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The number of newly approved antimicrobial compounds has been steadily decreasing over the past 50 years emphasizing the need for novel antimicrobial substances. Here we present M e x , a method for the high-throughput discovery of novel antimicrobials, that relies on E. coli self-screening to determine the bioactivity of more than ten thousand naturally occurring peptides. Analysis of thousands of E. coli growth curves using next-generation sequencing enables the identification of more than 1000 previously unknown antimicrobial peptides. Additionally, by incorporating the kinetics of growth inhibition, a first indication of the mode of action is obtained, which has implications for the ultimate usefulness of the peptides in question. The most promising peptides of the screen are chemically synthesized and their activity is determined in standardized susceptibility assays. Ten out of 15 investigated peptides efficiently eradicate bacteria at a minimal inhibitory concentration in the lower µ m or upper n m range. This work represents a step-change in the high-throughput discovery of functionally diverse antimicrobials.

Background Patients with uncontrolled asthma despite the use of inhaled corticosteroids (ICS), may have a variety of biological pathways driving their airway inflammation. Londamocitinib (AZD4604), a selective, inhaled, Janus kinase 1 inhibitor, has been designed to target a broad inflammatory cytokine profile including those classically unresponsive to ICS. The ARTEMISIA mechanistic study aims to provide a clear understanding of the pathways impacted by londamocitinib in the lung, determine how this impact is reflected in the nose and periphery, and identify candidate biomarkers of londamocitinib-treatment response in asthma. This article reports the design and objectives of the ARTEMISIA study. Methods ARTEMISIA is a placebo-controlled, double-blind study of adults with moderate-to-severe asthma aiming to assess the effects of inhaled londamocitinib on Type 2 (T2) and non-T2 driven inflammatory pathways. Extensive parallel bio-sampling of the lung target tissue, nasal mucosa, blood and urine will be performed prior to the first dose and after 4-weeks of treatment with either londamocitinib or placebo. The main objectives of the study are to evaluate the effect of londamocitinib on gene expression in endobronchial brushings and signal transducer and activator of transcription (STAT) phosphorylation in endobronchial biopsies. Key exploratory objectives include investigating the correlation between inflammatory phenotype-specific bronchial epithelial gene signatures and other biomarkers in the lung and peripheral samples; as well as analysis of transcriptomic, proteomic, and metabolomic biomarkers in the nose, blood, and urine. Discussion ARTEMISIA commenced recruitment in 2024 and is poised to deliver a deep understanding of the mechanism of action of londamocitinib and its potential to impact on a population of asthmatics with high unmet need. The multiomic analysis of paired central and peripheral samples may reveal novel insights into the connection and translation between these compartments, deepen understanding of airways disease, and identify novel candidate biomarkers for asthma and JAK activity. In addition to sampling the airway directly, with parallel nasal and peripheral bio-sampling mirrored by the Phase 2a AJAX study (NCT06020014), the ARTEMISIA study may provide a unique link between bronchial assessed mechanisms of action and clinical outcomes. Trial registration NCT06435273 (ClinicalTrials.gov). Registered 24th May 2024.

Background Liquid biopsy is a minimally-invasive method of sampling bodily fluids, capable of revealing evidence of cancer. The distribution of cell-free DNA (cfDNA) fragment lengths has been shown to differ between healthy subjects and cancer patients, whereby the distributional shift correlates with the sample’s tumour content. These fragmentomic data have not yet been utilised to directly quantify the proportion of tumour-derived cfDNA in a liquid biopsy. Results We used statistical learning to predict tumour content from Fourier and wavelet transforms of cfDNA length distributions in samples from 118 cancer patients. The model was validated on an independent dilution series of patient plasma. Conclusions This proof of concept suggests that our fragmentomic methodology could be useful for predicting tumour content in liquid biopsies.

High-density lipoproteins (HDL) contain hundreds of lipid species and proteins and exert many potentially vasoprotective and antidiabetogenic activities on cells. To resolve structure-function-disease relationships of HDL, we characterized HDL of 51 healthy subjects and 98 patients with diabetes (T2DM), coronary heart disease (CHD), or both for protein and lipid composition, as well as functionality in 5 cell types. The integration of 40 clinical characteristics, 34 nuclear magnetic resonance (NMR) features, 182 proteins, 227 lipid species, and 12 functional read-outs by high-dimensional statistical modeling revealed, first, that CHD and T2DM are associated with different changes of HDL in size distribution, protein and lipid composition, and function. Second, different cellular functions of HDL are weakly correlated with each other and determined by different structural components. Cholesterol efflux capacity (CEC) was no proxy of other functions. Third, 3 potentially novel determinants of HDL function were identified and validated by the use of artificially reconstituted HDL, namely the sphingadienine-based sphingomyelin SM 42:3 and glycosylphosphatidylinositol-phospholipase D1 for the ability of HDL to inhibit starvation-induced apoptosis of human aortic endothelial cells and apolipoprotein F for the ability of HDL to promote maximal respiration of brown adipocytes.

ObjectivesDespite several effective targeted therapies, biomarkers that predict whether a patient with psoriatic arthritis (PsA) will respond to a particular treatment are currently lacking.MethodsWe analysed proteomics data from serum samples of nearly 2000 patients with PsA in placebo-controlled phase-III clinical trials of the interleukin-17 inhibitor secukinumab. To discover predictive biomarkers of clinical response, we used statistical learning with controlled feature selection. The top candidate was validated using an ELISA and was separately assessed in a trial of almost 800 patients with PsA treated with secukinumab or the tumour necrosis factor inhibitor adalimumab.ResultsSerum levels of beta-defensin 2 (BD-2) at baseline were found to be robustly associated with subsequent clinical response (eg, American College of Rheumatology definition of 20%, 50% and 70% improvement) to secukinumab, but not to placebo. This finding was validated in two independent clinical studies not used for discovery. Although BD-2 is known to be associated with psoriasis severity, the predictivity of BD-2 was independent of baseline Psoriasis Area and Severity Index. The association between BD-2 and response to secukinumab was observed as early as 4 weeks and maintained up to 52 weeks. BD-2 was also found to predict response to treatment with adalimumab. Unlike in PsA, BD-2 was not predictive of response to secukinumab in rheumatoid arthritis.ConclusionsIn PsA, BD-2 at baseline is quantitatively associated with clinical response to secukinumab. Patients with high levels of BD-2 at baseline reach and sustain higher rates of clinical response after treatment with secukinumab.