Anand Kumar Andiappan

Background: Basophils are important mediators in the pathobiology of atopic diseases, yet the heterogeneity of basophils within the perinatal environment remains incompletely understood. In our study, we aimed to perform comprehensive profiling of paired maternal and cord blood basophils using a single‐cell omics approach to look into their distinct transcriptional signatures. Methods: Basophils were isolated from four paired maternal‐cord blood samples at the time of delivery, including one mother who delivered monozygotic twins. Basophil characterization of maternal blood (MB) and cord blood (CB) was analyzed in flow cytometry. Single cell RNA sequencing (scRNA‐seq) was performed to further examine the basophils composition and heterogeneity in MB and CB. Results: We observed that CB basophils have lower expression of FCER1A, and no cell‐bound or circulating IgE levels as compared to MB. Our scRNA‐seq data show that MB and CB basophils are different in both transcriptomic and functional profiles. CB basophils exhibit greater heterogeneity than MB. Low interindividual variability between twin CB basophil samples confirmed that the transcriptomic difference between MB and CB basophils is biologically meaningful. Trajectory pseudotime analysis revealed that CB basophils have an earlier pseudotime, potentially indicating a more naïve state. In addition, we demonstrated that CB basophils exhibit distinct functional capacities. Conclusion: Our data identified protein and gene signatures in MB and CB basophils. We demonstrated that CB has greater heterogeneity than MB basophils, thereby highlighting the relevance of single cell profiling of basophils in the context of the maternal‐fetal interface.

The COVID-19 pandemic highlighted the urgent need to develop effective and broad-spectrum antiviral therapies against coronaviruses. One strategy to address this concern is a combination therapy using repurposed drugs against zoonotic viruses with pandemic potential. We previously demonstrated that the combination of Remdesivir and Ivermectin is highly potent and synergistic in inhibiting the replication of murine hepatitis virus (MHV) in RAW264.7 macrophages. This study investigated the interactions between the drug combination, coronavirus and host by proteomics and RNA sequencing of MHV-infected H2.35 murine liver epithelial cells. Time-of-addition and time-of-removal assays suggested that the drug combination likely affected the synthesis of viral RNA and viral protein. This combination drastically diminished the live virus titer greater than the respective monotherapies in MHV-infected H2.35 cells (by ~4 log₁₀), as well as in SARS-CoV-2-infected VeroE6 cells and human nasal epithelial cells. Proteomic and transcriptomic analyses revealed that viral protein and RNA levels were significantly depressed upon combination treatment. The drug combination exhibited considerable negative effects upon host RNA processes and resulted in the upregulation of host protein processes (e.g., response to unfolded protein; protein insertion into ER membrane). Molecular pathways affected by the combination treatment were markedly distinct from the monotherapies and indicated that Ivermectin enhances Remdesivir by modulating critical host processes to synergistically exert its inhibitory effect on the coronavirus replication cycle.

Prenatal stress (PS) is a repeated exposure to aversive situations during pregnancy, including high emotional strain, which is suspected to affect homeostatic systems in infants. Paediatric eczema develops quickly after birth at flexural sites subjected to continuous mechanical constraints^1,2. Although epidemiological studies have suggested an association between PS and a higher risk of eczema in children^3,4,5,6, no causative biological link has yet been identified. Here we show that eczema at birth originates from molecular dysregulations of neuroimmune circuits in utero, triggered by fluctuations in the maternal hypothalamic–pituitary–adrenal axis. We found that offspring of stressed pregnant dams have dysregulated mast cells and skin-projecting neurons and quickly develop eczema in response to harmless mechanical friction. We demonstrated that PS transiently modulates amniotic fluid corticosterone concentrations, which directly alters the activation program of skin mast cells expressing the glucocorticoid receptor Nr3c1 and the adjacent sensory neurons conveying mechanosensation. Therapeutic normalization of maternal corticosterone concentrations or genetic depletion of Mcpt5⁺ mast cells during stressed gestation prevents fetal immune dysregulation and protects against eczema development after birth. Our findings support a new model in which early-onset paediatric eczema originates from dysregulations in the fetal immune system, caused by fluctuations in maternal glucocorticoids induced by stress.

Rationale and Objectives: Iron availability and metabolism are important in the pathogenesis of bacterial infections. More recently, links have been reported between iron and the severity of viral infections. In this study, we characterize a crucial relationship between iron metabolism and IAV infection and disease. Methods: Iron-related gene expression was assessed in human airway epithelial cells (AEC) infected with IAV. AECs were cultured with ferric iron, iron-loaded transferrin, or iron chelator, deferoxamine (DFO), prior to infection with IAV. Mice were placed on a high iron diet for 8 weeks prior to infection with IAV or treated with anti-transferrin receptor-1 (TFR1) antibody during IAV infection. The effects of iron modulation and depletion of TFR1-mediated responses on IAV infection were assessed. Measurements and main results: Iron-related gene expression and metabolism are altered systemically and in lung tissues and AECs during IAV infections. Increasing iron availability increases viral titer in AECs, while DFO protects against iron-induced increased susceptibility to infection. Increasing systemic iron loading, which increases iron levels in the lung, increases viral titer, proinflammatory responses, airway inflammation, and worsens IAV-induced disease in terms of lung function and weight loss in vivo . Inhibition of TFR1 protects against IAV-induced disease in vivo . Conclusion: IAV infections remain a major threat to human health and global economies. Strategies that boost protective, or reduce pathogenic, host responses may provide broadly effective, long-term therapeutic options. We have identified a key role for iron metabolism in modifying host responses to IAV that can be harnessed to protect against disease. Key Messages: Iron metabolism is altered systemically, and in airway epithelial cells and lung tissues, during IAV infection. Increased iron availability increases viral titer both in vitro and in vivo Systemic iron loading worsens IAV-induced inflammation and disease outcomes in vivo, highlighting iron as a crucial factor for modulating IAV infections and disease. Host epithelial cells and lung tissues reduce TFRC gene expression, whilst the number and proportion of TFR1 ^hi expressing cells increase, in response to IAV infection. Neutralising TFR1 protects against IAV-induced disease in vivo, highlighting TFR1 as a potential therapeutic target for IAV infections.

Background: Atopic Dermatitis (AD) is a chronic condition characterised by recurrent skin lesions and itch, significantly impacting patients’ quality of life. AD is most common during childhood, with global prevalence ranging between 10% and 20%. Objective: Identifying protein biomarkers underlying AD in children, to better understand the pathogenesis. Methods: Cross-sectional study using untargeted tandem mass spectrometry to analyse skin tape protein extracts from 71 10-year-old Singaporean children. Subjects were stratified into three groups, no history of AD (NoAD=29), history of AD but no symptoms at time of sample collection (PastAD=22) and present AD (PresentAD=20). Significant proteins were shortlisted and functional analyses were performed. Results: 397 differentially expressed proteins were identified (p-valueConclusions: Multiple protein biomarkers and pathways, including keratinization, inflammatory responses and oxidative stress response, are involved in the onset and persistence of AD. Despite resolution of disease manifestations in PastAD subjects, functional analyses show that some pathways and biomarkers indicative of AD persist.

We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgroups: antipsychotic-responsive, clozapine-responsive, and clozapine-resistant. The cohort comprises 146 schizophrenia patients (49 antipsychotics-responsive, 68 clozapine-responsive, 29 clozapine-resistant) and 49 healthy controls. Protein levels of immune biomarkers were quantified using the Olink Target 96 Inflammation Panel (Olink®, Uppsala, Sweden). To predict labels, a support vector machine (SVM) classifier is trained on the Olink®data matrix and evaluated via leave-one-out cross-validation. Associated protein biomarkers are identified via recursive feature elimination. We constructed three separate predictive models for binary classification: one to discern healthy controls from individuals with schizophrenia (AUC = 0.74), another to differentiate individuals who were responsive to antipsychotics (AUC = 0.88), and a third to distinguish treatment-resistant individuals (AUC = 0.78). Employing machine learning techniques, we identified features capable of distinguishing between treatment response subgroups. In this study, SVM demonstrates the power of machine learning to uncover subtle signals often overlooked by traditional statistics. Unlike t-tests, it handles multiple features simultaneously, capturing complex data relationships. Chosen for simplicity, robustness, and reliance on strong feature sets, its integration with explainable AI techniques like SHapely Additive exPlanations enhances model interpretability, especially for biomarker screening. This study highlights the potential of integrating machine learning techniques in clinical practice. Not only does it deepen our understanding of schizophrenia’s heterogeneity, but it also holds promise for enhancing predictive accuracy, thereby facilitating more targeted and effective interventions in the treatment of this complex mental health disorder.

Background In this research study, we apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgroups: antipsychotic-responsive, clozapine-responsive, and clozapine-resistant. Methods The study comprises 146 schizophrenia patients (49 antipsychotics-responsive, 68 clozapine-responsive, 29 clozapine-resistant) and 49 healthy controls. Protein levels of immune biomarkers were quantified using the Olink Target 96 Inflammation Panel. To predict labels, a support vector machine classifier is trained on the Olink data matrix and evaluated via leave-one-out cross-validation. Associated protein biomarkers are identified via recursive feature elimination. Findings We constructed three separate predictive models for binary classification: one to discern healthy controls from individuals with schizophrenia (AUC = 0.74), another to differentiate individuals who were responsive to antipsychotics (AUC = 0.88), and a third to distinguish treatment-resistant individuals (AUC = 0.78). Employing machine learning techniques, we identified features capable of distinguishing between treatment response subgroups. Interpretation In this study, support vector machine demonstrates the power of machine learning to uncover subtle signals often overlooked by traditional statistics. Unlike t-tests, it handles multiple features simultaneously, capturing complex data relationships. Chosen for simplicity, robustness, and reliance on strong feature sets, its integration with artificial intelligence techniques like SHapely Additive exPlanations enhances model interpretability, especially for biomarker screening. This study highlights the potential of integrating machine learning techniques in clinical practice. Not only does it deepen our understanding of schizophrenia's heterogeneity, but it also holds promise for enhancing predictive accuracy, thereby facilitating more targeted and effective interventions in the treatment of this complex mental health disorder.

The scaffolding protein CARD11 is a critical mediator of antigen receptor signaling in lymphocytes. Hypomorphic (partial loss-of-function) mutations in CARD11 are associated with the development of severe atopic dermatitis, in which T cell receptor signaling is reduced and helper T cell differentiation is skewed to an allergy-associated type 2 phenotype. Here, we found that the docking protein DOK3 plays a key role in the pathogenesis of atopic dermatitis by suppressing CARD11 activity. DOK3 interacted with CARD11 and decreased its phosphorylation in T cells by recruiting the catalytic subunit of protein phosphatase 4, thereby dampening downstream signaling. Knocking out Dok3 enhanced the production of the cytokine IFN-γ by T cells, which conferred protection against experimental atopic dermatitis–like skin inflammation in mice. The expression of DOK3 was increased in T cells isolated from patients with atopic dermatitis and inversely correlated with IFNG expression. A subset of hypomorphic CARD11 variants found in patients with atopic dermatitis bound more strongly than wild-type CARD11 to DOK3. Our findings suggest that the strength of the interaction of DOK3 with CARD11 may predispose individuals to developing atopic dermatitis.