The zinc cluster transcription factor Rha1 is a positive filamentation regulator in Candida albicans

Molecular Determinants Involved in Candida albicans Biofilm Formation and Regulation

Candida albicans is known for its pathogenicity, although it lives within the human body as a commensal member. The commensal nature of C. albicans is well controlled and regulated by the host's immune system as they live in the harmonized microenvironment. However, the development of certain unusual microhabitat conditions (change in pH, co-inhabiting microorganisms' population ratio, debilitated host-immune system) pokes this commensal fungus to transform into a pathogen in such a way that it starts to propagate very rapidly and tries to breach the epithelial barrier to enter the host's systemic circulations. In addition, Candida is infamous as a major nosocomial (hospital-acquired infection) agent because it enters the human body through venous catheters or medical prostheses. The hysterical mode of C. albicans growth builds its microcolony or biofilm, which is pathogenic for the host. Biofilms propose additional resistance mechanisms from host immunity or extracellular chemicals to aid their survival. Differential gene expressions and regulations within the biofilms cause altered morphology and metabolism. The genes associated with adhesiveness, hyphal/pseudo-hyphal growth, persister cell transformation, and biofilm formation by C. albicans are controlled by myriads of cell-signaling regulators. These genes' transcription is controlled by different molecular determinants like transcription factors and regulators. Therefore, this review has focused discussion on host-immune-sensing molecular determinants of Candida during biofilm formation, regulatory descriptors (secondary messengers, regulatory RNAs, transcription factors) of Candida involved in biofilm formation that could enable small-molecule drug discovery against these molecular determinants, and lead to disrupt the well-structured Candida biofilms effectively.

Bibliometric analysis and thematic review of Candida pathogenesis: Fundamental omics to applications as potential antifungal drugs and vaccines

Invasive candidiasis caused by the pathogenic Candida yeast species has resulted in elevating global mortality. The pathogenicity of Candida spp. is not only originated from its primary invasive yeast-to-hyphal transition; virulence factors (transcription factors, adhesins, invasins, and enzymes), biofilm, antifungal drug resistance, stress tolerance, and metabolic adaptation have also contributed to a greater clinical burden. However, the current research theme in fungal pathogenicity could hardly be delineated with the increasing research output. Therefore, our study analysed the research trends in Candida pathogenesis over the past 37 years via a bibliometric approach against the Scopus and Web of Science databases. Based on the 3993 unique documents retrieved, significant international collaborations among researchers were observed, especially between Germany (Bernhard Hube) and the UK (Julian Naglik), whose focuses are on Candida proteinases, adhesins, and candidalysin. The prominent researchers (Neils Gow, Alistair Brown, and Frank Odds) at the University of Exeter and the University of Aberdeen (second top performing affiliation) UK contribute significantly to the mechanisms of Candida adaptation, tolerance, and stress response. However, the science mapping of co-citation analysis performed herein could not identify a hub representative of subsequent work since the clusters were semi-redundant. The co-word analysis that was otherwise adopted, revealed three research clusters; the cluster-based thematic analyses indicated the severeness of Candida biofilm and antifungal resistance as well as the elevating trend on molecular mechanism elucidation for drug screening and repurposing. Importantly, the in vivo pathogen adaptation and interactions with hosts are crucial for potential vaccine development.

CRISPR-Cas9 Editing Induces Loss of Heterozygosity in the Pathogenic Yeast Candida parapsilosis

Genetic manipulation is often used to study gene function. However, unplanned genome changes (including single nucleotide polymorphisms [SNPs], aneuploidy, and loss of heterozygosity [LOH]) can affect the phenotypic traits of the engineered strains. Here, we compared the effect of classical deletion methods (replacing target alleles with selectable markers by homologous recombination) with CRISPR-Cas9 editing in the diploid human-pathogenic yeast Candida parapsilosis. We sequenced the genomes of 9 isolates that were modified using classic recombination methods and 12 that were edited using CRISPR-Cas9. As a control, the genomes of eight isolates that were transformed with a Cas9-expressing plasmid in the absence of a guide RNA were also sequenced. Following gene manipulation using classic homologous recombination, only one strain exhibited extensive LOH near the targeted gene (8.9 kb), whereas another contained multiple LOH events not associated with the intended modification. In contrast, large regions of LOH (up to >1,100 kb) were observed in most CRISPR-Cas9-edited strains. LOH most commonly occurred adjacent to the Cas9 cut site and extended to the telomere in four isolates. In two isolates, we observed LOH on chromosomes that were not targeted by CRISPR-Cas9. Among the CRISPR-edited isolates, two exhibited cysteine and methionine auxotrophy caused by LOH at a heterozygous site in MET10, approximately 11 and 157 kb downstream from the Cas9 target site, respectively. C. parapsilosis isolates have relatively low levels of heterozygosity. However, our results show that mutation complementation to confirm observed phenotypes is required when using CRISPR-Cas9. IMPORTANCE CRISPR-Cas9 has greatly streamlined gene editing and is now the gold standard and first choice for genetic engineering. However, we show that in diploid species, extra care should be taken in confirming the cause of any phenotypic changes observed. We show that the Cas9-induced double-strand break is often associated with loss of heterozygosity in the asexual diploid human fungal pathogen Candida parapsilosis. This can result in deleterious heterozygous variants (e.g., stop gain in one allele) becoming homozygous, resulting in unplanned phenotypic changes. Our results stress the importance of mutation complementation even when using CRISPR-Cas9.

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies. This ranges from yeast white and opaque forms to hyphal and pseudohyphal filamentous morphologies. The software is based upon a fully convolutional one-stage (FCOS) object detector, a deep learning technique that uses an extensive set of images that we manually annotated with the location and morphology of each cell. We developed a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple yeast forms to complex filamentous architectures. Candescence achieves very good performance (~85% recall; 81% precision) on this difficult learning set, where some images contain hundreds of cells with substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology and how they intermix, we used a second technique from deep learning entitled generative adversarial networks. The resultant models allow us to identify and explore technical variables, developmental trajectories, and morphological switches. Importantly, the model allows us to quantitatively capture morphological plasticity observed with genetically modified strains or strains grown in different media and environments. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology. IMPORTANCE The fungus Candida albicans can "shape shift" between 12 morphologies in response to environmental variables. The cytoprotective capacity provided by this polymorphism makes C. albicans a formidable pathogen to treat clinically. Microscopy images of C. albicans colonies can contain hundreds of cells in different morphological states. Manual annotation of images can be difficult, especially as a result of densely packed and filamentous colonies and of technical artifacts from the microscopy itself. Manual annotation is inherently subjective, depending on the experience and opinion of annotators. Here, we built a deep learning approach entitled Candescence to parse images in an automated, quantitative, and objective fashion: each cell in an image is located and labeled with its morphology. Candescence effectively replaces simple rules based on visual phenotypes (size, shape, and shading) with neural circuitry capable of capturing subtle but salient features in images that may be too complex for human annotators.