GEO Accession viewer

NCBI > GEO > Accession Display

Not logged in | Login

GEO help: Mouse over screen elements for information.

Sample GSM2939999

Query DataSets for GSM2939999

Status

Public on Jan 19, 2018

Title

14-1M 6minRep1

Sample type

SRA

Source name

Cell Lysate

Organism

Pseudomonas aeruginosa

Characteristics

phage: Infected by 14-1
time point: 6min
phase of infection: Middle

Treatment protocol

RNA-Sequencing was performed on four phage-negative controls taken immediately before infection. Samples were also taken from synchronized infections by phages LUZ19, YuA, PEV2, and 14-1 to represent early, middle and late transcription as described by Blasdel et al. (2017, 2018). Briefly, synchronicity was controlled for by ensuring that fewer than 5% of host cells still survived before the early time point was taken.

Growth protocol

The Pseudomonas aeruginosa strain PAO1 was used as a host for phages LUZ19, YuA, PEV2, 14-1, and PhiKZ. Cultures were grown to an OD600 0.3 (1.2 x 10^8 / mL) in Lysogeny broth (10 g/L Tryptone, 5 g/L Yeast extract, 5 g/L NaCl) and infected at a multiplicity of infection of 50 to ensure synchronous infection. P. aeruginosa strain PAK was used as a host for phages PAK_P3 and PAK_P4.

Extracted molecule

total RNA

Extraction protocol

At time points after the addition of phage chosen to reflect each phase of infection, cells were immediately halted with stop solution (1%, 9% ethanol, 90% sample Blasdel (2018), pelleted, and re-suspended in TRIzol to lyse cells as well as inactivate RNAses and start an organic extraction. The resulting nucleic acid was digested with TURBO DNAse to remove DNA, rRNA was removed with the Ribo-Zero kit.
Stranded cDNA was prepared with the TruSeq Stranded mRNA Library Prep Kit ahead of sequencing according to manufacturers instructions

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2000

Description

Non-ribosomal RNA

Data processing

After trimming, sequencing reads were aligned separately to both the phage and host genomes with the CLC Genomics workbench v7.5.1 (QIAGEN Bioinformatics, Aarhus, Denmark)
Alignments were then summarized into count tables of Total Gene Reads that map to phage or host gene features respectively. Each statistical comparison presented was performed using the DESeq2 R/Bioconductor package to normalize host transcript populations to host transcript populations, before testing for differential expression as described by Blasdel et al. (2017).
The core analysis compares directional host total gene read counts for four phage negative controls sampled at a point immediately before infection to samples representing the synchronized late infections of LUZ19, YuA, PEV2 and 14-1. The DESeq2 test for the statistical significance of differential expression between the negative controls and phage-infected samples here is used to specifically highlight gene features that are differentially expressed in common between the different phage infections. As the four phages used have radically different metabolisms, genome contents, and methods for coopting host mechanisms, they are unlikely to all mediate differential expression of any given host gene in the same way. Thus, any differential expression of host genes that is common to all of the independent infections is likely to be mediated by the one biological entity all of the infections have in common, the host.
Given that there are two biological entities within the infected cell whose transcripts are sequenced together, and which change in total abundance relative to each other, meaningfully assessing differential expression for host genes becomes non-intuitively complex. Indeed, we observe a global depletion of all host transcripts relative to the total (phage and host) transcript population present in the cell due to the influx of phage transcripts. Therefore, two approaches could theoretically be used to measure the change in the abundance of a host transcript during a phage infection. We could measure changes (1) relative to either only other host transcripts or (2) relative to the total transcript population in the cell, including new phage transcripts. We have here adopted the first, asking which host transcripts are up or down regulated relative to other host transcripts by normalizing host transcripts before infection to host transcripts after infection. This has the effect of artificially enriching host reads in the phage infected condition by normalizing away the influx of phage transcripts, so as to compare it to the uninfected condition as if those phage transcripts were not present. The answer to this question focuses on how the host transcripts are being being targeted for either increased transcription or decreased degradation by at least one of the two biological entities in the cell, by ignoring the replacement of host transcripts with phage transcripts. Analyzing the change in a transcript's abundance relative to the total transcript population could not be addressed analysis of multiple phage infections, since depletion of host transcripts relative to the total happens at different rates during different phage infections. This effectively prevents us from elucidating a transcript's differential ability to compete for ribosome activity through changes relative abundance. For example, as was reported for prophage transcripts by Blasdel et al. (2017) during phage infection, a transcript could be targeted for increased transcription even while the stronger degradation of all non-phage transcripts reduces its abundance relative to the total transcript population in the cell.
Genome_build: Pseudomonas aeruginosa PAO1 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000006765.1)
Supplementary_files_format_and_content: Total gene read counts, Results of differential expression analysis

Submission date

Jan 18, 2018

Last update date

Jan 24, 2018

Contact name

Bob Blasdel

E-mail(s)

blasdelb@gmail.com

Organization name

KU Leuven

Street address

Kasteelpark Arenberg 21, bus 2462