Strategies for molecular classification of asthma using bipartite network analysis of cytokine expression

Finite Mixtures of Latent Trait Analyzers With Concomitant Variables for Bipartite Networks: An Analysis of COVID-19 Data

Networks consist of interconnected units, known as nodes, and allow to formally describe interactions within a system. Specifically, bipartite networks depict relationships between two distinct sets of nodes, designated as sending and receiving nodes. An integral aspect of bipartite network analysis often involves identifying clusters of nodes with similar behaviors. The computational complexity of models for large bipartite networks poses a challenge. To mitigate this challenge, we employ a Mixture of Latent Trait Analyzers (MLTA) for node clustering. Our approach extends the MLTA to include covariates and introduces a double EM algorithm for estimation. Applying our method to COVID-19 data, with sending nodes representing patients and receiving nodes representing preventive measures, enables dimensionality reduction and the identification of meaningful groups. We present simulation results demonstrating the accuracy of the proposed method.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Relationship between asthma status and antibody response pattern to 23-valent pneumococcal vaccination

Objective: Asthma poses an increased risk for serious pneumococcal disease, but little is known about the influence of asthma status on the 23-valent serotype-specific pneumococcal antibody response. We examined differences in antibody titers between pre- and post-vaccination with 23-valent pneumococcal polysaccharide vaccine (PPSV-23) in relation to asthma status. Methods: Asthma status was retrospectively ascertained by the Predetermined Asthma Criteria in an existing vaccine cohort through comprehensive medical record review. Twenty-three serotype-specific pneumococcal antibody titers measured at baseline and 4-6 weeks post-vaccination were analyzed. Vaccine responses to PPSV-23 were calculated from pre- to post-vaccine titers for each of the serotypes. Results: Of the 64 eligible and enrolled subjects, 18 (28%) had asthma. Controls (i.e., subjects without asthma) demonstrated a statistically significant fold change response compared to their baseline for all serotypes, while those with asthma did not mount a significant response to serotypes 7F, 22F, and 23F. The overall vaccine response as measured by fold change over baseline was lower in subjects with asthma than controls. Conclusions: Poorer humoral immune responses to PPSV-23 as measured by fold change were more likely to be observed in subjects with asthma compared to controls. We recommend the consideration of asthma status when interpreting vaccine response for immune competence workup through larger studies. Further studies are warranted to replicate these findings.

The soft computing-based approach to investigate allergic diseases: a systematic review

BACKGROUND

Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.

OBJECTIVE

The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.

METHODS

The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.

RESULTS

The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of disease.

CONCLUSIONS

Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

Severe Asthma: Have We Made Progress?

Tremendous efforts have been invested in research to (1) discover risk factors, biomarkers, and clinical characteristics; (2) understand the pathophysiology and treatment response variability in severe asthma; and (3) design new therapies. However, to combat severe asthma, many questions concerning the pathogenesis of severe asthma, including its natural history, genetic and environmental risk factors, and disease mechanisms, must be answered. In this article we highlight some of the major discoveries concerning the pathogenesis of severe asthma and its therapeutic development. We conclude that discoveries on numerous fronts of severe asthma, from disease heterogeneity, features of airway remodeling, cytokine mediators and signaling pathways underlying disease pathogenesis, disease mechanisms, potential biomarkers, to new therapeutic targets, demonstrate that progress has been made in understanding and developing more effective treatments for this difficult-to-treat disease.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Symptom-Based Clustering in Chronic Rhinosinusitis Relates to History of Aspirin Sensitivity and Postsurgical Outcomes

BACKGROUND

Symptoms burden in chronic rhinosinusitis (CRS) may be assessed by interviews or by means of validated tools such as the 22-item SinoNasal Outcome Test (SNOT-22). However, when only the total SNOT-22 scores are used, the pattern of symptom distribution and heterogeneity in patient symptoms is lost.

OBJECTIVES

To use a standardized symptom assessment tool (SNOT-22) on preoperative symptoms to understand symptom heterogeneity in CRS and to aid in characterization of distinguishing clinical features between subgroups.

METHODS

This was a retrospective review of 97 surgical patients with CRS. Symptom-based clusters were derived on the basis of presurgical SNOT-22 scores using unsupervised analysis and network graphs. Comparison between clusters was performed for clinical and demographic parameters, postsurgical symptom scores, and presence or absence of a history of aspirin sensitivity.

RESULTS

Unsupervised analysis reveals coclustering of specific symptoms in the SNOT-22 tool. Using symptom-based clustering, patients with CRS were stratified into severe overall (mean total score, 90.8), severe sinonasal (score, 62), moderate sinonasal (score, 40), moderate nonsinonasal (score, 37) and mild sinonasal (score, 16) clusters. The last 2 clusters were associated with lack of history of aspirin sensitivity. The first cluster had a rapid relapse in symptoms postoperatively, and the last cluster demonstrated minimal symptomatic improvement after surgery.

CONCLUSION

Symptom-based clusters in CRS reveal a distinct grouping of symptom burden that may relate to aspirin sensitivity and treatment outcomes.

Systems biology approach for subtyping asthma; where do we stand now?

PURPOSE OF REVIEW

The purpose of this review is to discuss the present systems biology approach to asthma and how it is helping to define asthma subtypes. Although the general concept of systems biology will be discussed, the article will focus on recent developments in the field related to asthma.

RECENT FINDINGS

The most recent work in systems biology and asthma has occurred in the area of genomics (e.g., pharmacogenomics and gene-environment interactions), protein interaction networks [e.g., interleukin (IL)-33/IL-1 receptor-like 1 signaling], cluster analysis of asthma patients (e.g., application of severe asthma research program clusters to a general urban asthma population), and multiscale approaches to asthma encompassing data from the molecule to whole organ (e.g., modeling of airways hyperresponsiveness).

SUMMARY

The results of recent work in this area have led to new insight into gene-cytokine and protein-protein networks involved in asthma, a better determination of key clinical factors associated with asthma subtypes, and the beginning of sophisticated multiscale approaches to modeling, understanding and predicting the behavior of the asthmatic lung.

Aligning mouse models of asthma to human endotypes of disease

Substantial gains in understanding the pathophysiologic mechanisms underlying asthma have been made using preclinical mouse models. However, because asthma is a complex, heterogeneous syndrome that is rarely due to a single allergen and that often presents in the absence of atopy, few of the promising therapeutics that demonstrated effectiveness in mouse models have translated into new treatments for patients. This has resulted in an urgent need to characterize T helper (Th) 2-low, non-eosinophilic subsets of asthma, to study models that are resistant to conventional treatments such as corticosteroids and to develop therapies targeting patients with severe disease. Classifying asthma based on underlying pathophysiologic mechanisms, known as endotyping, offers a stratified approach for the development of new therapies for asthma. In preclinical research, new models of asthma are being utilized that more closely resemble the clinical features of different asthma endotypes, including the presence of interleukin-17 and a Th17 response, a biomarker of severe disease. These models utilize more physiologically relevant sensitizing agents, exacerbating factors and allergens, as well as incorporate time points that better reflect the natural history and chronicity of clinical asthma. Importantly, some models better represent non-classical asthma endotypes that facilitate the study of non-Th2-driven pathology and resemble the complex nature of clinical asthma, including corticosteroid resistance. Placing mouse asthma models into the context of human asthma endotypes will afford a more relevant approach to the understanding of pathophysiological mechanisms of disease that will afford the development of new therapies for those asthmatics that remain difficult to treat.

TGF- β: an important mediator of allergic disease and a molecule with dual activity in cancer development

The transforming growth factor- β (TGF- β ) superfamily is a family of structurally related proteins that includes TGF- β , activins/inhibins, and bone morphogenic proteins (BMPs). Members of the TGF- β superfamily regulate cellular functions such as proliferation, apoptosis, differentiation, and migration and thus play key roles in organismal development. TGF- β is involved in several human diseases, including autoimmune disorders and vascular diseases. Activation of the TGF- β receptor induces phosphorylation of serine/threonine residues and triggers phosphorylation of intracellular effectors (Smads). Once activated, Smad proteins translocate to the nucleus and induce transcription of their target genes, regulating various processes and cellular functions. Recently, there has been an attempt to correlate the effect of TGF- β with various pathological entities such as allergic diseases and cancer, yielding a new area of research known as "allergooncology," which investigates the mechanisms by which allergic diseases may influence the progression of certain cancers. This knowledge could generate new therapeutic strategies aimed at correcting the pathologies in which TGF- β is involved. Here, we review recent studies that suggest an important role for TGF- β in both allergic disease and cancer progression.

Introduction to asthma and phenotyping

Asthma is an inflammatory disorder characterized by airway obstruction, airway hyperresponsiveness, and airway inflammation, all of which are variable among patients and variable in time within any specific patient. Understanding the mechanism that underlies this observed variability, and using that understanding to advance the science of asthma and the care of asthmatic patients, is an essential purpose of developing phenotypes. Clinical phenotypes have been used for decades, but overlap each other, and do not map cleanly to either pathophysiologic mechanism or with therapeutic response. Molecular phenotyping, although as yet only partially developed, offers the promise of dissecting the mechanistic underpinnings of the variability of asthma and of providing predictive therapeutics for the benefit of patients with this common and troubling disease.

From phenotypes to endotypes to asthma treatment

PURPOSE OF REVIEW

The current guidelines for asthma diagnosis and management do not recognize that different phenotypes of asthma exist, with significant variations in the manifestation of airway inflammation, symptoms, severity, and response to treatment. This article will critically review new approaches to classify asthma together with the emerging endotype-driven therapeutic strategies.

RECENT FINDINGS

Several new approaches for classifying asthma are available, from precision and deep phenotyping to identification of novel causal pathways and translation of biomarkers into pathway-specific diagnostic tests. New phenotypes, such as epigenetic phenotypes, asthmatic granulomatosis, or neurophenotypes are described. Large clinical trials testing the endotype-driven approach are increasingly successful, but the dissociated effect and the drug efficacy at the target site remain unsolved issues. Profiling the Th2 low and the resident cell compartment of asthma are major unmet needs in asthma endotyping.

SUMMARY

Each of the hallmark characteristics of asthma (inflammation, remodeling, airway hyperreactivity) is the expression of a complex network of molecules, very diverse both within any given patient in time and between any two patients. Some of these networks are repetitive across individuals with asthma and specific for clinical expression, gene-environment interaction and inflammatory cell profiles represent novel endotype-specific diagnostic and therapeutic strategies.

Identification of innate immune response endotypes in asthma: implications for personalized medicine

Asthma is an idiopathic disease characterized by episodic inflammation and reversible airway obstruction triggered by exposure to environmental agents. Because this disease is heterogeneous in onset, exacerbations, inflammatory states, and response to therapy, there is intense interest in developing personalized approaches to its management. Of focus in this review, the recognition that a component of the pathophysiology of asthma is mediated by inflammation has implications for understanding its etiology and individualizing its therapy. Despite understanding how Th2 polarization mediates asthma exacerbations by aeroallergen exposure, we do not yet fully understand how RNA virus infections produce asthmatic exacerbations. This review will summarize the explosion of information that has revealed how patterns produced by RNA virus infection trigger the innate immune response (IIR) in sentinel airway cells. When the IIR is triggered, these cells elaborate inflammatory cytokines and protective mucosal interferons whose actions activate long-lived adaptive immunity and limit organismal replication. Recent work has shown the multifaceted way that dysregulation of the IIR is linked to viral-induced exacerbation, steroid insensitivity, and T helper polarization of adaptive immunity. New developments in quantitative proteomics now enable accurate identification of subgroups of individuals that demonstrate activation of IIR ("innate endotype"). Potential applications to clinical research are proposed. Together, these developments open realistic prospects for how identification of the IIR endotype may inform asthma therapy in the future.

Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records

OBJECTIVE

Mental illness is the leading cause of disability in the USA, but boundaries between different mental illnesses are notoriously difficult to define. Electronic medical records (EMRs) have recently emerged as a powerful new source of information for defining the phenotypic signatures of specific diseases. We investigated how EMR-based text mining and statistical analysis could elucidate the phenotypic boundaries of three important neuropsychiatric illnesses-autism, bipolar disorder, and schizophrenia.

METHODS

We analyzed the medical records of over 7000 patients at two facilities using an automated text-processing pipeline to annotate the clinical notes with Unified Medical Language System codes and then searching for enriched codes, and associations among codes, that were representative of the three disorders. We used dimensionality-reduction techniques on individual patient records to understand individual-level phenotypic variation within each disorder, as well as the degree of overlap among disorders.

RESULTS

We demonstrate that automated EMR mining can be used to extract relevant drugs and phenotypes associated with neuropsychiatric disorders and characteristic patterns of associations among them. Patient-level analyses suggest a clear separation between autism and the other disorders, while revealing significant overlap between schizophrenia and bipolar disorder. They also enable localization of individual patients within the phenotypic 'landscape' of each disorder.

CONCLUSIONS

Because EMRs reflect the realities of patient care rather than idealized conceptualizations of disease states, we argue that automated EMR mining can help define the boundaries between different mental illnesses, facilitate cohort building for clinical and genomic studies, and reveal how clear expert-defined disease boundaries are in practice.