{"doi":"10.3389/fmicb.2024.1504449","title":"Editorial: Recent advancements in mycobacterial diseases research","abstract":"According to the World Health Organization, the number of global tuberculosis (TB) cases in 2021 reached 10.6 million, a 4.5% rise from the number of cases recorded in 2020 (World Health Organization Global Tuberculosis Report-2022). This indicates the urgent need to develop measures to curtail the disease. The diverse host immune response to Mycobacterium tuberculosis (Mtb), the causative agent of TB, is influenced by both host and bacterial factors and the immune response of the host to infection [1].A pathologic hallmark of Mtb infection in humans is the formation of granulomas, a highly organized cellular structure at the site of infection [2; 3]. Although granulomas are considered hostprotective because they restrict Mtb into a confined space, the bacteria can utilize those granulomas to escape from killing by host immune cells. Thus, the fate of Mtb and the course of infection and disease progression is largely determined at the level of granulomas [4; 5; 6]. Granulomas are heterogeneous, comprised of a variable mixture of immune and non-immune cells as shown in the spectrum of human TB disease and recapitulated in various animal models of Mtb infection. However, granulomas consistently evolve, undergoing caseous necrosis and cavitation in active pulmonary TB patients, however, this is only observed in non-human primates and rabbit models of Mtb infection [2; 3]. While cavitary granulomas facilitate bacterial dissemination, highly cellular "solid" granulomas contain bacteria at the core. Importantly, the granulomas are encased by a layer of fibrotic material, comprised of various types of collagens and elastin secreted by the macrophages and fibroblasts surrounding the granulomas [4; 5; 6]. These fibrotic cores leave residual parenchymal tissue damage, which hinders successful antibiotic penetration into the granuloma to kill Mtb. However, the mechanism of collagen formation and the nature of collagen fibers that constitute the fibrotic zone of granulomas are not completely understood. Here, Song et.al describes an automated quantitative assay to determine fibrosis characteristics of various granuloma types using 16 tissue types of TB patients and lung sections from a Marmoset model of Mtb infection. The authors have used a conventional Masson trichrome staining method to visualize the extent of fibrosis in different types of lung granulomas. The authors used a novel, stain-free second harmonic generation (SHG) two-photon excited fluorescence (TPEF) microscopy to further characterize the nature of fibrosis in the granulomas. Results from these analyses show that in the fibrotic area, aggregated collagens, made of short and thick clusters of 200-620nm in size were the predominant form, compared to the long and thick disseminated collagens of 200-300nm size. Furthermore, MMP-9 which codes for matrix metalloproteinase-9, was found to be upregulated in the granulomas of various tissues. This study contributes a valuable quantitative imaging tool to gain insight into the fibrotic dynamics of TB granulomas. Further exploration and refinement of such tools to understand the mechanism of fibrosis in TB granulomas would facilitate the development of novel strategies to prevent TB sequelae and efficiently kill Mtb within those granulomas.The host immune cell recognizes mycobacteria through various pattern recognition receptors (PRRs), such as the Toll-like receptor (TLR). TLR recognizes pathogen-associated molecular patterns (PAMPs), including lipoprotein and lipopolysaccharides that are present in bacteria [7]. TLR1 recognizes tri-acylated lipopeptide, and TLR6 recognizes di-acyl lipopeptide [8]. TLR1 recognizes the 19 kD lipoprotein of Mtb [9], and TLR2 recognizes 19 kD lipoproteins, lipoarabinomannan and other Mtb PAMPs [10]. In this special issue, a study by Varshney et al showed an association between TLR2 deletion (-196 to -174) and TLR1 743 A > G gene polymorphism and drug-resistant pulmonary TB in a population","journal":"Frontiers in Microbiology","year":2024,"id":489153,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9494,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":915234,"name":"Jotam G. Pasipanodya","orcid":"0000-0002-8984-8379","position":1,"is_corresponding":false},{"id":336002,"name":"Selvakumar Subbian","orcid":"0000-0003-2021-8632","position":2,"is_corresponding":false},{"id":239632,"name":"Vishwanath Venketaraman","orcid":"0000-0002-2586-1160","position":3,"is_corresponding":false},{"id":566005,"name":"Matt D. Johansen","orcid":"0000-0001-5553-5270","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T02:08:23.929823Z","pmid":"39479208","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}