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. 2021 May 24;10(11):2262. doi: 10.3390/jcm10112262

Gene Expression as a Guide to the Development of Novel Therapies in Primary Glomerular Diseases

Panagiotis Garantziotis 1,2, Stavros A P Doumas 2, Ioannis Boletis 3, Eleni Frangou 2,4,5,*
Editors: Smaragdi Marinaki, Sophia Lionaki
PMCID: PMC8197155  PMID: 34073694

Abstract

Despite improvements in understanding the pathogenic mechanisms of primary glomerular diseases, therapy still remains nonspecific. We sought to identify novel therapies targeting kidney-intrinsic injury of distinct primary glomerulonephritides through computational systems biology approaches. We defined the unique transcriptional landscape within kidneys from patients with focal segmental glomerulosclerosis (FSGS), minimal change disease (MCD), immunoglobulin A nephropathy (IgAN), membranous nephropathy (MN) and thin basement membrane nephropathy (TBMN). Differentially expressed genes were functionally annotated with enrichment analysis, and distinct biological processes and pathways implicated in each primary glomerular disease were uncovered. Finally, we identified novel drugs and small-molecule compounds that may reverse each glomerulonephritis phenotype, suggesting they should be further tested as precise therapy in primary glomerular diseases.

Keywords: primary glomerulonephritis, focal segmental glomerulosclerosis, minimal change disease, IgA nephropathy, membranous nephropathy, thin basement membrane nephropathy, gene expression, computational systems biology, drug discovery and development, drug repurposing

1. Introduction

Primary glomerulonephritides encompass a heterogeneous group of glomerular diseases characterized by abnormal activation of innate and/or adaptive immune responses due to kidney-intrinsic factors [1]. Although relatively rare, they represent the most common cause of end-stage renal disease in young adults and are associated with increased morbidity, mortality and healthcare costs [2]. Current clinicopathological classification includes minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), immunoglobulin and complement-mediated glomerular diseases with a membranoproliferative glomerular pattern (MPGN), immunoglobulin A nephropathy (IgAN) and thin basement membrane nephropathy (TBMN) [1]. Despite improvements in understanding the underlying pathogenic mechanism of each glomerular disease, therapy still remains nonspecific and includes general supportive measures coupled with immunosuppression [3,4]. Disease-specific therapy targeting kidney-intrinsic injury still remains a major challenge in nephrology.

Recent advances in omics technologies have provided insights into the molecular mechanisms underlying complex traits, such as glomerular diseases. Gene expression represents the intermediate phenotype between genetic variation and disease phenotypic variation and thus may inform about genetic and environmental effects on cells and tissues. Specifically, the comparison of gene expression variation between distinct conditions can delineate transcriptional differences and specific molecular pathways [5]. To this end, high-throughput genome-wide gene expression studies have described the transcriptome of the peripheral blood and kidneys of animal models and patients with glomerular diseases and uncovered molecular pathways implicated in their pathogenesis [6]. However, gene expression patterns unique to each primary glomerular disease remain to be defined.

Computational systems biology combines knowledge-driven experimental data with simulation-based analyses and tests hypotheses with in silico experiments. This provides a powerful tool to understand complex biological processes and identify novel drugs or drugs to be repurposed [7]. The Connectivity Map (CMap) project was the first approach that provided genome-wide gene expression responses from 4 human cell lines treated with 1 out of 1309 FDA-approved drugs or small-molecule compounds at different doses [8]. The NIH Library of Integrated Network-based Cell-Signatures (LINCS) program expanded the CMap project to include over a million signatures through the use of the L1000 high-throughput transcriptomic technology; it identified gene expression alterations (before and after treatment) of more than 60 human cell lines with more than 20,000 drugs/small-molecule compounds [9,10]. By the use of a multivariate method to compute signatures, the LINCS L1000 Characteristic Direction Signatures Search engine (L1000CDS2) further prioritized thousands of small-molecule signatures and their pairwise combinations, predicted to either mimic or reverse the gene expression signature of a disease or condition [11]. Recently, connectivity mapping identified BI-2536 as a potential drug to treat diabetic nephropathy [12].

Herein, we employed the Nephroseq classic v4 web-based analysis engine [13], a platform for integrative data mining of comprehensive kidney disease gene expression datasets, and identified datasets that compared kidney gene expression patterns from patients with primary glomerular diseases vs. healthy individuals. We defined the unique transcriptional landscape of FSGS, MCD, IgAN, MN and TBMN through the comparison of kidney gene expression patterns between the yielded datasets. Differentially expressed genes were functionally annotated with enrichment analysis, and distinct biological processes and pathways implicated in each primary glomerular disease were established. Finally, using the L1000CDS2 engine [11], we identified putative novel drugs or small-molecule compounds that may reverse each glomerulonephritis phenotype, suggesting they should be further tested as precise therapy in primary glomerular diseases.

2. Materials and Methods

2.1. Search Strategy

To identify publicly available gene expression profiles of human glomeruli from patients with primary glomerular diseases, we used the Nephroseq classic v4 web-based engine [13]. Gene expression datasets generated using the Affymetrix Microarray Technology were only included in the study. Specifically, the keywords “human”, “kidney”, “glomerulus”, “microarray” and “affymetrix” were used in the engine to search for gene expression patterns of diseases. Expression profiles from minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), immunoglobulin A nephropathy (IgAN) and thin basement membrane nephropathy (TBMN), when compared to expression profiles from healthy kidneys, were yielded. We applied a p-value cutoff of 0.05, as provided by the Nephroseq classic v4 web-based engine, to define the statistically significant differentially expressed genes (DEGs). All DEGs that demonstrated statistical significance, irrespective of their fold-change, were included in the analysis. Comparison between DEGs was performed with Venny2.1 [14] and InteractiVenn [15].

2.2. Functional Enrichment Analysis and Drug Prediction

Enrichment analysis of DEGs was performed using g:Profiler [16]. The visualization of enrichment analysis was carried out using the GraphPad Prism version 5.0 [17]. To identify compounds that efficiently reverse the disease-specific transcriptional signatures, the L1000CDS2 engine was used [11]. To identify the possible mechanism of action, disease target, side effects and FDA approval of the drugs/small molecules, we used the Large-Scale Visualization of Drug-induced Transcriptomic signatures (L1000FWD) engine [18], the DrugBank resource [19] and the FDA website.

3. Results

3.1. Computational Systems Biology Approaches Reveal Glomerulonephritis-Specific Gene Signatures

We initially employed the Nephroseq classic v4 web-based analysis engine [13] to identify datasets of kidney gene expression patterns from patients with primary glomerular diseases and healthy individuals. Therefore, we collected previously published datasets that compared kidney gene expression from patients with focal segmental glomerulosclerosis (FSGS) (a total of 39 patients) [20,21], minimal change disease (MCD) (a total of 21 patients) [20,21], IgA nephropathy (IgAN) (a total of 54 patients) [21,22], membranous nephropathy (MN) (21 patients) [21] or thin basement membrane nephropathy (TBMN) (3 patients) [21], with a total of 36 healthy individuals. Clinical parameters and demographics of patients and healthy individuals are presented in Table S1.

To define the unique transcriptional landscape of each primary glomerular disease, we compared gene expression patterns between the yielded datasets. Differentially expressed genes (DEGs) that were exclusively identified in one glomerular disease (and not in others) defined the glomerulonephritis-specific gene signature. Accordingly, 202, 150, 671, 2058 and 1233 DEGs defined the FSGS-specific, MCD-specific, IgAN-specific, MN-specific and TBMN-specific gene signature, respectively, suggesting a unique pathogenic involvement highly specific to the respective glomerular disease. Interestingly, no common DEGs across all diseases were identified (Figure 1, Table S2). These data demonstrate that gene expression patterns within kidneys in primary glomerular diseases are deregulated in a glomerulonephritis-specific manner. Experimental validation of differential transcripts and their functionality in kidneys is further needed.

Figure 1.

Figure 1

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Glomerulonephritis-specific differentially expressed genes. Gene expression patterns between primary glomerular diseases were compared. Differentially expressed genes (DEGs) that were exclusively identified in one glomerular disease (and not in others) defined the glomerulonephritis-specific gene signatures. FSGS—focal segmental glomerulosclerosis; MCD—minimal change disease; IgAN—immunoglobulin A nephropathy; MN—membranous nephropathy; TBMN—thin basement membrane nephropathy.

3.2. Unique Biological Processes and Pathways Are Implicated in Distinct Primary Glomerular Diseases

Next, to reveal altered molecular pathways between primary glomerular diseases, glomerulonephritis-specific DEGs were functionally interpreted through enrichment analysis using g:Profiler [16]. To this end, the FSGS-specific gene signature was functionally enriched in gene ontology (GO) biological processes linked to cell–cell junction assembly and the enzyme-linked receptor protein signaling pathway. Similarly, the MCD-specific gene signature was functionally enriched in glycine serine threonine metabolism; the IgAN-specific gene signature was enriched in regulation of postsynaptic membrane potential and the cAMP signaling pathway; the MN-specific gene signature was enriched in IL-17 and MAPK signaling pathways, whereas the TBMN-specific gene signature was enriched in pathways linked to B-cell receptor signaling and regulation of actin cytoskeleton (Figure 2). Collectively, these data demonstrate the involvement of unique biological processes and pathways in distinct glomerular diseases, suggesting that these processes and pathways may represent disease-specific novel therapeutic targets.

Figure 2.

Figure 2

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Functional enrichment analysis of glomerulonephritis-specific differentially expressed genes (DEGs) reveals the involvement of unique biological processes and pathways in distinct primary glomerular diseases. Bar-plot diagrams demonstrating functional enrichment analysis of DEGs from the (a) FSGS-specific gene signature; (b) MCD-specific gene signature; (c) IgAN-specific gene signature; (d) MN-specific gene signature; (e) TBMN-specific gene signature. Intersection size demonstrates the number of genes identified in each enriched term. Colors denote adjusted p-values of terms as defined by the color-coding heatmap. FSGS—Focal Segmental Glomerulosclerosis; MCD—Minimal Change Disease; IgAN—Immunoglobulin A nephropathy; MN—Membranous nephropathy; TBMN—Thin Basement Membrane Nephropathy.

To identify putative novel drugs or small-molecule compounds that may reverse each glomerulonephritis phenotype, we used the L1000 Characteristic Direction Signature Search Engine (L1000CDS2) [16]. The L1000CDS2 was queried with upregulated and downregulated genes from each glomerular disease to prioritize matching signatures obtained from more than 20,000 drug and small-molecule treatments of multiple human cell lines. Queries generated predictions for the top 50 molecules that could reverse the input of each glomerular disease (Table S3).

To identify glomerulonephritis-specific novel drugs or small-molecule compounds, we compared the top 50 drug signatures between the 5 glomerular diseases. Drugs or small-molecule compounds that were predicted to reverse only one glomerulonephritis-specific gene signature and not others defined the glomerulonephritis-specific drug signature. Thus, 17, 9, 13, 8 and 27 drugs/small-molecule compounds were predicted to reverse the FSGS-specific, MCD-specific, IgAN-specific, MN-specific and TBMN-specific gene signature, respectively (Figure 3, Table 1).

Figure 3.

Figure 3

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Drugs and small-molecule compounds predicted to reverse gene expression patterns of primary glomerulonephritides in a disease-specific manner. The comparison of prioritized drugs/small-molecule compounds that may reverse gene expression patterns from primary glomerular diseases revealed glomerulonephritis-specific drugs and drugs that may reverse gene expression patterns from all glomerulonephritides. FSGS—focal segmental glomerulosclerosis; MCD—minimal change disease; IgAN—immunoglobulin A nephropathy; MN—membranous nephropathy; TBMN—thin basement membrane nephropathy.

Table 1.

Glomerulonephritis-specific drugs. Drugs and small-molecule compounds predicted to reverse the glomerulonephritides-specific gene signatures. They are presented according to their ranking and score during prioritization. Cell line tested and dose and time of treatment applied to each cell line are demonstrated in separate columns.

Specific Drugs Rank Score Perturbation (Drug) Cell Line Dose Time
FSGS-specific 3 0.0344 Tanespimycin HEPG2 10.0 um 24 h
4 0.0323 JAK3 Inhibitor VI PC3 10.0 um 6 h
48 0.0237 BRD-A17065207 SKB 10.0 um 24 h
10 0.0301 Ponatinib HEPG2 3.33 um 24 h
11 0.0301 NVP-AUY922 HEPG2 0.37 um 24 h
23 0.0258 BRD-K53414658 A375 10.0 um 24 h
28 0.0258 KIN001-265 HEPG2 0.37 um 24 h
31 0.0258 CGP-60474 MCF10A 1.11 um 24 h
33 0.0258 GSK-1059615 BT20 10.0 um 24 h
34 0.0258 AZD-5438 BT20 10.0 um 24 h
36 0.0258 GSK-2126458 LNCAP 1.11 um 24 h
38 0.0258 AZ-628 A375 3.33 um 24 h
39 0.0237 Z-Leu3-VS HCC515 10.0 um 24 h
41 0.0237 CGP 71683 hydrochloride HCC515 10.0 um 24 h
42 0.0237 KETOROLAC TROMETHAMINE HCC515 10.0 um 24 h
45 0.0237 16-HYDROXYTRIPTOLIDE HA1E 0.08 um 24 h
46 0.0237 89671 HA1E 0.09 um 24 h
MCD-specific 11 0.0453 BRD-K13810148 MCF7 10.0 um 24 h
16 0.0453 Pracinostat A549 10.0 um 24 h
18 0.0425 NORETHINDRONE VCAP 10.0 um 24 h
25 0.0425 5-Fluorocytosine VCAP 10.0 um 24 h
30 0.0397 TWS119 HCC515 10.0 um 24 h
37 0.0397 DL-PDMP A375 64.0 um 24 h
42 0.0397 Taxifolin-(+/−) VCAP 10.0 um 24 h
44 0.0397 EI-293 PC3 10.0 um 24 h
46 0.0397 Neratinib HCC515 3.33 um 24 h
IgAN-specific 3 0.0296 BMS-754807 A375 10.0 um 24 h
4 0.0296 BRD-K19295594 A375 11.1 um 24 h
11 0.0281 BRD-K65814004 MCF7 10.0 um 24 h
18 0.0266 DG-041 A375 40.0 um 24 h
25 0.0258 15-Deoxy-12,14-prostaglandin J2 A375 10.0 um 24 h
27 0.0258 Dovitinib A375 1.11 um 24 h
28 0.0258 Mitoxantrone A375 0.12 um 24 h
32 0.0251 7b-cis A375 10.0 um 24 h
39 0.0243 MK-0591 A375 80.0 um 24 h
41 0.0243 Piperlongumine (HPLC) PC3 10.0 um 24 h
43 0.0243 S1367 MCF7 10.0 um 24 h
46 0.0243 NVP-BEZ235 A549 0.37 um 24 h
47 0.0243 SB590885 HT29 3.33 um 24 h
MN-specific 9 0.0183 Foretinib HT29 10.0 um 24 h
34 0.0151 WH-4-025 HT29 10.0 um 24 h
13 0.0170 GDC-0980 HT29 10.0 um 24 h
23 0.0157 TPCA-1 HT29 10.0 um 24 h
25 0.0157 S1175 MCF7 10.0 um 24 h
31 0.0154 BRD-K41859756 HT29 10.0 um 24 h
41 0.0148 Palbociclib HME1 10.0 um 24 h
49 0.0145 Erlotinib MCF10A 3.33 um 24 h
TBMN-specific 14 0.0243 Narciclasine PC3 10.0 um 6 h
10 0.0252 BRD-A93236127 MCF7 10.0 um 24 h
12 0.0247 BRD-A62809825 HT29 10.0 um 24 h
13 0.0247 Homoharringtonine HT29 10.0 um 6 h
16 0.0238 ARP 101 MCF7 10.0 um 24 h
17 0.0228 CAY10594 HT29 20.0 um 24 h
18 0.0228 BRD-K91370081 SKB 10.0 um 24 h
19 0.0223 BRD-K32896438 MCF7 10.0 um 24 h
20 0.0223 BRD-K23478508 MCF7 10.0 um 24 h
21 0.0223 BRD-K53308430 VCAP 10.0 um 24 h
22 0.0218 Wortmannin PC3 10.0 um 24 h
23 0.0218 BJM-ctd2-9 SW620 10.0 um 6 h
24 0.0218 HY-10518 VCAP 10.0 um 24 h
26 0.0218 BRD-K06009608 VCAP 10.0 um 24 h
28 0.0218 LDN-193189 SKBR3 10.0 um 3 h
50 0.0204 BRD-K92317137 PC3 10.0 um 6 h
30 0.0213 PK-11195 HT29 160.0 um 24 h
31 0.0213 MST- 312 PC3 11.1 um 24 h
32 0.0213 BRD-K80786583 MCF7 10.0 um 24 h
33 0.0213 BRD-K78122587 MCF7 10.0 um 24 h
37 0.0213 GDC-0941 LNCAP 1.11 um 24 h
38 0.0209 PERHEXILINE MALEATE HT29 10.0 um 24 h
40 0.0209 Dorsomorphin dihydrochloride MCF7 10.0 um 24 h
41 0.0209 BRD-U33728988 SKB 10.0 um 24 h
44 0.0209 BRD-A80502530 MCF7 10.0 um 24 h
45 0.0209 PHA-665752 HT29 0.04 um 24 h
47 0.0209 JW-7-24-1 A549 10.0 um 24 h
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Interestingly, PD-0325901 (an investigational MEK inhibitor [23]), PD-184352 (an investigational MAPK inhibitor [24]) and KU 0060648 trihydrochloride were predicted to reverse gene expression patterns of all glomerular diseases (Table 2).

Table 2.

Drugs that may reverse gene expression patterns from all glomerular diseases. Drugs and small-molecule compounds are presented according to their ranking and score during prioritization. Cell line tested and dose and time of treatment applied to each cell line are demonstrated in separate columns.

Disease Rank Score Perturbation (Drug) Cell Line Dose Time
FSGS 7 0.0323 PD-0325901 HEPG2 10.0 um 24 h
MCD 1 0.0567 HT29 0.12 um 24 h
IgAN 45 0.0243 A375 1.11 um 24 h
TBNM 36 0.0213 HT29 0.12 um 24 h
MN 5 0.0192 HT29 0.12 um 24 h
FSGS 9 0.0301 PD-184352 A375 3.33 um 24 h
MCD 7 0.0482 HT29 10.0 um 24 h
IgAN 16 0.0273 HT29 10.0 um 24 h
TBNM 27 0.0218 HT29 10.0 um 24 h
MN 12 0.0170 HT29 10.0 um 24 h
FSGS 44 0.0237 KU 0060648 trihydrochloride A375 10.0 um 24 h
MCD 20 0.0425 A375 10.0 um 24 h
IgAN 24 0.0258 A375 10.0 um 24 h
TBNM 8 0.0257 MCF7 10.0 um 24 h
MN 20 0.0157 A375 10.0 um 24 h
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Finally, the potential mechanism of action of identified drugs and small molecules, the disease they may target, their possible side effects and their possible FDA approval were identified through the L1000FWD engine [18], the DrugBank resource [19] and the FDA website (Table 3).

Table 3.

Potential mechanism of action, target disease, side effects and FDA approval of drugs and small molecules. MOA: mechanism of action; N/A: not available.

Perturbation (Drug) Mechanism of Action (MOA) FDA Approval Disease Target Side Effects
Tanespimycin HSP inhibitor Yes Multiple myeloma N/A
JAK3 Inhibitor VI Unknown No Unknown N/A
BRD-A17065207 Protein synthesis inhibitor No Unknown N/A
Ponatinib Bcr-Abl kinase inhibitor Yes Chronic myeloid leukemia Hypertension
FLT3 inhibitor Acute lymphoblastic leukemia Rash
PDGFR tyrosine kinase receptor inhibitor Abdominal pain
Fatigue
Headache
Dry skin
Constipation
Arthralgia
Nausea
Pyrexia
Thrombocytopenia
Anemia
Neutropenia
Lymphopenia
Leukopenia
NVP-AUY922 HSP inhibitor No Unknown N/A
BRD-K53414658 VEGFR inhibitor No Unknown N/A
KIN001-265 Unknown No Unknown N/A
CGP-60474 Unknown No Unknown N/A
GSK-1059615 Unknown No Unknown N/A
AZD-5438 CDK inhibitor No Unknown N/A
GSK-2126458 Unknown No Unknown N/A
AZ-628 RAF inhibitor No Unknown N/A
Z-Leu3-VS Unknown No Unknown N/A
CGP 71683 hydrochloride Neuropeptide receptor antagonist No Unknown N/A
Ketorolac TROMETHAMINE Cyclooxygenase inhibitor Yes Unknown Abdominal pain
Dyspepsia
Nausea
Headaches
Vomiting
Epigastric pain
Gastrointestinal bleeding
Drowsiness
Acute renal failure
Hypertension
Respiratory depression
Coma
16-HYDROXYTRIPTOLIDE RNA polymerase inhibitor No Unknown N/A
89671 Unknown No Unknown N/A
BRD-K13810148 HDAC inhibitor No Unknown N/A
Pracinostat HDAC inhibitor No Unknown N/A
Norethindrone Progesterone receptor agonist Yes Menopause Adverse effects of contraceptives
Osteoporosis nausea
Vaginal atrophy vomiting
5-Fluorocytosine Inhibition of RNA and DNA biosynthesis Yes Bacterial septicemia Nausea
Endocarditis Diarrhea
Urinary tract infections Vomiting
Meningitis Abdominal pain
TWS119 Glycogen synthase kinase-3β inhibitor No Unknown N/A
DL-PDMP Unknown No Unknown N/A
Taxifolin-(+/−) Opioid receptor antagonist No Unknown N/A
EI-293 Unknown No Unknown N/A
Neratinib EGFR inhibitor Yes Breast cancer Hepatotoxicity
Diarrhea
Vomiting
Dehydration
Cellulitis
Renal failure
Erysipelas
Alanine aminotransferase increase
Aspartate aminotransferase increase
Nausea
Fatigue
Abdominal pain
BMS-754807 IGF-1 inhibitor No Unknown N/A
BRD-K19295594 BCL inhibitor No Unknown N/A
MCL1 inhibitor
BRD-K65814004 Nitric oxide synthase inhibitor No Unknown N/A
DG-041 Unknown No Unknown N/A
15-Deoxy-12,14-prostaglandin J2 Natural peroxisome No Unknown N/A
Proliferator-activated
receptor-γ (PPAR-γ) agonist
Dovitinib EGFR inhibitor No Unknown N/A
FGFR inhibitor
FLT3 inhibitor
PDGFR tyrosine kinase receptor inhibitor
VEGFR inhibitor
Mitoxantrone Topoisomerase inhibitor Yes Multiple sclerosis Leukopenia with infection
Prostate cancer
Acute myeloid leukemia
7b-cis Unknown No Unknown N/A
MK-0591 Leukotriene synthesis inhibitor No Unknown N/A
Piperlongumine (HPLC) Unknown No Unknown N/A
S1367 Topoisomerase inhibitor No Unknown N/A
NVP-BEZ235 mTOR inhibitor, PI3K inhibitor No Unknown N/A
SB590885 Unknown No Unknown N/A
Foretinib VEGFR inhibitor No Unknown N/A
WH-4-025 Unknown No Unknown N/A
GDC-0980 mTOR inhibitor, PI3K inhibitor No Unknown N/A
TPCA-1 IKK inhibitor No Unknown N/A
S1175 HSP inhibitor No Unknown N/A
BRD-K41859756 HSP inhibitor No Unknown N/A
Palbociclib CDK inhibitor Yes Breast cancer Neutropenia
Leukopenia
Anemia
Fatigue
Nausea
Diarrhea
Respiratory infection
Headache
Thrombocytopenia
Vomiting
Decreased appetite
Erlotinib EGFR inhibitor Yes Non-small-cell lung cancer Diarrhea
pancreatic cancer Rash
Liver transaminase elevation
Narciclasine Unknown No Unknown N/A
BRD-A93236127 ATPase inhibitor No Unknown N/A
BRD-A62809825 Unknown No Unknown N/A
Homoharringtonine Protein synthesis inhibitor Yes Chronic myeloid leukemia Myelosuppression
Bleeding
Hyperglycemia
Fetal harm
ARP 101 Unknown No Unknown N/A
CAY10594 Unknown No Unknown N/A
BRD-K91370081 DNA synthesis inhibitor No Unknown N/A
BRD-K32896438 Unknown No Unknown N/A
BRD-K23478508 ATPase inhibitor Yes Congestive heart failure Nausea
Atrial fibrillation Vomiting
Visual changes
Arrhythmia
BRD-K53308430 Unknown No Unknown N/A
Wortmannin PI3K inhibitor No Unknown N/A
BJM-ctd2-9 Androgen receptor antagonist No Unknown N/A
HY-10518 Unknown No Unknown N/A
BRD-K06009608 Unknown No Unknown N/A
LDN-193189 Bone morphogenic protein inhibitor No Unknown N/A
BRD-K92317137 Unknown No Unknown N/A
PK-11195 Benozodiazepine receptor antagonist No Unknown N/A
MST-312 Unknown No Unknown N/A
BRD-K80786583 Unknown No Unknown N/A
BRD-K78122587 T-type calcium channel blocker No Unknown N/A
GDC-0941 PI3K inhibitor No Unknown N/A
PERHEXILINE MALEATE Unknown No Unknown N/A
Dorsomorphin dihydrochloride Unknown No Unknown N/A
BRD-U33728988 Unknown No Unknown N/A
BRD-A80502530 Unknown No Unknown N/A
PHA-665752 c-MET inhibitor No Unknown N/A
JW-7-24-1 Unknown No Unknown N/A
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Collectively, our data reveal novel, not previously identified, drugs and small-molecule compounds that may reverse the phenotype of primary glomerular diseases in a disease-specific manner. Further investigation is called for to define the therapeutic implications of our findings.

4. Discussion

Herein, we employed the Nephroseq classic v4 web-based analysis engine and identified gene expression datasets within kidneys from patients with primary glomerular diseases vs. healthy individuals. The comparison of gene expression patterns defined the FSGS-specific, MCD-specific, IgAN-specific, MN-specific and TBMN-specific gene signatures. Functional enrichment analysis of differentially expressed genes identified distinct biological processes and pathways implicated in each primary glomerular disease. Finally, by the use of computational systems biology resources, we revealed novel, not previously identified, drugs and small-molecule compounds that may reverse the phenotype of primary glomerular diseases in a glomerulonephritis-specific manner.

We elected to use the Nephroseq classic v4 web-based analysis engine that gives access to genome-wide gene expression datasets generated by renal researchers [13]. We yielded datasets generated from hybridization-based analyses of human kidneys from patients with primary glomerular diseases when compared to healthy kidneys. Specifically, we obtained datasets for MCD, FSGS, MN, IgAN and TBMN. Although these diseases are characterized by distinct pathogenic mechanisms and express distinct manifestations, therapy is common and includes general measures to reduce proteinuria and manage hypertension, edema, hyperlipidemia and hypercoagulability, coupled with immunosuppression, such as steroids, alkylating agents, mycophenolate mofetil, calcineurin inhibitors and others) [3,4,25]. To reveal glomerulonephritis-specific processes and pathways implicated in each glomerular disease that could serve as glomerulonephritis-specific targets, we compared the kidney transcriptome between the yielded primary glomerular diseases. We uncovered DEGs exclusively expressed in each glomerular disease that defined each glomerulonephritis-specific gene signature. Gene signatures were further interpreted with functional enrichment analysis, by which delineated processes and pathways unique to each glomerular disease were uncovered.

FSGS and MCD are primary podocytopathies that underlie idiopathic nephrotic syndrome. FSGS is a heterogeneous disease that represents a common phenotypic expression of different clinical and histological syndromes caused by distinct causes, including mutations in podocyte components, circulating factors, viral infections, toxicities and maladaptive responses [26,27,28]. Specifically, the pathogenesis involves a factor disturbing podocyte function and permeability and a T-cell abnormality leading to cytokine production [29,30]. Cardiotrophin-like cytokine-1 (CLC-1) and soluble urokinase plasminogen activator surface receptor (suPAR) are implicated in the pathogenesis of FSGS [31,32]. MCD represents the most common cause of nephritic syndrome in children. Underlying causes may include Hodgkin disease, atopy, exposure to allergens and nonsteroidal anti-inflammatory drugs [33,34,35]. In MCD, deregulated T-cell function can be attributed to altered expression of NF-κB and NFRKB [36,37]. Our data demonstrate that gene expression deregulation within kidneys of human FSGS is associated with biological processes linked to cell–cell junction assembly and the enzyme-linked receptor protein signaling pathway. Gene expression deregulation within kidneys from human MCD is linked to glycine, serine and threonine metabolism. These suggest that the identified processes and pathways may represent targeted therapies for FSGS or MCD.

The current model of IgAN pathogenesis involves a multiple-hit hypothesis model. Genetic and environmental factors predispose individuals to abnormal immune responses to pathogens. Mucosal-derived pathogen-associated molecular patterns induce polyclonal lymphocyte proliferation in a TLR-mediated manner. IgA-secreting plasma cells migrate to mucosa, releasing dimeric IgA into the lumen, whereas misdirected cells are released to systemic circulation, secreting poorly O-galactosylated IgA1. Circulating galactose-deficient IgA1 forms immune complexes with specific antibodies, resulting in immune-complex deposition or in situ formation in the mesangium, inducing mesangial cell proliferation and increased production of mesangial matrix [38,39]. Despite improvements in understanding the pathogenesis of IgAN, this multiple-hit hypothesis model does not explain kidney injury associated with mesangial IgA deposition. Our analysis demonstrates that within kidneys of patients with IgAN, gene expression deregulation is associated with neuroactive ligand-receptor interaction and the cAMP signaling pathways, suggesting they could represent targets of kidney injury in IgAN.

MN is an autoimmune disease characterized by the formation of immune deposits and complement-mediated proteinuria [40]. Epitopes and antigens, such as phospholipase A2 receptor (PLA2R) [41], thrombospondin domain-containing 7A (THSD7A) [42] and neutral endopeptidase [43], have been identified. The role of complement activation mediating podocyte injury in MN is well established [44,45]. Recently, the altered TH17/Treg ratio was suggested as a mechanism involved in the pathogenesis of idiopathic MN [46]. In agreement, we reveal that within kidneys of patients with MN, the transcriptome deregulation is functionally enriched in IL17 signaling pathways. We also uncover the role of CLEC7A (dectin) and MAPK signaling pathway, suggesting their involvement in tissue injury in idiopathic MN.

TBMN refers to familial and sporadic isolated hematuria associated with an attenuated glomerular basement membrane. It is an autosomal dominant condition caused by mutations in COL4A3 or COL4A4 gene [47]. In this study, we demonstrate that within kidneys from patients with TBMN, DEGs are functionally enriched in pathways of B-cell receptor signaling and regulation of actin cytoskeleton.

Next, we used the identified glomerulonephritis-specific gene signatures, and through computational systems biology approaches, we sought to discover novel drugs or drugs to be repurposed in a disease-specific manner. Specifically, we used the L1000CDS2 engine [11] and prioritized the top 50 molecules predicted to reverse upregulated and downregulated DEGs of each glomerular disease. Then, drug signatures of the five glomerulonephritides were compared, and glomerulonephritis-specific drugs were identified. Among other drugs, the third-generation kinase inhibitor indicated for the treatment of chronic myeloid leukemia, ponatinib [48], and the JAK3 kinase inhibitor IV [49] were predicted to specifically reverse the FSGS-specific gene signature; pracinostat, an oral histone deacetylase inhibitor [50] was predicted to specifically reverse the MCD-specific gene signature; BMS-754807, an inhibitor of insulin-like growth factor-1R/IR [51], was predicted to specifically reverse the IgAN-specific gene signature; TCPA-1, a dual inhibitor of STAT3 and NF-κB [52], was predicted to specifically reverse the MN-specific gene signature; and wortmannin, an autophagy inhibitor [53], was predicted to specifically reverse the TBMN-specific gene signature. This computational analysis prioritized novel drugs and drugs to be repurposed as glomerulonephritis-specific agents. Further investigation is ongoing to define the therapeutic implications of our findings.

Collectively, through computational systems biology approaches, we defined the FSGS-specific, MCD-specific, IgAN-specific, MN-specific and TBMN-specific gene signatures within human kidneys and identified distinct biological processes and pathways implicated in each disease. We also revealed novel, not previously identified, drugs and small-molecule compounds that may reverse the phenotype of primary glomerular diseases in a glomerulonephritis-specific manner. Experimental validation of the differential transcripts and their functionality is further needed. To this end, future studies will define the pathogenic and therapeutic implications of our findings.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/jcm10112262/s1, Table S1: Clinical parameters and demographics of patients and healthy individuals; Table S2: Glomerulonephritis-specific differentially expressed genes; Table S3: Drugs and small-molecule compounds predicted to reverse each primary glomerular disease.

Click here for additional data file. (66.1KB, zip)

Author Contributions

Conceptualization, E.F.; methodology, E.F. and P.G.; software, E.F., P.G. and S.A.P.D.; validation, P.G.; formal analysis, E.F. and P.G.; investigation, E.F. and P.G.; resources, E.F.; data curation, E.F.; writing—original draft preparation, E.F.; writing—review and editing, P.G., S.A.P.D. and I.B.; visualization, S.A.P.D.; supervision, E.F.; project administration, E.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable due to the nature of the study.

Informed Consent Statement

Not applicable due to the nature of the study.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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