Presentation about RNA biomarkers in Chronic Kidney Disease, given at ICAHN Renal Research Conference on 4/29/2022

1. RNA BIOMARKERS
IN CHRONIC
KIDNEY DISEASE
CHRISTOS ARGYROPOULOS MD, PHD, FASN
ASSOCIATE PROFESSOR
CHIEF NEPHROLOGY, DEPARTMENT OF INTERNAL MEDICINE
UNIVERSITY OF NEW MEXICO HEALTH SCIENCES CENTER
April 29th 2022, RENAL RESEARCH CONFERENCE ICAHN SOM

2. Disclosures
Consultation fees: Bayer, Otsuka, Bayer, Baxter, Quanta
Writing support: Astra Zeneca
Research Support: Dialysis Clinic, Inc

3. Learning Objectives
Extracellular noncoding RNAs as biomarkers and mediators in Kidney Disease
1. Classes of noncoding RNA (ncRNA)
2. Biological roles and function of ncRNA
3. Select clinical and preclinical studies about ncRNA in kidney diseases
Measuring (extracellular) ncRNAs
1. Rationale for extracellular noncoding RNA profiling
2. Detection platforms
3. Work underway at the University of New Mexico Nephrology/Clinical and Translational Science Center

4. Noncoding RNAs (ncRNA)
RNAs that do not encode for proteins
The vast majority of RNA found in a cell
Classified by size:
 Short noncoding RNAs (short ncRNA) : <
200nt
 Long noncoding RNAs (lncRNA) > 200 nt
long (up t
Classified by structure
 Linear (with or without secondary structure)
 Circular
https://doi.org/10.1016/j.kint.2018.06.033

5. From DNA to
ncRNA OR
mRNA, to
protein AND/OR
regulation
https://doi.org/10.1016/j.tig.2015.03.007
https://doi.org/10.3389/fgene.2019.00496

6. Biological Functions Of lncRNA
Post-transcriptional Gene Regulation
Supports cellular organelle function (e.g. rRNA synthesis in
mitochondria)
Structural and regulation (transcriptional) level function in the
nucleus and its organelles
Maintains genome integrity (telomeres)
https://www.nature.com/articles/s41580-020-00315-9.pdf

7. LncRNA regulates genes at the transcriptional and post-
transcriptional level
https://www.nature.com/articles/s41580-020-00315-9

8. Classification of circular RNAs
Name Type Location Joint site Sequence feature Function
ecRNA exon cytoplasms 3′–5′ phosphodiester bond Formed by cyclization of exons containing the
reverse complementary sequence of introns
and selective cyclization.
Functioning as miRNA sponges; Interact
with RNA-binding proteins (RBPs);
Participates in translation.
CiRNA intron nucleus 2′-5′ phosphodiester bond 5′ splice site enriched with 7 GU motifs and 3′
branch site contains 11 C motifs.
Regulation of gene transcription.
ElciRNA exon–
intron
nucleus 3′-5′ phosphodiester bond Formed by cyclization of exons containing the
reverse complementary sequence of introns
and selective cyclization.
Regulation of gene transcription.
https://peerj.com/articles/5503/
Length is variable: 100nt – 4,000nt

9. Of lines and circles:
making circRNAs
https://peerj.com/articles/5503/

10. Function of
circRNAs
• RBP binding
• Competition with mRNAs or
lncRNA for access to the
splicing mechanism
• Sponging of microRNAs
• Regulation of transcription
• Translation to small peptides
(“rolling circle translation”)
• Reverse transcription to cDNA
and reintegration as
pseudogene
https://www.nature.com/articles/s41419-021-03743-3/

11. Small
noncoding
RNA (sncRNA)
 MicroRNA (miRNA) : ~22 nt
 Small interfering RNA (siRNA):
~22nt
 Small Nuclear RNA (snRNA):
~60-220nt
 Small nucleolar RNA (snoRNA):
60-220nt
 Transfer RNA derived small
RNA (tsRNAs): 15-50nt
 PIWI interacting RNA (piRNA):
24-31 nt
https://www.frontiersin.org/articles/10.3389/fgene.2019.00364/full
https://pubmed.ncbi.nlm.nih.gov/35145038/

12. MiRNA
biogenesis
• Short (21-23nt) non-coding RNAs
• First miRNA (lin-4) identified in
1993 as a regulator of larval
development in C. elegans
• Second miRNA (let-7) isolated in
2000
• In 2014: 28,645 miRNAs in
animals, plants and some viruses
(~1900 in humans)
• Last update of miRBase: 38,589
https://www.nature.com/articles/nrd4359

13. Post-
transcriptional
regulation by
miRNAs
https://www.nature.com/articles/nrg2290
https://pubmed.ncbi.nlm.nih.gov/35247033/
https://www.nature.com/articles/nrd4359

14. Global interference with
intrarenal miRNA production is
associated with experimental
renal disease
Zhdanova et al Kidney Int. 2011 Oct;80(7):719-30

15. Selective Deletion of Dicer from the (mouse)
proximal tubule …
PROTECTS AGAINST ISCHEMIC AKI
BUT EXACERBATES RENAL INJURY AND
FIBROSIS IN DIABETIC KIDNEY DISEASE
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2865746/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6337000/

16. miRNA regulation of the TGF-beta
pathway in tissue fibrosis
https://www.frontiersin.org/articles/10.3389/fmolb.2021.707461/full
https://pubmed.ncbi.nlm.nih.gov/27108839/
Canonical TGF pathway Non-Canonical TGF pathway

17. Noncoding RNAs play key roles in
kidney physiology
ALDOSTERONE SIGNALING THICK ASCENDING LOOP OF HENLE
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796954/
miR-9
miR-374

18. A short history of lncRNA in kidney injury
https://www.frontiersin.org/articles/10.3389/fphys.2022.849403/full

19. miRNA
sponging as the
main
mechanism of
action of
circRNA in
kidney diseases
CircRNA Function Effect or application Expression
Hypertensive nephropathy
circNr1h4 miR-155-5p sponge Increased Far1 expression and reduced production of ROS
Downregulated in the kidneys of mice with salt-
sensitive hypertension and in cultured mouse
kidney collecting duct cells
rno_circRNA_014746
Unknown Potential roles in blood pressure regulation
Upregulated in the kidneys of hypertensive
rats
rno_circRNA_004804
rno_circRNA_004811
Diabetic kidney disease
circRNA_15698 miR-185 sponge
Silencing reduced expression of collagen I, collagen IV
and fibronectin
Upregulated in db/db mouse kidneys and in
cultured mesangial cells under high glucose
conditions
circACTR2 Unknown
Silencing reduced pyroptosis, IL-1β secretion and fibrosis
development
Upregulated in human proximal tubule cells under
high glucose conditions
SLE and lupus nephritis
circ_0000479 Unknown
Promotes disease activity in SLE by regulating metabolism and
Wnt signalling
Upregulated in PBMCs from patients with SLE
circHLA-C Predicted to sponge miR-150
Positively associated with proteinuria, kidney function,
disease activity scores and glomerular injury
Upregulated in kidney biopsy samples from
patients with lupus nephritis
Membranous nephropathy
circ_101319 Unknown Associated with disease activity
Upregulated in PBMCs from patients with
membranous nephropathy
AKI
circ-0114427 miR-494 sponge Increased ATF3 expression and IL-6 secretion
Upregulated in mice with cisplatin-induced
AKI and in cultured proximal tubular cells
exposed to cisplatin
circYAP1 miR-21-5p sponge
Decreased cellular growth and increased secretion of
inflammatory cytokines
Downregulated in blood from patients with AKI
ciRs-126 miR-126-5p sponge Biomarker of AKI Upregulated in blood from patients with AKI
Kidney transplantation
hsa_circ_0001334 Unknown Biomarker of acute TCMR
Upregulated in the urine of patients with
acute TCMR

20. Going around in
circles to make
a short story
long : ncRNA in
kidney diseases
https://journals.physiology.org/doi/full/10.1152/ajpcell.00048.2019

21. Noncoding RNAs as
biomarkers
EXPLORING THE EXTRACELLULAR SPACE TO GAIN
INSIGHTS ABOUT THE INTRACELLULAR SPACE

22. The FDA perspective about the
need for better kidney biomarkers
“DKD is currently diagnosed by the presence of albuminuria and/or changes in serum
creatinine indicating decline in estimated glomerular filtration rate ( eGFR)”
“However, risk assessment based on these and other available clinical parameters is
insufficient, particularly in patients with early stages of disease. The proposed panel is
intended to be used to enrich clinical trials of early stage diabetic kidney disease with
patients who are more likely to progress. “
https://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/UCM508790.pdf

23. Why bother with miRNAs in Kidney Disease?
Mechanistic studies in CKD progression
Most renal/extrarenal short RNAs are
handled by the kidneys
Filtration markers
Damage markers
More stable in circulation than mRNAs
Tissue specific expression
Availability of analytical platforms
Sequence conservation
10/16/2022
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6032378/

24. Many (?Most) Renal And “Extrarenal”
miRNAs are handled by the kidneys
Thompson JD, et al. Nucleic Acid Ther 2012;22:255–64.
Water FM van de et al Drug Metab Dispos 2006;34:1393–7.
Hanss et al PNAS, pp. 1921–1926, 1998
Extremely fast kinetics and electrophysiological
experiments consistent with the notion of a nucleic
channel in the PT

25. Why bother
with ncRNAs
other than
miRNAs in
kidney
diseases?
“Sponging” function of lncRNA and circRNA  one
implicitly monitors the activity of many miRNAs (like
seeing the negative of a photographic film)
circRNAs are more stable than lncRNA and sncRNA (they
are not sensitive to exonucleases, only endonucleases)
circRNA are often co-expressed with the linear
counterparts (so one can get more bang for the back)
circRNAs dynamic changes tend to be smaller than those
of lncRNA and miRNA (“less noisy”)
circRNA levels are much lower than the levels of lncRNAs
from the same source and/or levels of miRNA

26. PCR BASED DETECTION OF
sncRNA and circRNA
STEM LOOP PRIMER METHODS
https://www.future-science.com/doi/10.2144/btn-2019-0065
RT-PCR ON THE CIRCLE
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5891140/

27. RNA-seq for sncRNAs: infinite
variations in the ligation scheme
LINEAR APPROACHES
(TRUE-SEQ ETC)
CIRCULARIZATION APPROACH
(REAL-SEQ)
https://www.frontiersin.org/articles/10.3389/fgene.2015.00352/full https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1488-z

28. miRNA from human urine are measured reproducibly using either
RNAseq or RT-PCR and exhibit regulatory activity
Argyropoulos et al J. Clin. Med. 2015, 4(7), 1498-1517; doi:10.3390/jcm4071498
Gracia et al Scientific Reports 7 (2017) doi:10.1038/srep40601

29. RNAs of
various
nephron
specific
biomarkers in
urine derive
from EVs
https://doi.org/10.1016/j.ekir.2017.11.019

30. Differentially expressed miRNAs in DKD of
T1D derive from exosome, share sequence
motifs and map to pathways implicated in
kidney fibrosis
https://doi.org/10.1016/j.ekir.2017.11.019

31. Combinatorial miRNA biomarkers have a
high discrimination power in CKD
Metric Single miRNA Multiple
miRNAs
Sensitivity 0.85
(0.82-0.88)
0.88
(0.82-0.93)
Specificity 0.79
(0.74 – 0.83)
0.81
(0.74 – 0.86)
AUC 0.89
(0.86 – 0.92)
0.91
(0.88 – 0.93)
https://www.frontiersin.org/articles/10.3389/fmed.2021.782561/full
4,098 patients with CKD and 2,450 patients without CKD

32. RNA biomarker discovery activities at
UNM: clinical cohorts with outcomes data
COMPASS cohort :
UNM CTSC ran study
MIRROR-TRIC : MicroRNAs in progressive
renal disease – a pilot study in CRIC
CRIC Pilot Project
MIRROR-TRANSPLANT: MiRNA Profiling in
Renal Transplant Recipients
18 patients with histologic changes of chronic
allograft nephropathy sequenced upon detection
of IFTA
IRB modification to add longitudinal follow up
and image bank
UNM CTSC supported pilot

33. RNA biomarker discovery activities at UNM:
laboratory protocols, bioinformatics and metrology
Modeling sequencing and variation in Illumina
sequencing for short RNAs
In silico discovery of RNA biomarkers using network
modeling techniques
Development of workflows and bioinformatics tools for
the detection of nearly every category of ncRNA from a
single library preparation using Nanopore Sequencing
(patents pending)

34. COMPASS : Understanding kidney
disease locally, while developing
biomarkers for global use
Design to understand the development of CKD
in a hotspot in Northern New Mexico
Population level sampling based on the NKF
KEEP program
Targeted enrollment 500 patients with 3 years of
follow up
Clinical cohort activities interrupted by COVID19
Total 218 pts with baseline visit, 100 with follow-
up
exRNA/miRNA exploratory biomarker discovery
activities: 95 patients sequenced plasma + urine
 exosomes/non-exosomal/total plasma + urine ~ 600
sequencing profiles
https://pubmed.ncbi.nlm.nih.gov/29486722/
Concentration of β2M versus eGFR and markers of inflammation (high
sensitivity C-reactive protein or hsCRP) in all evaluable participants
Laboratory markers of glycemic control (HgbA1C), and insulin sensitivity
(HOMeostatic model Assessment (HOMA) for assessing beta-cell
function and Insulin Resistance or HOMA-IR, C-peptide index) versus
eGFR in all screened participants
Relation between emerging renal filtration markers (β2M and CysC) in
participants with CKD
Relation of β2M, CysC and insulin sensitivity at each time point to rate
of decline of the eGFR (ΔeGFR)
Relation between inflammation (hsCRP), eGFR and ΔeGFR
Relation of laboratory markers of CKD (anemia, calcium, phosphorus,
parathyroid hormone, vitamin D), abnormal serum electrolytes,
osmoregulation and urinary biomarkers (hematuria, albuminuria,
proteinuria, urine electrolytes) to eGFR and ΔeGFR
Relation of progressive CKD (ΔeGFR > 3 ml/min/1.73m
2
per year) status
in the community to personal, medical and family history risk factors
Secondary Biomarker Endpoints

35. MIRROR-TRIC: Pilot Study Design
Forty patients selected in a 2 x 2 factorial design
Across rate of decline of eGFR : “fast” vs “slow” progressors
Across proteinuria: mild-moderate (A1-A2) vs A3
All patients on RAASi
Close to optimal BP (<140/80 : pre-SPRINT) and glycemic
control (A1c: 6.5 – 8.5%)

36. Endpoints
Aim Exposure(s)/Confounders Outcome(s) Type of Analysis
Aim 1 microRNA profiles/Albuminuria ΔeGFR (category and as a
continuous)
Categorical (GFR slope category) and
longitudinal (eGFR repeated measures)
Aim 2 microRNA profiles/eGFR value Albuminuria (category and as a
continuous)
Cross-Sectional
Aim 2 microRNA profiles/proteinuria eGFR (category< 60, >60 and as a
continuous)
Cross-Sectional
Aim 3 microRNA profiles/
albuminuria/mGFR
Cys-C, B2M, BTP and other assays Cross-Sectional and Longitudinal

37. CONSORT
Diagram
10/16/2022 37

38. MIRROR – TRANSPLANT: Design
INCLUSION
Renal allograft recipient
Age 18-80
Underwent transplant surgery 6 months or
more prior to enrollment
Follow up in the outpatient transplant clinic
at the University of New Mexico Hospital
Biopsy proven CAN (meeting Banff 2007
criteria for IF/TA)(*)
EXCLUSION
Recent transplant surgery (less than 6 months)
Recent acute rejection episode (in the last 3 months)
Positivity for Hepatitis B, C, HIV, BK Virus (active)
Active liver disease
Malignancy
Plans for dialysis in the next three months
Incarcerated patients
Pregnant women
Other organ transplant
Self-reported cognitive impairment or dementia
Active infection (systemic, or involving any level of the
genitourinary tract)
Known, severe/symptomatic prostatic hypertrophy
Less than three creatinine/eGFR values over a period of 12
months, after the nadir creatinine
Known dementia or other condition that precludes informed
consent

39. SPECIFIC AIMS
Aim 1: To validate urine miRNA signatures of CAN progression.
◦ miRNAs previously identified by other investigators
Aim 2: To explore novel urine miRNA signatures of CAN
progression.
◦ Discovery of novel miRNAs missed by previous investigations
Aim 3: To provide a context for the interpretation of miRNAs
associations in CAN using novel computational approaches.
◦ What do these miRNA changes mean?
◦ Deconvolution and image analysis to correlate with histology to commence in the
summer of 2022
3/7/2016 39
MIRROR-
TRANSPLANT

40. Developing RNAseq into a
clinical/point of care diagnostic
CHALLENGES - OPPORTUNITIES
RNA-seq data are becoming more and
more abundant
There is poor reproducibility of findings
between and within research groups
Systematic measurement bias
confound findings
Multiple platforms now exist for
sequencing
Biochemistry workflows before the
devices are similar
SOLUTIONS
Build a model of the measurement from first
principles
Establish testable predictions that may be
verified in existing datasets
Establish correspondence between model
parameters and experimental steps
Use this model to understand and correct
systematic and random bias in RNA-seq
Embed the model into more general frameworks
for applications:
◦ Epidemiological
◦ biomarker discovery and validation
◦ Medical diagnostics

41. Conceptual model of the RNA-seq
experiment (ligation workflow)
X1 , X2 , … , Xn
Λ1 , Λ2 , … , Λn
B1 , B2 , … , Bn
Y1 , Y2 , … , Yn
Abundance in
original preparation
Abundance in
barcode (ligated)
sample
Abundance in PCR
amplified library
Abundance in
capture probes
Abundance of
counts in fastq files
(ligation efficiency)
fi
(number of PCR cycles)
N
(PCR efficiency) qi
Probability of capture si
Number of probes (K)
Library dilution factor (d)
Probability of signal
generation r
L1
𝑁
, L2
𝑁
, … , Ln
𝑁
https://doi.org/10.1093/nar/gkx199

42. Using distributional
regression to
estimate the value
of correction factors
to remove RNAseq
protocol bias
Hafner M, Renwick N, Brown M,
Mihailović A, Holoch D, Lin C, et al.
RNA-ligase-dependent biases in
miRNA representation in deep-
sequenced small RNA cDNA
libraries. RNA. 2011;17: 1697–
1712. doi:10.1261/rna.2799511
Fuchs RT, Sun Z, Zhuang F, Robb
GB. Bias in Ligation-Based Small
RNA Sequencing Library
Construction Is Determined by
Adaptor and RNA Structure. PLoS
ONE. 2015;10: e0126049.
doi:10.1371/journal.pone.0126049

43. Distributional
Regression bias
correction
transfers across
experiments
Use equimolar mix to estimate the value of the
correction factors
Corrected more than 40% of the bias
Opens up the possibility for the use of RNA-
seq for absolute quantification
https://doi.org/10.1093/nar/gkx199

44. Analysis of Differential Expression
without the analysis method noise
https://doi.org/10.1093/nar/gkx199

45. Approach for selecting miRNA candidate
biomarkers for kidney disease
Causes of kidney disease:
◦ Filtering out diabetes/glycemic relevant miRNAs
miRNAs mechanistically relevant to renal disease
◦ Targeting genes in CKD pathways
◦ Targeting genes expressed in glomerular/tubular cells (proteome/transcriptome)
◦ Correlated expression in kidney and biofluids in public datasets (PubMED/GEO)
Filter out miRNAs from potential non-renal sources
◦ Blood : endothelial cells
◦ Urine: bladder
Data stewardship/preservation of supervised/validation analysis
◦ miRNA transfer to UNM only after candidates have been selected for validation (relevant to
MIRROR-TRIC)
10/16/2022 45

46. Complex
miRNA gene
targeting
profile
patterns
miRNA
(n=2366)
Genes
(n=18776)
10/16/2022

47. Context
specificity in
over-
representation
analysis
10/16/2022 47

48. In silico ncRNA prediction for
biomarker discovery
Goal to predict ncRNA/disease associations
by combining bioinformatics (gene/ncRNA
interaction) and gene expression profile (e.g.
GEO database)
Hypothesis generation for disease ncRNA
biomarkers that can be validated in human
datasets
Tremendously speed up discovery by not
requiring development and validation cohorts
In silico activity is the development cohort
Hybrid recommender system developed
internally (patent to be submitted – results
from an early version of our system)
Non renal malignancies

49. Summary
Multiple subtypes of
ncRNA are
pathophysiologically
implicated in various forms
of kidney disease
miRNA, circRNAs and
lncRNA often coregulate
the same biological
processes
ncRNA may be detected in
biofluids and can
differentiate clinical states
of kidney diseases
Extracellular ncRNAs map
to pathways that are
known from mechanistic
studies to be implicated in
the pathogenesis of kidney
diseases
Combinatorial signatures
rather than single molecule
assays appear to be more
sensitive and specific for
the detection of clinical
disease
Sequencing methodologies
hold promise as clinical
diagnostics for the
simultaneous profiling of
many subtypes of ncRNAs

50. Acknowledgements
UNM RNA Kidney Biomarker Discovery Group:
oHamza Mir (data analyst)
oMonica Balasch (M3 UNM and ASN KIDNEY TREKS Mentee)
oMorgan Mackenzie (undergraduate student/lab technician/master of pores)
oAutumn Roberts (Division Admin) for handling complicated MolBio orders during multiple, consecutive COVID19 surges
UNM Clinical and Translational Science Center:
o T1 Lab team: Susan Tigert and Debbie Lovato (expert maintenance of complex equipment and for our socially distanced sequencing)
o Community Engagement and Research Core (execution of the COMPASS study)
o Participant Clinical Interactions Core (execution of the MIRROR-TRANSPLANT study)
Institute for Systems Biology:
o Professor Kai Wang & Dr Vikas Ghai
Pacific Northwest Research Institute:
o Professor David Galas & Dr Alton Etheridge
Funding: DCI Inc, UNM CTSC Pilot Program, CRIC study team
My fellowship mentor : Dr John (Nick) Johnson Col(ret) who got me into the field of RNA hunting