Manuscript Title: Transcriptomics of SGLT2-positive early proximal tubule segments in mice: response to type 1 diabetes, SGLT1/2 inhibition or GLP1 receptor agonism

Under review!

Authors

Young Chul Kim¹ ,² *, Vivek Das³, Sadhana Kanoo¹ ,2, Huazhen Yao⁴, Stephanie M. Stanford⁵, Nunzio Bottini⁵, Anil Karihaloo⁷, Volker Vallon¹,²,⁶ *

^* Contributed equally

¹ Division of Nephrology & Hypertension, Department of Medicine, University of California San Diego, La Jolla, CA, USA

² VA San Diego Healthcare System, San Diego, CA, USA

³ Novo Nordisk A/S, Søborg, Denmark

⁴ Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA

⁵ Division of Rheumatology, Allergy & Immunology, Department of Medicine, University of California San Diego, La Jolla, CA, USA

⁶ Department of Pharmacology, University of California San Diego, La Jolla, CA, USA

⁷ Novo Nordisk 33 Hayden Ave, Lexington, MA, USA

Data and code information

AKITA_LCM_RNASeq_Treatment

This study includes 54 mouse kidney samples stratified into 9 groups based on disease, treatment, and genotype explanatory variables. The table below summarizes the experimental design.  

Disease	Treatment	Genotype	Samples
Non-Diabetic	Vehicle	WT	6
Non-Diabetic	Dapagliflozin	WT	6
Non-Diabetic	Vehicle	KO	6
Non-Diabetic	Dapagliflozin	KO	6
Diabetic	Vehicle	WT	6
Diabetic	Dapagliflozin	WT	6
Diabetic	Semaglutide	WT	6
Diabetic	Vehicle	KO	6
Diabetic	Dapagliflozin	KO	6

The full metadata is available in the manifest file.

Data

The raw sequencing data is available at GSE279174

Results

The table below lists all of the results files

 

File	Description
Data/	Input FASTQ files
Genome/	Reference genome files
workspace_EDA/	Exploratory data analysis and results
workspace_RING/	RING Rdata object files for differential analysis using DESeq2
Manifest_LCM_AKITA.xlsx	Manifest file

Note: The differential expression analysis was first performed with all samples included in the DESeq model. After inspecting the exploratory data analysis plots, it was clear some groups had much higher within-group variation than others. This would affect the performance of the DESeq model, so subsequent comparisons were made using only the samples within the respective groups which were being compared. See the FAQ section of the DESeq2 manual for more information.

Support and Acknowledgement

Vivek Das from Novo Nordisk A/S executed the preprocessing steps with the consultancy Zifo RnD Solutions (Riya Saju, James Ashmore) using in-house Seven Bridges pipeline.

For queries Vivek Das: [email protected]