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KCTD proteins have redundant functions in controlling cellular growth.

GSE262642 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/04/25 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 6 conditions, mostly in duplicateGroups 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.
Summary (written by the submitters)
We explored functional redundancy of three structurally related KCTD proteins, KCTD2, KCTD5 and KCTD17 by progressively knocking them out in HEK 293 cells using CRISPR/Cas9 genome editing. After validating knockout, we assessed the effects of progressive knockout on cell growth and gene expression. We noted progressive effects of knockout of KCTD isoforms on cell growth were most pervasive when all three isoforms were deleted suggesting some functions were conserved between them.
Read the full summary ↓
This was also reflected in progressive changes in gene expression. Our previous work indicated that Gb1 was involved in transcriptional control of gene expression, so we compared gene expression patterns between GNB1 and KCTD KO. Knockout of GNB1 lead to numerous changes in the expression levels of other G protein subunit genes while knockout of KCTD isoforms, had the opposite effect, presumably because of their role in regulating levels of Gb1. Our work demonstrates a unique relationship between KCTD proteins and Gb1 and a global role for this subfamily of KCTD proteins in maintaining the ability of cells to survive and proliferate.
This dataset
Open GSE262642 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 GSE262642_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 PRJNA1092688 and SRA study SRP498257. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
Parental_KCTD ×2 KCTD-5-KO ×2 KCTD2-5-KO ×2 KCTD2-5-17-KO ×2 GNB1-KO ×2 Parental,GNB1-KO ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 of them. 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.