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Defining the Effects of PKC Modulator HIV Latency-Reversing Agents on Natural Killer Cells

GSE287754 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2025/01/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 39 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
39 samples: 39 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Defining the Effects of PKC Modulator HIV Latency-Reversing Agents on Natural Killer CellsPathogens & immunity · 2024
Summary (written by the submitters)
Latency reversing agents (LRAs) such as protein kinase C (PKC) modulators can reduce rebound-competent HIV reservoirs in small animal models. Furthermore, administration of natural killer (NK) cells following LRA treatment improves this reservoir reduction. It is currently unknown why the combination of a PKC modulator and NK cells is so potent and whether exposure to PKC modulators may augment NK cell function in some way.
Read the full summary ↓
Here, we compare the transcriptional profiles of PKC-treated NK and CD4+ T cells to assess their generalized cellular effects. Transcriptomic profiles from both cell types displayed signatures of cellular activation and enrichment of genes associated with the NFκB pathway. However, the transcriptomic effects of PKC stimulation on NK cells are much milder than that in CD4+ T cells. This finding, combined with our experimental data testing killing capacity of PKC-stimulated NK cells against K562 cells and infected CD4+ T cells suggests that their contribution in “kick and kill” strategies is primarily due to upregulating HIV expression in CD4+ T cells, rather than directly enhancing the effector functions of NK cells. This suggests that PKC modulators are primarily augmenting the “kick” rather than the “kill” arm of this HIV cure approach.
Published in
Defining the Effects of PKC Modulator HIV Latency-Reversing Agents on Natural Killer Cells
Dimapasoc M, Moran JA, Cole SW et al. · Pathogens & immunity 2024 · PMID 38765786 · doi:10.20411/pai.v9i1.673
This dataset
Open GSE287754 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE287754_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1214395 and SRA study SRP559245. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38765786) ↗
Study design
39 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 39 sample titles: 39 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.