Sayyab, S. | bioRxiv Science

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↗

bioRxiv2016

A computational method for detection of structural variants using Deviant Reads and read pair Orientation: DevRO

BackgroundNext generation sequencing (NGS) technology has made it possible to perform high-resolution screens for structural variants. Computational methods for detection of structural variants utilize paired-end mapping information, depth of coverage, split reads, or some combination of such data. The available methods are particularly designed to detect structural variants in single genomes or multiple genomes in a pairwise manner. The aim of this study was to develop a bioinformatics pipeline for detection of large structural variants using multiple pooled populations.\n\nResultsHere we describe the method \"DevRO\", developed to enable identification of structural variants using short insert paired-ends and long-range mate-pairs. DevRO uses paired-end mapping information from both types of libraries for identification of inversions, deletions and duplications followed by read depth information to screen for copy number variants. DevRO can detect structural variants in multiple populations without the need for pairwise comparisons. It uses a combined approach based on (i) paired-end mapping and (ii) depth of coverage that gives power to the study as compared to traditional methods that are based on either of these. DevRO is also designed to detect deletions in the reference assembly, which is an added functionality as compared to available methods.\n\nConclusionWe report a bioinformatics pipeline \"DevRO\" for detection of structural variants using paired-end mapping and depth of coverage methods tested on sequencing reads from multiple pooled rabbit populations. This method is useful when large numbers of populations have been re-sequenced as compared to traditional methods that can detect structural variants in a pairwise manner.

bioinformatics↗