The Transcription Factor Stp2 Is Important for Candida albicans Biofilm Establishment and Sustainability.

The fungal pathogen Candida albicans forms polymorphic biofilms where hyphal morphogenesis and metabolic adaptation are tightly coordinated by a complex intertwined network of transcription factors. The sensing and metabolism of amino acids play important roles during various phases of biofilm development - from adhesion to maturation. Stp2 is a transcription factor that activates the expression of amino acid permease genes and is required for environmental alkalinization and hyphal growth in vitro and during macrophage phagocytosis. While it is well established that Stp2 is activated in response to external amino acids, its role in biofilm formation remains unknown. In addition to widely used techniques, we applied newly developed approaches for automated image analysis to quantify Stp2-regulated filamentation and biofilm growth. Our results show that in the stp2Δ deletion mutant adherence to abiotic surfaces and initial germ tube formation were strongly impaired, but formed mature biofilms with cell density and morphological structures comparable to the control strains. Stp2-dependent nutrient adaptation appeared to play an important role in biofilm development: stp2Δ biofilms formed under continuous nutrient flow displayed an overall reduction in biofilm formation, whereas under steady conditions the mutant strain formed biofilms with lower metabolic activity, resulting in increased cell survival and biofilm longevity. A deletion of STP2 led to increased rapamycin susceptibility and transcriptional activation of GCN4, the transcriptional regulator of the general amino acid control pathway, demonstrating a connection of Stp2 to other nutrient-responsive pathways. In summary, the transcription factor Stp2 is important for C. albicans biofilm formation, where it contributes to adherence and induction of morphogenesis, and mediates nutrient adaption and cell longevity in mature biofilms.

Enolase From Aspergillus fumigatus Is a Moonlighting Protein That Binds the Human Plasma Complement Proteins Factor H, FHL-1, C4BP, and Plasminogen.

The opportunistic fungal pathogen Aspergillus fumigatus can cause severe infections, particularly in immunocompromised individuals. Upon infection, A. fumigatus faces the powerful and directly acting immune defense of the human host. The mechanisms on how A. fumigatus evades innate immune attack and complement are still poorly understood. Here, we identify A. fumigatus enolase, AfEno1, which was also characterized as fungal allergen, as a surface ligand for human plasma complement regulators. AfEno1 binds factor H, factor-H-like protein 1 (FHL-1), C4b binding protein (C4BP), and plasminogen. Factor H attaches to AfEno1 via two regions, via short conserved repeats (SCRs) 6-7 and 19-20, and FHL-1 contacts AfEno1 via SCRs 6-7. Both regulators when bound to AfEno1 retain cofactor activity and assist in C3b inactivation. Similarly, the classical pathway regulator C4BP binds to AfEno1 and bound to AfEno1; C4BP assists in C4b inactivation. Plasminogen which binds to AfEno1 via lysine residues is accessible for the tissue-type plasminogen activator (tPA), and active plasmin cleaves the chromogenic substrate S2251, degrades fibrinogen, and inactivates C3 and C3b. Plasmin attached to swollen A. fumigatus conidia damages human A549 lung epithelial cells, reduces the cellular metabolic activity, and induces cell retraction, which results in exposure of the extracellular matrix. Thus, A. fumigatus AfEno1 is a moonlighting protein and virulence factor which recruits several human regulators. The attached human regulators allow the fungal pathogen to control complement at the level of C3 and to damage endothelial cell layers and tissue components.

Automated tracking of label-free cells with enhanced recognition of whole tracks.

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

Coding of Experimental Conditions in Microfluidic Droplet Assays Using Colored Beads and Machine Learning Supported Image Analysis.

To efficiently exploit the potential of several millions of droplets that can be considered as individual bioreactors in microfluidic experiments, methods to encode different experimental conditions in droplets are needed. The approach presented here is based on coencapsulation of colored polystyrene beads with biological samples. The decoding of the droplets, as well as content quantification, are performed by automated analysis of triggered images of individual droplets in-flow using bright-field microscopy. The decoding strategy combines bead classification using a random forest classifier and Bayesian inference to identify different codes and thus experimental conditions. Antibiotic susceptibility testing of nine different antibiotics and the determination of the minimal inhibitory concentration of a specific antibiotic against a laboratory strain of Escherichia coli are presented as a proof-of-principle. It is demonstrated that this method allows successful encoding and decoding of 20 different experimental conditions within a large droplet population of more than 105 droplets per condition. The decoding strategy correctly assigns 99.6% of droplets to the correct condition and a method for the determination of minimal inhibitory concentration using droplet microfluidics is established. The current encoding and decoding pipeline can readily be extended to more codes by adding more bead colors or color combinations.

Aspf2 From Aspergillus fumigatus Recruits Human Immune Regulators for Immune Evasion and Cell Damage.

The opportunistic fungal pathogen Aspergillus fumigatus can cause life-threatening infections, particularly in immunocompromised patients. Most pathogenic microbes control host innate immune responses at the earliest time, already before infiltrating host immune cells arrive at the site of infection. Here, we identify Aspf2 as the first A. fumigatus Factor H-binding protein. Aspf2 recruits several human plasma regulators, Factor H, factor-H-like protein 1 (FHL-1), FHR1, and plasminogen. Factor H contacts Aspf2 via two regions located in SCRs6-7 and SCR20. FHL-1 binds via SCRs6-7, and FHR1 via SCRs3-5. Factor H and FHL-1 attached to Aspf2-maintained cofactor activity and assisted in C3b inactivation. A Δaspf2 knockout strain was generated which bound Factor H with 28% and FHL-1 with 42% lower intensity. In agreement with less immune regulator acquisition, when challenged with complement-active normal human serum, Δaspf2 conidia had substantially more C3b (>57%) deposited on their surface. Consequently, Δaspf2 conidia were more efficiently phagocytosed (>20%) and killed (44%) by human neutrophils as wild-type conidia. Furthermore, Aspf2 recruited human plasminogen and, when activated by tissue-type plasminogen activator, newly generated plasmin cleaved the chromogenic substrate S2251 and degraded fibrinogen. Furthermore, plasmin attached to conidia damaged human lung epithelial cells, induced cell retraction, and caused matrix exposure. Thus, Aspf2 is a central immune evasion protein and plasminogen ligand of A. fumigatus. By blocking host innate immune attack and by disrupting human lung epithelial cell layers, Aspf2 assists in early steps of fungal infection and likely allows tissue penetration.

Migration and interaction tracking for quantitative analysis of phagocyte-pathogen confrontation assays.

Invasive fungal infections are emerging as a significant health risk for humans. The innate immune system is the first line of defense against invading micro-organisms and involves the recruitment of phagocytes, which engulf and kill pathogens, to the site of infection. To gain a quantitative understanding of the interplay between phagocytes and fungal pathogens, live-cell imaging is a modern approach to monitor the dynamic process of phagocytosis in time and space. However, this requires the processing of large amounts of video data that is tedious to be performed manually. Here, we present a novel framework, called AMIT (algorithm for migration and interaction tracking), that enables automated high-throughput analysis of multi-channel time-lapse microscopy videos of phagocyte-pathogen confrontation assays. The framework is based on our previously developed segmentation and tracking framework for non-rigid cells in brightfield microscopy (Brandes et al., 2015). We here present an advancement of this framework to segment and track different cell types in different video channels as well as to track the interactions between different cell types. For the confrontation assays of polymorphonuclear neutrophils (PMNs) and Candida glabrata considered in this work, the main focus lies on the correct detection of phagocytic events. To achieve this, we introduced different PMN states and a state-transition model that represents the basic principles of phagocyte-pathogen interactions. The framework is validated by a direct comparison of the automatically detected phagocytic activity of PMNs to a manual analysis and by a qualitative comparison with previously published analyses (Duggan et al., 2105; Essig et al., 2015). We demonstrate the potential of our algorithm by comprehensive quantitative and multivariate analyses of confrontation assays involving human PMNs and the fungus C. glabrata.

Human neutrophils dump Candida glabrata after intracellular killing.

Interaction between fungal pathogens and human phagocytes can lead to remarkably variable outcomes, ranging from intracellular killing to prolonged survival and replication of the pathogen in the host cell. Using live cell imaging we observed primary human neutrophils that release phagocytosed Candida glabrata yeast cells after intracellular killing. This process, for which we propose the name "dumping", adds a new outcome to phagocyte-fungus interaction which may be of potential immunological importance as it allows professional antigen presenting cells to take up and process neutrophil-inactivated pathogens that in their viable state are able to evade intracellular degradation in these cells.