Background:Idiopathic pulmonary fibrosis (IPF) is a rare, incurable lung disease with a median survival of 3-5 years after diagnosis. Treatment options are limited. Genetic association studies can identify new genes involved in disease that might represent potential new drug targets, and it has been shown that drug targets with support from genetic studies are more likely to be successful in clinical development. Previous genome-wide association studies (GWAS) of IPF susceptibility have identified more than 20 signals implicating genes involved in multiple mechanisms, including telomere dysfunction, cell-cell adhesion, host defence immunity, various signalling pathways and, more recently, mitotic spindle assembly complex. Aim:To leverage new datasets and genotype imputation to discover further genes involved in development of IPF that could yield new pathobiological avenues for exploration and to guide future drug target discovery. Methods:We conducted a GWAS of IPF susceptibility including seven IPF case-control studies comprising 5,159 IPF cases and 27,459 controls of European ancestry, where IPF diagnosis was made by a respiratory clinician according to international guidelines. Genotypes were obtained from Whole Genome Sequencing (WGS) or from array-based imputation to the TOPMed WGS reference panel. New signals were replicated in independent biobanks with IPF defined using Electronic Healthcare Records. Bayesian fine-mapping was performed to identify the most likely causal variant(s) and bioinformatic investigation undertaken to map associated variants to putative causal genes. Results:We identified three novel genetic signals of association with IPF susceptibility. Genes prioritised by functional evidence at these signals included MUC1, which encodes a large transmembrane glycoprotein and known biomarker of lung fibrosis, and NTN4 encoding Netrin-4 whose known roles include angiogenesis. The third signal may map to SLC6A6, a taurine and beta-alanine transporter gene, previously implicated in retinal, cardiac and kidney dysfunction. Conclusion:Our study has identified new associations not previously identified by previous large biobank-based studies thereby highlighting the value of utilising clinically-curated IPF case-control studies, and new genotype imputation. We present new evidence for disease-driving roles of MUC1 and of endothelial cell and vascular changes in IPF.

Legionella pneumophila is an intracellular pathogen that causes Legionnaires disease, a severe pneumonia acquired primarily through contaminated water systems. Public health interventions rely on accurate estimates of the incubation period and dose-response (DR) relationship, yet currently used approaches in the literature underestimate incubation periods by assuming Markovian rupture times for infected macrophages. Here, we develop the first non-Markovian, two-scale within-host framework for Legionnaires disease, coupling stochastic intracellular replication in individual macrophages with extracellular Legionella-macrophage population dynamics. At the cellular level, we model intracellular replication using a stochastic logistic birth-death (SLBD) process, coupled with non-Markovian rupture-time distributions (Erlang and Burr). The Erlang distribution preserves tractability via the method of separation, whereas the Burr distribution captures heavy-tailed rupture times consistent with experimental data. Simulations are implemented using a renewal-based non-Markovian Gillespie algorithm. At the host level, successive infection and rupture events describe population-scale infection dynamics, enabling estimation of DR curves and incubation-period distributions. Across six model variants, DR predictions remain robust, with ID50 estimates narrowly ranging between 8.79 and 8.94 Legionella, consistent with guinea pig challenge data. In contrast, incubation-period estimates show strong dependence on rupture-time assumptions: non-Markovian models predict median incubation periods of 5-6 days, correcting the previous 2-3 day underestimation and aligning with human outbreak data (2-10 days, up to 13 days). Sensitivity analysis identifies rupture size, phagocytosis rates, and threshold effects as key determinants of incubation-period results. By relaxing exponential assumptions, our framework provides biologically realistic within-host dynamics that improve epidemiological predictions. These results refine the quantitative basis for outbreak investigations and environmental risk assessment and are generalizable to other intracellular pathogens such as Coxiella burnetii and Francisella tularensis. ### Competing Interest Statement The authors have declared no competing interest.

Introduction:The effect of coding polymorphisms of the β2-adrenergic receptor gene (ADRB2) on functional properties of the receptor is well established. We recently reported a genome-wide significant association between Thr164Ile and lung function, but the contribution of this variant to other traits remains unclear. Methods:To identify pleiotropic effects of ADRB2 Thr164Ile and other coding variants, we performed respiratory-focused and phenome-wide association studies in UK Biobank. In addition, we used available Olink proteomic data to characterise enriched pathways and upstream regulators of proteins associated with ADRB2 polymorphisms. Results:The minor T allele of Thr164Ile was associated with reduced lung function, but not COPD or asthma or risk of exacerbations on long-acting β-agonist treatment. It was also associated with nonrespiratory traits including increased eosinophil counts and blood lipid measurements, including increased cholesterol, reduced triglycerides and reduced apolipoprotein A. Proteins associated with Thr164Ile (p≤0.01) were enriched for various pathways, with the eosinophil-raising allele associated with reduced neutrophil degranulation, immunoregulatory interactions between lymphoid and nonlymphoid cells, tumour necrosis factor binding and DAP12 interactions, as well as activation of lipid metabolism pathways, including FXR/RXR activation and LXR/RXR activation. A gene-based analysis of rare, nonsynonymous ADRB2 variants, identified a novel association with nonrheumatic pulmonary valve disorders, but no association with lung function. Discussion:In conclusion, the lung function-lowering allele of Thr164Ile is associated with traits and proteins indicative of a role in immune and lipid metabolism pathways, suggesting potential targets for therapeutic intervention.

The COVID-19 pandemic has had varying impacts across different regions, necessitating localised data-driven responses. SARS-CoV-2 was first identified in a person in Wuhan, China, in December 2019 and spread globally within three months. While there were similarities in the pandemic's impact across regions, key differences motivated systematic quantitative analysis of diverse geographical data to inform responses. Malawi reported its first COVID-19 case on 2 April 2020 but had significantly less data than Global North countries to inform its response. Here, we present a modelling analysis of SARS-CoV-2 epidemiology and phylogenetics in Malawi between 2 April 2020 and 19 October 2022. We carried out this analysis using open-source tools and open data on confirmed cases, deaths, geography, demographics, and viral genomics. R was used for data visualisation, while Generalised Additive Models (GAMs) estimated incidence trends, growth rates, and doubling times. Phylogenetic analysis was conducted using IQ-TREE, TreeTime, and interactive tree of life. This analysis identifies five major COVID-19 waves in Malawi, driven by different lineages: (1) Early variants, (2) Beta, (3) Delta, (4) Omicron BA.1, and (5) Other Omicron. While the Alpha variant was present, it did not cause a major wave, likely due to competition from the more infectious Delta variant, since Alpha circulated in Malawi when Beta was phasing out and Delta emerging. Case Fatality Ratios were higher for Delta, and lower for Omicron, than for earlier lineages. Phylogeny reveals separation of the tree into major lineages as would be expected, and early emergence of Omicron, as is consistent with proximity to the likely origin of this variant. Both variant prevalence and overall rates of confirmed cases and confirmed deaths were highly geographically heterogeneous. We suggest that real-time analyses should be considered in Malawi and other countries, where similar computational and data resources are available.

During the COVID-19 pandemic, the emergence of novel variants of concern (VoCs) prompted different responses from governments across the world aimed at mitigating the impacts of more transmissible or more harmful strains. We model the invasion of a novel VoC into a population with heterogeneous vaccine- and infection-acquired immunity using a multi-type branching process framework with immigration. We define the number of cases needed to be reached to ensure stochastic extinction of this strain is unlikely and, therefore, the strain has become established in the population. To estimate the first-passage time distribution to reach this number of cases we use a mixture of stochastic simulations and analytic results. The first-passage time distribution gives a time window that is useful for policymakers planning interventions aimed at suppressing or delaying the introduction of novel VoC. We apply our method to a model of COVID-19 in the United Kingdom, though our results are applicable to other pathogens and settings.

This review outlines recent trends on invasive meningococcal disease (IMD) discussed at the latest meeting of the Global Meningococcal Initiative (GMI). There has been a re-emergence of the Hajj strain sublineage (serogroup W; ST-11 clonal complex), with travel to the Kingdom of Saudi Arabia being a critical factor in transmission. The epidemiology of IMD has also changed following the COVID-19 pandemic, with annual IMD cases increasing in many countries. For example, the highest number of IMD cases since 2014 was reported in the USA in 2023-2024. Atypical presentations of IMD have been prominent irrespective of the pandemic. For instance, an increase in cases of meningococcal epiglottitis has been reported in France in 2022-2023 (serogroups W and Y). When considering vaccination, the GMI has identified a need for broader meningococcal serogroup B (MenB) immunisation owing to the potential impact of the vaccines on reducing IMD incidence caused by other serogroups than MenB. There is also a case for using MenB vaccination to protect against Neisseria gonorrhoeae infection based on initial evidence, albeit further studies will need to be conducted.

The genus Neisseria includes two major human pathogens: N. meningitidis causing bacterial meningitis/septicemia and N. gonorrhoeae causing gonorrhoea. Mathematical models have been used to simulate their transmission and control strategies, and the recent observation of a meningococcal B (MenB) vaccine being partially effective against gonorrhoea has led to an increased modeling interest. Here we conducted a systematic review of the literature, focusing on studies that model vaccination strategies with MenB vaccines against Neisseria incidence and antimicrobial resistance. Using journal, preprint, and grey literature repositories, we identified 52 studies that we reviewed for validity, model approaches and assumptions. Most studies showed a good quality of evidence, and the variety of approaches along with their different modeling angles, was assuring especially for gonorrhoea studies. We identified options for future research, including the combination of both meningococcal and gonococcal infections in studies to have better estimates for vaccine benefits, and the spill over of gonorrhoea infections from the heterosexual to the MSM community and vice versa. Cost-effectiveness studies looking at at-risk and the wider populations can then be used to inform vaccine policies on gonorrhoea, as they have for meningococcal disease.

RATIONALE: Impaired lung function predicts mortality and is a diagnostic criterion for chronic obstructive pulmonary disease (COPD). Proteins are often the target of pharmacological interventions, therefore identifying causal links between proteins and lung function could inform understanding of COPD pathophysiology and suggest therapeutic targets. We aim to infer the potential impact of circulating protein levels on lung function, using strictly defined cis protein quantitative trait loci (cis-pQTLs) as genetic instrumental variables for Mendelian randomisation (MR). METHODS: We applied two-sample MR by integrating protein GWAS data (2,923 proteins, 48,195 UK Biobank European participants) with lung function GWAS data (four lung function traits, 149,166 European participants from 36 non-UK Biobank cohorts). We selected strictly defined cis-pQTLs, within 100 kilobase pairs of a transcription start site and strongly associated (P≤5×10-9) with protein levels, and applied single-cis-MR analysis (Wald ratio method). Sensitivity analyses included colocalization analysis (to distinguish causal effects from genomic confounding by linkage disequilibrium), and bidirectional MR to explore possible reverse causation. Replication analysis was conducted where possible. We used the Drug-Gene Interaction Database and phenome-wide association studies (PheWAS) to inform biological and clinical interpretation of identified proteins. RESULTS: We curated 1,841 proteins with a suitable cis-pQTL instrument, and evaluated evidence for causal effects of these proteins on four lung function traits. The single-cis MR analysis implicated 18 proteins for lung function at a Bonferroni-corrected threshold (Wald ratio estimator P<2.72×10-5). Of 10 proteins previously implicated by reported lung function signals, surfactant protein D (SFTPD) has been highlighted in previous respiratory MR analyses and variants in SFTPD have been previously reported to be associated with emphysema; our PheWAS suggested that this variant has a relatively specific effect on lung function as it was associated with no non-respiratory traits at a FDR<1%. In contrast to previous expression QTL evidence, our study suggested that ITGAV inhibition could reduce FEV1/FVC; we note that reduced lung function was also seen in a recent trial of an ITGAV inhibitor (NCT01371305). Our MR analysis implicated 8 novel proteins not implicated by previous GWAS (CCND2, DTD1, PILRA, PTPRK, TDRKH, GRHPR, NUDT5, SLITRK6); in our PheWAS the variants instrumenting these protein levels were associated with a wide range of traits. CONCLUSIONS: Our protein-based approach identified proteins that may be causally related for lung function variability. We highlight known protein drug targets, and identify several new proteins which are potentially therapeutic targets but warrant further follow up for potential utility and safety.

RATIONALE While genome-wide association studies (GWAS) have identified genetic variants associated with lung function, the extent to which age affects these associations remains underexplored. We performed a multi-ancestry, age-stratified GWAS and meta-regression of 930 known genetic variants associated with forced expiratory volume (FEV1), forced vital capacity (FVC), and FEV1/FVC to explore whether the effects of these variants on lung function showed heterogeneity by age at lung function measurement. METHODS Age-stratified association testing (using 10-year age bands) for 930 known lung function variants was performed in 798,308 individuals, from 44 cohorts and 5 ancestries (European, African, Admixed American, East Asian, South Asian). A meta-regression of the variant-lung function coefficients on mean age was performed, with adjustment for axes of genetic ancestry. Standardised measurements of genetic effects on lung function were used to account for variance in measurement distributions among age groups. Significance of the mean age term from the meta-regression indicated heterogeneity of a variant-lung function effect by age. We used quantile-quantile plots and Bernoulli tests to assess whether more signals demonstrated evidence of an age-dependent effect than we would expect by chance. RESULTS Of the 930 genetic variants known to be associated with FEV1, FVC, and FEV1/FVC, 110 showed significant age interactions (Pinteraction<0.05), a higher proportion than one would expect by chance (FEV1 P=3.62x10-7; FVC P=2.5x10-9; FEV1/FVC=0.044). For example, an intergenic variant near MGC57346−CRHR1 (rs11079718-A) had a smaller (negative) effect on FEV1 in children, little or no effect on FEV1 in adults aged 20-40 years, and a larger (positive) effect on FEV1 in older adults (Pinteraction=8.82x10−8). An intronic variant in BCL2L1 (rs6060627-T) was associated with a higher FVC in children (positive effect) but a lower (negative effect) FVC in adults (Pinteraction=0.001). An intergenic variant near MIR548F3 (rs62817-C) was associated with higher FEV1/FVC (positive effect) in adults aged 20-40, but a lower (negative effect) FEV1/FVC in adults aged over 40 years (Pinteraction=0.0055). CONCLUSIONS Around 12% of lung function-associated genetic variants exhibited effects on lung function that varied by age. Investigating how genetic variants influence lung function across the lifespan could contribute towards understanding of the molecular pathways involved and inform age-appropriate interventions. More powerful studies of this kind will depend on larger sample sizes in children and young adults.

Background: Understanding the spatiotemporal variation of COVID-19 transmission and its determinants is crucial for gaining deeper insights into the dynamics of disease spread. Regional and temporal differences in demographics, socioeconomic conditions, and environmental factors shaped the trajectory of the COVID-19 pandemic, underscoring the importance of advanced spatiotemporal modelling. This research aims to construct a spatiotemporal model to examine the relationship between age groups, poverty, population density, precipitation, and COVID-19 risk, as well as to pinpoint high-risk areas. Methods: Here we present a spatiotemporal statistical analysis using COVID-19 case data from Malawi recorded from 2 April 2020 to 27 March 2022. Bayesian spatiotemporal models were fitted, with weekly confirmed cases as the response variable and demographic, socioeconomic, and environmental factors as predictors. Results: The findings reveal that spatial and temporal factors, along with age, population density, and poverty, significantly affect observed COVID-19 incidence in Malawi, whereas precipitation does not. The greatest risk was observed during colder months (June-July), December festive season, and January. Urban centres and lake-shore districts were disproportionately impacted, with individuals aged 40-49 at particularly high risk. Conclusions: These results emphasise the need to prioritise vaccinations for working-age populations in urban and tourist areas during high-risk periods. Moreover, ensuring adherence to public health guidelines and enhancing healthcare services in these districts is critical. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Schlumberger Foundation-Faculty for the Future and the Wellcome Trust. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of the National Commission for Science and Technology, Malawi (Approval No: P.02/23/733) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Links to all data used in this study are provided in the manuscript.

RATIONALE Lung function predicts mortality and is a diagnostic criterion for COPD. Identification of causal genes and the variants and pathways that impact gene function and regulation can inform therapeutic interventions for COPD. Genome-wide association studies (GWAS) of imputed genotypes discovered 1,020 mostly common genetic variants associated with lung function. Whole-exome sequencing (WES) is better suited to study rarer protein-coding variants that may not be well imputed. We analysed the UK Biobank WES data to identify putative causal genes for lung function, not yet detected by GWAS, and to fine map the architecture of causal variants within genes. METHODS We included 343,104 European UK Biobank samples with WES data and four quantitative lung function phenotypes: FEV1, FVC, FEV1/FVC and peak expiratory flow (PEF). We performed single variant tests of 6.7 million variants with minor allele frequency (MAF) <1% and gene-based collapsing tests to enhance power to detect rare variant effects in aggregate. For gene-based tests models were run using MAF filters of <1%, <0.1%, <0.01%, <0.001% and singletons, and 2 variant function criteria: (i) predicted loss-of-function (pLoF); (ii) pLoF + deleterious missense. Qualifying variants were tested in aggregate for 18,468 genes using burden testing and methods accommodating opposing effect directions. RESULTSHMCN1 (Hemicentin-1), previously implicated by a common (MAF 24%) intronic variant, harboured a novel rare (MAF 0.03%) missense variant associated with FEV1/FVC (P=4.78×10-9) and was highly significant in gene-based testing (P=3.27×10-51) with a burden of over 900 pLoF and missense variants of MAF<0.01% contributing. This was notable amongst the other 17 genes with a significant gene-based result (P<2.69×10-6; Bonferroni correction for genes tested), with the next most significant result for LRP1 (P=6.73×10-15) driven by only 2 variants. In total rare variant testing implicated 28 genes, 8 of which have not been previously reported in GWAS. CONCLUSION We discover novel genes associated with lung function and highlight novel variants at known genes. In particular, we show allelic series for lung function – that is, statistically independent genetic variants that each implicate the same gene and show dose-response effects on lung function, such as the common, rare and very rare variants independently implicating HMCN1. Allelic series provide strong evidence to inform functional genomic studies and drug discovery.

SNV rs12477314 (C>T; 1000G MAF = 0.14), which maps to an intergenic region on 2q37.3, is a genome-wide significant association signal for pulmonary function in genome-wide association study meta-analyses. Bioinformatic analysis revealed that the intergenic region in proximity to the sentinel SNV is enriched for histone methylation markers suggestive of active enhancer regions modifiable by DNA methylation. The aim of this study was to investigate the functionality of putative enhancer/s and their potential interaction with CpG islands in the genomic region tagged by rs12477314 and their relevance to lung disease, in particular COPD. Two independent CRISPR/Cas9n-targeted deletions of the putative enhancer/s were performed in an airway epithelial cell line (NCI-H460). Deletion clones were subjected to RNA-Seq, and differential expression gene (DEG) datasets were generated using the Cufflinks version 2.2.1 pipeline (p-FDR < 0.05). Biological pathway analysis was performed using Qiagen's Ingenuity Pathway Analysis. Associations with the blood proteome were explored in UK Biobank. Our results suggest that the deleted regions are co-acting enhancers regulating overlapping gene expression patterns. The DEG datasets from the two genomic deletions are enriched for similar canonical pathways, which may contribute to a pro-inflammatory phenotype. Pathway-based regulatory effects analysis of the two DEG datasets resulted in identifying potential downstream biological processes. There was overlap between the pathways identified in protein association datasets and the DEG datasets. Our results suggest that the genomic region tagged by SNV rs12477314 constitutes a regulatory region responsible for regulating biological pathways conducive to a systemic inflammatory phenotype.

Interpreting viral mechanism of SARS-CoV-2 based on human body level is critical for developing more efficient interventions. Due to the limitation of data, limited models consider the viral dynamics of early phase of infection. The Human Challenge Study Killingley et al. (2022) enables us to garner data from the inoculation to the 14th day after the infection, which provides an overview of the SARS-CoV-2 within host infection dynamics. In the Human Challenge Study, each volunteer was inoculated with 10TCID50, approximately 55PFU, of a wild type of virus (Killingley et al. (2022)), and the data indicates that the viral load reduced below the detectable level within a day. The simplified within host models developed by Xu et al. (2023) explain the data from the Human Challenge Study (Killingley et al. (2022)). However, they do not explain the viral decay from Day 0 to Day 1. Hence, in this paper, we aim to develop a new viral mechanism to explain this phenomenon. Based on the simplified within host models developed by Xu et al. (2023), we consider that the virus will first go through an adjustment phase and then start to replicate. A new dose-response model is developed to evaluate the probability of infection by constructing a boundary problem. We will discuss this viral mechanism and fit the model to the data of the Human Challenge Study (Killingley et al. (2022)) by adopting AMC-SMC (approximate Bayesian computation-sequential Monte Carlo). Based on the results of parameter inference, we estimate that the adjusted viral load is around 1% of the inoculated viral load. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement all authors were supported by the TRACK: Transport Risk Assessment for COVID Knowledge project - EPSRC, EP/V032658/1 - ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Killingley et al. (2022), Safety, tolerability and viral kinetics during sars-cov-2 human challenge in young adults, Nature Medicine. https://www.nature.com/articles/s41591-022-01780-9 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used in the present study are publicly available.

RATIONALE: Impaired lung function predicts mortality and is a diagnostic criterion for chronic obstructive pulmonary disease (COPD). Proteins are often the target of pharmacological interventions, therefore identifying causal links between proteins and lung function could inform understanding of COPD pathophysiology and suggest therapeutic targets. We aim to infer the potential impact of circulating protein levels on lung function, using strictly defined cis protein quantitative trait loci (cis-pQTLs) as genetic instrumental variables for Mendelian randomisation (MR). METHODS: We applied two-sample MR by integrating protein GWAS data (2,923 proteins, 48,195 UK Biobank European participants) with lung function GWAS data (four lung function traits, 149,166 European participants from 36 non-UK Biobank cohorts). We selected strictly defined cis-pQTLs, within 100 kilobase pairs of a transcription start site and strongly associated (P<5E-9) with protein levels, and applied single-cis-MR analysis (Wald ratio method). Sensitivity analyses included colocalization analysis (to distinguish causal effects from genomic confounding by linkage disequilibrium), and bidirectional MR to explore possible reverse causation. Replication analysis was conducted where possible. We used the Drug-Gene Interaction Database and phenome-wide association studies (PheWAS) to inform biological and clinical interpretation of identified proteins. RESULTS: We curated 1,841 proteins with a suitable cis-pQTL instrument, and evaluated evidence for causal effects of these proteins on four lung function traits. The single-cis MR analysis implicated 16 proteins for lung function at a Bonferroni-corrected threshold (Wald ratio estimator P<1.71E-5), with evidence from colocalization. Of these, 10 proteins have been previously implicated either by lung function GWAS, or from other MR analyses with colocalization. Surfactant protein D (SFTPD) has been highlighted in previous respiratory MR analyses and variants in SFTPD have been previously reported to be associated with emphysema; our PheWAS suggested that this variant has a relatively specific effect on lung function as it was associated with no non-respiratory traits at a FDR<1%. In contrast to previous expression QTL evidence, our study suggested that ITGAV inhibition could reduce FEV1/FVC; we note that reduced lung function was also seen in a recent trial of an ITGAV inhibitor ([NCT01371305][1]). Our MR analysis implicated six proteins not implicated by previous lung function GWAS or MR (DTD1, PILRA, PTPRK, TDPRK, GRHPR, NUDT5). CONCLUSIONS: Our protein-based approach identified proteins that may be causally related for lung function variability. We highlight known protein drug targets, and identify several new proteins which are potentially therapeutic targets but warrant further follow up for potential utility and safety. ### Competing Interest Statement Richard J. Packer, Martin D Tobin and Anna L Guyatt receive collaborative funding from Orion Pharma, unrelated to the submitted work. ### Funding Statement This research was supported by a Wellcome Discovery Award (WT 225221/Z/22/Z). The research was partially supported by the NIHR Leicester Biomedical Research Centre and through an NIHR Senior Investigator Award to M.D.T. and I.P.H.; views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The funders had no role in the design of the study. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study is carried out in UK Biobank, which has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval (21/NW/0157). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01371305&atom=%2Fmedrxiv%2Fearly%2F2025%2F02%2F10%2F2025.02.07.25321860.atom

ABSTRACT Background Asthma is a heterogeneous disease characterized by overlapping clinical and inflammatory features. Objective This study aimed to provide insight into the systemic inflammatory profile in asthma, greater understanding of asthma endotypes and the contribution of genetic risk factors to both. Methods 4205 patients with asthma aged 16–60 were recruited from UK centers; serum cytokines were quantified from 708, including cytokines associated with Type 1, 2 and 17 inflammation. 3037 patients were genotyped for 25 single nucleotide polymorphisms associated with moderate‐severe asthma. Results Serum cytokines associated with Th2 inflammation showed high coordinated expression for example, IL‐4/IL‐5 (R2 = 0.513). The upper quartile of the serum cytokine data identified 43.7% of patients had high levels for multiple Th2 cytokines. However, the groups defined by serum cytokine profile were not clinically different. Childhood‐onset asthma was characterized by elevated total IgE, allergic rhinitis and dermatitis. Exacerbation prone patients had a higher BMI, smoking pack‐years, asthma control questionnaire score and reduced lung function. Patients with blood eosinophils of > 300 cells/µL had elevated total IgE and lower smoking pack‐years. None of these groups had a differential serum cytokine profile. Asthma risk alleles for; rs61816764 (FLG) and rs9303277 (IKFZ3) were associated with childhood onset disease (p = 2.67 × 10−4 and 2.20 × 10−7; retrospectively). No genetic variant was associated with cytokine levels. Conclusion Systemic inflammation in asthma is complex. Patients had multiple overlapping inflammatory profiles suggesting several disease mechanisms. Genetic risk factors for moderate‐severe asthma confirmed previous associations with childhood onset of asthma.

Rationale Genome-wide association studies (GWAS) identify new genomic signals that drive respiratory disease development. Drug targets supported by genetic studies, including GWAS, are twice as likely to be successful in clinical development [PMID:26121088]. However, a major challenge is prioritisation of GWAS signals and candidate causal genes for pre-clinical experimental follow-up. We identified 1,020 GWAS signals for lung function traits, implicating hundreds of genes and pathways [PMID:36914875]. We describe our prioritisation of non-coding GWAS signals using bioinformatics and CRISPR-based manipulation in a disease-relevant organoid model to identify candidate causal genes with confidence for further functional investigation. Methods Evidence from gene and protein expression datasets, functional annotation, rare variants and nearby genes implicated by Mendelian disease or mouse knockout phenotypes, were combined to map each of the 1020 signals to genes (Variant-to-Gene mapping, V2G). We integrated i) association data for additional respiratory traits (including asthma, IPF, COPD, P<1×10-3), ii) overlap with open chromatin (ATAC-seq, DNA footprinting) and enhancer RNA (eRNA), iii) functionally-informed fine-mapping and iv) significance of lung function association, to identify a long-list of signals for further characterisation. A subset of signals with the strongest V2G evidence (≥3 sources) were first evaluated for their effects on expression of their predicted target gene using CRISPR-interference (CRISPRi). Regions containing prioritised genetic variants were silenced in human alveolar type 2 cell organoids using CRISPRi with dCas9-KRAB and gRNA lentiviral constructs [PMID:34612202], followed by qRT-PCR to assess transcription. Results Of 1020 lung function GWAS signals, 135 had strong V2G mapping (≥3 sources of evidence), 270 were associated with additional respiratory traits, 96 overlapped with DNA footprints or eRNA and 1009 overlapped with open chromatin markers in lung cells. Forty-two signals were prioritised for further investigation based on one or more of those criteria. Of 12 signals prioritised by strong V2G only, CRISPRi-mediated silencing revealed that 9 affected the expression of the predicted target. For example, silencing rs34933034, resulted in an approximately 80% reduction of CSK expression compared to controls (CTRL=1.009±0.149, n=6; KD=0.198±0.226, n=8). In contrast, silencing rs12522114 increased MOCS2-DT expression sevenfold (CTRL=1.011±0.164, n=6; KD=7.271±8.641, n=9). Conclusions We prioritised a viable number of GWAS signals for CRISPRi and validated in silico V2G mapping in a disease-relevant organoid model. Our next tranche of CRISPRi experiments (ongoing) will use a newer dCas9-KRAB-MECP2 CRISPRi tool in single cells for Perturb-seq to evaluate global impacts on gene expression of GWAS signals with high functional priority but low V2G confidence.

Understanding the incubation period of Legionnaires’ disease is vital for accurate source-term identification. Traditionally, researchers estimate the dose-dependent incubation period from human outbreak data, but this method suffers from the inability to estimate the exposure dose retrospectively for each case. This challenge limit the precision of incubation-period analysis using human case data. Existing within-host models, such as ordinary differential equation (ODE)-based and discrete-event stochastic approaches, estimate the dose-dependent incubation period of Legionnaires’ disease. However, discrete-event models, while useful, are so computationally costly that the within-host dynamics must be simplified to solely the Legionella and macrophage interactions. This simplification makes the computation feasible, but precludes cytokine interactions and adaptive immune response modelling. In this paper, we develop a new approach to model the within-host dynamics of Legion-naires’ disease that focuses on reducing computational cost while maintaining accuracy. Specifically, we propose a hybrid framework that integrates and improves upon existing ODE and discrete event within-host models of Legionnaires’ disease. By integrating the previously developed ODE and discrete-event stochastic models with stochastic differential equation (SDE) models, we create a unified system that adapts dynamically throughout the infection process. We quantify the points at which each model becomes the optimal tool for describing the infection, resulting in a flexible simulation of disease dynamics. Our hybrid model aligns with observed human incubation-period data and is the first framework of its kind in this context. This advancement offers a more robust platform for testing additional biological assumptions and improving our understanding of Legionnaires’ disease. ### Competing Interest Statement The authors have declared no competing interest.