Revealing parallel host-pathogen transcriptional dynamics using sorted subpopulations and single, Candida albicans infected macrophages
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ABSTRACT:
PROVIDER: PRJNA437988 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6827190_1.fastq.gz | Fastqsanger.gz | |||
| SRR6827190_2.fastq.gz | Fastqsanger.gz | |||
| SRR6827200_1.fastq.gz | Fastqsanger.gz | |||
| SRR6827200_2.fastq.gz | Fastqsanger.gz | |||
| SRR6827210_1.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: The purpose of this study was to simulataneously examine the host and fungal pathogen transcriptional profiles of four distinct infection fates during macrophage and Candida albicans interactions Methods: Membrane stained (Deep Red),primary, bone marrow derived, murine macrophages and Candida albicans expressing GFP and mCherry were exposed to each other over a four hour time course. Samples were collected at 0, 1, 2 and 4 hours and sorted for four infection subpopulations: 1. Macrophages which phagocytosed live C. albicans (GFP+ /mCherry+ /Deep Red +), 2. Macrophages which phagocytosed dead C. albicans (GFP- /mCherry+ /Deep Red +), 3. Uninfected macrophages(GFP- /mCherry- /Deep Red +) and 4. Unengulfed C. albicans (GFP+ /mCherry + /Deep Red -). Unexposed controls were also collected for some time points (i.e. macrophages never exposed to C. albicans and C. albicans never exposed to macrophages). Single macrophages infected with live or dead C. albicans were also sorted. Smart-seq2 was used to create libraries for both infection subpopulation and single, infected cell samples that were sequenced on Illumina’s Miseqand Nextseq. Basic quality assessment of Illumina reads and sample demultiplexing was done with Picard version 1.107 and Trimmomatic. Samples profiling exclusively the mouse transcriptional response were aligned to the mouse transcriptome generated from the v. Dec. 2011 GRCm38/mm10 and a collection of mouse rRNA sequences from the UCSC genome website. Samples profiling exclusively the yeast transcriptional response were aligned to the C. albicans transcriptome strain SC5314 version A21-s02-m09-r10 downloaded from Candida Genome Database. Samples containing both macrophages and C. albicans were aligned to a “composite transcriptome” made by combining the mouse transcriptome and C. albicans transcriptomes described above and alignment was done via BWA (version 0.7.10-r789.) Multi-reads (reads that aligned to both host and pathogen transcripts) were discarded. Then, each host or pathogen sample file were aligned to its corresponding reference using Bowtie2 and RSEM (v.1.2.21). Transcript abundance was estimated using transcripts per million (TPM). For subpopulation samples, TPM was calculated using edgeR, all as implemented in the Trinity package version 2.1.. Genes were considered differentially expressed only if they had a 4-fold change difference (> 4 FC) in TPM values and a false discovery rate below or equal to 0.001 (FDR < 0.001), unless specified otherwise. For single macrophages infected with C. albicans, samples were aligned to the combined transcriptome as described above and RSEM was used to calculate TPM. Results: We were able to simultaneously measure the host and fungal pathogen transcriptional profiles of four distinct infection fates during macrophage and Candida albicans interactions Conclusions: Our study represents an analysis of both distinct infection populations of macrophages and fungus.
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Project description:The interaction between fungal pathogen and host during infection is a complex and dynamic process. To resolve this, we chose the zebrafish model organism as the host to study C. albicans infection via systems biology approach. Transcriptome microarray data and histological analysis of surviving fish were sampled at different post-infection time points. The dynamic variations of significant genes expression profiles in C. albicans and zebrafish were concurrently analyzed by principal component analysis (PCA). The PCA results clearly indicated that the infection of C. albicans can be divided into three phases that include adhesion, invasion and damage phases. The results were highly consistent with subsequent histological analysis. Furthermore, we found the primary ontology function of genes with significant variations in both C. albicans and zebrafish is iron related. Most of the iron related genes in C. albicans were over-expressed in late stage, while most of the iron related genes in zebrafish were suppressed at the same phase of infection. It suggested that the iron homeostasis function of the host was shut-down when massive hemorrhage in zebrafish occurred during later stages of infection. At the same time, the iron scavenging function of C. albicans was activated. This implied the competition for iron is an important issue between the host and the fungal pathogen during infection. When we administered excess iron into the microenvironment of infection site, the infection process was significantly delayed. That indicated the virulence of C. albicans is correlated with its protein-bond iron scavenging strategies. Our finings not only provided dynamic mechanistic views of the iron competition but also highlighted the potential regulatory schemes in fungal pathogenesis. Each fish was intraperitoneally injected with C. albicans cells and these infected fish were collected at 0.5, 1, 2, 4, 6, 8, 12, 16, 18 hpi. 0.625M-NM-<g of Cy3 cRNA for C. albicans array and 1.65 M-NM-<g of Cy3 cRNA for zebrafish array was fragmented to an average size of about 50-100 nucleotides by incubation with fragmentation buffer at 60M-BM-0C for 30 minutes. Each time points contain three biological repeat. For C. albicans array, each biological repeat has two technical replicates.
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