Relationship between cytokine expression patterns and clinical outcomes: two population-based birth cohorts

A systems immunology approach to investigate cytokine responses to viruses and bacteria and their association with disease

Patterns of human immune responses to viruses and bacteria and how this impacts risk of infections or onset/exacerbation of chronic respiratory diseases are poorly understood. In a population-based birth cohort, we measured peripheral blood mononuclear cell responses (28 cytokines) to respiratory viruses and bacteria, Toll-like receptor ligands and phytohemagglutinin, in 307 children. Cytokine responses were highly variable with > 1000-fold differences between children. Machine learning revealed clear distinction between virus-associated and bacteria-associated stimuli. Cytokines clustered into three functional groups (anti-viral, pro-inflammatory and T-cell derived). To investigate mechanisms potentially explaining such variable responses, we investigated cytokine Quantitative Trait Loci (cQTLs) of IL-6 responses to bacteria and identified nine (eight novel) loci. Our integrative approach describing stimuli, cytokines and children as variables revealed robust immunologically and microbiologically plausible clustering, providing a framework for a greater understanding of host-responses to infection, including novel genetic associations with respiratory disease.

Artificial intelligence in the diagnosis of pediatric allergic diseases

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self-care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting-up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of 'big data', we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.

Longitudinal patterns of cytokine expression at the individual level in humans after laparoscopic sleeve gastrectomy

The study of the human response to injury has been hampered by the inherent heterogeneity in the models and methods used. By studying a standard injury longitudinally, using individual patient‐level analysis, we endeavoured to better describe its dynamics. We analysed clinical variables, clinical laboratory and plasma cytokines from 20 patients at five time points. Clustering analysis showed two prototype patterns of cytokine behaviour: a concordant type, where cytokines behave the same way for all patients (notably IL‐0 and TNFα), and a variable type, where different patterns of expression are seen for different patients (notably IL‐8, IL‐6 and IL‐1RA). Analysis of the cytokines at the individual patient‐level showed a strong four‐way correlation between IL‐1RA, GCSF, MIP‐1β and MCP‐1. As it holds for most patients and not just on average, this suggests that they form a network which may play a central role in the response to gastro‐intestinal injuries in humans. In conclusion, the longitudinal analysis of cytokines in a standard model allowed the identification of their underlying patterns of expression. We propose that the two prototype patterns shown may reflect the mechanism that separates the common and individual aspects of the injury response.

Trajectories of childhood immune development and respiratory health relevant to asthma and allergy

Events in early life contribute to subsequent risk of asthma; however, the causes and trajectories of childhood wheeze are heterogeneous and do not always result in asthma. Similarly, not all atopic individuals develop wheeze, and vice versa. The reasons for these differences are unclear. Using unsupervised model-based cluster analysis, we identified latent clusters within a prospective birth cohort with deep immunological and respiratory phenotyping. We characterised each cluster in terms of immunological profile and disease risk, and replicated our results in external cohorts from the UK and USA. We discovered three distinct trajectories, one of which is a high-risk ‘atopic’ cluster with increased propensity for allergic diseases throughout childhood. Atopy contributes varyingly to later wheeze depending on cluster membership. Our findings demonstrate the utility of unsupervised analysis in elucidating heterogeneity in asthma pathogenesis and provide a foundation for improving management and prevention of childhood asthma.

Asthma causes wheezy and troubled breathing, and can be life-threatening. Scientists and doctors understand that asthma begins in early childhood. Chest infections, exposure to bacteria, viruses, and allergies may cause or trigger asthma. One person with asthma may not have the same origins as another. But it is not yet clear how various triggers may interact to trigger or exacerbate asthma.

To disentangle how these factors contribute to asthma, experts have tried to group people with asthma into subgroups. Unfortunately, the groups often vary from expert to expert. Now, some scientists are using computers to sort patients with asthma. The scientists let the computers decide the best criteria for sorting patients. This way the machines may identify patterns that are not obvious to humans.

Using this computer-based approach, Tang et al. sorted Australian children with asthma into 3 groups based on their early life allergies and respiratory health. One group has high-risk asthma with frequent chest infections and strong allergic responses. The other two groups are low-risk, but they respond differently to allergy and infection. Common tests used by doctors to diagnose patients with allergy or asthma may not work the same with all three groups. The bacteria found in the nose influence the risk of asthma, even in patients who are well, and the way this occurs varies by group. Similar groups were also found among children with asthma in the United States and the United Kingdom.

Learning more about subgroups of patients with asthma may help other scientists and doctors design better ways to diagnose, treat, or prevent asthma. Working together with scientists around the world to determine how to best describe subgroups of people according to asthma type and risk is a critical step in the process. Tang et al. hope other scientist will test whether these three groups are also found in people from other parts of the world.

Developments in the field of allergy mechanisms in 2015 through the eyes of Clinical & Experimental Allergy

In the first of two papers we described the development in the field of allergy mechanisms as described by Clinical and Experimental Allergy in 2015. Experimental models of allergic disease, basic mechanisms, clinical mechanisms and allergens are all covered. A second paper will cover clinical aspects.

Stability of pro- and anti-inflammatory immune biomarkers for human cohort studies

BACKGROUND

Although discovery research has identified the importance of dozens of pro- and anti-inflammatory immune mediators in the pathogenesis, maintenance, exacerbation and resolution of inflammatory diseases, most human cohort studies have incorporated few or no immunological intermediate phenotypes in their analyses. Significant hindrances have been (1) the limited panel of biomarkers known to be readily detected in healthy human populations and (2) the stability, hence utility, of such biomarkers to repeated analysis.

METHODS

The frequency and stability of 14 plasma biomarkers linked to in vivo immune regulation of allergic and autoimmune inflammatory disorders was determined in 140 healthy pediatric and adult participants. The impact of initial and multiple subsequent freeze/thaw cycles on pro-inflammatory (CCL2, CXCL10, IL-18, TNFα, IL-6), anti-inflammatory (IL-10, sTNF-RII, IL-1Ra), acute phase proteins (CRP, PTX3) and other biomarkers (sST2, IL-1RAcP) was subsequently quantified.

RESULTS

Multiple biomarkers capable of providing an innate immune signature of inflammation were readily detected directly ex vivo in healthy individuals. These biomarker levels were unaffected when comparing paired data sets from freshly obtained, never frozen plasma or serum and matched aliquots despite extensive freeze/thaw cycles. Neither age nor sex affected stability. Similarly, no quantitative differences were found following repetitive analysis of inflammatory biomarkers in culture samples obtained following in vitro stimulation with TLR and RLR ligands.

CONCLUSIONS

A broad panel of in vivo and ex vivo cytokine, chemokine and acute phase protein biomarkers that have been linked to human chronic inflammatory disorders are readily detected in vivo and remain stable for analysis despite multiple freeze thaw cycles. These data provide the foundation and confidence for large scale analyses of panels of inflammatory biomarkers to provide better understanding of immunological mechanisms underlying health versus disease.

Impact of interleukin 13 (IL13) genetic polymorphism Arg130Gln on total serum immunoglobulin (IgE) levels and interferon (IFN)-γ gene expression

This cross-sectional study was designed to investigate the extent of genetic susceptibility by targeting variants in interleukin (IL)-4/IL-13 signalling pathways leading to atopic disease in early childhood. We evaluated involvement of five single nucleotide polymorphisms IL4 C-590T, IL13 C-1055T, IL13 Arg130Gln, IL4RA Ile50Val and IL4RA Gln576Arg, in the control of serum total and antigen-specific immunoglobulin (Ig)E levels. Furthermore, we analysed their association with changes in gene expression of five cytokines having key roles in inflammatory and anti-inflammatory immune response [IL-4, IL-13, interferon (IFN)-γ, IL-8 and IL-10]. Total and antigen-specific IgE levels in serum and gene expression of selected cytokines in peripheral blood were measured in 386 children aged 1-8 years. TaqMan allelic discrimination, amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) and restriction fragment length polymorphisms (RFLP) methods validated by sequencing were used for genotyping. All genotypes for children with total and antigen-specific IgE levels in the normal range were in Hardy-Weinberg equilibrium. Gene expression analyses were carried out using TaqMan gene expression assays. We found elevated total IgE levels in carriers of IL13 Arg130Gln variant allele [odds ratio (OR) = 1·84; 95% confidence interval (CI) = 1·16-2·93]. This effect was more apparent for boys (OR = 2·31; 95% CI = 1·25-4·28). However, no significant association was observed for the other four variants examined. We found up-regulation of IFN-γ in children with elevated serum total IgE levels carrying the Arg130 allele (P = 0·005). No differences were found for IL4, IL8 or IL10, while IL13 gene expression was under the detection limit. IL13 Arg130Gln genotypes can play a role in genetic susceptibility to allergy via regulation of serum total IgE levels and affecting IFN-γ gene expression.

Relationship between cytokine expression patterns and clinical outcomes: two population-based birth cohorts

BACKGROUND

Models that incorporate patterns of multiple cytokine responses to allergens, rather than individual cytokine production, may better predict sensitization and asthma.

OBJECTIVE

To characterize the patterns of peripheral blood mononuclear cells' (PBMCs) cytokine responses to house dust mite (HDM) allergens among children from two population-based birth cohorts using machine learning techniques.

METHODS

PBMCs collected at 8 years of age from the UK Manchester Asthma and Allergy Study (n = 268) and at 14 years of age from the Australian Raine Study (n = 1374) were cultured with HDM extract (10 μg/ml). Cytokine expression (IL-13, IL-5, IFN-γ, and IL10) was measured in the supernatant. Cytokine patterns were identified using a Gaussian mixture model clustering, and classification stability was assessed by bootstrapping.

RESULTS

A six-class model indicated complex latent structure of cytokine expression. Based on the characteristics of each class, we designated them as follows: 'Nonresponders' (n = 905, 55%); 'IL-10 responders' (n = 49, 3%); 'IFN-γ and IL-13 medium responders' (n = 56, 3.4%); 'IL-13 medium responders' (n = 351, 21.4%); 'IL-5 and IL-13 medium responders' (n = 77, 4.7%); and 'IL-13 and IL-5 high responders' (n = 204, 12.4%). 'IL-13 and IL-5 high responders' were at much higher risk of HDM sensitization and asthma compared to all other classes, with 88% of children assigned to this class being sensitized and 28.5% having asthma.

CONCLUSION

Using model-based clustering, we identified several distinct patterns of cytokine response to HDM and observed interplay between cytokine expression level, cytokine patterns (especially IL-13 and IL-5), and clinical outcomes. 'IL-13 and IL-5 high responders' class was strongly associated with HDM sensitization. However, among HDM-sensitized children, one-third showed no PBMC response to HDM, and the majority of HDM-sensitized children did not have asthma or wheeze. Our findings suggest that positive HDM 'allergy tests' and asthma are associated with a broad range of immunophenotypes, which may have important implications for the use of cytokine-targeted treatment approaches.

Identification of Asthma Subtypes Using Clustering Methodologies

Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches.