Abderrahman Hachani

The increasing availability of microbial multi-omics datasets has created new opportunities to explore complex biological systems. However, exploration remains limited by the lack of accessible, reproducible workflows that integrate multiple omics layers and deliver easily interpretable visualizations of functional and pathway-level insights. Here, we present an R-based workflow for integrated analysis and network-based interpretation of microbial multi-omics data. The workflow enables microbiologists to analyse transcriptomic, proteomic and metabolomic datasets either individually or in combination, apply univariate and multivariate approaches for biomarker discovery and generate interpretable visualizations of functional and pathway-level signatures. Implemented as a multi-step R Markdown workflow, the resource leverages established open-source tools, including mixOmics for biomarker identification and omics integration and clusterProfiler for functional enrichment analysis. Functional integration is then performed on the enrichment results themselves rather than on individual features. By combining functional annotations from distinct omics layers, strains or conditions into a unified network, it enables simultaneous visualization of functional connectivity and of the sources from which each response was recovered, so that conserved and condition-specific responses can be identified directly rather than reconciled across separate outputs. A dedicated Shiny application makes this framework fully dynamic, allowing users to interactively filter, interpret and prioritize shared and condition-specific biological responses. We demonstrate its utility using a publicly available Streptococcus pyogenes dataset, revealing both shared and strain-specific functional responses to human serum. This resource provides an adaptable framework for systematic multi-omics analysis, improving accessibility and reproducibility and facilitating functional interpretation of microbial responses to diverse environments.

Methylglyoxal (MG) is a small and highly reactive byproduct of multiple metabolic reactions. In mammalian cells, MG serves important roles in cell signaling, but elevated levels of MG are toxic and contribute to the progression of many diseases, including autoimmune conditions and cancer. The study of MG is hampered by the lack of simple, accurate, and well-validated detection techniques. Here, we evaluate the previously reported methyl diaminobenzene-BODIPY (MBo) fluorescent probe by testing the detection of MG in solution, cell lysates, and live cells, using both plate reader and flow cytometry methods. We show that the MBo probe detects MG with a low nanomolar limit of detection in PBS and can quantitate low micromolar variations in MG abundance in both live cells and in lysed end point cellular assays. Our results highlight considerations for the use of MBo in common assays, including background assessments when using culture media containing serum, differential sensitivity with a range of lysis buffers, effects of fixation in flow cytometric assays, and depletion of residual MG in tissue culture media. Overall, this study establishes a framework using MBo to accurately assess MG in cellular systems.

Campylobacter jejuni is a leading global cause of acute foodborne gastroenteritis however, C. jejuni lacks some of the classic virulence determinants associated with other common enteric bacterial pathogens. In recent years an increasing number of C. jejuni isolates have been identified to encode Type Six Secretion System (T6SS), an apparatus utilised by Gram-negative bacteria to secrete toxic bacterial effectors into neighbouring cells. Despite the prevalence of the T6SS and previous investigations, the roles of the C. jejuni T6SS are still not well characterised especially when compared to our knowledge of other clinically relevant T6SS-positive bacterial species. Additionally, as of yet, no C. jejuni T6SS cargo effectors have been characterised. In this study, we show the C. jejuni 488 strain T6SS displays contact-dependent antagonistic behaviour towards T6SS-negative C. jejuni , Campylobacter coli , Escherichia coli and Enterococcus faecium strains suggesting the presence of the T6SS contributes to the competitive capacity of this C. jejuni T6SS-positive strain. Moreover, this antagonistic activity is linked to the functionality of CJ488_0980 and CJ488_0982, two novel putative Tox-REase-7 domain-containing effectors, which were identified through bioinformatical analysis of the C. jejuni 488 strain genome. Additionally, our investigations propose the C. jejuni 488 T6SS contributes to interaction, invasion and intracellular survival in human intestinal epithelial cells (IEC). Collectively, these initial findings are the first examples of in vitro investigation of putative cargo effectors in Campylobacter spp. and provide valuable insights into the roles of C. jejuni T6SS effectors in bacterial competition and pathogenesis. This study highlights the importance of T6SS as an emerging virulence determinant in Campylobacter spp. warranting further investigation.

Bloodstream infections caused by Staphylococcus aureus remain a leading cause of mortality worldwide. Our understanding of S. aureus survival and persistence in human serum, a cell-free fraction of blood hostile for bacteria, is still limited. Here, we applied multivariate data integration methods and network analysis to a multi-omic data set generated from five clinically prevalent S. aureus genotypes exposed to human serum. We observed, and then confirmed using isogenic mutants the significant roles of gapdhB , sucA , sirA , sstD , and perR in bacterial survival in serum. These data show that metabolic versatility in carbon source usage, iron transport, and resistance to oxidative stress is interlinked and central to S. aureus fitness in serum, representing potential S. aureus vulnerabilities that could be exploited therapeutically. IMPORTANCE: Bloodstream infections caused by Staphylococcus aureus are associated with mortality rates of up to 30%. However, the molecular mechanisms that enable this pathogen to survive in human serum—a nutrient-limited and immunologically hostile environment—remain poorly understood. By integrating multi-omic data from five clinically relevant S. aureus genotypes and validating key signatures using mutants, we identified conserved genetic determinants critical for bacterial survival in serum. Our findings highlight the interconnected roles of carbohydrate metabolic flexibility, iron acquisition, and oxidative stress resistance in shaping S. aureus adaptation to serum. This work advances our understanding of microbial strategies to survive in the bloodstream and demonstrates the potential of multi-omic integration to uncover therapeutic vulnerabilities in bacterial pathogens.

Mucosal-associated invariant T cells (MAIT cells) mediate tissue homeostasis and antimicrobial immunity. However, the cells that express major histocompatibility complex (MHC) class I–related protein 1 (MR1) and present microbial vitamin B–derived antigens (VitBAg) to MAIT cells remain unknown. We found that MR1 expression varied across tissues and cell types. Macrophages from the lung and peritoneal cavity expressed the highest levels of MR1 and were the most efficient at capturing and presenting VitBAg to MAIT cells. Expression of MR1 in macrophages was regulated transcriptionally and induced by the tissue environment and microbiota. Depletion of MR1 in macrophages, dendritic cells, and monocytes changed the composition of the microbiota and impaired MAIT cell responses against bacterial infection. We concluded that macrophages are key for MR1 antigen presentation and MAIT cell immunity.

The increasing availability of microbial multi-omics datasets has created new opportunities to explore complex biological systems. However, exploration remains limited by the lack of accessible, reproducible workflows that integrate multiple omics layers and deliver easily interpretable visualisations of functional and pathway-level insights. Here, we present an R-based workflow for integrated analysis and network-based pathway visualisation of microbial multi-omic data. The workflow enables microbiologists to analyse transcriptomic, proteomic, and metabolomic datasets either individually or in combination, apply univariate and multivariate approaches for biomarker discovery, and generate easily interpretable visualisations of functional and pathway-level signatures. Implemented as multi-step R Markdown, it leverages widely-used open-source tools, including mixOmics for biomarker identification and omics integration and clusterProfiler for pathway and functional enrichment analyses, with a new network-based integration and visualisation. Its flexible design supports a range of experimental structures and facilitates comparisons across strains, omics layers, and conditions, making it suitable for researchers with limited computational expertise. We demonstrate its utility using a publicly available Streptococcus pyogenes dataset, revealing both shared and strain-specific functional responses to human serum. This workflow provides a comprehensive and adaptable framework for systematic multi-omics analysis, improving accessibility and reproducibility and facilitating functional interpretation of microbial responses to diverse environments. Data summary: The code for this workflow is available on GitHub ( https://github.com/warasinee/Multiomics_Case_Study ). Datasets from our previously published study (1) were used to showcase the functionality and practical utility of the workflow. The multi-omics Streptococcus pyogenes dataset used in this study is available in the following public repositories: Gene Expression Omnibus ( GSE152821 ; GSE152822 ; GSE152823 ; GSE152824 , GSE152826 ), Proteomics Identifications Database ( PXD020863 ), and MetaobLights ( MTBLS2324 ) (1). Impact Statement: High-throughput omics technologies are transforming our understanding of how microbes adapt to diverse environments and cause disease. The integration of diverse omics layers at a systems level, combining transcriptomics, proteomics, and metabolomics data to identify signature molecules, pathways, and their interactions, remains challenging. Here, we present an R-based bioinformatic workflow designed for microbiology research, which connects existing tools and customised functions to streamline data integration and interpretation. The workflow links biomarkers to functional pathways, visualises results in an interactive network context, and is designed for flexibility and reproducibility. This practical resource lowers technical barriers to microbial multi-omics analysis, providing user-friendly access for dataset exploration and integration and supporting interpretation of system-level microbial adaptation in environmental, clinical, and industrial contexts.

COVID-19 has triggered the rapid adoption of human organoid-based tissue culture models to overcome the limitations of the commonly used Vero cell line that did not fully recapitulate SARS-CoV-2 infection of human tissues. As the primary site of SARS-CoV-2 infection, the human nasal epithelium (HNE) cultivated in vitro and differentiated at air–liquid interface (ALI) is an ideal model to study infection processes and for testing anti-viral antibodies and drugs. However, the need for primary basal cells to establish the ALI-HNE limits the scalability of this model system. To try and bypass this bottleneck, we devised an ALI-differentiated form of the human adenocarcinoma cell line Calu-3, reported to model most aspects of authentic SARS-CoV-2 infection, including viral entry. The ALI-Calu-3 were tested for infection by a panel of SARS-CoV-2 variants, including ancestral (VIC01) and early pandemic lineages (VIC2089, Beta, Delta), and Omicron subvariants (BA2.75, BA4, BA5, XBB1.5). All tested lineages infected the ALI-HNE. In stark contrast, infection of the ALI-Calu-3 by Omicron subvariants BA4 and XBB1.5 was reduced. These data support the use of ALI-Calu-3 as a complementary, intermediary model for most but not all SARS-CoV-2 lineages, and places the ALI-HNE as the benchmark culture model for SARS-CoV-2 infection.

Staphylococcus aureus is a leading cause of healthcare-associated pneumonia, contributing significantly to morbidity and mortality worldwide. As a ubiquitous colonizer of the upper respiratory tract, S. aureus must undergo substantial metabolic adaptation to achieve persistent infection in the distinctive microenvironment of the lung. We observed that fumC, which encodes the enzyme that converts fumarate to malate, is highly conserved with low mutation rates in S. aureus isolates from chronic lung infections. Fumarate, a pro-inflammatory metabolite produced by macrophages during infection, is regulated by the host fumarate hydratase (FH) to limit inflammation. Here, we demonstrate that fumarate, which accumulates in the chronically infected lung, is detrimental to S. aureus, blocking primary metabolic pathways such as glycolysis and oxidative phosphorylation (OXPHOS). This creates a metabolic bottleneck that drives staphylococcal FH (FumC) activity for airway adaptation. FumC not only degrades fumarate but also directs its utilization into critical pathways including the tricarboxylic acid (TCA) cycle, gluconeogenesis and hexosamine synthesis to maintain metabolic fitness and form a protective biofilm. Itaconate, another abundant immunometabolite in the infected airway enhances FumC activity, in synergy with fumarate. In a mouse model of pneumonia, a ΔfumC mutant displays significant attenuation compared to its parent and complemented strains, particularly in fumarate- and itaconate-replete conditions. Our findings underscore the pivotal role of immunometabolites in promoting S. aureus pulmonary adaptation.

Understanding Staphylococcus aureus adaptation is foundational for addressing the challenges posed by this pathogen. Using evolutionary convergence analysis on 7099 S. aureus genomes we uncovered frequent, reversible loss-of-function (LoF) mutations caused by simple sequence repeats (SSRs). Functional assays showed SSRs are potent, length-dependent switches that activate and deactivate genes, a mechanism exemplified by frequent LoF mutations in mutL (DNA mismatch repair) that causes a hypermutator phenotype. Analysis of over 600 episodes of human S. aureus colonisation and infection confirmed laboratory findings and showed that mutL SSR hypermutability behaves like a phase-variable evolutionary capacitor that unlocks subsequent adaptive SSR variations. We also experimentally demonstrate that SSRs facilitate foreign DNA uptake ( hsdR restriction endonuclease) and lead to gentamicin-resistant small colony variants that are known to cause persistent infections (menF menadione biosynthesis). Furthermore, the phenomenon is widespread, with 27 of 29 diverse human microbiome samples harbouring S. aureus with distinct SSR subpopulations, indicative of niche-specific adaptations. Thus, SSRs are potent reservoirs of previously unrecognized S. aureus genetic heterogeneity that confer rapid adaptive capacity, including in antigens targeted by past vaccine trials. A deeper understanding of SSR evolutionary trajectories has the potential to improve treatments and to anticipate S. aureus responses to new therapies.

Phosphoinositide 3-kinase delta (PI3Kδ) is essential for immune cell functions, preventing immunodeficiency and inflammation; however, its role in dendritic cell (DC)-mediated immune regulation remains unknown. Here, we report a key role for DC-intrinsic PI3Kδ in linking microbial recognition with antigen presentation for effective T-cell priming. Using genetic and functional assays, we demonstrate that PI3Kδ deficiency in DCs leads to broad dysregulation of intestinal CD4⁺ T-cell immunity, characterized by impaired regulatory T-cell expansion and increased susceptibility to colitis. DC-based studies show that PI3Kδ links pattern-recognition-receptor signaling to MHC class I- and II-restricted presentation of phagosome-associated antigens by facilitating NOX2-dependent oxidative burst through RAC2, while mitigating inflammasome activation. In contrast, PI3Kδ deficiency disrupts phagosomal pH balance, leading to accelerated acidification and proteolysis of antigens, impairing T-cell activation. Our study identifies PI3Kδ as a key coordinator of phagosome dynamics, important for DC adaptive programming that shapes T-cell responses and supports intestinal immune homeostasis.