Experimental models have often been at the origin of immunological paradigms such as the M1/M2 dichotomy following macrophage polarization. However, this clear dichotomy in animal models is not as obvious in humans, and the separating line between M1-like and M2-like macrophages is rather represented by a continuum, where boundaries are still unclear. Indeed, human infectious diseases, are characterized by either a back and forth or often a mixed profile between the pro-inflammatory microenvironment (dominated by interleukin (IL)-1β, IL-6, IL-12, IL-23 and Tumor Necrosis Factor (TNF)-α cytokines) and tissue injury driven by classically activated macrophages (M1-like) and wound healing driven by alternatively activated macrophages (M2-like) in an anti-inflammatory environment (dominated by IL-10, Transforming growth factor (TGF)-β, chemokine ligand (CCL)1, CCL2, CCL17, CCL18, and CCL22). This review brews the complexity of the situation during infectious diseases by stressing on this continuum between M1-like and M2-like extremes. We first discuss the basic biology of macrophage polarization, function, and role in the inflammatory process and its resolution. Secondly, we discuss the relevance of the macrophage polarization continuum during infectious and neglected diseases, and the possibility to interfere with such activation states as a promising therapeutic strategy in the treatment of such diseases.

Hackathons are collaborative events that bring together diverse groups to solve predefined challenges. The COVID‐19 pandemic caused by SARS‐CoV‐2 has emphasized the need for portable and reproducible genomics analysis pipelines to study the genetic susceptibility of the human host and investigate human‐SARS‐CoV‐2 protein interactions. To build and strengthen institutional capacities in OMICS data analysis applied to host‐pathogen interaction (HPI), the PHINDaccess project organized two hackathons in 2020 and 2021. These hackathons are aimed at developing bioinformatics pipelines related to the SARS‐CoV‐2 viral genome, its phylodynamic transmission, and the identification of human genome host variants, with a focus on addressing global health challenges, particularly in low‐ and middle‐income countries (LMIC). This paper outlines the preparation, proceedings, and lessons learned from these hackathons, including the challenges faced by participants and our recommendations based on our experience for organizing hackathons in LMIC and beyond.

Leishmania (L.) major modulates host microRNA (miR) expression to promote intracellular survival, yet the roles of specific miRs during early infection of human macrophages remain poorly defined. We investigated the dynamic regulation of miR-21-5p and miR-146a-5p in THP-1-derived macrophages infected with L. major over an early post-infection time course (3–48 h), alongside miR-30a-3p and miR-101-3p. Both miR-21-5p and miR-146a-5p were significantly upregulated during the early stages of infection (3–24 h) before declining by 48 h. Using antisense LNA inhibitors, we achieved independent single and dual silencing of both miRs, allowing us to dissect their functional contribution to host gene expression. In silico network analysis identified 887 shared and specific target genes, including NFKB1, MYC, STAT3, HIF1A, TNF, CXCL8, IL18, and XBP1. Silencing of miR-21-5p and miR-146a-5p at early time points relieved inhibitory control over several of these targets, notably increasing NF-κB, MYC, IL18 and XBP1 expression, and reducing TNF and sXBP1, consistent with their role as negative regulators of innate immune signaling. These findings identify miR-21-5p and miR-146a-5p as early, dynamic regulators of the human macrophage transcriptional response to L. major infection and identify their target genes as candidate biomarkers and therapeutic targets in cutaneous leishmaniasis.

Wastewater-based metagenomics offers a comprehensive, population-level, and unbiased window into circulating microorganisms, complementing clinical surveillance by capturing signals across an entire connected population rather than only individuals seeking care. In Tunisia, previous wastewater investigations have relied primarily on targeted, single-site approaches, leaving the spatial and seasonal dynamics of wastewater microbiomes largely uncharacterized. This study presents the first longitudinal, multi-site shotgun metagenomic characterization of raw influent wastewater in Tunisia, sampling three treatment plants across the Greater Tunis area, serving populations ranging from approximately 60,000 to 450,000 inhabitants, over eight sampling events spanning winter and summer. Across 24 samples, Bacteria dominated the classified reads, with Proteobacteria as the dominant phylum and Aliarcobacter as the single most abundant genus. Genus-level richness varied significantly among treatment plants, forming a gradient that paralleled estimated population served, while community evenness remained comparatively stable across treatment plants and showed only a modest difference between the two sampling campaigns. Community structure differed significantly by both treatment plant and sampling campaign (winter vs. summer), and an exploratory genus-level differential abundance analysis identified largely distinct sets of taxa associated with each factor, with viral genera modestly overrepresented among campaign-associated taxa. Targeted screening detected clinically relevant bacterial genera and a standard fecal contamination marker in all 24 samples. Among genera previously reported as antimicrobial resistance (AMR) gene carriers, Acidovorax was significantly most abundant at one treatment plant. Dedicated screening of the assembled contigs against the CARD and ResFinder databases identified 129 non-redundant annotation events at 113 independent genomic loci (122 distinct database gene labels), and sample-resolved read mapping indicated that resistome composition differed between treatment plants and between the two sampling campaigns. These findings establish a population-level metagenomic baseline for wastewater microbiomes in Tunisia, demonstrating how longitudinal, multi-site sequencing can resolve spatial and between-campaign temporal structure relevant to environmental surveillance to support data-driven pathogen prioritization and pave the way for compliance with a One Health framework.