Distinct Endotypes Research Articles

Atopic Dermatitis: Striving for Reliable Biomarkers.

Atopic dermatitis (AD) is a highly heterogeneous inflammatory disease regarding both its pathophysiology and clinical manifestations. However, it is treated according to the “one-size-fits-all” approach, which may restrict response to treatment. Thus, there is an unmet need for the stratification of patients with AD into distinct endotypes and clinical phenotypes based on biomarkers that will contribute to the development of precision medicine in AD. The development of reliable biomarkers that may distinguish which patients with AD are most likely to benefit from specific targeted therapies is a complex procedure and to date none of the identified candidate biomarkers for AD has been validated for use in routine clinical practice. Reliable biomarkers in AD are expected to improve diagnosis, evaluate disease severity, predict the course of disease, the development of comorbidities, or the therapeutic response, resulting in effective and personalized treatment of AD. Among the studied AD potential biomarkers, thymus and activation-regulated chemokine/C-C motif ligand 17 (TARC/CCL17) has the greatest evidence-based support for becoming a reliable biomarker in AD correlated with disease severity in both children and adults. In this review, we present the most prominent candidate biomarkers in AD and their suggested use.

Nasopharyngeal lipidomic endotypes of infants with bronchiolitis and risk of childhood asthma: a multicentre prospective study

BackgroundBronchiolitis is the leading cause of hospitalisation of US infants and an important risk factor for childhood asthma. Recent evidence suggests that bronchiolitis is clinically heterogeneous. We sought to derive...

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

BackgroundWhile the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights.MethodsWe developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation.ResultsSix stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001).ConclusionsSix stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care.Trial registrationThe core study was registered with ClinicalTrials.gov, number NCT02210221, registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

Identifying endotypes of individuals after an attack of pancreatitis based on unsupervised machine learning of multiplex cytokine profiles

After an attack of pancreatitis, individuals may develop metabolic sequelae (eg, new-onset diabetes) and/or pancreatic cancer. These new-onset morbidities are, at least in part, driven by low-grade inflammation. The aim was to study the profiles of cytokines/chemokines in individuals after an attack of pancreatitis. A commercially available panel including 31 cytokines/chemokines was investigated. Random forest classifier and unsupervised hierarchical clustering were applied to study participants (who had no persistent organ failure and did not require ICU admission) according to their cytokine/chemokine profiles. Pancreatitis-related characteristics, detailed body composition (determined using 3.0 T magnetic resonance imaging), markers of glucose, lipid, and iron metabolism, gut and pancreatic hormones, as well as liver and pancreatic enzymes, were compared between clusters. Bootstrap validation was employed. A total of 160 participants, including 107 postpancreatitis individuals (investigated at a median of 18 months after the last attack of pancreatitis) and 53 healthy volunteers, were studied. Twenty-two cytokines/chemokines were significantly different between postpancreatitis and health. Two distinct endotypes of individuals after an attack of pancreatitis were identified–‟inflammatory” and ‟noninflammatory.” Sixteen cytokines/chemokines were significantly higher in the inflammatory endotype compared with the noninflammatory endotype. No cytokine/chemokine was significantly higher in the noninflammatory endotype. The inflammatory endotype was characterized by significantly elevated insulin (P= 0.001), glucose-dependent insulinotropic peptide (P = 0.001), peptide YY (P = 0.017), and ghrelin (P = 0.014). The noninflammatory endotype was characterized by significantly elevated hepcidin (P= 0.016). Pancreatitis-related factors, body composition, and other studied parameters did not differ significantly between the 2 endotypes. Individuals with a similar phenotype and clinical course of pancreatitis have differing cytokine/chemokine profiles after clinical resolution of the disease. People with the inflammatory endotype have distinct changes in the pancreatic and gut hormones known to be involved in the pathogenesis of new-onset morbidities after an attack of pancreatitis.

Omics technologies in allergy and asthma research: An EAACI position paper.

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

1286-P: Plasma-Induced Signatures Measured at Type 1 Diabetes (T1D) Onset Correlate with Post-onset Beta-Cell Function

Significant phenotypic diversity exists among T1D patients, supporting the existence of distinct disease endotypes (e.g. duration of the post-onset partial remission period) . To broaden our understanding of T1D heterogeneity and its relationship to disease progression and therapeutic responsiveness, we studied 566 new onset T1D patients (241 females, 325 males; mean age 16.5 +/- 8.2 years) that had participated in TrialNet clinical trials: TN02/MMF DZB; TN05/anti-CD20; TN08/GAD-alum; TN09/CTLA4-Ig; TN14/Canakinumab; and TN19/ATG-GCSF. Baseline samples were analyzed with a sensitive bioassay, where subject plasma is used to induce transcription in a well-controlled peripheral blood mononuclear cell population. The response was measured with a microarray, data were normalized, and transcripts exhibiting the highest median absolute deviation within each trial were identified. Weighted gene correlation network analysis was used to cluster patterns of variation into modules of co-expressed transcripts. Co-expression networks were correlated with clinical data collected over the 24-months following clinical onset. In the placebo arms of all 6 trials, we identified modules that significantly correlated with post-onset C-peptide AUC, post-onset insulin dose, Insulin-Dose Adjusted A1c, and/or the rate of C-peptide decline. This suggests that the baseline analyses identified modules that differentiated subjects in terms of post-onset beta-cell function, supporting the existence of T1D endotypes. While TN02, TN08, and TN14 did not show overall efficacy in slowing post-onset C-peptide decline, the module correlations with phenotypes related to beta-cell function were fewer in number and of lesser statistical significance in the treatment arms of all 6 trials. This suggests that there was greater beta-cell function in the treatment arms of all trials, and that varying degrees of immunomodulation had occurred even in those trials that did not show efficacy. Disclosure A.Bedrat: None. S.Jia: None. M.Roethle: None. K.Dew: None. S.M.Cabrera: None. C.Lin: None. M.Hessner: None. Funding National Institutes of Health (R01DK121528)

Molecular Taxonomy of Systemic Lupus Erythematosus Through Data-Driven Patient Stratification: Molecular Endotypes and Cluster-Tailored Drugs.

ObjectivesTreatment of Systemic Lupus Erythematosus (SLE) is characterized by a largely empirical approach and relative paucity of novel compound development. We sought to stratify SLE patients based on their molecular phenotype and identify putative therapeutic compounds for each molecular fingerprint.MethodsBy the use of whole blood RNA-seq data from 120 SLE patients, and in a data-driven, clinically unbiased manner, we established modules of commonly regulated genes (molecular endotypes) and re-stratified patients through hierarchical clustering. Disease activity and severity were assessed using SLEDAI-2K and Lupus Severity Index, respectively. Through an in silico drug prediction pipeline, we investigated drugs currently in use, tested in lupus clinical trials, and listed in the iLINCS prediction databases, for their ability to reverse the gene expression signatures in each molecular endotype. Drug repurposing analysis was also performed to identify perturbagens that counteract group-specific SLE signatures.ResultsMolecular taxonomy identified five lupus endotypes, each characterized by a unique gene module enrichment pattern. Neutrophilic signature group consisted primarily of patients with active lupus nephritis, while the B-cell expression group included patients with constitutional features. Patients with moderate severity and serologic activity exhibited a signature enriched for metabolic processes. Mild disease was distributed in two groups, exhibiting enhanced basic cellular functions, myelopoiesis, and autophagy. Bortezomib was predicted to reverse disturbances in the “neutrophilic” cluster, azathioprine and ixazomib in the “B-cell” cluster, and fostamatinib in the “metabolic” patient subgroup.ConclusionThe clinical spectrum of SLE encompasses distinct molecular endotypes, each defined by unique pathophysiologic aberrancies potentially reversible by distinct compounds.

Cytotoxic CD4+ tissue-resident memory T cells are associated with asthma severity.

Abstract Patients with severe uncontrolled asthma represent a distinct endotype with persistent airway inflammation and remodeling that is refractory to corticosteroid treatment. To determine T cell subsets and effector molecules that drive pathogenesis of severe asthma, we performed single-cell transcriptome analysis of &amp;gt;50,000 airway CD4+ T cells isolated from bronchoalveolar lavage (BAL) samples from 30 patients with mild and severe asthma. We observed striking heterogeneity in the nature of CD4+ T cells present in asthmatics' airways with tissue-resident memory (TRM) cells making a dominant contribution. Notably, in severe asthmatics a subset of CD4+ TRM cells (CD103-expressing) was significantly increased, comprising nearly 65% of all CD4+ T cells in the airways of male patients with severe asthma when compared to mild asthma (13%). This subset was enriched for transcripts linked to T cell receptor (TCR) activation (HLA-DRB1, HLA-DPA1, CD40LG) and cytotoxicity (GZMB, GZMH), and following stimulation expressed high levels of transcripts encoding for pro-inflammatory non-TH2 cytokines (CCL3, CCL4, CCL5, TNF, LIGHT) that could fuel persistent airway inflammation and remodeling. Our findings indicate the need to look beyond the traditional T2 model of severe asthma to better understand the heterogeneity of this disease. This work was supported by (i) NIH research grant R01HL114093 (PV) and equipment grants (S10RR027366 - BD FACSAria Fusion, and S10OD025052 - Illumina Novaseq 6000); the William K. Bowes Jr. Foundation (P.V.). The WATCH study is supported by the Southampton NIHR Biomedical Research Centre and the Southampton NIHR Clinical Research Facility which are funded by the NIHR and are a partnership between the University of Southampton and University Hospital Southampton NHS Foundation Trust.

Chronic Allergen Challenge Induces Corticosteroid Insensitivity With Persistent Airway Remodeling and Type 2 Inflammation.

Type 2-high severe asthma is described as a distinct endotype with Th2 inflammation, high eosinophil lung infiltration, impaired lung function, and reduced corticosteroid sensitivity. While the inflammatory milieu is similar to mild asthma, patients with type 2-high severe asthma likely have underlying mechanisms that sustain asthma pathophysiology despite corticosteroid treatments. Acute and chronic allergen models induce robust type 2 inflammatory responses, however differences in corticosteroid sensitivity remains poorly understood. In the present study, we sensitized and challenged mice with ovalbumin (OVA; acute model) or mixed allergens (MA; chronic model). Corticosteroid sensitivity was assessed by administering vehicle, 1, or 3 mg/kg fluticasone propionate (FP) and examining key asthmatic features such as airway inflammation, remodeling, hyperresponsiveness, and antioxidant capacity. Both acute and chronic allergen exposure exhibited enhanced AHR, immune cell infiltration, airway inflammation, and remodeling, but corticosteroids were unable to fully alleviate inflammation, AHR, and airway smooth muscle mass in MA-challenged mice. While there were no differences in antioxidant capacity, persistent IL-4+ Th2 cell population suggests the MA model induces type 2 inflammation that is insensitive to corticosteroids. Our data indicate that chronic allergen exposure is associated with more persistent type 2 immune responses and corticosteroid insensitivity. Understanding differences between acute and chronic allergen models could unlock underlying mechanisms related to type 2-high severe asthma.

Hidradenitis suppurativa: Morphologic and vascular study of nodular inflammatory lesions by means of optical coherence tomography.

Hidradenitis suppurativa (HS) is an inflammatory disease characterized by a recurrent-remission trend and clinical lesions that range from asymptomatic to inflamed, deep-seated nodules with scarring and suppuration. The aim of our study was to identify morphologic and vascular features of HS nodules by means of dynamic optical coherence tomography (D-OCT) and to define if they are correlated to patient endotype and risk of disease progression. A set of standardized clinical pictures and D-OCT images were acquired from 57 inflammatory nodules of 40 patients affected by HS. A set of 20 clinical and D-OCT images were acquired from 20 healthy volunteers as a control group. The comparison of D-OCT features among HS and control group was analysed. The correlation between HS patient endotype and D-OCT features of the lesions was calculated. D-OCT enabled to identify vascular and morphological aspects characterizing HS nodular inflammatory lesions. In addition, several D-OCT features were significantly different among distinct disease endotypes. The characterization of HS nodular inflammatory lesions through D-OCT, corresponding to blood vessel dilation and inflammatory associated hyper-vascularization, may have important clinical consequences in the assessment of HS risk of progression, therapeutic decisions and treatment efficacy monitoring.

High-Dimensional Analysis Reveals Distinct Endotypes in Patients With Idiopathic Inflammatory Myopathies.

The idiopathic inflammatory myopathies (IIM) are a rare clinically heterogeneous group of conditions affecting the skin, muscle, joint, and lung in various combinations. While myositis specific autoantibodies are well described, we postulate that broader immune endotypes exist in IIM spanning B cell, T cell, and monocyte compartments. This study aims to identify immune endotypes through detailed immunophenotyping of peripheral blood mononuclear cells (PBMCs) in IIM patients compared to healthy controls. We collected PBMCs from 17 patients with a clinical diagnosis of inflammatory myositis and characterized the B, T, and myeloid cell subsets using mass cytometry by time of flight (CyTOF). Data were analyzed using a combination of the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE), cluster identification, characterization, and regression (CITRUS), and marker enrichment modeling (MEM); supervised biaxial gating validated populations identified by these methods to be differentially abundant between groups. Using these approaches, we identified shared immunologic features across all IIM patients, despite different clinical features, as well as two distinct immune endotypes. All IIM patients had decreased surface expression of RP105/CD180 on B cells and a reduction in circulating CD3+CXCR3+ subsets relative to healthy controls. One IIM endotype featured CXCR4 upregulation across all cellular compartments. The second endotype was hallmarked by an increased frequency of CD19+CD21loCD11c+ and CD3+CD4+PD1+ subsets. The experimental and analytical methods we describe here are broadly applicable to studying other immune-mediated diseases (e.g., autoimmunity, immunodeficiency) or protective immune responses (e.g., infection, vaccination).

Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes.

BackgroundIn vitro, animal model and clinical evidence suggests that tuberculosis is not a monomorphic disease, and that host response to tuberculosis is protean with multiple distinct molecular pathways and pathologies (endotypes). We applied unbiased clustering to identify separate tuberculosis endotypes with classifiable gene expression patterns and clinical outcomes.MethodsA cohort comprised of microarray gene expression data from microbiologically confirmed tuberculosis patients was used to identify putative endotypes. One microarray cohort with longitudinal clinical outcomes was reserved for validation, as were two RNA-sequencing (seq) cohorts. Finally, a separate cohort of tuberculosis patients with functional immune responses was evaluated to clarify stimulated from unstimulated immune responses.ResultsA discovery cohort, including 435 tuberculosis patients and 533 asymptomatic controls, identified two tuberculosis endotypes. Endotype A is characterised by increased expression of genes related to inflammation and immunity and decreased metabolism and proliferation; in contrast, endotype B has increased activity of metabolism and proliferation pathways. An independent RNA-seq validation cohort, including 118 tuberculosis patients and 179 controls, validated the discovery results. Gene expression signatures for treatment failure were elevated in endotype A in the discovery cohort, and a separate validation cohort confirmed that endotype A patients had slower time to culture conversion, and a reduced cure rate. These observations suggest that endotypes reflect functional immunity, supported by the observation that tuberculosis patients with a hyperinflammatory endotype have less responsive cytokine production upon stimulation.ConclusionThese findings provide evidence that metabolic and immune profiling could inform optimisation of endotype-specific host-directed therapies for tuberculosis.

Heterogeneity of Type 1 Diabetes at Diagnosis Supports Existence of Age-Related Endotypes.

Previous findings suggest that there are age-related endotypes of type 1 diabetes with different underlying etiopathological mechanisms in those diagnosed at age <7 years compared with those diagnosed at age ≥13 years. We set out to explore whether variation in demographic, clinical, autoimmune, and genetic characteristics of children and adolescents with newly diagnosed type 1 diabetes support the existence of these proposed endotypes. We used data from the Finnish Pediatric Diabetes Register to analyze characteristics of 6,015 children and adolescents diagnosed with type 1 diabetes between 2003 and 2019. We described and compared demographic data, clinical characteristics at diagnosis, autoantibody profiles, and HLA class II-associated disease risk between three groups formed based on age at diagnosis: <7, 7-12, and ≥13 years. We found significant age-related differences in most of the characteristics analyzed. Children diagnosed at age <7 years were characterized by a higher prevalence of affected first-degree relatives, stronger HLA-conferred disease susceptibility, and higher number of autoantibodies at diagnosis, in particular a higher frequency of insulin autoantibodies, when compared with older children. Those diagnosed at age ≥13 years had a considerably higher male preponderance, higher frequency of glutamic acid decarboxylase autoantibodies, longer duration of symptoms before diagnosis, and more severe metabolic decompensation, reflected, for example, by a higher frequency of diabetic ketoacidosis. Our findings suggest that the heterogeneity of type 1 diabetes is associated with the underlying disease process and support the existence of distinct endotypes of type 1 diabetes related to age at diagnosis.

Recent advances in the immunopathogenesis of severe asthma

Severe asthma is a heterogeneous disease encompassing different phenotypes and endotypes. Although patients with severe asthma constitute a small proportion of the total population with asthma, they largely account for the morbidity and mortality associated with asthma, indicating a clear unmet need. Being distinct from mild and moderate disease, new insights into the immunopathogenesis of severe asthma are needed. The disease endotypes have provided better insights into the immunopathogenic mechanisms underlying severe asthma. Current stratified approach of treating severe asthma based on phenotypes is met with shortcomings, necessitating unbiased multidimensional endotyping to cope with disease complexity. Therefore, in this review, we explore the distinct endotypes and their mechanistic pathways that characterize the heterogeneity observed in severe asthma.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures.

SummaryBackgroundInter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.MethodsBlood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.FindingsGene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts.InterpretationThe severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

Distinct Endotypes of Pediatric Rhinitis Based on Cluster Analysis.

Despite the wide spectrum of pediatric rhinitis, endotyping of rhinitis based on type 2 inflammation and bronchial hyper-responsiveness (BHR) is lacking. This study aimed to investigate endotypes of pediatric rhinitis using cluster analysis. Cluster analysis was performed on data from 241 children with rhinitis by using 12 variables reflecting clinical characteristics of skin prick, laboratory, and pulmonary function tests. After extracting clusters, between-cluster differences in clinical features, such as nasal symptom scores and asthma comorbidity, were assessed to investigate the association between the endotypes and clinical features. Four clusters were extracted by hierarchical cluster analysis. Cluster 1 (n = 32 [13.3%]) was the non-allergic rhinitis dominant cluster with low type 2 inflammation and the lowest rate of BHR. Patients in cluster 1 had the mildest nasal symptoms and no asthma comorbidity. Cluster 2 (n = 114 [47.3%]) was the largest cluster and exhibited intermediate type 2 inflammation and low BHR. Cluster 3 (n = 65 [27.0%]) showed high type 2 inflammation and intermediate BHR. However, the severity of nasal symptoms and asthma comorbidity in this cluster were comparable with those in cluster 2. Cluster 4 (n = 30 [12.4%]) revealed high type 2 inflammation and BHR with potential functional airway impairment. Additionally, cluster 4 displayed the most severe nasal symptoms and frequent asthma comorbidity. Four distinct endotypes of pediatric rhinitis based on allergen sensitization, type 2 inflammation, and BHR correlate to symptoms and asthma comorbidity. These endotypes may aid clinicians in understanding the wide spectrum of pediatric rhinitis.

Revisiting Asian chronic rhinosinusitis in the era of type 2 biologics.

Chronic rhinosinusitis (CRS) is a highly heterogeneous disorder exhibiting considerable epidemiological, clinical and immunopathological variations across patients with distinct ethnic backgrounds and in different geographic locations. Asian CRS patients present less eosinophilic and type 2 (T2) inflammation, but more prominent neutrophilic inflammation compared with patients in Western countries. Although several biologics targeting important elements of T2 inflammation, such as IL-4, IL-5, IL-13 and IgE, demonstrate promising benefit for Caucasian patients with recurrent nasal polyps, their efficacy in Asian patients remains poorly defined. The distinct endotypes in Asian patients warrant the identification and selection of patients who would benefit from T2 biologics in Asian countries. Additionally, developing novel treatments targeting neutrophilic, type 1, and type 3 inflammation may benefit approximately 50% of Asian CRS patients with non-T2 inflammation. In this review, we summarized and discussed recent progress in the study of Asian CRS endotypes in comparison with those in patients in Western countries, and the methods of identifying Asian patients with eosinophilic or T2 inflammation. T2 biologic treatment of Asian CRS patients, potential therapeutic candidates targeting non-T2 inflammation in Asian CRS patients and the progress on developing other T2 biologics were discussed.

Large-scale plasma proteomics can reveal distinct endotypes in chronic obstructive pulmonary disease and severe asthma.

BackgroundChronic airway diseases including chronic obstructive pulmonary disease (COPD) and asthma are heterogenous in nature and endotypes within are underpinned by complex biology. This study aimed to investigate the utility of proteomic profiling of plasma combined with bioinformatic mining, and to define molecular endotypes and expand our knowledge of the underlying biology in chronic respiratory diseases.MethodsThe plasma proteome was evaluated using an aptamer‐based affinity proteomics platform (SOMAscan®), representing 1238 proteins in 34 subjects with stable COPD and 51 subjects with stable but severe asthma. For each disease, we evaluated a range of clinical/demographic characteristics including bronchodilator reversibility, blood eosinophilia levels, and smoking history. We applied modified bioinformatic approaches used in the evaluation of RNA transcriptomics.ResultsSubjects with COPD and severe asthma were distinguished from each other by 365 different protein abundancies, with differential pathway networks and upstream modulators. Furthermore, molecular endotypes within each disease could be defined. The protein groups that defined these endotypes had both known and novel biology including groups significantly enriched in exosomal markers derived from immune/inflammatory cells. Finally, we observed associations to clinical characteristics that previously have been under‐explored.ConclusionThis investigational study evaluating the plasma proteome in clinically‐phenotyped subjects with chronic airway diseases provides support that such a method can be used to define molecular endotypes and pathobiological mechanisms that underpins these endotypes. It provided new concepts about the complexity of molecular pathways that define these diseases. In the longer term, such information will help to refine treatment options for defined groups.

Targeted phospholipidomic analysis of synovial fluid as a tool for osteoarthritis deep phenotyping

ObjectiveThe aim of this study was to carry out a targeted phospholipidomic analysis on synovial fluid (SF) from patients with different grades of osteoarthritis (OA) and controls, in order to search for specific phospholipid profiles that may be useful for the deep phenotyping of this disease. DesignMultiple reaction monitoring-mass spectrometry (MRM/MS) was applied to explore the potential phospholipidomic differences in the SF of knee OA patients (n ​= ​15) (subclassified into early- and late-stage OA) and non-OA controls (n ​= ​4). Multivariate statistical analyses conducted by partial least squares discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA) were performed to identify significantly altered phospholipids in OA, characterize phospholipidomic profiles associated with the radiographic stage of the disease and describe potential endotypes at early stages. ResultsSignificant discrimination of phospholipid profiles between non-OA controls and the early- and late-stage OA groups were found by PLS-DA and HCA. Compared to SF from non-OA controls, OA patients showed higher levels of most quantified phospholipid species, including phosphatidylcholines (PC), phosphatidylserines and phosphatidylinositols. Furthermore, several PC species showed significant differences in abundance between the two OA subgroups and were negatively correlated with cartilage damage. Finally, two distinct endotypes of early-stage OA were identified based on the phospholipidomic profile of SF. ConclusionsOur data provides a novel insight into the phospholipid profiles of OA synovial fluid, revealing specific alterations associated with the radiographic stage of the disease. This targeted phospholipidomic profiling also facilitated the characterization of two different OA endotypes at early stages of the disease.

Airway Bacteria Quantification Using Polymerase Chain Reaction Combined with Neutrophil and Eosinophil Counts Identifies Distinct COPD Endotypes.

Background: Chronic obstructive pulmonary disease (COPD) inflammatory endotypes are associated with different airway microbiomes. We used quantitative polymerase chain reaction (qPCR) analysis of sputum samples to establish the bacterial load upper limit in healthy controls; these values determined the bacterial colonisation prevalence in a longitudinal COPD cohort. Bacteriology combined with sputum inflammatory cells counts were used to investigate COPD endotypes. Methods: Sixty COPD patients and 15 healthy non-smoking controls were recruited. Sputum was analysed by qPCR (for Haemophilus influenzae, Moraxella catarrhalis, Streptococcus pneumoniae and Psuedomonas aeruginosa) and sputum differential cell counts at baseline and 6 months. Results: At baseline and 6 months, 23.1% and 25.6% of COPD patients were colonised with H. influenzae, while colonisation with other bacterial species was less common, e.g., S. pneumoniae—1.9% and 5.1%, respectively. H. influenzae + ve patients had higher neutrophil counts at baseline (90.1% vs. 67.3%, p < 0.01), with similar results at 6 months. COPD patients with sputum eosinophil counts ≥3% at ≥1 visit rarely showed bacterial colonisation. Conclusions: The prevalence of H. influenzae colonisation was approximately 25%, with low colonisation for other bacterial species. H. influenzae colonisation was associated with sputum neutrophilia, while eosinophilic inflammation and H. influenzae colonisation rarely coexisted.