www.kidney-international.org c l i n i ca l i nves t iga t ionfactor activation variability in minimal change
OPENPrecision nephrology identified tumor necrosis
disease and focal segmental glomerulosclerosis
Laura H. Mariani1,45, Sean Eddy1,45, Fadhl M. AlAkwaa1, Phillip J. McCown1, Jennifer L. Harder1, Viji Nair1,
Felix Eichinger1, Sebastian Martini1, Adebowale D. Ademola2, Vincent Boima3, Heather N. Reich4,
Jamal El Saghir1, Bradley Godfrey1, Wenjun Ju1, Emily C. Tanner1, Virginia Vega-Warner1, Noel L. Wys1,
Sharon G. Adler5, Gerald B. Appel6, Ambarish Athavale7, Meredith A. Atkinson8, Serena M. Bagnasco9,
Laura Barisoni10, Elizabeth Brown11, Daniel C. Cattran4, Gaia M. Coppock12, Katherine M. Dell13,
Vimal K. Derebail14, Fernando C. Fervenza15, Alessia Fornoni16, Crystal A. Gadegbeku17,
Keisha L. Gibson18, Laurence A. Greenbaum19, Sangeeta R. Hingorani20, Michelle A. Hladunewich4,
Jeffrey B. Hodgin21, Marie C. Hogan15, Lawrence B. Holzman12, J. Ashley Jefferson22, Frederick J. Kaskel23,
Jeffrey B. Kopp24, Richard A. Lafayette25, Kevin V. Lemley26, John C. Lieske15, Jen-Jar Lin27,
Rajarasee Menon28, Kevin E. Meyers29, Patrick H. Nachman30, Cynthia C. Nast31,
Michelle M. O’Shaughnessy32, Edgar A. Otto1, Kimberly J. Reidy23, Kamalanathan K. Sambandam11,33,
John R. Sedor34,35,36,37, Christine B. Sethna38, Pamela Singer38, Tarak Srivastava39, Cheryl L. Tran40,
Katherine R. Tuttle22,41, Suzanne M. Vento42, Chia-shi Wang19, Akinlolu O. Ojo43, Dwomoa Adu3,
Debbie S. Gipson44, Howard Trachtman1 and Matthias Kretzler1,28
1Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; 2Department of Paediatrics,
Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria; 3Department of Medicine and
Therapeutics, University of Ghana Medical School, College of Health Sciences, University of Ghana, Accra, Ghana; 4Division of
Nephrology, Department of Medicine, University Health Network and University of Toronto, Toronto, Ontario, Canada; 5Division of
Nephrology and Hypertension at Harbor-UCLA Medical Center and The Lundquist Institute for Biomedical Innovation, Torrance,
California, USA; 6Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA;
7Division of Nephrology-Hypertension, University of San Diego, California, San Diego, California, USA; 8Division of Pediatric Nephrology,
Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; 9Department of Pathology, Johns Hopkins University School of
Medicine, Baltimore, Maryland, USA; 10Department of Pathology and Medicine, Division of Nephrology, Duke University School of
Medicine, Durham, North Carolina, USA; 11Division of Nephrology, Department of Pediatrics, University of Texas Southwestern Medical
Center, Dallas, Texas, USA; 12Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania,
Philadelphia, Pennsylvania, USA; 13Center for Pediatric Nephrology, Cleveland Clinic, Case Western Reserve University, Cleveland, Ohio,
USA; 14University of North Carolina Kidney Center, Division of Nephrology and Hypertension, University of North Carolina at Chapel Hill,
Chapel Hill, North Carolina, USA; 15Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester,
Minnesota, USA; 16Katz Family Division of Nephrology and Hypertension, Department of Medicine, University of Miami Miller School of
Medicine, Miami, Florida, USA; 17Department of Kidney Medicine, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland,
Ohio, USA; 18Pediatric Nephrology Division, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA; 19Division
of Nephrology, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA; 20Division of Nephrology,
Department of Pediatrics, University of Washington, Seattle, Washington, USA; 21Department of Pathology, University of Michigan, Ann
Arbor, Michigan, USA; 22Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington, USA; 23Division
of Pediatric Nephrology, Montefiore Medical Center, Bronx, New York, USA; 24National Institute of Diabetes and Digestive Diseases,
National Institutes of Health, Bethesda, Maryland, USA; 25Department of Medicine, Division of Nephrology, Stanford University, Stanford,
CA, USA; 26Department of Pediatrics, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA;
27Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA; 28Department of
Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA; 29Division of Nephrology, Children’s
Hospital of Philadelphia, Philadelphia, Pennsylvania, USA; 30Division of Nephrology and Hypertension, Department of Medicine,
University of Minnesota, Minneapolis, Minnesota, USA; 31Department of Pathology, Cedars-Sinai Medical Center, Los Angeles, California,
USA; 32Department of Medicine, Division of Renal Medicine, Cork University Hospital, Cork, Ireland; 33Division of Nephrology, Department
of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA; 34Lerner Research Institutes, Cleveland Clinic,Correspondence: Matthias Kretzler, University of Michigan, MSRB II, 4544-D, 45LHM and SE are co-first authors.
1150 West Medical Center Drive, Ann Arbor, Michigan 48109, USA. E-mail: Received 8 February 2022; revised 25 October 2022; accepted 28
kretzler@umich.edu; or Laura H. Mariani, Department of Internal Medicine, October 2022
Division of Nephrology, University of Michigan, MSRB II, 4544-C, 1150 West
Medical Center Drive, Ann Arbor, Michigan 48109, USA. E-mail: lmariani@
med.umich.edu
Kidney International (2022) -, -–- 1
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndromeCleveland, Ohio, USA; 35Department of Molecular Medicine, Case Western Reserve University, Cleveland, Ohio, USA; 36Department of
Physiology, Case Western Reserve University, Cleveland, Ohio, USA; 37Department of Biophysics, Case Western Reserve University,
Cleveland, Ohio, USA; 38Division of Pediatric Nephrology, Cohen Children’s Medical Center, New Hyde Park, New York, USA; 39Section of
Nephrology, Children’s Mercy Hospital, Kansas City, Missouri, USA; 40Pediatric Nephrology, Mayo Clinic, Rochester, Minnesota, USA;
41Providence Medical Research Center, Providence Health Care, University of Washington, Spokane, Washington, USA; 42Division of
Nephrology, Department of Pediatrics, New York University School of Medicine, New York, New York, USA; 43Department of Population
Health, School of Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA; and 44Division of Nephrology, Department of
Pediatrics, University of Michigan, Ann Arbor, Michigan, USAThe diagnosis of nephrotic syndrome relies on clinical
presentation and descriptive patterns of injury on kidney
biopsies, but not specific to underlying pathobiology.
Consequently, there are variable rates of progression and
response to therapy within diagnoses. Here, an unbiased
transcriptomic-driven approach was used to identify
molecular pathways which are shared by subgroups of
patients with either minimal change disease (MCD) or focal
segmental glomerulosclerosis (FSGS). Kidney tissue
transcriptomic profile-based clustering identified three
patient subgroups with shared molecular signatures across
independent, North American, European, and African
cohorts. One subgroup had significantly greater disease
progression (Hazard Ratio 5.2) which persisted after
adjusting for diagnosis and clinical measures (Hazard Ratio
3.8). Inclusion in this subgroup was retained even when
clustering was limited to those with less than 25%
interstitial fibrosis. The molecular profile of this subgroup
was largely consistent with tumor necrosis factor (TNF)
pathway activation. Two TNF pathway urine markers were
identified, tissue inhibitor of metalloproteinases-1 (TIMP-1)
and monocyte chemoattractant protein-1 (MCP-1), that
could be used to predict an individual’s TNF pathway
activation score. Kidney organoids and single-nucleus RNA-
sequencing of participant kidney biopsies, validated TNF-
dependent increases in pathway activation score, transcript
and protein levels of TIMP-1 and MCP-1, in resident kidney
cells. Thus, molecular profiling identified a subgroup of
patients with either MCD or FSGS who shared kidney TNF
pathway activation and poor outcomes. A clinical trial
testing targeted therapies in patients selected using
urinary markers of TNF pathway activation is ongoing.
Kidney International (2022) -, -–-; https://doi.org/10.1016/
j.kint.2022.10.023
KEYWORDS: data integration; nephrotic syndrome; TNF; transcriptomics
Copyright ª 2022, International Society of Nephrology. Published by
Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).N ephrotic syndrome is characterized by proteinuria,hypoalbuminemia, hyperlipidemia, and edema. Twonephrotic diseases, minimal change disease (MCD)
and focal segmental glomerulosclerosis (FSGS), currently
diagnosed based on histopathologic features, have broadly
overlapping clinical presentations and treatment approaches.12
Within each diagnosis, however, some patients respond well
to current therapy, whereas others either do not respond or
relapse upon stopping therapy with variable risk of loss of
kidney function. These clinical observations suggest a het-
erogeneous biology underlying current disease classifica-
tion.2–4 Because of limited understanding of pathobiology,
interpretation of the clinical outcome variability in observa-
tional studies and clinical trials is challenging,5,6 and molec-
ularly-driven,7,8 personalized6 treatments for MCD and FSGS
are unavailable.
Nevertheless, nephrotic syndrome is well positioned for
implementing precision medicine. Clinically procured kidney
biopsy tissue allows for unbiased identification of molecular
signatures that can be linked to histopathology, noninvasive
biomarkers, and evaluated against clinical outcomes. With the
goal of identifying molecular pathways that are shared by
subgroups of patients with MCD and FSGS, this study
implemented a multidimensional data integration approach
(Figure 1) in the prospective North American Nephrotic
Syndrome Study Network (NEPTUNE),9 then replicated in
the European Renal cDNA Bank (ERCB)10,11 and the Human
Heredity and Health in Africa Kidney Disease Research
Network cohort (H3Africa).12,13
The study aims to agnostically identify groups of patients
with shared molecular signatures, to identify the relevant
pathways from that signature that could then be evaluated in
individual patients using potential noninvasive markers. Such
markers of the disease mechanism may enable targeted
therapeutic interventions.METHODS
Study participants
The study included 220 NEPTUNE,9 35 H3Africa,12 and 30
ERCB10,11 participants with biopsy-proven MCD or FSGS and
compartment-enriched genome-wide kidney mRNA expression
profiles.
NEPTUNE (NCT01209000) is a prospective study of children
and adults with proteinuria, recruited from 21 sites at the time of
their first clinically indicated kidney biopsy.9,14 ERCB is a European
study of adults recruited at 28 sites, with biopsy tissue for gene
expression profiling and cross-sectional clinical information, at the
time of a clinically indicated kidney biopsy.10,11 H3Africa12 is a
prospective study of participants aged 15 years and above, eligible for
a kidney biopsy, recruited from 13 clinical centers in Nigeria and
Ghana with an estimated glomerular filtration rate (eGFR) of $15
ml/min per 1.73 m2 and proteinuria (albuminuria >500 mg/d).12Kidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion
Step 1: Kidney biopsy tissues were micro- -
dissected into glomerular and tubulointerstitial
compartments for RNA extraction and profiling
Step 2: Clustering by tissue gene expression Clustering
data identified patient subgroups based on
molecular profiles 1 2 3
1
Step 3: Each subgroup, representing patients 2
with similar molecular profiles, was tested for * 3 
association with clinical phenotypes
days
Network
Step 4: Functional context of cluster-specific Regulator
differentially expressed gene profiles was
explored to identify network regulators and
expression profiles
Organoids SnRNAseq
Step 5: Activation of the identified biological
networks in kidney cells was tested in kidney
organoids and confirmed using single nuclear
RNA-seq from kidney biopsies
Step 6: Responsible network kidney 1 a c eb d
transcript levels were tested for correlation with
non-invasive surrogate (urine) markers for 2 a c eb d
potential prediction accuracy of the markers in
identifying molecular subgroup affiliation 3 a c eb d
Step 7: Urinary markers can be used to match
patients with therapies targeting pathways 
associated with their molecular disease
subgroup
Figure 1 | Analysis strategy. Flowchart of tubulointerstitial
compartment gene expression to identify molecular subgroups and
associated noninvasive urinary markers. eGFR, estimated glomerular
filtration rate; snRNA-seq, single-nucleus RNA sequencing.
eGFRFor all cohorts, informed consent was obtained from individual
patients or parents/guardians on approval by institutional review
boards or local ethics committees of participating institutions.
Clinical data
NEPTUNE participants were followed 2 to 3 times per year, for up
to 5 years. Medical history, medication use, laboratory results,
blood and urine samples for the measurement of serum creatinine,
and urine protein-to-creatinine ratio (UPCR) were collected at
each study visit. eGFR (ml/min per 1.73 m2) was calculated using
the Chronic Kidney Disease Epidemiology Collaboration formula
for participants $18 years old and the modified chronic kidney
disease in children-Schwartz formula for participants <18 years
old, with an average taken for young adults aged 18 to 26 years.15–17
The endpoint of kidney functional loss was defined by a 40%
reduction in eGFR or the onset of kidney failure (initiation of
dialysis, receipt of kidney transplant or eGFR <15 ml/min per 1.73
m2 measured at 2 sequential visits).18 Complete remission was
defined as UPCR <0.3 mg/mg on a single-void urine or a 24-hour
urine collection. In ERCB and H3Africa, clinical information,
including demographics and clinical laboratory results, was ob-
tained at the time of biopsy.Kidney International (2022) -, -–-Kidney pathology
Diagnosis in NEPTUNE was assigned by study pathologists based on
biopsy reports and/or digital images. The degree of interstitial
fibrosis (IF) was visually assessed and scored by 2 to 5 pathologists
using biopsy whole slide images of trichrome, periodic acid–Schiff,
or silver-stained sections, recorded as the percent of cortex
involved, and averaged across the pathologists’ measures.19,20
Transcriptome profiling
In NEPTUNE, RNA sequencing (RNA-seq) was performed on
manually microdissected kidney biopsy tissue that separated tubu-
lointerstitial and glomerular compartments of the research core. For
H3Africa, a 5-mm cortical segment (not needed for clinical diag-
nosis) was manually microdissected, RNA-isolated, and sequenced to
generate RNA-seq profiles (see Supplementary Methods).
In ERCB, compartment-specific transcriptomic profiles of the
research core were generated using the Affymetrix microarray plat-
form. NEPTUNE RNA-seq and ERCB microarray data are available
at Nephroseq.org and through Gene Expression Omnibus21
(GSE219195, GSE197307, GSE104954, GSE104948).
Cluster analysis, differential expression, and functional
enrichment analysis
R Software (R Core Team [2013]) was used for Kmeans, Prediction
Analysis of Microarrays (PAM), and hierarchical clustering ana-
lyses.22,23 Optimal clustering was determined using delta-K and the
ConsensusClusterPlus package.22 The linear models for microarray
data (limma) package24 was used for differential expression analysis.
Differentially expressed genes (absolute fold change >1.5 and q
value <0.05) between clusters were analyzed for the enrichment of
canonical pathways using the Ingenuity Pathway Analysis Software
Suite (IPA; Qiagen).25 Previously published26 single-cell RNA-seq
cell-type selective expression clusters from adult reference kidney
tissue can be accessed at http://nephrocell.miktmc.org/ and GEO
(GSE14098).
Tumor necrosis factor activation score
A tumor necrosis factor (TNF) activation network was generated
from expert curated interactions from NETPro annotations in the
Genomatix Genome Analyzer database (Precigen Bioinformatics).
From the database, 272 causally downstream genes or proteins that
increased expression from TNF exposure were used to generate a
TNF activation score. Individual gene expression values were first
Z-transformed. The TNF activation score for each participant was
the average Z-score of the 272 genes in their kidney RNA-seq
profile.
Urine marker profiling
Urine proteins were identified and measured using the multiplex
Luminex platform (Eve Technologies) composed of a panel of 54
urinary cytokines, matrix metalloproteinases, and tissue inhibitor of
metalloproteinases (see Supplementary Methods). A candidate pro-
tein had to satisfy the following criteria to be considered a potential
noninvasive marker of TNF activation: (i) protein must be a product
of a gene expressed downstream of TNF; (ii) urine protein expres-
sion must correlate with the corresponding kidney tissue gene
expression (mRNA levels), and (iii) gene expression must correlate
with the TNF activation score.
Putative urine markers were assayed in duplicate using Quanti-
kine enzyme-linked immunosorbent assay kit human chemokine (C-
C motif) ligand 2 (CCL2)/monocyte chemoattractant protein-13
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndrome(MCP-1) (DCP00) and tissue inhibitor of metalloproteinases 1
(TIMP-1) (DTM100; R&D Systems). Absorbance was measured with
a VersaMax enzyme-linked immunosorbent assay plate reader, and
results were calculated with SoftMax Pro (Molecular Devices).
Markers were normalized to urine creatinine concentration and log2
transformed.
Statistical analysis of the association with clinical data and
urine markers
Descriptive statistics were used to characterize baseline (time of
biopsy) participant characteristics by molecular cluster. Differ-
ences in Kaplan-Meier curves, by molecular cluster, were tested by
the log-rank test. Univariate Cox proportional hazards models
were fit separately for time from biopsy to complete remission
and time to the composite of end-stage kidney disease and a 40%
decline in eGFR to assess the association of molecular cluster and
TNF score with clinical outcomes. Models were adjusted for
diagnosis (MCD, FSGS), eGFR, and UPCR. IF and glomerular
sclerosis were assumed to be on the causal pathway and therefore
not included in the models. Pearson’s correlation was used to
assess the relationship between the TNF score, marker tissue
mRNA expression, and urinary concentration. Linear regression
models were fit to assess the association of urinary markers and
clinical features with TNF score, and to calculate a predicted
score. Pearson’s correlation was used to correlate the predicted
and observed TNF activation scores. Analyses were performed
using STATA, v12.1.
Single-nuclear RNA-seq
Nuclei were prepared from the biopsy tissue of 10 NEPTUNE par-
ticipants (5 from cluster 3, with high TNF activity scores [defined as
TNF High], and 5 from clusters 1 and 2, with lower TNF activity
scores [defined as TNF Low]) stored in RNAlater using protocols
from the Kidney Precision Medicine Project27,28 (Supplementary
Methods, GSE213030). Nuclear cluster annotation was determined
by defining enriched genes in each cell cluster and comparing cluster
selective gene profiles with previously identified human kidney cell
marker gene sets.26–28
Kidney organoid culture, treatment, and analysis
Kidney organoids were generated from UM77-2 human embry-
onic stem cells as previously described.29 Organoids were treated
with TNF (R&D Systems, Cat# 10291-TA) resuspended in
phosphate-buffered saline on day 23 at the indicated concentra-
tions. Organoid supernatants were removed at specified times,
RNA extracted, and sequenced (Supplementary Methods). Orga-
noid culture supernatants and cell lysates were diluted 150-fold to
measure MCP-1 and 10-fold to measure TIMP-1 using the
enzyme-linked immunosorbent assay as described above. Quan-
titative real-time polymerase chain reaction analysis was per-
formed in triplicate using TaqMan Fast Universal PCR Master Mix
(2X) for CCL2, TIMP-1, and glyceraldehyde-3-phosphate
dehydrogenase.RESULTS
Unbiased consensus clustering of gene expression profiles
identifies shared molecular signatures
Transcriptomic profiles of microdissected kidney biopsy com-
partments were used to group participants into distinct sub-
clusters. Because of the known association of tubulointerstitial4
changes with risk of loss of eGFR, tubulointerstitial transcrip-
tional data were analyzed first. Transcriptomes fromNEPTUNE
participants clustered into 3 groups (n ¼ 85, 76, and 59,
respectively), with 1 cluster (T3) demonstrating the highest
cluster stability (Figure 2a). The delta-K revealed that the 3-
cluster solution was optimal across clustering approaches
(Supplementary Figure S1A–C). To validate the molecular pro-
files identified in NEPTUNE, Kmeans, PAM, and hierarchical
consensus clustering22,23 were also applied to the tubulointer-
stitial transcriptome data from 2 independent FSGS/MCD co-
horts, ERCB (N ¼ 30) and H3Africa (N ¼ 35), resulting in 3
distinct clusters (Figure 2b and c) with high cluster stability.
Glomerular compartment clustering also identified 3 clusters
(Supplementary Figure S2A and B), with a transcriptional
signature largely shared with the tubulointerstitium (Figure 2d).
Differential expression analysis of the tubulointerstitial tran-
scripts between T3 and the other 2 clusters in each cohort
showed a robust, directionally conservedmolecular signal across
cohorts (correlation of fold change 0.94, P < 0.001, for
NEPTUNEvs. ERCB and 0.93, P< 0.001, forNEPTUNEvs.H3;
Figure 2e and f). Finally, of the 179NEPTUNE participants with
measured IF, clustering was repeated in only those participants
with IF <25% (n ¼ 148). All 26 participants, originally in T3
from this group, again clustered together.
NEPTUNE participants in T3 were older and had a lower
eGFR, greater IF, and higher UPCR at biopsy (Table 1;
Supplementary Figure S3). In ERCB and H3Africa, partici-
pants in T3 also had a lower eGFR and were older. Although
T3 had a greater proportion of FSGS in all 3 cohorts, it also
included participants with MCD (Figure 2g, Supplementary
Figure S1D and E). In an unadjusted survival model,
NEPTUNE T3 participants were more likely to reach the
composite of end-stage kidney disease or a 40% decline in
eGFR (unadjusted hazard ratio [HR]: 5.23 [95% confidence
interval: 1.9, 14.5], P < 0.001, for overall differences in
curves; Figure 2h) and fewer complete proteinuria remission
events were observed (unadjusted HR: 0.73 [95% confidence
interval: 0.43, 1.26], P ¼ 0.068, for overall difference in
curves; Figure 2i) compared with T1.
Biological and molecular relevance of cluster 3
Differential mRNA expression profiles were used to elucidate
the molecular functions associated with cluster T3. In
NEPTUNE, there were 2721 transcripts in T3 with an abso-
lute 1.5-fold change and q < 0.05 (2199 upregulated, 522
downregulated) compared with T1 and T2 (Supplementary
Table S1A). This gene set was analyzed to identify enriched
canonical pathways, predicted upstream regulators,30 and
gene interaction networks. These analyses converged on TNF
pathway activation.
In signal transduction pathway over-representation
(enrichment), the granulocyte adhesion and diapedesis
signal transduction pathway had the highest enrichment score
(log(p) ¼ 21.4). Of the 180 genes in this pathway, 70
(38.9%) were found differentially regulated in T3, including
TNF (2.4-fold upregulated in T3, q < 0.001, Figure 3a26).Kidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion
a NEPTUNE  (N = 220) b ERCB (N = 30) c H3Africa (N = 35)
T3 T2 T1 T3 T2 T1 T3 T1 T2
d e
NEPTUNE
5.0 5.0
R = 0.84,P < 2.2e−16 R = 0.94, P < 2.2e-16
2.5 2.5
0.0 0.0
−2.5 −2.5
−2 −1 0 1 −2 0 2
Fold change glomerular compartment NEPTUNE Fold change tubular compartment ERCB
f g NEPTUNE (N = 220)
5.0
R = 0.93, P < 2.2e-16 Cluster 1: 
Figure 2
85
2.5 FSGS: 114
0.0 Cluster 2: 
76
−2.5 MCD: 106
−2.5 0.0 2.5 Cluster 3: 
Fold change tubular compartment H3Africa 59
h i
Kaplan-Meier survival for composite ESKD/40% decline in eGFR Kaplan-Meier survival for complete remission
0 10 20 30 40 0 10 20 30 40
Follow-up times (mo) Follow-up times (mo)
Number at risk Number at risk
Cluster 1 69 61 57 48 38 Cluster 1 62 28 20 18 12
Cluster 2 68 68 60 53 43 Cluster 2 60 30 17 11 8
Cluster 3 45 40 35 30 19 Cluster 3 51 23 18 14 7
Cluster T1 Cluster T2 Cluster T3 Cluster T1 Cluster T2 Cluster T3
Figure 2 | Kidney transcriptomic cluster membership and unadjusted Kaplan-Meier curves. Consensus clustering using Kmeans
identified optimal cluster membership from tubulointerstitial transcriptomic profiles with 3 clusters (clusters were designated by tissue
compartment and cluster number, T1, T2, T3 for tubulointerstitial clusters) in a cluster matrix from (a) North American Nephrotic Syndrome
Study Network (NEPTUNE), (b) European Renal cDNA Bank (ERCB), and (c) Human Heredity and Health in Africa Kidney Disease Research
Network (H3Africa) cohorts. The values ranged from 0 (pale yellow, samples do not cluster together) to 1 (brown, samples demonstrate high
affinity and cluster together). Scatter plots show a strong correlation of significant fold change differences of genes differentially expressed in
(d) tubulointerstitial and glomerular compartments in NEPTUNE; (e) tubulointerstitium cluster 3 (T3) compared with T2 and T1 from NEPTUNE
(y-axis) and cluster T3 compared with T1 and T2 from ERCB (x-axis); and, similarly, for (f) H3Africa (x-axis). (g) Alluvial plot of correspondence
between the diagnosis and cluster membership of participants in the NEPTUNE cohort. Unadjusted Kaplan-Meier survival curves by the
NEPTUNE tubulointerstitial cluster for (h) composite endpoint of 40% loss of estimated glomerular filtration rate (eGFR) or end-stage kidney
disease (ESKD) and (i) complete remission. FSGS, focal segmental glomerulosclerosis; UPCR, urine protein-to-creatinine ratio.
Kidney International (2022) -, -–- 5
Fold change tubular Fold change tubular 
compartment NEPTUNE compartment NEPTUNE
Probability
0.00 0.25 0.50 0.75 1.00
Fold change tubular 
compartment NEPTUNE
Cumulative incidence
0.0 0.25 0.50 0.75 1.0
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndrome
Table 1 | Clinical characteristics of participants summarized by cluster identity and cohort
NEPTUNE All (N [ 220) Cluster 1 (N [ 85) Cluster 2 (N [ 76) Cluster 3 (N [ 59) P value
Age, mean (SD) 28.2 (21.7) 22.3 (19.5) 26.8 (22.6) 38.5 (20.0) <0.0001
Female, n (%) 89 (40) 33 (39) 30 (39) 26 (44) 0.82
Adult, n (%) 112 (59) 31 (36) 35 (46) 46 (78) <0.0001
eGFR, mean (SD) 89.5 (46.8) 102.1 (40.7) 104.2 (48.9) 52.3 (29.2) <0.0001
Albumin, mean (SD) 3.62 (0.89) 3.68 (0.86) 3.62 (0.89) 3.54 (0.95) 0.3916
FSGS, n (%) 114 (52) 32 (38) 37 (49) 45 (76) <0.0001
UPCR, median (IQR) 2.76 (1.0, 7.04) 1.9 (0.6, 7.1) 2.56 (1.02, 5.5) 5.35 (2.0, 10.6) <0.0001
% IF, median (IQR) 4 (0, 16) 1.5 (0,5) 3 (0, 8) 20 (10, 55) <0.0001
Disease duration before 29.47 (69.46) 26.78 (46.56) 18 (50.6) 49 (108) 0.30
biopsy, mo, mean (SD)
On RAAS blockade, n (%) 86 (39) 34 (40) 26 (34) 26 (44) 0.0005
On IST, n (%) 101 (46) 49 (58) 37 (49) 15 (25) 0.0005
ERCB Cluster 1 (N [ 18) Cluster 2 (N [ 4) Cluster 3 (N [ 8) P value
Age, mean (SD) 42.2 (19.0) 29.4 (6.6) 49.2 (18.3) 0.63
Female, n (%) 10 (56) 1 (25) 3 (38) 0.59
eGFR, mean (SD) 92.2 (34.5) 119.0 (4.0) 43.2 (31.0) 0.02
FSGS, n (%) 8 (44) 2 (50) 7 (87) 0.135
H3Africa Cluster 1 (N [ 14) Cluster 2 (N [ 16) Cluster 3 (N [ 5) P value
Age, mean (SD) 24.1 (10.2) 30.0 (14.1) 30.8 (10.7) 0.19
Female, n (%) 3 (21) 4 (25) 0 (0) 0.90
eGFR, mean (SD) 89.8 (42.6) 109.8 (14.7) 25.8 (11.7) 0.03
FSGS, n (%) 7 (50) 4 (25) 4 (80) 0.07
UPCR, median (IQR) 1.4 (1.1, 2.2) 1.1 (0.3, 2.8) 3.6 (1.8, 7.5) 0.05
% IF, median (IQR) 0 (0, 9) 0 (0, 1) 40 (0, 40) 0.01
Disease duration less than 6 mo, n (%) 2 (14) 4 (25) 4 (80) 0.02
IST in the past 6 mo, n (%) 5 (36) 4 (25) 1 (20) 0.71
eGFR, estimated glomerular filtration rate (ml/min per 1.73 m2); ERCB, European Renal cDNA Bank; FSGS, focal segmental glomerulosclerosis; H3Africa, Human Heredity and
Health in Africa Kidney Disease Research Network cohort; IF, interstitial fibrosis; IQR, interquartile range; IST, immunosuppressive therapy; NEPTUNE, North American Nephrotic
Syndrome Study Network; RAAS, renin angiotensin aldosterone system; UPCR, urine protein-to-creatinine ratio (mg/mg).Causal analysis of predicted upstream regulators predicted
TNF as the top biological mediator activated in T3 (IPA
network Z-score ¼ 13.2, enrichment P ¼ 2.09E–120). An
expanded causal mechanistic network centered on down-
stream effects of predicted TNF activation (Figure 3b)
explained 48% (1299 of 2721) of the differentially expressed
genes between T3 and T1 and 2. Regulated transcripts
included multiple transcription factors previously implicated
in chronic kidney disease progression, such as nuclear factor
kappa B (including NFkB 1 (p105/p50), RELA (p65) sub-
units)11,31,32 and signal transducer and activator of tran-
scription (STAT1 and 3).33 In the gene interaction network
analysis, TNF was identified as the hub gene connecting the
T3 regulated gene set (Figure 3c). Mapping the upstream
regulators using differential expression profiles from each
cohort (Supplementary Table S1A) recapitulated the
NEPTUNE signal in the ERCB and H3Africa cohorts, with
TNF identified as the top upstream regulator (Supplementary
Table S1B).
Next, previously published cell-selective transcripts from
human kidney single-cell RNA-seq data sets26 were used to
interrogate the cluster-specific tubulointerstitial expression
profiles in NEPTUNE. The top 10 genes selectively enriched
in kidney cell types26 served as cell-type–specific markers.
Bulk RNA-seq expression data were filtered for markers from
each cell type to see which cell types contributed most to the6
transcriptional signal in T3. Increased contributions in T3
were observed from kidney cells (fibroblasts, endothelial,
parietal epithelial, ascending thin loop of Henle, and
descending loop of Henle) and immune cell lineages, whereas
genes specific for proximal tubules, intercalated cells, thick
ascending loop of Henle, distal convoluted tubule, connecting
tubule, principal and transitioning cells were expressed at
lower levels in T3 (Figure 3d). Taken together, these findings
demonstrate that TNF activation in T3 represents a molecular
signature derived from both resident kidney and immune
cells.
Patient-level TNF activation score and relationship to cluster
information
Multiple lines of evidence converged on TNF; therefore,
individual-level kidney TNF activation was assessed. Using a
rich knowledge base,34–36 in silico analysis extracted a set of 272
genes causally downstream of TNF activation (Supplementary
Table S2). Expression of these 272 genes was used as the
readout of TNF activation in kidney biopsies. A TNF activation
score was calculated for each participant19,33,37,38 and evaluated
across the 3 cohorts (Figure 4a). Consistent with TNF activa-
tion accounting for the clustering, the range of TNF activation
scores were similar, with the highest scores in cluster T3
(Figure 4a). The TNF activation score was also calculated from
glomerular samples and found to be strongly correlated withKidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion
a b
Legend
Increased activity predicted
Leads to activation
Directionality inconsistent in network
Effect not predicted
c d
CLUSTER 1 CLUSTER 2 CLUSTER 3
FIB
POD
MAC
MON
NKC
EC-A
EC-P
GEC
VSMC/MC
PT1-7
IC-B
IC-A
tPC-IC
TAL
DCT
CNT
TB--CCYeTll
T-Cell (activated)
T-CYT-MEM
PC
PC-CNT
ATL
PEC
DTL
ATL ascending thin loop of Henle
CNT Connecting tubule
DCT Distal convoluted tubule
DN Distal nephron
DTL Descending loop of Henle
DS Disease specific
DS_TAL Disease specific thick ascending
loop of Henle
EC Endothelial cell
IC Intercalated cell
MC Mesangial cell
PC Principal cell
PEC Parietal epithelial cell
POD Podocyte
PT Proximal tubular epithelial cell
TAL Thick ascending loop of Henle
vSMC Vascular smooth muscle cells
tPC_IC Transitional principal
intercalated cell
Kidney International (2022) -, -–- 7
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndrome
a
NEPTUNE 1.5ERCB H3Africa
1 1 1.0 Cluster
0.5 3
0 2
0 0.0 1
−1 −0.5
Patient ID
b
NEPTUNE
R = 0.76 P < 2.2e−16 ● ● ●●
1.0 ● ●
● ●
● ● ●● ●● ●
●● ●
0.5 ● ● ● ● ● ● ● ●
●●●● ● ● ● ● ● ● ●●
● ● ● ●● ● ●●●
●
●
●● ●●●●●●●● ● ●
●
0.0 ● ●● ● ●● ● ●● ● ●●
●● ● ●
● ●● ●
●●● ●● ● ● ●● ●
● ● ●
● ●● ● ● ●● ●
●●●●
●● ●● ●●●
● ● ● ● ●
● ●●●●●●●
●● ●
● ●
● ● ●
−0.5 ●● ● ●●● ●
● ●● ●
● ● ●
● ●● ●●● ● ● ●●
−1.0 ●
−1 0 1 2
Tubular TNF activity score
c
-1 0 1 2
Tubular TNF activity score
Figure 4 | Tumor necrosis factor (TNF) activity scores across all profiled participants. (a) From the indicated cohorts colored by cluster
membership in tubular transcriptomes. (b) Pearson’s correlation of TNF activity scores from the glomeruli (y-axis) and tubular (x-axis)
transcriptomes in the same North American Nephrotic Syndrome Study Network (NEPTUNE) participants. (c) Correlation of the TNF activity
score from the tubular transcriptome with interstitial fibrosis. The horizontal bar (red) indicates 25% interstitial fibrosis. ERCB, European Renal
cDNA Bank; H3Africa, Human Heredity and Health in Africa Kidney Disease Research Network cohort.
=
Figure 3 | Molecular and functional context of cluster T3 expression profiles. Differential expression profiles from T3 compared with T1 and
T2 in the North American Nephrotic Syndrome Study Network (NEPTUNE) cohort were generated, and enrichment analysis was performed using
Ingenuity Pathways Analysis. (a) Granulocyte adhesion and diapedesis was the top enriched canonical pathway; a subset of the pathway is shown
highlighting tumor necrosis factor (TNF) as an input to the pathway. Genes highlighted in red were upregulated in the differential expression
profile. (b) A mechanistic network of predicted upstream regulators from the differential expression profile indicating TNF as an input. (c) TNF was
identified in a gene interaction network (red indicates that the gene was upregulated in the differential expression profile, whereas green
indicates downregulation). (d) Cell selective gene expression markers were previously identified26 and were intersected with voom-transformed
gene (row) normalized expression data (yellow indicates higher expression, blue indicates lower expression) to elucidate probable cell
contribution to differential expression profiles.
8 Kidney International (2022) -, -–-
TNF activity score
Interstitial fibrosis Glomerular TNF activity score
0 20 40 60 80 100
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion
Table 2 | Unadjusted and adjusted Cox proportional hazards models for composite of end-stage kidney disease and 40%
decline in eGFR from baseline in the NEPTUNE study
Adjusted for MCD/FSGS,
Unadjusted model Adjusted for MCD/FSGS eGFR, and UPCR
Models Predictor HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
Model 1: cluster membership Cluster 1 Ref. Ref. Ref.
Cluster 2 1.7 (0.6, 5.1) 0.34 1.6 (0.5, 4.8) 0.40 2.3 (0.69, 7.57) 0.18
Cluster 3 5.2 (1.9, 14.5) 0.001 4.5 (1.6, 12.9) 0.005 3.80 (1.1, 13.1) 0.035
Model 2: TNF activation scorea TNF activation score 2.6 (1.5, 4.4) 0.001 2.3 (1.3, 4.1) 0.003 1.7 (0.9, 3.5) 0.12
CI, confidence interval; eGFR, estimated glomerular filtration rate (ml/min per 1.73 m2); FSGS, focal segmental glomerulosclerosis; HR, hazard ratio; MCD, minimal change
disease; NEPTUNE, North American Nephrotic Syndrome Study Network; TNF, tumor necrosis factor; UPCR, urine protein-to-creatinine ratio (mg/mg).
aHR for increase in Z-score by 1.the tubulointerstitial TNF activation score in the 2 cohorts
where matched gene expression samples were available
(Figure 4b). ATNF Z-score was generated using the PROGENy
TNF-pathway signature gene set.39 This 98-gene signature
shared 41 genes with our in silico TNF signature. Both signa-
tures were strongly correlated with one another (R2 > 0.94,
P < 0.0001) in the tubulointerstitial NEPTUNE transcriptomic
data.
Association of cluster 3 and TNF activation with IF and clinical
outcomes
The Spearman correlation of TNF activation scores with the
severity of IF in NEPTUNE was significant (n ¼ 179, rho ¼
0.59, P < 0.001; Figure 4c). However, among the 148 par-
ticipants with minimal IF (<25% of the kidney cortex),
elevated TNF activation scores (TNF activation score >0)
were observed in 47 (32%), indicating that the TNF acti-
vation score may be more sensitive to the early signs of
kidney damage that are not yet visible by histopathologic
examination.
To evaluate the extent to which the molecular information
from the kidney tissue captured the variability in loss of eGFR
over time observed in T3 versus T1 and 2, a survival model
was fit separately with cluster membership (model 1, Table 2)
and TNF activation score (model 2, Table 2) as primary
predictors of interest in NEPTUNE. After adjustments for
diagnosis (MCD vs. FSGS), baseline eGFR and UPCR, cluster
3 was associated with a higher hazard of reaching the com-
posite outcome (HR: 3.8, P ¼ 0.035). T2 was not significantly
different from T1. Similarly, an increase in TNF activation
score was associated with higher hazard of the composite
outcome (unadjusted HR: 2.6, P < 0.001). After adjusting for
diagnosis, the HR remained elevated (2.3, P ¼ 0.003). The
association was attenuated after further adjustment for eGFR
and UPCR (HR: 1.7, P ¼ 0.12), suggesting that these factors
may be on the causal pathway of GFR decline.
Identification of noninvasive surrogates of TNF activation
Based on prior work,40 an intrarenal pathway activation
signal might be reflected in participants’ urine profiles. In
NEPTUNE, genes in the TNF activation signature were
cross-referenced with the urine proteomic profile. Fourteen
genes in the TNF activation network had correspondingKidney International (2022) -, -–-urinary proteins (Figure 5a). Of these, intrarenal gene
expression of CCL2 and TIMP1 correlated with urine pro-
tein levels (r ¼ 0.58, P < 0.0001 and r ¼ 0.50, P < 0.0001,
respectively). Urinary MCP-1 (the protein encoded by
CCL2) and TIMP-1 were also correlated with the TNF
activation score (P < 0.0001, r $ 0.50 for both biomarkers;
Figure 5b and c, respectively). Mean levels of both markers
were higher in MCD and FSGS participants in cluster T3
(Supplementary Figure S4). Thus, these 2 urine proteins
were identified as potential noninvasive surrogates reflective
of intrarenal TNF activation.
TNF effect on kidney organoids
Human organoids were used to further support the rela-
tionship of the noninvasive surrogate markers to intrarenal
TNF activation. TNF treatment of organoids resulted in an
early, dose-dependent increase in TNF activation scores
(3 hours), which was slightly dampened but sustained for 20
hours in culture (Figure 6a). TNF activation was reflected in
the upregulation of CCL2 and TIMP1 mRNA expression
(Figure 6b) followed by increased detection of the encoded
proteins MCP-1 and TIMP-1 in the organoid culture medium
(Figure 6c).
TNF activation and expression of surrogate markers in kidney
cells
To test the cellular source of the noninvasive surrogate
markers in human kidney biopsies, single-nucleus RNA-seq
analysis of 10 NEPTUNE biopsies, 5 with high and 5 with
moderate-to-low TNF activation scores in the tubulointer-
stitial profiles, was performed (Supplementary Table S3). The
analysis identified 22 unique nuclear clusters representing the
major cell types of the kidney (Figure 7a28). In the TNF High
samples, cell-type–specific gene expression for both CCL2
and TIMP1 were higher across both immune and intrinsic
kidney cell types (Figure 7B), consistent with findings from
kidney organoids. Thus, the TNF-responsive surrogate
markers reflect alterations in inflammatory and resident
kidney cells in participants with TNF pathway activation.
Predictive ability of surrogate markers
NEPTUNE participants with TNF activation scores, and
urinary cytokine measurements obtained within 45 days after9
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndrome
a CCL4
CCL5
CCL22
CXCL1
CCL2
CXCL10
CCL11 TNF
IIIIL1RN
TIIIIMP2
IIIIL7
TIIIIMP1
MMP2
MMP9
b
R = 0.51, P = 3.6e-08
1
0
−1
6 8 10 12 14
Log2 uMCP1/Cr
c
R = 0.46, P = 3.1e-07
1
0
−1
−4 0 4 8
Log2 uTIMP1/Cr
Figure 5 | Noninvasive surrogate selection for tumor necrosis
factor (TNF) activation. (a) Fourteen genes upregulated in cluster
T3, were downstream of TNF activation, as characterized by curated
cause and effect relationships, and were present on the Luminex
panel used to profile urine profile from North American Nephrotic
Syndrome Study Network (NEPTUNE) participants. The TNF
activation score plotted against (b) Log2 uMCP1/Cr and (c) Log2
uTIMP1/Cr. MCP-1, monocyte chemoattractant protein-1; TIMP-1,
tissue inhibitor of metalloproteinases-1.
TNF activity score TNF activity scorebiopsy (N ¼ 90), were included in predictive models. Using a
combination of urine biomarkers (MCP-1 and TIMP-1),
eGFR and UPCR, a predicted TNF score was calculated and10highly correlated with the transcriptionally derived intrarenal
TNF activation score (r ¼ 0.61, P < 0.001; Figure 8). Thus, a
noninvasive biomarker signature of MCP-1 and TIMP-1,
coupled with routinely obtained clinical information, pre-
dicts intrarenal TNF activation profiles in participants with
FSGS and MCD.
DISCUSSION
This study used tissue transcriptomics to address the het-
erogeneity in disease progression under routine clinical care,
in children and adults with biopsied MCD and FSGS, to
agnostically identify patient subgroups with a shared molec-
ular signature. In the subgroup with the highest risk of eGFR
loss, activation in the kidney of a targetable pathway was
identified that could be quantified at an individual level, and
predicted noninvasively, through urine surrogate markers.
Such approaches for identifying molecular markers provide a
strategy for precision medicine in nephrology, where patients
can be matched to mechanistically relevant therapies.2
For the first time, from a cluster of participants with
shared molecular profiles in 3 geographically diverse cohorts,
a subgroup of high-risk participants, comprising both MCD
and FSGS diagnoses, was identified. Clustering remained
consistent when limiting to those with low levels of IF and,
after adjusting for diagnosis, eGFR and UPCR, this subgroup
had increased risk of GFR loss. This suggests that tran-
scriptomic data capture prognostic information not currently
captured by clinical-pathologic evaluation and potential dis-
ease pathways not targeted by current treatments. The path-
ways within the subgroup’s molecular profile could represent
a variety of biological processes, including both disease
initiating and progression mechanisms, both of which could
be valuable targets for therapy.
The kidney tissue molecular profile of the poor outcome
subgroup centered on TNF activation, a cytokine linked to a
range of diseases.41–44 From several lines of evidence,
including human and animal studies in kidneys,45,46 TNF is
produced in immune and resident kidney cells,47,48 has been
implicated early in disease causation49,50 and
progression,44,47,49,51 but has not been implemented in clin-
ical care. Although TNF activation was associated with the
degree of fibrosis in this study, many participants without
significant scarring had elevated TNF activation scores,
demonstrating that pathway activation may occur early in the
disease course. Two candidate urine markers of TNF activa-
tion were identified: MCP-1, a marker of active inflamma-
tion,52 and TIMP-1, associated with tissue remodeling and
scarring.53 These markers were elevated in the poor outcome
cluster and, along with existing clinical measures, accurately
predicted intrarenal TNF activation. Cell-specific transcrip-
tional signals characterizing the poor outcome cluster were
derived from both infiltrating immune cells and resident
kidney cells, including endothelial cells. This was confirmed
in the single-nucleus RNA-seq data where participants with
high TNF scores showed increased expression of CCL2 and
TIMP1 across multiple cell types. In addition, TNF-treatedKidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion
a
1.0 3 h 20 h
0.5
0.0
-0.5
-1.0
VC 1 5 25 VC 1 5 25
[TNF] ng/ml
b CCL2 TIMP1
* *
A.
TNF TNF
c MCP1
* TIMP1TIMP-1
*
TNF
TNF
Figure 6 | Tumor necrosis factor (TNF) effects on kidney organoids and surrogate markers. TNF directly stimulates TNF activation and
expression of the selected surrogate markers in human pluripotent stem-cell–derived kidney organoids. (a) TNF activation scores were
calculated from bulk RNA-sequencing data obtained from kidney organoids treated with vehicle control (VC) or 1, 5, 25 ng/ml TNF for 3 or 20
hours. Quantification of (b) CCL2 (left) and TIMP1 (right) transcript levels in kidney organoid cell lysates by quantitative real-time polymerase
chain reaction relative to control, and of (c) Monocyte chemoattractant protein-1 (MCP-1; left) and tissue inhibitor of metalloproteinases-1
(TIMP-1; right) protein levels in kidney organoid culture supernatant by the enzyme-linked immunosorbent assay normalized to total protein,
generated from the same samples after treatment with 5 ng/ml TNF or VC for 24 hours. Each data point was generated from a unique sample
and represents the average of analysis in triplicate. Long bar, mean; short bar, 1 SD; *P value < 0.05 by Student’s t test. Representative
experiment (1 of 4 independent) shown.
TNF score
MCP-1/total protein
ng/mg Relative quantity
TMP-1/total protein
ng/mg Relative quantity
[ [
[ [organoids had increased TNF pathway activation scores,
transcript and protein levels of TIMP-1 and MCP-1.
Case reports and small studies suggest that anti-TNF
therapy may be effective in a subset of patients with
nephrotic syndrome, but intrarenal TNF activation was notKidney International (2022) -, -–-assessed.54–56 The FONT trial (Novel Therapies for Resistant
FSGS) tested the TNF inhibitor adalimumab in patients with
multidrug-resistant FSGS.57,58 Of the 17 patients treated in
the combined phase I and phase II studies, 4 patients had
a $50% reduction in proteinuria; 2 patients achieved11
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndrome
a
10
R = 0.61, P < 0.001
DCT PT
5 TAL TAL
FIB
PC/IC PC/IC
ATL Immune
DTL DTL0 POD
PT EC
Immune ATL
EC DCT
POD
FIB
−5
−10 −5 0 5 10
UMAP_1
-1 -.5 0 .5 1
Predicted TNF activation score
b Figure 8 | Correlation of observed tumor necrosis factor (TNF)
CCL2 TIMP1 activation score with a predicted score based on urinary
biomarkers and clinical features. Linear regression models were
used to generate predicted tissue TNF activation scores based on
estimated glomerular filtration rate, urine protein-to-creatinine
ratio, urinary tissue inhibitor of metalloproteinases-1, and urinary
monocyte chemoattractant protein-1. Correlation was 0.61, P
value < 0.001.
igh low igh h F  h  lo
w
NFT TN
F F
TN TN
Figure 7 | Tumor necrosis factor (TNF) effects on kidney and
immune cell types by single-nucleus RNA sequencing (snRNA-
seq) from glomerular-depleted biopsies. (a) Uniform Manifold
Approximation and Projection (UMAP) plot of snRNA-seq profiles
from TI of selected North American Nephrotic Syndrome Study
Network (NEPTUNE) participants found to have elevated TNF
activation scores (TNF High) and low-to-moderate TNF activation
scores (TNF Low) and (b) single nuclear cluster expression of CCL2
and TIMP1 by TNF activation status. Cell-type–specific markers were
based on Lake et al.28 ATL, ascending thin limb cells; CCL2, CC
chemokine ligand 2; DCT, distal convoluted tubule cells; DTL,
descending thin limb cells; EC-GC, glomerular endothelial cells; FIB,
fibroblast cells; Immune, several types of immune cellsPC/IC,
principal cells/intercalated cells; POD, podocytes; PT, proximal
tubule cells; TAL, thick ascending limb cells; TIMP-1, tissue inhibitor
of metalloproteinases-1.
UMAP_2
Observed TNF activation score
-1 0 1 2dramatic improvements, from UPCR of 17 to 0.6 mg/mg in
one and from 3.6 to 0.6 mg/mg in the other. Although the
study was considered unsuccessful in demonstrating the ef-
ficacy of anti-TNF therapy for patients with FSGS as a group,
a response in any patient with this severe phenotype is
notable and consistent with underlying biologic heteroge-
neity within the diagnosis. Other clinical trials in MCD and
FSGS have also observed variable responses to other
interventions.59,60
These examples highlight the need for precision medicine
and for clinical trials to incorporate markers (serum, urine, or
genetic), indicating activation of the targeted injury pathway
to ensure alignment of the molecular profile of participants12with that of the intervention. Molecular categorization could
be combined with consensus clustering based on clinical and
laboratory data, as outlined here for nephrotic syndrome, and
recently applied to the Chronic Renal Insufficiency Cohort,61
for precise delineation of patient prognosis and optimization
of therapy.
The subgroup of highest clinical need, those with the
greatest risk of GFR loss, was the focus for this initial study.
Tubulointerstitial damage and fibrosis have been shown to
strongly associate with eGFR decline and treatment response
across diagnoses.19,62–64 Therefore, although MCD and FSGS
are glomerular diseases, the premise for this study was that
pathways associated with disease progression were more likely
to be detected in the tubulointerstitial profiles. Nevertheless,
the TNF activation signals in the glomerular and tubular
compartments were strongly correlated, capturing similar
activation in both compartments. Further, even when only the
participants with low fibrosis were clustered, their high-risk
subgroup membership persisted. Therefore, the tubulointer-
stitial TNF pathway merits investigation and may represent
both disease initiating and chronic progression mechanisms
in glomerular diseases.
Several limitations of the present study are acknowledged.
This study included only biopsy-proven MCD and FSGS. The
TNF pathway and associated urine markers are likely relevant
to other kidney diseases that could be addressed in future
studies using this pipeline. Relatedly, this study included all
MCD and FSGS participants to be broadly inclusive of the
spectrum of presentation at the time of a clinical biopsy.
However, patients with MCD, particularly children, who
respond well to therapy and may not receive a clinical biopsy
are not represented in this cohort. Similar analyses could
identify pathways relevant within clinical subgroups of theseKidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t iondiagnoses (e.g., only those with high degrees of proteinuria).
Future work using non–tissue-based omic technology could
identify molecular subclusters in nonbiopsied patients, not
included here. Genetic information is expanding our under-
standing of nephrotic syndrome. However, in NEPTUNE, a
low frequency of mutations in 21 monogenic nephrotic
syndrome–associated genes were detected,65 and likely pre-
vented us from finding an association of TNF with mono-
genic glomerular diseases. Similar data are unavailable for
ERCB and H3Africa. In many patients, a single pathway is
unlikely to drive disease progression and additional or com-
bination therapies may be appropriate. Future work using this
pipeline and data from this study can uncover additional
pathways and surrogate markers relevant to each of the
tubular and glomerular transcriptomic clusters.
As next steps, clinical studies are needed to validate the use
of predicted TNF activation scores as a target engagement
biomarker during the treatment of FSGS and other glomer-
ular diseases. Reprising the FONT trial design by limiting trial
eligibility to patients with high predicted TNF pathway acti-
vation would enable the assessment of response to TNF in-
hibition, stratified by TNF activation levels. A phase IIa proof
of concept trial with this design has been initiated for children
and adults with biopsy-proven FSGS and MCD (clinical-
trials.gov/NCT04009668).
In conclusion, this study provides a road map for imple-
menting precision medicine in primary podocytopathies. It
identified a molecularly defined subset of patients with
nephrotic syndrome who have poor clinical outcomes and
increased activation of a targetable pathway, TNF, as a key
driver of disease progression. Noninvasive markers, validated
in an organoid model system, are available to identify the
participants with TNF activation, an approach currently being
tested in an interventional trial. The concepts developed here
for FSGS/MCD, and the TNF pathway represents a first step
toward a comprehensive map of targetable pathways for
glomerular diseases and a move toward precision medicine
where the right medicine is administered to the right patient
with glomerular disease.
DISCLOSURE
MK reports grants from the National Institutes of Health (NIH), Chan
Zuckerberg Initiative, JDRF, AstraZeneca, NovoNordisk, Eli Lilly, Gilead,
Goldfinch Bio, Janssen, Boehringer Ingelheim, Moderna, European
Union Innovative Medicine Initiative, Certa, Chinook, amfAR, Angion,
RenalytixAI, Travere, Regeneron, and IONIS, outside the submitted
work. In addition, he has a patent PCT/EP2014/073413 “Biomarkers
and methods for progression prediction for chronic kidney disease”
licensed. Also, outside of submitted work, LHM has served on the
advisory board of Reata Pharmaceuticals, Calliditas Therapeutics,
Chinook Therapeutics, and Travere Therapeutics. SE reports grant
support from AstraZeneca, NovoNordisk, Eli Lilly, Gilead, Janssen,
Moderna, Certa, Chinook, amfAR, Angion, and IONIS, outside the
submitted work. HT consults for Travere Therapeutics, Goldfinch Bio,
Akebia, Natera, Otsuka, and Chemocentryx. KLG serves on Reata CKD
Advisory Board and the Travere Inc. FSGS and IgA Advisory
WorkGroup. VKD reports funding from Novartis, honoraria from
UpToDate, and has served on the advisory board of Retrophin andKidney International (2022) -, -–-Bayer. JCL reports grants from Dicerna, Allena, Orfan-Bridgebio,
Synlogic, Novobiome, OxThera, Oxidien, Alnylam, and Federation Bio,
outside the submitted work. LAG reports grants from Reata Phar-
maceuticals and Vertex Pharmaceuticals. He has served as a paid
consultant for Roche Pharmaceuticals. He is a paid member of a data
safety monitoring board for Travere Pharmaceuticals. DSG reports
grants from Travere Therapeutics, Reata, Goldfinch Bio, Novartis, and
Boehringer Ingelheim and serves on advisory or consultancy through
the University of Michigan with Roche, Genentech, AstraZeneca, and
Vertex. All the other authors declared no competing interests.ACKNOWLEDGMENTS
We thank Dr. Lalita Subramanian for help with writing, editing, and
formatting this manuscript. The Nephrotic Syndrome Study Network
Consortium (NEPTUNE), U54-DK-083912, is a part of the National
Institutes of Health (NIH) Rare Disease Clinical Research Network
(RDCRN), supported through collaboration between the Office of Rare
Diseases Research, National Center for Advancing Translational
Sciences, and the National Institute of Diabetes, Digestive, and Kidney
Diseases (NIDDK). Additional funding and/or programmatic support
for this project has also been provided by the Else Kröner-Fresenius
Foundation (ERCB), University of Michigan, the NephCure Kidney
International and the Halpin Foundation, and the Applied Systems
Biology Core at the University of Michigan George M. O’Brien Kidney
Translational Core Center (2P30-DK-08194). LHM is supported
through funding from NIH/NIDDK, K08 DK115891-01. We
acknowledge the role of the H3Africa Consortium in making this
research possible through the sharing of data and knowledge. The
NIH (USA) and Wellcome Trust (UK) have provided the core funding
for the H3Africa Consortium, and more information is available at
https://h3africa.org/. This research was supported by the following
grants from NIH/National Human Genome Research Institute (NHGRI)/
NIDDK: H3Africa Kidney Disease Study (U54 HG006939), H3Africa
Kidney Disease Cohort Study (U01 DK107131), H3Africa Kidney
Disease Collaborative Centers (9U54 DK116913). The views expressed
in this paper do not represent the views of the H3Africa Consortium
or their funders. ERCB, NEPTUNE, and H3Africa contributing members
are listed in the Supplementary Acknowledgment.AUTHOR CONTRIBUTIONS
All co-authors have contributed to the manuscript. LHM, SE, MK, KRT,
PHN, JCL, DCC, DA, KLG, JRS, HNR, SMB, LB, RAL, JBH, DSG, JLH, LBH,
HT, CAG conceptualized this study; LHM, SE, VB,WJ, SMB, JLH, J-JL,
FMA, MMO, VN, PJM contributed to data curation; LHM, SE, FMA, PJM,
WJ, JLH did the formal analysis; LHM, SE, MK, VB, DA, JCL, JRS, AF, LBH,
RAL, LB, DSG, AOO were involved in funding acquisition; LHM, SE, MK,
PJM, KRT, VB, KVL, LB, SGA, ADA, TS, C-SW, FCF, CLT, DCC, WJ, DA, KLG,
BG, KEM, LBH, SMB, RAL, JBH, MAA, J-JL investigated the findings;
FMA, SM, PJM, BG, JBH, VN, SE, JLH developed the methodology; LHM,
SE, KRT, VB, FE, KKS, TS, FCF, SMV, DA, KLG, KEM were involved in
project administration; KVL, LAG, SGA, ADA, AA, LHM, JCL, FCF, SMV,
CLT, DA, KLG, BG, AF, KEM, HNR, LBH, RAL, JBH, MAA, J-JL, MK
provided resources; SE, FMA, PJM worked on software used in the
study; SE, FE, FMA, PJM performed bioinformatic processing and
analyses in the study; LHM, SE, KRT, VB, ADA, AA, JCL, VKD, KLG, AF,
KEM, HNR, LBH, RAL, MK, JLH supervised the study; VB, ADA and WJ
worked on study validation; NLW, VV-W, ECT, JES performed kidney
organoid experiments; BG, RM, PJM, EAO coordinated processing and
analysis of single nuclear RNA-seq samples; LHM, SE, VB, CBS, FMA,
ADA, PJM, KEM, and LBH helped with visualization; LHM, SE, VB, PJM,
and JLH wrote the original draft; all authors reviewed and provided
valuable feedback on the manuscript.13
c l i n i ca l i nves t iga t i on LH Mariani et al.: Identifying TNF activation in nephrotic syndromeSUPPLEMENTARY MATERIAL
Supplementary File (PDF)
Supplementary Acknowledgments. European Renal cDNA Bank
(ERCB) members at the time of the study, Members of the Nephrotic
Syndrome Study Network (NEPTUNE) at the time of the study, and
Members of the Human Heredity and Health in Africa Kidney Disease
Research Network cohort (H3 Africa).
Supplementary Methods.
Supplementary References.
Figure S1. Cluster dendrogram and assignment of minimal change
disease (MCD) and focal segmental glomerulosclerosis (FSGS)
participants based on kidney biopsy tubulointerstitial gene
expression data in North American Nephrotic Syndrome Study
Network (NEPTUNE), European Renal cDNA Bank (ERCB), and Human
Heredity and Health in Africa Kidney Disease Research Network
cohort (H3 Africa).
Figure S2. Comparison of cluster assignment and clinical measures in
North American Nephrotic Syndrome Study Network (NEPTUNE) and
European Renal cDNA Bank (ERCB) using glomerular compartment
expression data.
Figure S3. Comparison of clinical factors and cluster assignment
across cohorts.
Figure S4. Urine marker levels by diagnosis and cluster.
Table S1A. List of differentially expressed genes (absolute FC >1.5,
q < 0.05) in cluster 3 versus clusters 1 and 2, in each cohort, used for
enrichment analyses.
Table S1B. Predicted upstream regulators based on DEG profiles of
patients in cluster 3 relative to clusters 1 and 2.
Table S2. Genes and gene products activated by tumor necrosis
factor (TNF) used to generate the TNF activation score.
Table S3. Clinical characteristics of North American Nephrotic
Syndrome Study Network (NEPTUNE) participants with biopsies used
for single-nucleus RNA-seq.REFERENCES
1. Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular
Diseases Work Group. KDIGO 2021 clinical practice guideline for the
management of glomerular diseases. Kidney Int. 2021;100(4S):S1–S276.
2. D’Agati VD, Alster JM, Jennette JC, et al. Association of histologic variants
in FSGS clinical trial with presenting features and outcomes. Clin J Am
Soc Nephrol. 2013;8:399–406.
3. Rosenberg AZ, Kopp JB. Focal segmental glomerulosclerosis. Clin J Am
Soc Nephrol. 2017;12:502–517.
4. Gipson DS, Troost JP, Lafayette RA, et al. Complete remission in the
nephrotic syndrome study network. Clin J Am Soc Nephrol. 2016;11:81–89.
5. Trachtman H, Nelson P, Adler S, et al. DUET: a phase 2 study evaluating
the efficacy and safety of sparsentan in patients with FSGS. J Am Soc
Nephrol. 2018;29:2745–2754.
6. Janiaud P, Serghiou S, Ioannidis JPA. New clinical trial designs in the era
of precision medicine: an overview of definitions, strengths, weaknesses,
and current use in oncology. Cancer Treat Rev. 2019;73:20–30.
7. Collins FS. Reengineering translational science: the time is right. Sci
Transl Med. 2011;3:90cm17.
8. Eddy S, Mariani LH, Kretzler M. Integrated multi-omics approaches to
improve classification of chronic kidney disease. Nat Rev Nephrol.
2020;16:657–668.
9. Barisoni L, Nast CC, Jennette JC, et al. Digital pathology evaluation in the
multicenter Nephrotic Syndrome Study Network (NEPTUNE). Clin J Am
Soc Nephrol. 2013;8:1449–1459.
10. Yasuda Y, Cohen CD, Henger A, et al. Gene expression profiling analysis
in nephrology: towards molecular definition of renal disease. Clin Exp
Nephrol. 2006;10:91–98.
11. Schmid H, Boucherot A, Yasuda Y, et al. Modular activation of nuclear
factor-kappaB transcriptional programs in human diabetic nephropathy.
Diabetes. 2006;55:2993–3003.
12. Osafo C, Raji YR, Burke D, et al. Human Heredity and Health (H3) in Africa
Kidney Disease Research Network: a focus on methods in Sub-Saharan
Africa. Clin J Am Soc Nephrol. 2015;10:2279–2287.1413. Osafo C, Raji YR, Olanrewaju T, et al. Genomic approaches to the
burden of kidney disease in Sub-Saharan Africa: the Human Heredity
and Health in Africa (H3Africa) Kidney Disease Research Network.
Kidney Int. 2016;90:2–5.
14. Gadegbeku CA, Gipson DS, Holzman LB, et al. Design of the
Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary
glomerular nephropathy by a multidisciplinary approach. Kidney Int.
2013;83:749–756.
15. Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate
glomerular filtration rate from serum creatinine: a new prediction
equation. Modification of Diet in Renal Disease Study Group. Ann Intern
Med. 1999;130:461–470.
16. Schwartz GJ, Work DF. Measurement and estimation of GFR in children
and adolescents. Clin J Am Soc Nephrol. 2009;4:1832–1843.
17. Ng DK, Schwartz GJ, Schneider MF, et al. Combination of pediatric and
adult formulas yield valid glomerular filtration rate estimates in young
adults with a history of pediatric chronic kidney disease. Kidney Int.
2018;94:170–177.
18. Zee J, Mansfield S, Mariani LH, Gillespie BW. Using all longitudinal data to
define time to specified percentages of estimated GFR decline: a
simulation study. Am J Kidney Dis. 2019;73:82–89.
19. Grayson PC, Eddy S, Taroni JN, et al. Metabolic pathways and immunom
etabolism in rare kidney diseases. Ann Rheum Dis. 2018;77:1226–1233.
20. Barisoni L, Troost JP, Nast C, et al. Reproducibility of the NEPTUNE
descriptor-based scoring system on whole-slide images and histologic
and ultrastructural digital images. Mod Pathol. 2016;29:671–684.
21. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene
expression and hybridization array data repository. Nucleic Acids Res.
2002;30:207–210.
22. Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool
with confidence assessments and item tracking. Bioinformatics. 2010;26:
1572–1573.
23. Monti S, Tamayo P, Mesirov JP, Golub T. Consensus clustering: a
resampling-based method for class discovery and visualization of gene
expression microarray data. Mach Learn. 2004;52:91–118.
24. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression
analyses for RNA-sequencing and microarray studies. Nucleic Acids Res.
2015;43:e47.
25. Calvano SE, Xiao W, Richards DR, et al. A network-based analysis of
systemic inflammation in humans. Nature. 2005;437:1032–1037.
26. Menon R, Otto EA, Hoover P, et al. Single cell transcriptomics identifies
focal segmental glomerulosclerosis remission endothelial biomarker. JCI
Insight. 2020;5:e133267.
27. Lake BB, Chen S, Hoshi M, et al. A single-nucleus RNA-sequencing
pipeline to decipher the molecular anatomy and pathophysiology of
human kidneys. Nat Commun. 2019;10:2832.
28. Lake BB, Menon R, Winfree S, et al. An atlas of healthy and injured cell
states and niches in the human kidney. bioRxiv. 2021, 2021.2007.2028.
454201.
29. Harder JL, Menon R, Otto EA, et al. Organoid single cell profiling
identifies a transcriptional signature of glomerular disease. JCI Insight.
2019;4:e122697.
30. Krämer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches
in Ingenuity Pathway Analysis. Bioinformatics. 2014;30:523–530.
31. Wiggins JE, Patel SR, Shedden KA, et al. NFkappaB promotes
inflammation, coagulation, and fibrosis in the aging glomerulus. J Am
Soc Nephrol. 2010;21:587–597.
32. Martini S, Nair V, Keller BJ, et al. Integrative biology identifies shared
transcriptional networks in CKD. J Am Soc Nephrol. 2014;25:2559–2572.
33. Tao J, Mariani L, Eddy S, et al. JAK-STAT signaling is activated in the
kidney and peripheral blood cells of patients with focal segmental
glomerulosclerosis. Kidney Int. 2018;94:795–808.
34. Gupta A, Puri S, Puri V. Bioinformatics unmasks the maneuverers of pain
pathways in acute kidney injury. Sci Rep. 2019;9:11872.
35. Schüler-Toprak S, Häring J, Inwald EC, et al. Agonists and knockdown of
estrogen receptor b differentially affect invasion of triple-negative breast
cancer cells in vitro. BMC Cancer. 2016;16:951.
36. Supper J, Gugenmus C, Wollnik J, et al. Detecting and visualizing gene
fusions. Methods. 2013;59:S24–S28.
37. Lee E, Chuang HY, Kim JW, et al. Inferring pathway activity toward
precise disease classification. PLoS Comput Biol. 2008;4:e1000217.
38. Tao J, Mariani L, Eddy S, et al. JAK-STAT activity in peripheral blood cells
and kidney tissue in IgA nephropathy. Clin J Am Soc Nephrol. 2020;15:
973–982.Kidney International (2022) -, -–-
LH Mariani et al.: Identifying TNF activation in nephrotic syndrome c l i n i ca l i nves t iga t ion39. Schubert M, Klinger B, Klünemann M, et al. Perturbation-response genes
reveal signaling footprints in cancer gene expression. Nat Commun.
2018;9:20.
40. Ju W, Nair V, Smith S, et al. Tissue transcriptome-driven identification of
epidermal growth factor as a chronic kidney disease biomarker. Sci Transl
Med. 2015;7:316ra193.
41. Ware CF. The TNF superfamily-2008. Cytokine Growth Factor Rev. 2008;19:
183–186.
42. Jäättelä M. Biologic activities and mechanisms of action of tumor
necrosis factor-alpha/cachectin. Lab Invest. 1991;64:724–742.
43. Ernandez T, Mayadas TN. Immunoregulatory role of TNFalpha in
inflammatory kidney diseases. Kidney Int. 2009;76:262–276.
44. Bakr A, Shokeir M, El-Chenawi F, et al. Tumor necrosis factor-alpha
production from mononuclear cells in nephrotic syndrome. Pediatr
Nephrol. 2003;18:516–520.
45. McCarthy ET, Sharma R, Sharma M, et al. TNF-alpha increases albumin
permeability of isolated rat glomeruli through the generation of
superoxide. J Am Soc Nephrol. 1998;9:433–438.
46. Le Berre L, Herve C, Buzelin F, et al. Renal macrophage activation and
Th2 polarization precedes the development of nephrotic syndrome in
Buffalo/Mna rats. Kidney Int. 2005;68:2079–2090.
47. Pedigo CE, Ducasa GM, Leclercq F, et al. Local TNF causes NFATc1-
dependent cholesterol-mediated podocyte injury. J Clin Invest. 2016;126:
3336–3350.
48. Baud L, Fouqueray B, Philippe C, Amrani A. Tumor necrosis factor alpha
and mesangial cells. Kidney Int. 1992;41:600–603.
49. Otalora L, Chavez E, Watford D, et al. Identification of glomerular and
podocyte-specific genes and pathways activated by sera of patients with
focal segmental glomerulosclerosis. PLoS One. 2019;14:e0222948.
50. Lee HH, Cho YI, Kim SY, et al. TNF-alpha-induced inflammation stimulates
apolipoprotein-A4 via activation of TNFR2 and NF-kappaB signaling in
kidney tubular cells. Sci Rep. 2017;7:8856.
51. Wen Y, Lu X, Ren J, et al. KLF4 in macrophages attenuates TNF a-mediated
kidney injury and fibrosis. J Am Soc Nephrol. 2019;30:1925–1938.
52. Kim MJ, Tam FWK. Urinary monocyte chemoattractant protein-1 in renal
disease. Clin Chim Acta. 2011;412:2022–2030.
53. Ahmed AK, Haylor JL, El Nahas AM, Johnson TS. Localization of matrix
metalloproteinases and their inhibitors in experimental progressive
kidney scarring. Kidney Int. 2007;71:755–763.Kidney International (2022) -, -–-54. Peyser A, Machardy N, Tarapore F, et al. Follow-up of phase I trial of
adalimumab and rosiglitazone in FSGS: III. Report of the FONT study
group. BMC Nephrol. 2010;11:2.
55. Raveh D, Shemesh O, Ashkenazi YJ, et al. Tumor necrosis factor-alpha
blocking agent as a treatment for nephrotic syndrome. Pediatr Nephrol.
2004;19:1281–1284.
56. Ito S, Tsutsumi A, Harada T, et al. Long-term remission of nephrotic
syndrome with etanercept for concomitant juvenile idiopathic arthritis.
Pediatr Nephrol. 2010;25:2175–2177.
57. Joy MS, Gipson DS, Powell L, et al. Phase 1 trial of adalimumab in
Focal Segmental Glomerulosclerosis (FSGS): II. Report of the FONT
(Novel Therapies for Resistant FSGS) study group. Am J Kidney Dis.
2010;55:50–60.
58. Trachtman H, Vento S, Herreshoff E, et al. Efficacy of galactose and
adalimumab in patients with resistant focal segmental
glomerulosclerosis: report of the font clinical trial group. BMC Nephrol.
2015;16:111.
59. Hogan J, Bomback AS, Mehta K, et al. Treatment of idiopathic FSGS
with adrenocorticotropic hormone gel. Clin J Am Soc Nephrol. 2013;8:
2072–2081.
60. Yu C-C, Fornoni A, Weins A, et al. Abatacept in B7-1–positive proteinuric
kidney disease. N Engl J Med. 2013;369:2416–2423.
61. Zheng Z, Waikar SS, Schmidt IM, et al. Subtyping CKD patients by
consensus clustering: the Chronic Renal Insufficiency Cohort (CRIC)
Study. J Am Soc Nephrol. 2021;32:639–653.
62. Zee J, Liu Q, Smith AR, et al. Kidney biopsy features most predictive of
clinical outcomes in the spectrum of minimal change disease and
focal segmental glomerulosclerosis. J Am Soc Nephrol. 2022;33:
1411–1426.
63. Kolachalama VB, Singh P, Lin CQ, et al. Association of pathological
fibrosis with renal survival using deep neural networks. Kidney Int Rep.
2018;3:464–475.
64. Srivastava A, Palsson R, Kaze AD, et al. The prognostic value of
histopathologic lesions in native kidney biopsy specimens: results from
the Boston Kidney Biopsy Cohort Study. J Am Soc Nephrol. 2018;29:
2213–2224.
65. Sampson MG, Gillies CE, Robertson CC, et al. Using population genetics
to interrogate the monogenic nephrotic syndrome diagnosis in a case
cohort. J Am Soc Nephrol. 2016;27:1970–1983.15