Asthmatics with exacerbation during acute respiratory illness exhibit unique transcriptional signatures within the nasal mucosa

Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Multiarray screening identifies plasma proteins associated with Th17 cell differentiation and viral defense in coincident asthma and obesity

BACKGROUND

The immunological mechanisms behind the clinical association between asthma and obesity in adolescence are not fully understood. This study aimed to find new plasma protein biomarkers associated specifically with coincident asthma and obesity in adolescents.

METHODS

This was a cross-sectional study in children and adolescents 10-19 years old (N = 390). Relative plasma concentrations of 113 protein biomarkers related to inflammation and immune response were determined by proximity extension assay (Target 96; Olink, Uppsala, Sweden). Differences in protein concentrations between healthy controls (n = 84), subjects with asthma (n = 138), subjects with obesity (n = 107), and subjects with both asthma and obesity (AO; n = 58) were analyzed by ANCOVA, adjusting for age and sex, and in a separate model adjusting also for the sum of specific IgE antibody concentrations to a mix of food allergens (fx5) and aeroallergens (Phadiatop). Proteins elevated in the AO group but not in the obesity or asthma groups were considered specifically elevated in asthma and obesity.

RESULTS

Five proteins were elevated specifically in the AO group compared to controls (here sorted from largest to smallest effect of asthma and obesity combined): CCL8, IL-33, IL-17C, FGF-23, and CLEC7A. The effects of adjusting also for specific IgE were small but IL-33, IL-17C, and FGF-23 were no longer statistically significant.

CONCLUSION

We identified several new potential plasma biomarkers specifically elevated in coincident asthma and obesity in adolescents. Four of the proteins, CCL8, IL-33, IL-17C, and CLEC7A, have previously been associated with viral mucosal host defense and Th17 cell differentiation.

Orai1/STIMs modulators in pulmonary vascular diseases

Calcium (Ca2+) is a secondary messenger that regulates various cellular processes. However, Ca2+ mishandling could lead to pathological conditions. Orai1 is a Ca2+channel contributing to the store-operated calcium entry (SOCE) and plays a critical role in Ca2+ homeostasis in several cell types. Dysregulation of Orai1 contributed to severe combined immune deficiency syndrome, some cancers, pulmonary arterial hypertension (PAH), and other cardiorespiratory diseases. During its activation process, Orai1 is mainly regulated by stromal interacting molecule (STIM) proteins, especially STIM1; however, many other regulatory partners have also been recently described. Increasing knowledge about these regulatory partners provides a better view of the downstream signalling pathways of SOCE and offers an excellent opportunity to decipher Orai1 dysregulation in these diseases. These proteins participate in other cellular functions, making them attractive therapeutic targets. This review mainly focuses on Orai1 regulatory partners in the physiological and pathological conditions of the pulmonary circulation and inflammation.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1-8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address this significant gap, we present PhyloFrame, the first-ever machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating big data tissue-specific functional interaction networks, global population variation data, and disease-relevant transcriptomic data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. These results demonstrate how AI can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Identification of long non-coding RNA using single nucleotide epimutation analysis: a novel gene discovery approach

BACKGROUND

Long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms related to tumorigenesis by functioning as oncogenes or tumor-suppressors or even harboring oncogenic and tumor-suppressing effects; representing a new class of cancer biomarkers and therapeutic targets. It is predicted that more than 35,000 ncRNA especially lncRNA are positioned at the intergenic regions of the human genome. Emerging research indicates that one of the key pathways controlling lncRNA expression and tissue specificity is epigenetic regulation.

METHODS

In the current article, a novel approach for lncRNA discovery based on the intergenic position of most lncRNAs and a single CpG site methylation level representing epigenetic characteristics has been suggested.

RESULTS

Using this method, a novel antisense lncRNA named LINC02892 presenting three transcripts without the capacity of coding a protein was found exhibiting nuclear, cytoplasmic, and exosome distributions.

CONCLUSION

The current discovery strategy could be applied to identify novel non-coding RNAs influenced by methylation aberrations.

PerSVade: personalized structural variant detection in any species of interest

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

Artificial intelligence-based decision support model for new drug development planning

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering applying a random forest classification algorithm, the accuracy and area under curve were 78% and 0.74, respectively. In particular, the DDR model was applied to predict the success probability of companies developing Coronavirus disease 2019 (COVID-19) vaccines. It was demonstrated that the higher the predicted score from the DDR model, the more progress in the clinical phase of the COVID-19 vaccine development. Although our approach has limitations that should be improved, it makes scientific as well as industrial contributions in that the DDR model can support rational decision-making prior to initiating drug development by considering not only technical aspects but also company-related variables.

Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

SARS-CoV-2, the causal agent of COVID-19, is primarily a pulmonary virus that can directly or indirectly infect several organs. Despite many studies carried out during the current COVID-19 pandemic, some pathological features of SARS-CoV-2 have remained unclear. It has been recently attempted to address the current knowledge gaps on the viral pathogenicity and pathological mechanisms via cellular-level tropism of SARS-CoV-2 using human proteomics, visualization of virus-host protein-protein interactions (PPIs), and enrichment analysis of experimental results. The synergistic use of models and methods that rely on graph theory has enabled the visualization and analysis of the molecular context of virus/host PPIs. We review current knowledge on the SARS-COV-2/host interactome cascade involved in the viral pathogenicity through the graph theory concept and highlight the hub proteins in the intra-viral network that create a subnet with a small number of host central proteins, leading to cell disintegration and infectivity. Then we discuss the putative principle of the "gene-for-gene and "network for network" concepts as platforms for future directions toward designing efficient anti-viral therapies.

Dysregulated Notch Signaling in the Airway Epithelium of Children with Wheeze

The airway epithelium of children with wheeze is characterized by defective repair that contributes to disease pathobiology. Dysregulation of developmental processes controlled by Notch has been identified in chronic asthma. However, its role in airway epithelial cells of young children with wheeze, particularly during repair, is yet to be determined. We hypothesized that Notch is dysregulated in primary airway epithelial cells (pAEC) of children with wheeze contributing to defective repair. This study investigated transcriptional and protein expression and function of Notch in pAEC isolated from children with and without wheeze. Primary AEC of children with and without wheeze were found to express all known Notch receptors and ligands, although pAEC from children with wheeze expressed significantly lower NOTCH2 (10-fold, p = 0.004) and higher JAG1 (3.5-fold, p = 0.002) mRNA levels. These dysregulations were maintained in vitro and cultures from children with wheeze displayed altered kinetics of both NOTCH2 and JAG1 expression during repair. Following Notch signaling inhibition, pAEC from children without wheeze failed to repair (wound closure rate of 76.9 ± 3.2%). Overexpression of NOTCH2 in pAEC from children with wheeze failed to rescue epithelial repair following wounding. This study illustrates the involvement of the Notch pathway in airway epithelial wound repair in health and disease, where its dysregulation may contribute to asthma development.

A small molecule produced by Lactobacillus species blocks Candida albicans filamentation by inhibiting a DYRK1-family kinase

The fungus Candida albicans is an opportunistic pathogen that can exploit imbalances in microbiome composition to invade its human host, causing pathologies ranging from vaginal candidiasis to fungal sepsis. Bacteria of the genus Lactobacillus are colonizers of human mucosa and can produce compounds with bioactivity against C. albicans. Here, we show that some Lactobacillus species produce a small molecule under laboratory conditions that blocks the C. albicans yeast-to-filament transition, an important virulence trait. It remains unexplored whether the compound is produced in the context of the human host. Bioassay-guided fractionation of Lactobacillus-conditioned medium linked this activity to 1-acetyl-β-carboline (1-ABC). We use genetic approaches to show that filamentation inhibition by 1-ABC requires Yak1, a DYRK1-family kinase. Additional biochemical characterization of structurally related 1-ethoxycarbonyl-β-carboline confirms that it inhibits Yak1 and blocks C. albicans biofilm formation. Thus, our findings reveal Lactobacillus-produced 1-ABC can prevent the yeast-to-filament transition in C. albicans through inhibition of Yak1.

Plant growth-promoting microbes — an industry view

Plant growth-promoting microbes can affect the plant microbiome, improving different properties of the plant such as yield and health. Many companies are commercializing these microbes as products called biologicals. Defining the product concept is one of the first and most important steps in making a biological product. Companies can use phenotyping and genotyping approaches to identify the microbe to make into a live bacterial product. Screening usually begins in the laboratory and often moves from high-throughput methods to more time and resource-intensive methods culminating in large scale field testing. Once the microbe is chosen, the fermentation process grows the bacteria to the necessary amounts, while the formulation process ensures a stable product in the desired form such as a liquid or powder. The products must show yield increases in the field over several seasons and conditions, but also must be easy to use and cost-effective to be adopted by farmers and other customers. Tying all these data together from the selection process to test results gives a customer a ‘reason to believe’ for the marketing and launch of a successful product.

Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis

Diagnostic errors are common and can lead to harmful treatments. We present a data-driven, generic approach for identifying patients at risk of being mis- or overdiagnosed, here exemplified by chronic obstructive pulmonary disease (COPD). It has been estimated that 5-60% of all COPD cases are misdiagnosed. High-throughput methods are therefore needed in this domain. We have used a national patient registry, which contains hospital diagnoses for 6.9 million patients across the entire Danish population for 21 years and identified statistically significant disease trajectories for COPD patients. Using 284,154 patients diagnosed with COPD, we identified frequent disease trajectories comprising time-ordered comorbidities. Interestingly, as many as 42,459 patients did not present with these time-ordered, common comorbidities. Comparison of the individual disease history for each non-follower to the COPD trajectories, demonstrated that 9597 patients were unusual. Survival analysis showed that this group died significantly earlier than COPD patients following a trajectory. Out of the 9597 patients, we identified one subgroup comprising 2185 patients at risk of misdiagnosed COPD without the typical events of COPD patients. In all, 10% of these patients were diagnosed with lung cancer, and it seems likely that they are underdiagnosed for lung cancer as their laboratory test values and survival pattern are similar to such patients. Furthermore, only 4% had a lung function test to confirm the COPD diagnosis. Another subgroup with 2368 patients were found to be at risk of "classically" overdiagnosed COPD that survive >5.5 years after the COPD diagnosis, but without the typical complications of COPD.

Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study

The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients' engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A_1c, estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.

The influence of evolutionary history on human health and disease

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Polygenic scores (PGS) have been widely used to predict disease risk using variants identified from genome-wide association studies (GWAS). To date, most GWAS have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European ancestry populations. Here, we derive a theoretical model of the relative accuracy (RA) of PGS across ancestries. We show through extensive simulations that the RA of PGS based on genome-wide significant SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of causal SNP effects and heritability. We find that LD and MAF differences between ancestries can explain between 70 and 80% of the loss of RA of European-based PGS in African ancestry for traits like body mass index and type 2 diabetes. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWAS are mostly shared across continents.

Bioinformatics Analysis and Identification of Underlying Biomarkers Potentially Linking Allergic Rhinitis and Asthma

Background

Rhinitis is the most common clinical manifestation of allergy, affecting more than 400 million people around the world. Rhinitis increases the risk of developing bronchial hyper-responsiveness and asthma. Previous studies have shown that rhinitis is closely related with the physiology, pathology, and pathogenesis of asthma. We analyzed co-expressed genes to explore the relationships between rhinitis and asthma and to find biomarkers of comorbid rhinitis and asthma.

Material/Methods

Asthma- and rhinitis-related differentially-expressed genes (DEGs) were identified by bioinformatic analysis of GSE104468 and GSE46171 datasets from the Gene Expression Omnibus (GEO) database. After assessment of Gene Ontology (GO) terms and pathway enrichment for DEGs, a protein–protein interaction (PPI) network was conducted via comprehensive target prediction and network analyses. We also evaluated co-expressed DEGs and corresponding predicted miRNAs involved in the developing process of rhinitis and asthma.

Results

We identified 687 and 1001 DEGs in bronchial and nasal epithelia samples of asthma patients, respectively. For patients with rhinitis, we found 245 DEGs. The hub-genes of PAX6, NMU, NTS, NMUR1, PMCH, and KRT6A may be associated with rhinitis, while CPA3, CTSG, POSTN, CLCA1, HDC, and MUC5B may be involved in asthma. The co-expressed DEGs of BPIFA1, CCL26, CPA3, and CST1, together with corresponding predicted miRNAs (e.g., miR-195-5p and miR-125a-3p) were found to be significantly correlated with rhinitis and asthma.

Conclusions

Rhinitis and asthma are related, and there are significant correlations of BPIFA1, CCL26, CPA3, and CST1 genes with novel biomarkers involved in the comorbidity of rhinitis and asthma.

Aberrant cell migration contributes to defective airway epithelial repair in childhood wheeze

Abnormal wound repair has been observed in the airway epithelium of patients with chronic respiratory diseases, including asthma. Therapies focusing on repairing vulnerable airways, particularly in early life, present a potentially novel treatment strategy. We report defective lower airway epithelial cell repair to strongly associate with common pre-school-aged and school-aged wheezing phenotypes, characterized by aberrant migration patterns and reduced integrin α5β1 expression. Next generation sequencing identified the PI3K/Akt pathway as the top upstream transcriptional regulator of integrin α5β1, where Akt activation enhanced repair and integrin α5β1 expression in primary cultures from children with wheeze. Conversely, inhibition of PI3K/Akt signaling in primary cultures from children without wheeze reduced α5β1 expression and attenuated repair. Importantly, the FDA-approved drug celecoxib - and its non-COX2-inhibiting analogue, dimethyl-celecoxib - stimulated the PI3K/Akt-integrin α5β1 axis and restored airway epithelial repair in cells from children with wheeze. When compared with published clinical data sets, the identified transcriptomic signature was also associated with viral-induced wheeze exacerbations highlighting the clinical potential of such therapy. Collectively, these results identify airway epithelial restitution via targeting the PI3K-integrin α5β1 axis as a potentially novel therapeutic avenue for childhood wheeze and asthma. We propose that the next step in the therapeutic development process should be a proof-of-concept clinical trial, since relevant animal models to test the crucial underlying premise are unavailable.

Distinct signatures of lung cancer types: aberrant mucin O-glycosylation and compromised immune response

BACKGROUND

Genomic initiatives such as The Cancer Genome Atlas (TCGA) contain data from -omics profiling of thousands of tumor samples, which may be used to decipher cancer signaling, and related alterations. Managing and analyzing data from large-scale projects, such as TCGA, is a demanding task. It is difficult to dissect the high complexity hidden in genomic data and to account for inter-tumor heterogeneity adequately.

METHODS

In this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patients from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC).

RESULTS

We used the gene expression data to identify co-expression modules and differentially expressed genes to discriminate between LUAD and LUSC. We identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, along with two dual role genes. Our results have been validated against other transcriptomics data of lung cancer patients.

CONCLUSIONS

Our integrative approach allowed us to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC might be linked to the activation of three oncogenic pathways, which promote the evasion of the antitumor immune response. Collectively, our results provide new future directions for the design of target therapies in lung cancer.

Gene expression of TMEM178, which encodes a negative regulator of NFATc1, decreases with the progression of asthma severity

In two independent microarray studies involving primary airway epithelial cells, the relative gene expression of TMEM178 decreases with the progression of asthma severity. Our manuscript creates a paradigm for future studies dissecting the role of Tmem178 in the pathogenesis of severe asthma.

A host gene expression approach for identifying triggers of asthma exacerbations

Rationale

Asthma exacerbations often occur due to infectious triggers, but determining whether infection is present and whether it is bacterial or viral remains clinically challenging. A diagnostic strategy that clarifies these uncertainties could enable personalized asthma treatment and mitigate antibiotic overuse.

Objectives

To explore the performance of validated peripheral blood gene expression signatures in discriminating bacterial, viral, and noninfectious triggers in subjects with asthma exacerbations.

Methods

Subjects with suspected asthma exacerbations of various etiologies were retrospectively selected for peripheral blood gene expression analysis from a pool of subjects previously enrolled in emergency departments with acute respiratory illness. RT-PCR quantified 87 gene targets, selected from microarray-based studies, followed by logistic regression modeling to define bacterial, viral, or noninfectious class. The model-predicted class was compared to clinical adjudication and procalcitonin.

Results

Of 46 subjects enrolled, 7 were clinically adjudicated as bacterial, 18 as viral, and 21 as noninfectious. Model prediction was congruent with clinical adjudication in 15/18 viral and 13/21 noninfectious cases, but only 1/7 bacterial cases. None of the adjudicated bacterial cases had confirmatory microbiology; the precise etiology in this group was uncertain. Procalcitonin classified only one subject in the cohort as bacterial. 47.8% of subjects received antibiotics.

Conclusions

Our model classified asthma exacerbations by the underlying bacterial, viral, and noninfectious host response. Compared to clinical adjudication, the majority of discordances occurred in the bacterial group, due to either imperfect adjudication or model misclassification. Bacterial infection was identified infrequently by all classification schemes, but nearly half of subjects were prescribed antibiotics. A gene expression-based approach may offer useful diagnostic information in this population and guide appropriate antibiotic use.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

The challenges of the expanded availability of genomic information: an agenda-setting paper

Rapid advances in microarray and sequencing technologies are making genotyping and genome sequencing more affordable and readily available. There is an expectation that genomic sequencing technologies improve personalized diagnosis and personalized drug therapy. Concurrently, provision of direct-to-consumer genetic testing by commercial providers has enabled individuals' direct access to their genomic data. The expanded availability of genomic data is perceived as influencing the relationship between the various parties involved including healthcare professionals, researchers, patients, individuals, families, industry, and government. This results in a need to revisit their roles and responsibilities. In a 1-day agenda-setting meeting organized by the COST Action IS1303 "Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives," participants discussed the main challenges associated with the expanded availability of genomic information, with a specific focus on public-private partnerships, and provided an outline from which to discuss in detail the identified challenges. This paper summarizes the points raised at this meeting in five main parts and highlights the key cross-cutting themes. In light of the increasing availability of genomic information, it is expected that this paper will provide timely direction for future research and policy making in this area.

Ethical sharing of health data in online platforms - which values should be considered?

Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology (ICT) platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes.With this in mind, the Science and Values Working Group of the COST Action CHIP ME 'Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives' (IS 1303) identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals' trust in research.We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment.

Dual RNA-seq reveals viral infections in asthmatic children without respiratory illness which are associated with changes in the airway transcriptome

Background

Respiratory illness caused by viral infection is associated with the development and exacerbation of childhood asthma. Little is known about the effects of respiratory viral infections in the absence of illness. Using quantitative PCR (qPCR) for common respiratory viruses and for two genes known to be highly upregulated in viral infections (CCL8/CXCL11), we screened 92 asthmatic and 69 healthy children without illness for respiratory virus infections.

Results

We found 21 viral qPCR-positive and 2 suspected virus-infected subjects with high expression of CCL8/CXCL11. We applied a dual RNA-seq workflow to these subjects, together with 25 viral qPCR-negative subjects, to compare qPCR with sequencing-based virus detection and to generate the airway transcriptome for analysis. RNA-seq virus detection achieved 86% sensitivity when compared to qPCR-based screening. We detected additional respiratory viruses in the two CCL8/CXCL11-high subjects and in two of the qPCR-negative subjects. Viral read counts varied widely and were used to stratify subjects into Virus-High and Virus-Low groups. Examination of the host airway transcriptome found that the Virus-High group was characterized by immune cell airway infiltration, downregulation of cilia genes, and dampening of type 2 inflammation. Even the Virus-Low group was differentiated from the No-Virus group by 100 genes, some involved in eIF2 signaling.

Conclusions

Respiratory virus infection without illness is not innocuous but may determine the airway function of these subjects by driving immune cell airway infiltration, cellular remodeling, and alteration of asthmogenic gene expression.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1140-8) contains supplementary material, which is available to authorized users.

Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control

RATIONALE

Maintaining optimal symptom control remains the primary objective of asthma treatment. Better understanding of the biologic underpinnings of asthma control may lead to the development of improved clinical and pharmaceutical approaches.

OBJECTIVES

To identify molecular pathways and interrelated genes whose differential expression was associated with asthma control.

METHODS

We performed gene set enrichment analyses of asthma control in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or unstimulated CD4+ T lymphocytes (CD4), and a self-reported asthma control score representing either the preceding 6 months (chronic) or 7 days (acute). Our study comprised a discovery WB cohort (n = 245, chronic) and three independent, nonoverlapping replication cohorts: a second WB set (n = 448, acute) and two CD4 sets (n = 300, chronic; n = 77, acute).

MEASUREMENTS AND MAIN RESULTS

In the WB discovery cohort, we found significant overrepresentation of genes associated with asthma control in 1,106 gene sets from the Molecular Signatures Database (false discovery rate, <5%). Of these, 583 (53%) replicated in at least one replication cohort (false discovery rate, <25%). Suboptimal control was associated with signatures of eosinophilic and granulocytic inflammatory signals, whereas optimal control signatures were enriched for immature lymphocytic patterns. These signatures included two related biologic processes related to activation by TREM-1 (triggering receptor expressed on myeloid cells 1) and lipopolysaccharide.

CONCLUSIONS

Together, these results demonstrate the existence of specific, reproducible transcriptomic components in blood that vary with degree of asthma control and implicate a novel biologic target (TREM-1).

The pan-cancer pathological regulatory landscape

Dysregulation of the normal gene expression program is the cause of a broad range of diseases, including cancer. Detecting the specific perturbed regulators that have an effect on the generation and the development of the disease is crucial for understanding the disease mechanism and for taking decisions on efficient preventive and curative therapies. Moreover, detecting such perturbations at the patient level is even more important from the perspective of personalized medicine. We applied the Transcription Factor Target Enrichment Analysis, a method that detects the activity of transcription factors based on the quantification of the collective transcriptional activation of their targets, to a large collection of 5607 cancer samples covering eleven cancer types. We produced for the first time a comprehensive catalogue of altered transcription factor activities in cancer, a considerable number of them significantly associated to patient’s survival. Moreover, we described several interesting TFs whose activity do not change substantially in the cancer with respect to the normal tissue but ultimately play an important role in patient prognostic determination, which suggest they might be promising therapeutic targets. An additional advantage of this method is that it allows obtaining personalized TF activity estimations for individual patients.

RNA sequencing analysis of human podocytes reveals glucocorticoid regulated gene networks targeting non-immune pathways

Glucocorticoids are steroids that reduce inflammation and are used as immunosuppressive drugs for many diseases. They are also the mainstay for the treatment of minimal change nephropathy (MCN), which is characterised by an absence of inflammation. Their mechanisms of action remain elusive. Evidence suggests that immunomodulatory drugs can directly act on glomerular epithelial cells or ‘podocytes’, the cell type which is the main target of injury in MCN. To understand the nature of glucocorticoid effects on non-immune cell functions, we generated RNA sequencing data from human podocyte cell lines and identified the genes that are significantly regulated in dexamethasone-treated podocytes compared to vehicle-treated cells. The upregulated genes are of functional relevance to cytoskeleton-related processes, whereas the downregulated genes mostly encode pro-inflammatory cytokines and growth factors. We observed a tendency for dexamethasone-upregulated genes to be downregulated in MCN patients. Integrative analysis revealed gene networks composed of critical signaling pathways that are likely targeted by dexamethasone in podocytes.

Airway molecular endotypes of asthma: dissecting the heterogeneity

PURPOSE OF REVIEW

This review will cover advances over the past year in defining airway endotypes in asthma by gene expression and the relationship between these endotypes and clinical traits.

RECENT FINDINGS

Expression profiling studies of asthmatic airway samples continue to reveal significant heterogeneity in airway inflammation and dysfunction. Recent studies have indicated multiple distinct, but related Th2 inflammatory asthma endotypes. Moreover, novel biomarkers of Th2 inflammation are being identified in more accessible nasal brushing and induced sputum cell samples. New data suggest the presence of multiple non-Th2-driven asthma molecular endotypes, including ones related to neutrophilic inflammation, airway remodeling, and chemosensory dysfunction. Many of these endotypes are associated with clinical disease features and treatment response.

SUMMARY

Molecular endotyping of asthmatic patients using gene expression profiling of airway samples is helping to uncover disease mechanisms and potential novel treatment targets. The advancement of endotyping methods holds the promise of future personalized treatment for asthma.

Genetic and genomic analyses as a basis for new diagnostic nosologies

For schizophrenia, bipolar disorder, and autism, clinical descriptions are precise and reliable, but there is great overlap among diagnoses in associated genetic polymorphisms and rare variants, treatment response, and other phenomenological findings such as brain imaging. It is widely hoped that new diagnostic categories can be developed which are more precise and predictive of important features of illness, particularly response to pharmacological agents. It is the intent of this paper to describe the diagnostic implications of some current genetic findings, and to describe how the genetic associations with diagnosis may be teased apart into new associations with biologically coherent diagnostic entities and scales, based on the various functional aspects of the associated genes and functional genomic data.

Pathobiology of severe asthma

Severe asthma (SA) afflicts a heterogeneous group of asthma patients who exhibit poor responses to traditional asthma medications. SA patients likely represent 5-10% of all asthma patients; however, they have a higher economic burden when compared with milder asthmatics. Considerable research has been performed on pathological pathways and structural changes associated with SA. Although limitations of the pathological approaches, ranging from sampling, to quantitative assessments, to heterogeneity of disease, have prevented a more definitive understanding of the underlying pathobiology, studies linking pathology to molecular markers to targeted therapies are beginning to solidify the identification of select molecular phenotypes. This review addresses the pathobiology of SA and discusses the current limitations of studies, the inflammatory cells and pathways linked to emerging phenotypes, and the structural and remodeling changes associated with severe disease. In all cases, an effort is made to link pathological findings to specific clinical/molecular phenotypes.

Human rhinovirus infection causes different DNA methylation changes in nasal epithelial cells from healthy and asthmatic subjects

Background

Mechanisms underlying the development of virus-induced asthma exacerbations remain unclear. To investigate if epigenetic mechanisms could be involved in virus-induced asthma exacerbations, we undertook DNA methylation profiling in asthmatic and healthy nasal epithelial cells (NECs) during Human Rhinovirus (HRV) infection in vitro.

Methods

Global and loci-specific methylation profiles were determined via Alu element and Infinium Human Methylation 450 K microarray, respectively. Principal components analysis identified the genomic loci influenced the most by disease-status and infection. Real-time PCR and pyrosequencing were used to confirm gene expression and DNA methylation, respectively.

Results

HRV infection significantly increased global DNA methylation in cells from asthmatic subjects only (43.6% to 44.1%, p = 0.04). Microarray analysis revealed 389 differentially methylated loci either based on disease status, or caused by virus infection. There were disease-associated DNA methylation patterns that were not affected by HRV infection as well as HRV-induced DNA methylation changes that were unique to each group. A common methylation locus stood out in response to HRV infection in both groups, where the small nucleolar RNA, H/ACA box 12 (SNORA12) is located. Further analysis indicated that a relationship existed between SNORA12 DNA methylation and gene expression in response to HRV infection.

Conclusions

We describe for the first time that Human rhinovirus infection causes DNA methylation changes in airway epithelial cells that differ between asthmatic and healthy subjects. These epigenetic differences may possibly explain the mechanism by which respiratory viruses cause asthma exacerbations.