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CD36 gates nocturnal hypertension by regulating inactive period blood pressure variation via renal AMPK/HDAC5/PER1 axis

GSE335135 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2026/06/15 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 37 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
Mus musculus (mouse)
Samples
37 samples: 37 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.
Summary (written by the submitters)
Nocturnal hypertension is a strong predictor of cardio cerebrovascular disease (CVD), yet its underlying mechanisms remain largely unknown. In this study, we identified CD36 as a key regulator of blood pressure (BP) rhythms and as a gatekeeper of nocturnal hypertension. Its deficiency disrupts circadian BP rhythms.We continuously monitored 48-hour BP using telemetry in CD36 global knockout (KO) mice and renal proximal tubule-specific CD36 KO mice.
Read the full summary ↓
We also collected serum samples and ambulatory blood pressure monitoring (ABPM) data from patients with nocturnal hypertension to assess the relationship between CD36 expression and nocturnal hypertension. A comprehensive screening of various factors affecting BP and circadian rhythms was conducted to explore the molecular mechanisms involved, utilizing RNA sequencing, calorimetry, enzyme-linked immunosorbent assay (ELISA), co-immunoprecipitation, immunofluorescence assays, and echocardiography. Our telemetry data revealed significantly elevated BP during the daytime (inactive period) in both CD36 global KO and renal proximal tubule specific KO mice compared to their control counterparts. Furthermore, levels of platelet CD36 negatively correlated with the severity of nocturnal hypertension in humans. Mechanistically, CD36 modulates BP rhythms through the AMPK/HDAC5/PER1 pathway: its deficiency activates phosphorylated AMPK, leading to the dissociation of the HDAC5-PER1 complex, enhancing PER1 nuclear accumulation, and desynchronizing the oscillations of intrarenal reninangiotensin-aldosterone system (RAAS) genes and clock-controlled genes (CCGs), ultimately disrupting BP rhythm. Our findings highlight CD36 as a critical integrator that links renal clocks to BP homeostasis, establishing it as a potential predictor and therapeutic target for nocturnal hypertension.
This dataset
Open GSE335135 in finder → Find more mouse 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 GSE335135_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 PRJNA1476468 and SRA study SRP707972. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
37 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 37 sample titles: 37 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.