Antimicrobial resistance (AMR) poses a major global health threat that demands the discovery of new antimicrobial agents. Antimicrobial peptides (AMPs) offer a promising therapeutic alternative due to their broad-spectrum activity and reduced likelihood of resistance development. In the current study, we developed COMPASS, a comprehensive database aggregating 75,381 unique AMP sequences from nine public repositories, and created AmpGPT2, a transformer-based generative model specifically fine-tuned for AMP sequence generation. Unlike directed approaches, which optimize antimicrobial sequences or certain properties, our foundational model learns general AMP sequence patterns through an undirected training strategy. AmpGPT2 generated peptide sequences, of which 95.41% were predicted to be AMPs by AMP Scanner, representing a substantial improvement over existing models. The generated peptides exhibit physicochemical properties consistent with natural AMPs, including appropriate length distributions and molecular characteristics. Experimental validation demonstrated that one of five tested peptides, which shares structural features with dermaseptin-family AMPs, exhibited significant concentration-dependent antimicrobial activity against Klebsiella pneumoniae and Pseudomonas aeruginosa, supporting the model’s potential for functional AMP discovery. Highlighting the persistent challenge of translating computational predictions into biological function, this work establishes a foundational framework for AMP discovery that can serve as a basis for subsequent directed optimization strategies, potentially accelerating the development of novel antimicrobial therapeutics.

BACKGROUND AND OBJECTIVE:Incident airflow limitation is frequently diagnosed at advanced stages. Identifying individuals at risk through primary care spirometry may enable earlier intervention, yet validated, pragmatic frameworks remain lacking. METHODS:We applied a framework of three mutually exclusive spirometric at-risk phenotypes, termed Three-Phenotype Spirometry-Based Identification (hereafter TriSpi), to 6123 participants from two population-based cohorts: KORA (n = 1973, 3-year Follow-up, derivation) and SHIP (n = 4150, 5-year Follow-up, validation). TriSpi+ individuals were defined as meeting criteria for one of the three phenotypes: Early airflow limitation (EAL, FEV1/FVC > 0.7 and < 10th percentile or < 0.7 and > 5th percentile), small airway dysfunction (SAD) defined using FEF50- or FEF75-based thresholds; and preserved-ratio impaired-spirometry (PRISm). Associations with incident airflow limitation were tested using Firth's regression. RESULTS:EAL, PRISm, and SAD (defined using either FEF50 or FEF75-based thresholds) were significantly associated with incident airflow limitation across cohorts and follow-ups. TriSpi+ individuals accounted for 26%-36% of the population, identifying 74%-93% of future cases, while TriSpi+ was associated with a 10-25-fold increase in risk. Negative predictive values exceeded 95% across definitions, and the number needed to screen among TriSpi+ individuals ranged from 7 to 13. EAL showed the strongest individual association (OR up to 53.2), while SAD was more common in younger adults. CONCLUSION:Combining definitions for EAL, PRISm, and SAD enables robust prediction of incident airflow limitation. TriSpi may serve as a scalable, pragmatic approach for early risk stratification in primary care, in absence of post-BD spirometry.

Influenza A virus (IAV)-induced exacerbations are a major contributor to morbidity in chronic obstructive pulmonary disease (COPD), yet the epithelial mechanisms that govern these events remain unknown. We profiled the response to IAV infection of differentiated airway epithelial cells from healthy donors and individuals with COPD at single-cell resolution. The analysis revealed infection-driven shifts across multiple epithelial compartments and distinct alterations in cell-cell communication in COPD, associated with an increased CXCL11 expression. Functional assays demonstrated that CXCL11 augments mucus-associated gene and protein expression, particularly MUC5AC, increases mucus secretion and viscosity and is associated with reduction of virus-related immune pathways. This highlights CXCL11 as a contributor to both mucus hypersecretion and impaired antiviral epithelial responses in COPD exacerbations.

Background:Community-acquired pneumonia (CAP) is a leading cause of morbidity and mortality. While tools predicting short-term prognosis exist, there is urgent need for the early identification of patients requiring close follow-up monitoring for post-acute mortality. We therefore conducted cluster analysis of baseline clinical data to investigate predictors of post-acute mortality in CAP. Methods:We analysed 7840 participants from the German CAPNETZ cohort, using self-organising map (SOM)-clustering and survival analyses. Random survival forest (RSF) models were used to identify key predictors of mortality, which were then analysed using time-dependent area under the curve and Cox proportional hazard regression models. Results:SOM-clustering based on 10 predictors identified 879 (12%, in four clusters) patients with high risk for post-acute (30-180 days) mortality. Across the cohort, age and urea were the most important predictors of post-acute mortality, while in the high-risk cohort, body mass index emerged as the strongest predictor, as identified by RSF modelling. In one high-risk cluster, there was an association with elevated platelet counts (HR: 1.13, 95% CI 1.03-1.21, p=0.01; increments of 40 platelets·nL-1, c14, 35% of high-risk patients), in another (c15, 50% of high-risk patients) with elevated urea (HR: 1.06, 95% CI 1.01-1.11, p=0.02) and C-reactive protein (CRP) (HR: 1.27, 95% CI 1.01-1.58, p=0.04). Conclusion:Using 10 clinical predictors for post-acute mortality in CAP, predictive SOM-clustering revealed several high-risk subgroups, with heterogeneous biomarkers, suggestive of differences in the underlying pathophysiology (thrombocytes, urea, CRP). Adapting medical therapy to these high-risk subgroups may reduce post-acute mortality following CAP.

BACKGROUND:Eosinopenia has been associated with adverse outcomes in community-acquired pneumonia (CAP). However, its relationship with hospital resource use remains unclear. RESEARCH QUESTION:What is the association between admission eosinophil counts and hospital resource use among adults with CAP? STUDY DESIGN AND METHODS:This prospective multicenter cohort study (Community-Acquired Pneumonia Network of Competence [CAPNETZ]; Identifier: 2024-07-11-CHV6) has enrolled patients ≥ 18 years of age with CAP in university hospitals in Germany since 2017. Associations between admission blood eosinophil counts and hospital resource use-ICU admission, mechanical ventilation, and length of stay-were assessed using multivariable regression models. The optimal eosinophil count threshold for stratifying patients by ICU admission and mechanical ventilation rates was identified, and outcomes were compared between patients above and below this threshold. RESULTS:Lower eosinophil counts at admission were associated with increased ICU admission (n = 1,639; P < .001), including among patients treated with systemic glucocorticoids (P = .002) and those not receiving glucocorticoids (P = .047). Lower eosinophil counts also were associated with higher rates of mechanical ventilation (P = .014) and longer hospital stays (P = .024). An eosinophil count threshold of 10 cells/μL was identified as the cutoff that best distinguished patients with higher vs lower risk of ICU admission and mechanical ventilation. Patients with eosinopenia (≤ 10 cells/μL) showed higher ICU admission rates (14.2% vs 8.5%; P < .001; adjusted OR, 1.78), increased mechanical ventilation rates (9.1% vs 5.2%; P = .003; adjusted OR, 1.82), and longer hospitalization (mean, 10.2 days vs 9.0 days; P = .013). INTERPRETATION:Our results show that admission eosinopenia (≤ 10 cells/μL) was associated with greater hospital resource use and may serve as a practical biomarker for health care resource planning. CLINICAL TRIAL REGISTRATION:German Clinical Trials Register; No.: DRKS00005274; URL: https://drks.de/.

Small extracellular vesicles (sEVs) play a role in the pathophysiology of viral respiratory infections and may be suitable biomarkers for COVID-19 and Influenza infections, or targets for treatment. We investigated differences in the surface proteome of plasma sEVs in patients with COVID-19 and Influenza. In a discovery cohort with 117 patients, we used a random forest (RF) classifier in order to discriminate COVID-19 and Influenza patients based on routine clinical parameters. Furthermore, plasma samples from these patients were analyzed with an EV Array containing 33 antibodies to capture sEVs, which were then visualized with a combination of CD9, CD63, and CD81 antibodies. We applied an RF classifier and a random depth-first search (RDFS) approach to extract markers with the best discriminatory potential. Data were then validated in an independent set of patient samples on a chip-based ExoView platform.In the initial cohort of 117 patients, leukocyte numbers, and heart rate discriminated best between COVID-19 and Influenza infection. In the plasma samples, 32 EV surface markers could be detected. Feature panels containing CD9, CD81, and CD141 allowed a discrimination between COVID-19 and Influenza. Consecutively, increased CD9 abundance was validated in a second, independent cohort, with the ExoView technology. The increased CD9 signal in Influenza patients was confirmed and shown to be mostly driven by CD9/CD41a double positive sEVs, hinting at a thrombocyte origin.We identified leukocyte numbers and heart rate, as well as CD9 as a sEV surface marker to differentiate COVID-19 from Influenza patients.

BackgroundHuman precision-cut lung slices (hPCLS) are a unique platform for functional, mechanistic, and drug discovery studies in the field of respiratory research. However, tissue availability, generation, and cultivation time represent important challenges for their usage. Therefore, the present study evaluated the efficacy of a specifically designed tissue preservation solution, TiProtec, complete or in absence (-) of iron chelators, for long-term cold storage of hPCLS.MethodshPCLS were generated from peritumor control tissues and stored in DMEM/F-12, TiProtec, or TiProtec (-) for up to 28 days. Viability, metabolic activity, and tissue structure were determined. Moreover, bulk-RNA sequencing was used to study transcriptional changes, regulated signaling pathways, and cellular composition after cold storage. Induction of cold storage-associated senescence was determined by transcriptomics and immunofluorescence (IF). Finally, cold-stored hPCLS were exposed to a fibrotic cocktail and early fibrotic changes were assessed by RT-qPCR and IF.ResultsHere, we found that TiProtec preserves the viability, metabolic activity, transcriptional profile, as well as cellular composition of hPCLS for up to 14 days. Cold storage did not significantly induce cellular senescence in hPCLS. Moreover, TiProtec downregulated pathways associated with cell death, inflammation, and hypoxia while activating pathways protective against oxidative stress. Cold-stored hPCLS remained responsive to fibrotic stimuli and upregulated extracellular matrix-related genes such as fibronectin and collagen 1 as well as alpha-smooth muscle actin, a marker for myofibroblasts.ConclusionsOptimized long-term cold storage of hPCLS preserves their viability, metabolic activity, transcriptional profile, and cellular composition for up to 14 days, specifically in TiProtec. Finally, our study demonstrated that cold-stored hPCLS can be used for on-demand mechanistic studies relevant for respiratory research.

SUMMARY:Legionella pneumophila has significantly contributed to multiple cases of pneumonia with a high rate of mortality globally. Its ability to exploit host mechanisms through several expressed virulence factors poses challenges for diagnosis, treatment, and outbreak control. To address this, we developed LegionProfiler, a computational tool that swiftly identifies virulence factor protein domains within genome assemblies of Legionella pneumophila serogroup 1 isolates and classifies them into high- or low-virulence groups. LegionProfiler automates the probing of genome assemblies for virulence-associated protein domains and determines the isolate's potential to cause severe pneumonia infection. The LegionProfiler workflow is made available through a user-friendly interface to enhance technical control of infectious sources and adds important insights to the general epidemiology of clinical isolates. It could also support the development of targeted therapeutic strategies that will improve patient treatment. AVAILABILITY AND IMPLEMENTATION:LegionProfiler is freely accessible as a web service at https://legionprofiler.uni-muenster.de, and can also be run locally in a Docker container. The source code can be found at https://imigitlab.uni-muenster.de/heiderlab/legionprofiler or at Zenodo (DOI:10.5281/zenodo.15592325). SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Community-acquired pneumonia (CAP) is one of the most frequent causes of death among infectious diseases worldwide. Analyzing a dataset of 5,223 CAP patients in a German multicenter cohort study, our research uniquely explores the twofold combined impact of meteorological conditions, air quality conditions, and pre-existing chronic obstructive pulmonary disease (COPD) on CAP admissions. Both the twofold compound effect of absolute values of meteorological and air quality conditions and, even more, their day-to-day changes significantly influence CAP admissions. Our study emphasizes the important role of air quality conditions over meteorological conditions in contributing to increased CAP admissions, with these weather conditions exerting their influence with a lag time of approximately three to four days. Individuals with pre-existing COPD face the highest risk of CAP admission in the general cohort. The implications of our findings extend to supporting at-risk individuals through protective measures and providing healthcare providers with valuable insights for resource planning during pneumonia-inducing weather conditions.

RATIONALE: Human precision-cut lung slices (hPCLS) are a unique platform for functional, mechanistic, and drug discovery studies in respiratory research. Their relevance lies in their ability to maintain all resident cellular compartments (epithelial, mesenchymal, and immune cells) as well as the extracellular matrix (ECM) in their native three-dimensional structure. However, tissue availability, transportation, generation, and cultivation time represent important challenges for their usage. To address this, the present study aimed to evaluate the efficacy of a specifically designed tissue preservation solution (TiProtec) in the absence (-) or presence (+) of iron chelators as an alternative for long-term cold storage of hPCLS. METHODS: 500 µm hPCLS were generated and stored either in DMEM/F-12 medium or TiProtec (-/+) for up to 28 days. Viability, metabolic activity, and tissue structure were longitudinally determined. Bulk-RNA sequencing was used to study transcriptional changes, regulated signaling pathways, and changes in cellular composition after cold storage. Moreover, the induction of cold storage-associated cellular senescence was determined by transcriptomics and immunofluorescence (IF). To evaluate their potential for mechanistic studies in lung research, we evaluated the response to a previously described fibrotic cocktail after 7 and 14 days of cold storage in TiProtec (-/+) by IF and RT-qPCR. RESULTS: We demonstrated that TiProtec (+) preserves the viability, metabolic activity, transcriptional profile, and cellular composition of hPCLS for up to 14 days when compared to freshly sliced hPCLS. Moreover, cold storage did not significantly induce cellular senescence in hPCLS. Notably, TiProtec (+) downregulated pathways associated with cell death and inflammation while activating pathways protective against oxidative stress. Finally, cold-stored hPCLS remained responsive for up to 14 days to a fibrotic cocktail upregulating the expression of fibrosis-associated proteins such as fibronectin, alpha-smooth muscle actin, and alpha-1 type I collagen. CONCLUSION: This study provides for the first time insights into the transcriptional and functional changes associated with cold storage preservation of hPCLS. Moreover, it contributes to an optimized use of hPCLS, enabling banking, sharing, and on-demand processing and usage of hPCLS for translational lung research.

Thromboembolic complications are common in severe COVID-19 and are thought to result from excessive neutrophil-extracellular-trap (NET)-driven immunothrombosis. Glycosylation plays a vital role in the efficiency of immunoglobulin A (IgA) effector functions, with significant implications for NET formation in infectious diseases. This study represents the first comprehensive analysis of plasma IgA glycosylation during severe SARS-CoV-2 or Influenza A infection, revealing lower sialylation and higher galactosylation of IgA1 O-glycans in acute respiratory distress syndrome (ARDS), regardless of the underlying cause of the disease. Importantly, N-glycans displayed an infection-specific pattern, with N47 of IgA2 showing diminished sialylation and bisection, and N340/N327 of IgA1/2 demonstrating lower fucosylation and antennarity along with higher non-complex glycans in COVID-19 compared to Influenza. Notably, COVID-19 IgA possessed strong ability to induce NET formation and its glycosylation patterns correlated with extracellular DNA levels in plasma of critically ill COVID-19 patients. Our data underscores the necessity of further research on the role of IgA glycosylation in the modulation of pathogen-specific immune responses in COVID-19 and other infectious diseases.

Community-acquired pneumonia (CAP) is one of the most frequent causes of death among infectious diseases worldwide. There is a growing concern about weather impacts on CAP. However, no studies have examined the effects of comorbidities and personal characteristics alongside the twofold impact of weather conditions (meteorological and air quality) on CAP. Our study investigates how personal characteristics (age, sex, and BMI) and comorbidities (asthma, chronic heart disease, COPD, diabetes, heart insufficiency, smoking, and tumor) and care influence the twofold compound impact of weather on CAP admissions. We match medical data from a German multicentre cohort of 10,660 CAP patients with daily regional weather data, using logistic regressions to calculate the “Pneumonia Risk Increase Factor” (PRIF). This factor quantifies the heightened risk of CAP admissions due to weather conditions. We demonstrate that individuals with specific personal characteristics and those with comorbidities are more susceptible to weather impacts in the context of CAP than their counterparts. People with COPD have a PRIF of 5.28, followed by people in care (5.23) and people with a high BMI (4.02). Air pollutants, particularly CO and PM2.5, play a significant role in increasing CAP hospitalizations. For meteorological conditions, air pressure and lower temperatures, combined with air pollutants, lead to high PRIFs. Our findings emphasize the increased weather vulnerability of old, high BMI, and males and people with comorbidities. This provides invaluable information to support at-risk individuals through protective measures and provides healthcare providers as well as health policymakers with insights for resource planning before and during pneumonia-contributing weather conditions.