furthermore, the IL-4-dependent induction of IL-24 observed in rhinitis patients was downregulated by IFN-γ, and therefore IL-24 represents a potential biomarker of allergic inflammation and a Th2 polarized condition of the epithelium.

Method of conditional moments (MCM) for the Chemical Master Equation: a unified framework for the method of moments and hybrid stochastic-deterministic models.

The time-evolution of continuous-time discrete-state biochemical processes is governed by the Chemical Master Equation (CME), which describes the probability of the molecular counts of each chemical species. As the corresponding number of discrete states is, for most processes, large, a direct numerical simulation of the CME is in general infeasible. In this paper we introduce the method of conditional moments (MCM), a novel approximation method for the solution of the CME. The MCM employs a discrete stochastic description for low-copy number species and a moment-based description for medium/high-copy number species. The moments of the medium/high-copy number species are conditioned on the state of the low abundance species, which allows us to capture complex correlation structures arising, e.g., for multi-attractor and oscillatory systems. We prove that the MCM provides a generalization of previous approximations of the CME based on hybrid modeling and moment-based methods. Furthermore, it improves upon these existing methods, as we illustrate using a model for the dynamics of stochastic single-gene expression. This application example shows that due to the more general structure, the MCM allows for the approximation of multi-modal distributions.

High-dimensional Bayesian parameter estimation: case study for a model of JAK2/STAT5 signaling.

In this work we present results of a detailed Bayesian parameter estimation for an analysis of ordinary differential equation models. These depend on many unknown parameters that have to be inferred from experimental data. The statistical inference in a high-dimensional parameter space is however conceptually and computationally challenging. To ensure rigorous assessment of model and prediction uncertainties we take advantage of both a profile posterior approach and Markov chain Monte Carlo sampling. We analyzed a dynamical model of the JAK2/STAT5 signal transduction pathway that contains more than one hundred parameters. Using the profile posterior we found that the corresponding posterior distribution is bimodal. To guarantee efficient mixing in the presence of multimodal posterior distributions we applied a multi-chain sampling approach. The Bayesian parameter estimation enables the assessment of prediction uncertainties and the design of additional experiments that enhance the explanatory power of the model. This study represents a proof of principle that detailed statistical analysis for quantitative dynamical modeling used in systems biology is feasible also in high-dimensional parameter spaces.

Integrative genetic and metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma.

BACKGROUND: Genome-wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites. METHODS: We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population-based KORA F4 study (N = 2925). In addition, we analysed effects of single-nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites. RESULTS: Increased levels of various phosphatidylcholines and decreased levels of various lyso-phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions. CONCLUSIONS: Our study demonstrated the potential of metabolomics to infer asthma-related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.

A strategy for combining minor genetic susceptibility genes to improve prediction of disease in type 1 diabetes.

Genome-wide association studies have identified gene regions associated with type 1 diabetes. The aim of this study was to determine how the combined allele frequency of multiple susceptibility genes can stratify islet autoimmunity and/or type 1 diabetes risk. Children of parents with type 1 diabetes and prospectively followed from birth for the development of islet autoantibodies and diabetes were genotyped for single-nucleotide polymorphisms at 12 type 1 diabetes susceptibility genes (ERBB3, PTPN2, IFIH1, PTPN22, KIAA0350, CD25, CTLA4, SH2B3, IL2, IL18RAP, IL10 and COBL). Non-human leukocyte antigen (HLA) risk score was defined by the total number of risk alleles at these genes. Receiver operator curve analysis showed that the non-HLA gene combinations were highly effective in discriminating diabetes and most effective in children with a high-risk HLA genotype. The greatest diabetes discrimination was obtained by the sum of risk alleles for eight genes (IFIH1, CTLA4, PTPN22, IL18RAP, SH2B3, KIAA0350, COBL and ERBB3) in the HLA-risk children. Non-HLA-risk allele scores stratified risk for developing islet autoantibodies and diabetes, and progression from islet autoimmunity to diabetes. Genotyping at multiple susceptibility loci in children from affected families can identify neonates with sufficient genetic risk of type 1 diabetes to be considered for early intervention.

Knowledge-based gene expression classification via matrix factorization.

MOTIVATION: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks. RESULTS: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.

How to extract marker genes from microarray data sets.

In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization (NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.

On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation.

In this paper, an automatic assignment tool, called BSS-AutoAssign, for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY) spectroscopy signals of proteins dissolved in water.