Fungal infections are increasing in prevalence worldwide. The paucity of available antifungal drug classes, combined with the increased occurrence of multidrug resistance in fungi, has led to new clinical challenges in the treatment of fungal infections. Candida auris is a recently emerged multidrug resistant human fungal pathogen that has become a worldwide public health threat. C. auris clinical isolates are often resistant to one or more antifungal drug classes, and thus, there is a high unmet medical need for the development of new therapeutic strategies effective against C. auris. Additionally, C. auris possesses several virulence traits, including the ability to form biofilms, further contributing to its drug resistance, and complicating the treatment of C. auris infections. Here we assessed red, green, and blue visible lights alone and in combination with photosensitizing compounds for their efficacies against C. auris biofilms. We found that (1) blue light inhibited and disrupted C. auris biofilms on its own and that the addition of photosensitizing compounds improved its antibiofilm potential; (2) red light inhibited and disrupted C. auris biofilms, but only in combination with photosensitizing compounds; and (3) green light inhibited C. auris biofilms in combination with photosensitizing compounds, but had no effects on disrupting C. auris biofilms. Taken together, our findings suggest that photodynamic therapy could be an effective non-drug therapeutic strategy against multidrug resistant C. auris biofilm infections.

RNA-sequencing (RNA-seq) is a gold-standard method to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3' Tag-Seq (also called TagSeq, 3' Tag-RNA-Seq, and Quant-Seq 3' mRNA-Seq) is one RNA-seq variation where the 3' end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample. We present a simple, easy-to-use, and publicly available computational workflow to analyze 3' Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC and aligned to the reference genome, and then read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots. This protocol is intended for users specifically interested in analyzing 3' Tag-Seq data, and thus normalizations based on transcript length are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.