Abstract As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

BACKGROUND:Early identification of children at risk of asthma attacks is important for optimizing treatment strategies. We aimed to integrate salivary microbiome and serum inflammatory mediator profiles with asthma attacks history to develop a comprehensive predictive model for future attacks. METHODS:This study contained a discovery (SysPharmPediA) and a replication phase (U-BIOPRED). School-aged children with asthma were classified into at risk and no-risk groups, based on the presence or absence of one or more severe attacks during one-year follow-up. Prediction models were developed using random forest on the training set (70%) with data on past asthma attacks, microbiome composition, serum inflammatory mediator levels, and their combinations and then tested on the rest of the population (30%). Outcomes were replicated in a subset of children with severe asthma from U-BIOPRED. RESULTS:Complete data were available for 154 children (SysPharmPediA = 121, U-BIOPRED = 33). In discovery, the model based on past attacks resulted in an area under the receiving characteristic curve (AUROCC) ~ 0.7. Models including six salivary bacteria or six inflammatory mediators achieved similar results. The combined model incorporating seven features, past asthma attacks, Capnocytophaga, Corynebacterium, and Cardiobacterium, TIMP-4, VEGF, and MIP-3β achieved the highest accuracy with AUROCC ~0.87. The combined model in the U-BIOPRED limited to available inflammatory mediators (VEGF), and incorporating past asthma attacks, Capnocytophaga, Corynebacterium, and Cardiobacterium, resulted in an AUROCC of 0.84. CONCLUSION:Serum inflammatory mediators and salivary microbiome complement asthma attacks history for predicting future attacks. These results highlight the imperative for continued investigation into oral microbiota and its interaction with the immune system.

BACKGROUND:Lung quantitative computed tomography (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways, using radiomultiomics, integrating qCT, multiomics and machine learning/artificial intelligence. METHODS:We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterise radiomultiomic-associated clusters (RACs). RESULTS:qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated body mass index, mild airflow limitation, decreased CT lung volume and increased lung density and upregulation of the complement pathway. RAC2 (n=34) subjects had airway wall thickness and a mild degree of airflow limitation, with upregulation of proliferative pathways including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B, and downregulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signalling and signalling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9. CONCLUSIONS:U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent a novel strategy to identify new molecular pathways in asthma pathobiology.

Introduction:The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods:Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results:Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion:The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

BackgroundChronic lung allograft dysfunction (CLAD) is the leading cause of poor long-term survival after lung transplantation (LT). Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) aimed to predict CLAD.MethodsTo predict CLAD, we investigated the clinicome of patients with LT; the exposome through assessment of airway microbiota in bronchoalveolar lavage cells and air pollution studies; the immunome with works on activation of dendritic cells, the role of T cells to promote the secretion of matrix metalloproteinase-9, and subpopulations of T and B cells; genome polymorphisms; blood transcriptome; plasma proteome studies and assessment of MSK1 expression.ResultsClinicome: the best multivariate logistic regression analysis model for early-onset CLAD in 422 LT eligible patients generated a ROC curve with an area under the curve of 0.77. Exposome: chronic exposure to air pollutants appears deleterious on lung function levels in LT recipients (LTRs), might be modified by macrolides, and increases mortality. Our findings established a link between the lung microbial ecosystem, human lung function, and clinical stability post-transplant. Immunome: a decreased expression of CLEC1A in human lung transplants is predictive of the development of chronic rejection and associated with a higher level of interleukin 17A; Immune cells support airway remodeling through the production of plasma MMP-9 levels, a potential predictive biomarker of CLAD. Blood CD9-expressing B cells appear to favor the maintenance of long-term stable graft function and are a potential new predictive biomarker of BOS-free survival. An early increase of blood CD4 + CD57 + ILT2+ T cells after LT may be associated with CLAD onset. Genome: Donor Club cell secretory protein G38A polymorphism is associated with a decreased risk of severe primary graft dysfunction after LT. Transcriptome: blood POU class 2 associating factor 1, T-cell leukemia/lymphoma domain, and B cell lymphocytes, were validated as predictive biomarkers of CLAD phenotypes more than 6 months before diagnosis. Proteome: blood A2MG is an independent predictor of CLAD, and MSK1 kinase overexpression is either a marker or a potential therapeutic target in CLAD.ConclusionSystems prediction of Chronic Lung Allograft Dysfunction generated multiple fingerprints that enabled the development of predictors of CLAD. These results open the way to the integration of these fingerprints into a predictive handprint.

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis b-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-b (transforming growth factor-b) (highest in the Veillonella cluster) and Wnt/b-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values,0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Background: The obese-asthma phenotype can be difficult-to-treat and severe in children; however, underlying mechanisms are unknown. Objective: Explore if gene expression patterns in children with mild/moderate vs severe (MMA vs SA) asthma differ by age and sex-adjusted BMI z-scores (BMIz), and if these differentially expressed patterns have functional significance. Methods: We performed a cross-sectional analysis of baseline clinical and blood transcriptome data from the U-BIOPRED cohort, including children aged 5-17 years with MMA (N=37) or SA (N=74). We ran gene-specific regression models for the outcome, asthma status (MMA vs SA), including age, sex, gene expression, BMIz, and a gene expression*BMIz interaction term. We then identified a list of top candidate genes (raw p<0.05) with differential expression by BMIz and assessed their functional significance using a pathway-centric approach. Results: We identified 157 down- and 258 up-regulated differentially expressed genes by BMIz. Pathway enrichment in severe asthma showed down-regulation of purinergic receptor pathways in children with lower BMIz, while DNA damage pathways were up-regulated in the higher BMIz group (Fig. 1). Conclusion: We found differential gene expression and implicated pathways for children with lower vs higher BMIz by asthma severity group, which could provide possible mechanisms underlying the obese-asthma phenotype. Larger studies are needed to validate this.

Background: Growing evidence indicates high comorbid anxiety and depression in patients with asthma. How-ever, the mechanisms underlying this comorbid condition remain unclear. The aim of this study was to inves-tigate the role of inflammation in comorbid anxiety and depression in three asthma patient cohorts of the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes (U-BIOPRED) project.Methods: U-BIOPRED was conducted by a European Union consortium of 16 academic institutions in 11 European countries. A subset dataset from subjects with valid anxiety and depression measures and a large blood biomarker dataset were analysed, including 198 non-smoking patients with severe asthma (SAn), 65 smoking patients with severe asthma (SAs), 61 non-smoking patients with mild-to-moderate asthma (MMA), and 20 healthy non-smokers (HC). The Hospital Anxiety and Depression Scale was used to measure anxiety and depression and a series of inflammatory markers were analysed by the SomaScan v3 platform (SomaLogic, Boulder, Colo). ANOVA and the Kruskal-Wallis test were used for multiple-group comparisons as appropriate.Results: There were significant group effects on anxiety and depression among the four cohort groups (p < 0.05). Anxiety and depression of SAn and SAs groups were significantly higher than that of MMA and HC groups (p < 0.05. There were significant differences in serum IL6, MCP1, CCL18, CCL17, IL8, and Eotaxin among the four groups (p < 0.05). Depression was significantly associated with IL6, MCP1, CCL18 level, and CCL17; whereas anxiety was associated with CCL17 only (p < 0.05).Conclusions: The current study suggests that severe asthma patients are associated with higher levels of anxiety and depression, and inflammatory responses may underlie this comorbid condition.

Free Article from Systems Biologists Seek Fuller Integration of Systems Biology Approaches in New Cancer Research Programs

Gregor Mendel is generally presented as the 'ignored and solitary founder of genetics'. This Moravian friar would have worked in strict isolation on the heredity of peas in the garden of his monastery, and his experiments would have been ignored by his contemporaries, before being 'rediscovered' independently by three botanists in 1900, 34 years after their publication. Historians have contributed to replace the genesis of Mendel's work in the context of his time, questioning the mythical image that prevailed in academic circles and the public perception. This paper recalls that Mendel benefitted from a very favourable context for the development of his experiments at St Thomas Monastery in Brno and was not isolated from the scientific community of his time. Although the notions on which his work was based were already present in scientific publications, this does not diminish the importance of Mendel's contribution to the development of modern biology. We provide a detailed analysis of the results of his experiments on the development of hybrid plants that he presented in two lectures at the Brno Society of Natural History in 1865, demonstrating that beyond his major contribution to the foundation of genetics, Mendel was one of the pioneers of systems biology.

A characteristic of living systems is their ability to self-organize. Using appropriate quantum mechanical and mathematical tools, we show that this ability may also be a characteristic of non-living systems, such as the electron, thus establishing an unsuspected relationship between biology and physics, whereby “biology precedes physics”. Planck's constant h is not a pure number but an elementary quantum of action, a “real atom” according to Henri Poincaré. Consequently, Louis de Broglie's equation λ = h/p must be rewritten in the form h = λp or lp. This approach gives a new meaning to quantum physics: the precise equivalence lp = h = Ed combined with the conceptual tools of Laurent Nottale's scale relativity allows the reunification of physics and support the development of promising engineering applications, notably in systems biology and medicine.

Rationale: There is a major unmet need for improving the care of children and adolescents with severe asthma and wheeze. Identifying factors contributing to disease severity may lead to improved diagnostics, biomarkers, or therapies. The airway microbiota may be such a key factor. Objectives: To compare the oropharyngeal airway microbiota of children and adolescents with severe and mild/moderate asthma/ wheeze. Methods: Oropharyngeal swab samples from school-age and preschool children in the European U-BIOPRED (Unbiased BIOmarkers in the PREDiction of respiratory disease outcomes) multicenter study of severe asthma, all receiving severityappropriate treatment, were examined using 16S ribosomal RNA gene sequencing. Bacterial taxa were defined as amplicon sequence variants. Results: We analyzed 241 samples from four cohorts: A) 86 school-age children with severe asthma; B) 39 school-age children with mild/moderate asthma; C) 65 preschool children with severe wheeze; and D) 51 preschool children with mild/moderate wheeze. The most common bacteria were Streptococcus (mean relative abundance, 33.5%), Veillonella (10.3%), Haemophilus (7.0%), Prevotella (5.9%), and Rothia (5.5%). Age group (school-age vs. preschool) was associated with the microbiota in b-diversity analysis (F = 3.32, P = 0.011) and in a differential abundance analysis (28 significant amplicon sequence variants). Among all children, we found no significant difference in the microbiota between children with severe and mild/moderate asthma/wheeze in univariable b-diversity analysis (F= 1.99, P = 0.08, N= 241), but a significant difference in amultivariable model (F = 2.66, P = 0.035), including the number of exacerbations in the previous year. Age was also significant when expressed as a microbial maturity score (Spearman Rho, 0.39; P= 4.6310210); however, this score was not associated with asthma/wheeze severity. Conclusions: There was a modest difference in the oropharyngeal airway microbiota between children with severe and mild/moderate asthma/wheeze across all children but not in individual age groups, and a strong association between the microbiota and age. This suggests the oropharyngeal airway microbiota as an interesting entity in studying asthma severity, but probably without the strength to serve as a biomarker for targeted intervention.

Introduction: Children with asthma or wheezing are reported to have airway microbial imbalances. This study aims to identify clusters in asthmatic and wheezing children using throat microbiota profiles. Methods: Throat samples were collected from the U-BIOPRED pediatric asthma and wheezing cohort [Fleming et al. ERJ 2015]. 16S rRNA sequencing was used to characterize the microbiome. Unsupervised hierarchical clustering was performed by the Bray-Curtis β-diversity [Abdel-Aziz et al. JACI 2021]. Enrichment scores of the MSigDB Hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Severe asthma and wheezing children were followed-up for 12-18 months, during which the frequency of exacerbations was assessed. Results: Throat samples of 241 children (median age: 6 years, 40% female) revealed 4 microbiome-driven clusters. Clusters 1-4 were Streptococcus-, Veillonella-, Rothia- and Haemophilus-dominant, respectively. The clusters showed significant differences in: atopy, eczema, FEV1 post-salb % pred, frequency of exacerbation/year during follow-up, and the TGF-β and the Wnt/β-Catenin signaling transcriptomic pathways in blood (Table 1). Conclusion: Oropharyngeal microbiome identified 4 distinct pediatric asthma/wheezing phenotypes that show associations with pathways involved in airway remodeling. This suggests that the airway microbiome may be associated asthma/wheezing pathophysiology.