Surfactant protein D inhibits growth, alters cell surface polysaccharide exposure and immune activation potential of Aspergillus fumigatus

© 2022 The Authors. Humoral immunity plays a defensive role against invading microbes. However, it has been largely overlooked with respect to Aspergillus fumigatus, an airborne fungal pathogen. Previously, we have demonstrated that surfactant protein D (SP-D), a major humoral component in human lung-alveoli, recognizes A. fumigatus conidial surface exposed melanin pigment. Through binding to melanin, SP-D opsonizes conidia, facilitates conidial phagocytosis, and induces the expression of protective pro-inflammatory cytokines in the phagocytic cells. In addition to melanin, SP-D also interacts with galactomannan (GM) and galactosaminogalactan (GAG), the cell wall polysaccharides exposed on germinating conidial surfaces. Therefore, we aimed at unravelling the biological significance of SP-D during the germination process. Here, we demonstrate that SP-D exerts direct fungistatic activity by restricting A. fumigatus hyphal growth. Conidial germination in the presence of SP-D significantly increased the exposure of cell wall polysaccharides chitin, α-1,3-glucan and GAG, and decreased β-1,3-glucan exposure on hyphae, but that of GM was unaltered. Hyphae grown in presence of SP-D showed positive immunolabelling for SP-D. Additionally, SP-D treated hyphae induced lower levels of pro-inflammatory cytokine, but increased IL-10 (anti-inflammatory cytokine) and IL-8 (a chemokine) secretion by human peripheral blood mononuclear cells (PBMCs), compared to control hyphae. Moreover, germ tube surface modifications due to SP-D treatment resulted in an increased hyphal susceptibility to voriconazole, an antifungal drug. It appears that SP-D exerts its anti-A. fumigatus functions via a range of mechanisms including hyphal growth-restriction, hyphal surface modification, masking of hyphal surface polysaccharides and thus altering hyphal immunostimulatory properties.Pasteur Roux-Cantarini Fellowship; UtechS Photonic BioImaging (Imagopole), C2RT, Institut Pasteur, supported by the French National Research Agency (France BioImaging; ANR-10–INBS–04; Investments for the Future)

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

International audienceBackground: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.Aims: This narrative review aims to explore the current use of ML In clinical microbiology.Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019.Content: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes.Implications: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings