Biology subjects

Lerm, M.

Publications and source records attributed to Lerm, M..

4 recordsLinked to original sources

bioRxiv2026

Epigenetic Crosstalk Between BCG-Infected Macrophages and Naive Monocytes Potentiates Antimycobacterial Activity

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a leading cause of death from infectious disease. Infected cells secrete extracellular vesicles (EVs), nanosized membrane-bound particles containing bioactive molecules known to mediate intercellular communication and influence immune regulation during infection. We hypothesized that EVs secreted from Mycobacterium bovis Bacillus Calmette Guerin (BCG)-infected macrophages could epigenetically reprogram naive monocytes and enhance their mycobactericidal activity against Mtb. A transwell co-culture system was used to enable communication via EVs and soluble factors, between BCG-infected macrophages and naive monocytes (recipient macrophages). To assess the impact of these factors, we examined epigenetic reprogramming and the ability to control Mtb growth. BCG-infected macrophages released EVs with a distinct proteomic profile mapping to multiple tuberculosis-related pathways. Recipient macrophages exhibited altered DNA methylation patterns compared to those co-cultured with untreated, Staphylococcus aureus-infected or hydrogen peroxide-exposed macrophages. The proteomic cargo of EVs and differentially methylated genes in recipient cells showed significant interactions with TNF as a central hub enriched in both the phagosome and tuberculosis pathway. The epigenetic reprogramming was accompanied by a trend towards improved control of Mtb in vitro. BCG infection induces release of EVs that, together with soluble factors, induce targeted epigenetic remodeling in recipient macrophages. In this study, we demonstrate that extracellular vesicles (EVs) released during Mycobacterium bovis-infection carry a directed proteomic cargo mapping to several tuberculosis-related pathways. Furthermore, we show that these proteins interact with differentially methylated genes in EV-recipient macrophages. Our findings indicate that infected macrophages transmit epigenetic information via EVs. This work provides new insights into intercellular and epigenetic mechanisms linked to innate immune memory.

molecular biology↗

bioRxiv2025

System-level health profiling from blood DNA methylation with explainable deep learning

Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability. Here we develop an explainable framework that quantifies respiratory, cardiovascular, and metabolic status as bounded health scores (0-1) derived from sex-specific clinical reference ranges and disease penalties, and then predicts these scores from whole-blood DNAm. Using Generation Scotland and case-control samples (n = 14,496 individuals), we screened 39 covariates for disease relevance and DNAm predictability, yielding system- relevant panels that were aggregated into scores. We compressed DNAm profiles with a protein-interaction-guided autoencoder, and trained health predictors on 128- dimensional embeddings using fully connected networks. On held-out samples, models reproduced the composite scores with strong rank agreement (Spearman {rho} = 0.87, R2 = 0.71 for respiratory health; {rho} = 0.82, R2 = 0.66 for cardiovascular; {rho} = 0.81, R2 = 0.64 for metabolic) and recover expected population structure in a generally healthy cohort, with clear separation between "single-system low" and "multi-system low" phenotypes, and graded coupling across systems without redundancy. Further, the top features retrieved from each explainable predictor aligned with system biology: airway epithelial repair, hypoxia and inflammatory trafficking for respiratory; endothelial remodeling and cardiomyocyte programs for cardiovascular; glucose-lipid metabolism and metaflammation for metabolic. These results show that DNAm embeddings can yield accurate, transparent, and system-aware health profiling from blood, providing actionable summaries while revealing the molecular processes the models use to infer multi-system status. This approach positions DNAm embeddings plus interpretable penalty targets as a practical bridge from epigenomic signal to system-level triage and is extensible for evaluation in larger, more diverse cohorts.

bioinformatics↗

bioRxiv2021

CD4+CCR6+ T cells dominate the BCG-induced transcriptional signature

The century-old Mycobacterium bovis Bacillus Calmette-Guerin (BCG) remains the only licensed vaccine against tuberculosis (TB). Despite this, there is still a lot to learn about the immune response induced by BCG, both in terms of phenotype and specificity. Here, we investigated immune responses in adult individuals pre and 8 months post BCG vaccination. We specifically determined changes in gene expression, cell subset composition, DNA methylome, and the TCR repertoire induced in PBMCs and CD4 memory T cells associated with antigen stimulation by either BCG or a Mycobacterium tuberculosis (Mtb)-derived peptide pool. Following BCG vaccination, we observed increased frequencies of CCR6+ CD4 T cells, which includes both Th1* and Th17 subsets, and mucosal associated invariant T cells (MAITs). A large number of immune response genes and pathways were upregulated post BCG vaccination with similar patterns observed in both PBMCs and memory CD4 T cells, thus suggesting a substantial role for CD4 T cells in the cellular response to BCG. These upregulated genes and associated pathways were also reflected in the DNA methylome. We described both qualitative and quantitative changes in the BCG-specific TCR repertoire post vaccination, and importantly found evidence for similar TCR repertoires across different subjects. The immune signatures defined herein can be used to track and further characterize immune responses induced by BCG, and can serve as reference for benchmarking novel vaccination strategies.

immunology↗

bioRxiv2021

DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types

BackgroundFlow cytometry is a classical approach used to define cell types in peripheral blood. While DNA methylation signatures have been extensively employed in recent years as an alternative to flow cytometry to define cell populations in peripheral blood, this approach has not been tested in lung-derived samples. Here, we compared bronchoalveolar lavage with a more cost-effective and less invasive technique based on sputum induction and developed a DNA methylome-based algorithm that can be used to deconvolute the cell types in such samples. ResultsWe analyzed the DNA methylome profiles of alveolar macrophages and lymphocytes cells isolated from the pulmonary compartment. The cells were isolated using two different methods, sputum induction and bronchoalveolar lavage. A strong positive correlation between the DNA methylome profiles of cells obtained with the two isolation methods was observed, and in two of the donors, in which the correlation was best, a later analyses demonstrated that those subjects the samples were consistently derived from the lower part of the lungs. We also identified unique patterns of CpG methylation in DNA obtained from the two cell populations, which can be used as a signature to discriminate between the alveolar macrophages and lymphocytes by means of open-source algorithms. We validated our findings with external data and obtained results consistent with the previous findings. ConclusionsOur analysis opens up a new possibility to identify different cell populations from lung samples and promotes sputum induction as a tool to study immune cell populations from the lung.

genomics↗