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Trachoma is a leading cause of infection-related blindness worldwide. This disease is caused by recurrent Chlamydia trachomatis (Ct) infections of the conjunctiva and develops in two phases: i) active (acute trachoma, characterized by follicular conjunctivitis), then long-term: ii) scarring (chronic trachoma, characterized by conjunctival fibrosis, corneal opacification and eyelid malposition). Scarring trachoma is driven by the number and severity of reinfections. The immune system plays a pivotal role in trachoma including exacerbation of the disease. Hence the immune system may also be key to developing a trachoma vaccine. Therefore, we characterized clinical and local immune response kinetics in a non-human primate model of acute conjunctival Ct infection and disease. The conjunctiva of non-human primate (NHP, Cynomolgus monkeys-Macaca fascicularis-) were inoculated with Ct (B/Tunis-864 strain, B serovar). Clinical ocular monitoring was performed using a standardized photographic grading system, and local immune responses were assessed using multi-parameter flow cytometry of conjunctival cells, tear fluid cytokines, immunoglobulins, and Ct quantification. Clinical findings were similar to those observed during acute trachoma in humans, with the development of typical follicular conjunctivitis from the 4th week post-exposure to the 11th week. Immunologic analysis indicated an early phase influx of T cells in the conjunctiva and elevated interleukins 4, 8, and 5, followed by a late phase monocytic influx accompanied with a decrease in other immune cells, and tear fluid cytokines returning to initial levels. Our NHP model accurately reproduces the clinical signs of acute trachoma, allowing for an accurate assessment of the local immune responses in infected eyes. A progressive immune response occurred for weeks after exposure to Ct, which subsided into a persistent innate immune response. An understanding of these local responses is the first step towards using the model to assess new vaccine and therapeutic strategies for disease prevention.

Quantitative analysis of bacterial dynamics in time-lapse microscopy requires robust tracking pipelines, yet selecting and optimizing algorithms for specific experiments remains challenging. Indeed, Microbiologists are confronted with numerous algorithms that must be carefully chosen and parameterized to achieve optimal tracking for their experiments. We present an automated methodology to determine optimal tracking configurations for microbiological applications. It is based on TrackMate 8, a novel version of the TrackMate Fiji plugin extended with microbiology-specific tools. Our approach systematically evaluates algorithm-parameter combinations optimizing biologically relevant metrics (e.g., cell-cycle accuracy, bacteria morphology) and includes: (1) integration of deep-learning algorithms (Omnipose, YOLO, Trackastra) adequate for bacteria images in TrackMate, (2) a TrackMate-Helper extension for parameter optimization, and (3) a tracking and segmentation editor for tracking ground-truth generation. We demonstrate the effectiveness of the methodology on two use cases showing its adaptability to diverse experimental conditions. This methodology enables microbiologists with a widely applicable, automated framework to optimize tracking pipelines, facilitating quantitative analysis in bacterial imaging.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Fixed upload of badly formatted Supplemental Information file.* https://zenodo.org/records/17909896* https://zenodo.org/records/17911259Funder Information DeclaredAgence Nationale de la Recherche, ANR-24-INBS-0005 FBI BIOGEN, ANR-10-PATH-003 HELDIVPAT, ANR-10-LBX-62 IBEID, ANR-16-CONV-0005 INCEPTION, ANR-17-EURE-0012 EURIP, ANR-19-CE44-0014O2-TABOOEuropean Research Council, DESTOP European Research Council (ERC) Advanced grant (101097791), PGNfromSHAPEtoVIR, FP7-202283, IMI 2 Joint Undertaking (JU) under Grant Agreement No 853989Fondation pour la Recherche Médicale, EQU202403018034, FDT202504020138Gates Foundation, IMI 2 Joint Undertaking (JU) under Grant Agreement No 853989

Background: Trachoma -the leading cause of blindness worldwide as a result of infection- is caused by repeated Chlamydia trachomatis (Ct) conjunctival infections. Disease develops in two phases: i) active (acute trachoma, characterized by follicular conjunctivitis), then long-term ii) scarring (chronic trachoma, characterized by conjunctival fibrosis, corneal opacification and eyelid malposition). Scarring trachoma is driven by the number and the severity of reinfections. The immune system is a pivotal aspect of disease, involved in disease aggravation, but also key for exploitation in development of a trachoma vaccine. Therefore, we characterized clinical and local immune response kinetics in a non-human primate model of acute conjunctival Ct infection and disease. Methodology/Principal Findings: The conjunctiva of non-human primate (NHP, Cynomolgus monkeys -Macaca fascicularis-) were inoculated with Ct (B/Tunis-864 strain, B serovar). Clinical ocular monitoring was performed using a standardized photographic grading system, and local immune responses were assessed using multi-parameter flow cytometry of conjunctival cells, tear fluid cytokines, immunoglobulins, and Ct quantification. Clinical findings were similar to those observed during acute trachoma in humans, with the development of typical follicular conjunctivitis from the 4th week post-exposure to the 11th week. Immunologic analysis revealed an early phase influx of T cells in the conjunctiva and elevated interleukins 4, 8, and 5, before a later phase monocytic influx accompanied by a decrease in other immune cells, and tear fluid cytokines returning to initial levels. Conclusion/Significance: Our NHP model accurately reproduces acute trachoma clinical signs, allowing for the precise assessment of the local immune responses in infected eyes. A progressing immune response occurred for weeks after exposure to Ct, which subsided into persistence of innate immune responses. Understanding these local responses is the first step towards using the model to assess new vaccine and therapeutic strategies to prevent disease.Competing Interest StatementFunding for this work was recieved from the Thea foundation (EP, ML, AR). Additionnally roles of consultants and occasional speakers for the Thea foundation is disclosed (ML, AR). These competing interests do not alter adherence to PLOS policies on sharing data and materials.