This document discusses using systems approaches and translational nephrology to investigate acute kidney injury (AKI). It summarizes research from the Wales Kidney Research Unit exploring ischemic preconditioning (IPC) as a potential prophylactic therapy for AKI. The research optimized an IPC protocol, identified protective gene expression signatures with IPC, and used Ingenuity Pathway Analysis to predict drugs that may recapitulate the protective IPC phenotype. Some predicted drugs were tested and found to reduce kidney injury in an AKI rat model, supporting drug repurposing approaches identified through transcriptional profiling and pathway analysis.
3. Biomedical Research Units and Centres:
“formed through partnerships between
leading NHS organisations and
universities. Conduct translational
research to transform scientific
breakthroughs into life-saving treatments
for patients”
Wales Kidney Research Unit
(WKRU)
The only UK Biomedical Research Unit
or Centre with a focus on those
affected by kidney diseases
16. Sham
n=6
IRI
n=6
D-IPC
n=6
I-IPC
n=6
RNA sequencing Illumnia total RNA
Riberzero Gold Chemistry
54.4
million
reeds
Reads
post trim
53.6
million
Forward
reads
53.6%
Reverse
46.4%
26.9%
duplication
20.3 million paired
end reads
(40.6% target
mapped reads)
58.8
million
reeds
Reads
post trim
58.0
million
19.9%
duplication
23.2 million paired
end reads
(42.2% target
mapped reads)
21.1%
duplication
25.8 million
paired end reads
(42.5% target
mapped reads)
18.4%
duplication
23.3 million
paired end reads
(42.1% target
mapped reads)
Sham IRI D-IPC I-IPC
59.2
million
reeds
65.0
million
reeds
Reads
post trim
54.0
million
Reads
post trim
57.4
million
Forward
reads
51.8%
Reverse
48.2%
Forward
reads
52.6%
Reverse
47.4%
Forward
reads
52.5%
Reverse
47.5%
Average number of
reads generated
from each group
analysis
Trimmomatic
Stat RNA-seq aligner
DESeq-2
• 24 kidneys, 4 experimental groups
• Illumina RNA Ribozero Gold Chemistry
• Ave Seq Depth = 30M paired-end reads
• After trimming, alignment & mapping
≈ 22M paired-end reads
mRNA sequencing
Foxwell et al. (unpublished data)
17. IRI relative to Sham I-IPC relative to Sham D-IPC relative to Sham
Number of transcripts
p<0.05
log2FC >1 1667
log2FC <-1 1029
Number of transcripts
p<0.05
log2FC >1 1307
log2FC <-1 718
Number of transcripts
p<0.05
log2FC >1 894
log2FC <-1 166
-6 -4 -2 0 2 4 6
20
40
60
80
log2 (FoldChange)
-log10(p-value)
-6 -4 -2 0 2 4 6
20
40
60
80
log2 (FoldChange)
-log10(p-value)
-6 -4 -2 0 2 4 6
20
40
60
80
log2 (FoldChange)
-log10(p-value)
Expression analysis of mRNA sequencing data
Foxwell et al. (unpublished data)
883
955
22
33
825
50 165
IRI
D-IPC I-IPC
20. Dataset Molecules
2,358 genes Number of molecules with a p<0.05 and log2FC of <-
1 or >1
Canonical Pathways
Overlap of dataset molecules with known pathways
Upstream regulators
whose differential regulation would result in the
expression change seen in dataset
Diseases and Biological Functions/ Tox Functions
and Tox Lists
Linking dataset differential expression to biological
processes, diseases and disease endpoints
Regulatory Effect
Links regulators to dataset and finally the disease of
function
Functional enrichment analysis was
completed using omics analysis
platform IPA:
Ingenuity Pathway Analysis
• 16,780 transcripts uploaded
• Significance cut-off criteria of
p<0.05 and log2FC of <-1 or >1
• 2,358 transcripts mapped:
• 852 downregulated
• 1506 upregulated
IPA: Core Analysis Work Flow
Foxwell et al. (unpublished data)
24. Data set molecules
Regulators
Canonical Pathways
Cellular Processes
Diseases
Data set molecules
Regulators
Canonical Pathways
Cellular Processes
Diseases
IRI vs Sham
Core Analysis
Preconditioning vs Sham
Core Analysis
Comparative Analysis
Comparative Analysis
Foxwell et al. (unpublished data)