Transcriptome Sequencing Approaches to Elucidate Host-Microbe Interactions in Opportunistic Human Fungal Pathogens

Overlooked Candida glabrata petites are echinocandin tolerant, induce host inflammatory responses, and display poor in vivo fitness

IMPORTANCE

Candida glabrata is a major fungal pathogen, which is able to lose mitochondria and form small and slow-growing colonies, called "petite." This attenuated growth rate has created controversies and questioned the clinical importance of petiteness. Herein, we have employed multiple omics technologies and in vivo mouse models to critically assess the clinical importance of petite phenotype. Our WGS identifies multiple genes potentially underpinning petite phenotype. Interestingly, petite C. glabrata cells engulfed by macrophages are dormant and, therefore, are not killed by the frontline antifungal drugs. Interestingly, macrophages infected with petite cells mount distinct transcriptomic responses. Consistent with our ex vivo observations, mitochondrial-proficient parental strains outcompete petites during systemic and gut colonization. Retrospective examination of C. glabrata isolates identified petite prevalence a rare entity, which can significantly vary from country to country. Collectively, our study overcomes the existing controversies and provides novel insights regarding the clinical relevance of petite C. glabrata isolates.

Multiplexed target enrichment of coding and non-coding transcriptomes enables studying Candida spp. infections from human derived samples

The study of transcriptomic interactions between host and pathogens in in vivo conditions is challenged by the low relative amounts of the pathogen RNA. Yeast opportunistic pathogens of the genus Candida can cause life-threatening systemic infections in immunocompromised patients, and are of growing medical concern. Four phylogenetically diverse species account for over 90% of Candida infections, and their specific interactions with various human tissues are still poorly understood. To enable in vivo transcriptomic analysis in these species, we designed and validated pan-Candida target capture probes to enrich protein-coding and non-coding transcriptomes. The probe-based enrichment approach outperformed enrichment based on differential lysis of host cells, and showed similar enrichment performance as an existing capture design, yet achieving better fidelity of expression levels, enabling species multiplexing and capturing of lncRNAs. In addition, we show that our probe-based enrichment strategy allows robust genotype-based identification of the infecting strain present in the sample.

Next-Generation Sequencing Applications for the Study of Fungal Pathogens

Next-generation sequencing (NGS) has become a widely used technology in biological research. NGS applications for clinical pathogen detection have become vital technologies. It is increasingly common to perform fast, accurate, and specific detection of clinical specimens using NGS. Pathogenic fungi with high virulence and drug resistance cause life-threatening clinical infections. NGS has had a significant biotechnological impact on detecting bacteria and viruses but is not equally applicable to fungi. There is a particularly urgent clinical need to use NGS to help identify fungi causing infections and prevent negative impacts. This review summarizes current research on NGS applications for fungi and offers a visual method of fungal detection. With the development of NGS and solutions for overcoming sequencing limitations, we suggest clinicians test specimens as soon as possible when encountering infections of unknown cause, suspected infections in vital organs, or rapidly progressive disease.

Small open reading frames in plant research: from prediction to functional characterization

Gene prediction is a laborious and time-consuming task. The advancement of sequencing technologies and bioinformatics tools, coupled with accelerated rate of ribosome profiling and mass spectrometry development, have made identification of small open reading frames (sORFs) (< 100 codons) in various plant genomes possible. The past 50 years have seen sORFs being isolated from many organisms. However, to date, a comprehensive sORF annotation pipeline is as yet unavailable, hence, addressed in our review. Here, we also provide current information on classification and functions of plant sORFs and their potential applications in crop improvement programs.

The long non-coding RNA landscape of Candida yeast pathogens

Long non-coding RNAs (lncRNAs) constitute a poorly studied class of transcripts with emerging roles in key cellular processes. Despite efforts to characterize lncRNAs across a wide range of species, these molecules remain largely unexplored in most eukaryotic microbes, including yeast pathogens of the Candida clade. Here, we analyze thousands of publicly available sequencing datasets to infer and characterize the lncRNA repertoires of five major Candida pathogens: Candida albicans, Candida tropicalis, Candida parapsilosis, Candida auris and Candida glabrata. Our results indicate that genomes of these species encode hundreds of lncRNAs that show levels of evolutionary constraint intermediate between those of intergenic genomic regions and protein-coding genes. Despite their low sequence conservation across the studied species, some lncRNAs are syntenic and are enriched in shared sequence motifs. We find co-expression of lncRNAs with certain protein-coding transcripts, hinting at potential functional associations. Finally, we identify lncRNAs that are differentially expressed during infection of human epithelial cells for four of the studied species. Our comprehensive bioinformatic analyses of Candida lncRNAs pave the way for future functional characterization of these transcripts.

Long non-coding RNAs (lncRNAs) play roles in key cellular processes, but remain largely unexplored in fungal pathogens such as Candida. Here, Hovhannisyan and Gabaldón analyze thousands of sequencing datasets to infer and characterize the lncRNA repertoires of five Candida species, paving the way for their future functional characterization.

Candida pathogens induce protective mitochondria-associated type I interferon signalling and a damage-driven response in vaginal epithelial cells

Vaginal candidiasis is an extremely common disease predominantly caused by four phylogenetically diverse species: Candida albicans; Candida glabrata; Candida parapsilosis; and Candida tropicalis. Using a time course infection model of vaginal epithelial cells and dual RNA sequencing, we show that these species exhibit distinct pathogenicity patterns, which are defined by highly species-specific transcriptional profiles during infection of vaginal epithelial cells. In contrast, host cells exhibit a homogeneous response to all species at the early stages of infection, which is characterized by sublethal mitochondrial signalling inducing a protective type I interferon response. At the later stages, the transcriptional response of the host diverges in a species-dependent manner. This divergence is primarily driven by the extent of epithelial damage elicited by species-specific mechanisms, such as secretion of the toxin candidalysin by C. albicans. Our results uncover a dynamic, biphasic response of vaginal epithelial cells to Candida species, which is characterized by protective mitochondria-associated type I interferon signalling and a species-specific damage-driven response.

Recent trends in molecular diagnostics of yeast infections: from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside.