Gene Expression as a Guide to the Development of Novel Therapies in Primary Glomerular Diseases
1
Department of Clinical Immunology and Rheumatology, Medical University Hannover, 30625 Hannover, Germany
2
Lab of Autoimmunity and Inflammation, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
3
Department of Nephrology and Renal Transplantation, University of Athens Medical School, Laiko Hospital, 11527 Athens, Greece
4
Department of Nephrology, Limassol General Hospital, 3304 Limassol, Cyprus
5
Medical School, University of Nicosia, 2417 Nicosia, Cyprus
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(11), 2262; https://doi.org/10.3390/jcm10112262
Submission received: 7 April 2021
/
Revised: 10 May 2021
/
Accepted: 19 May 2021
/
Published: 24 May 2021
(This article belongs to the Special Issue Optimizing Drug Therapy and Developing Novel Therapeutic Interventions in Nephrology)
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.
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.
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.
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).
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).
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).
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.
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.
References
- Ponticelli, C.; Glassock, R.J. A definition, modern classification, and global epidemiology of primary glomerulonephritis. In Treatment of Primary Glomerulonephritis; Oxford University Press: Oxford, UK, 2019. [Google Scholar]
- Floege, J.; Amann, K. Primary glomerulonephritides. Lancet 2016, 387, 2036–2048. [Google Scholar] [CrossRef]
- Floege, J.; Barbour, S.J.; Cattran, D.C.; Hogan, J.J.; Nachman, P.H.; Tang, S.C.W.; Wetzels, J.F.M.; Cheung, M.; Wheeler, D.C.; Winkelmayer, W.C.; et al. Management and treatment of glomerular diseases (part 1): Conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2019, 95, 268–280. [Google Scholar] [CrossRef] [Green Version]
- Rovin, B.H.; Caster, D.J.; Cattran, D.C.; Gibson, K.L.; Hogan, J.J.; Moeller, M.J.; Roccatello, D.; Cheung, M.; Wheeler, D.C.; Winkelmayer, W.C.; et al. Management and treatment of glomerular diseases (part 2): Conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int. 2019, 95, 281–295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Frangou, E.A.; Bertsias, G.K.; Boumpas, D.T. Gene expression and regulation in systemic lupus erythematosus. Eur. J. Clin. Investig. 2013, 43, 1084–1096. [Google Scholar] [CrossRef] [PubMed]
- Tryggvason, S.H.; Guo, J.; Nukui, M.; Norlin, J.; Haraldsson, B.; Jörnvall, H.; Tryggvason, K.; He, L. A meta-analysis of expression signatures in glomerular disease. Kidney Int. 2013, 84, 591–599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kitano, H. Computational systems biology. Nature 2002, 420, 206–210. [Google Scholar] [CrossRef] [PubMed]
- Lamb, J.; Crawford, E.D.; Peck, D.; Modell, J.W.; Blat, I.C.; Wrobel, M.J.; Lerner, J.; Brunet, J.-P.; Subramanian, A.; Ross, K.N.; et al. The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science 2006, 313, 1929–1935. [Google Scholar] [CrossRef] [Green Version]
- Keenan, A.B.; Jenkins, S.L.; Jagodnik, K.M.; Koplev, S.; He, E.; Torre, D.; Wang, Z.; Dohlman, A.B.; Silverstein, M.C.; Lachmann, A.; et al. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst. 2018, 6, 13–24. [Google Scholar] [CrossRef] [Green Version]
- Peyvandipour, A.; Saberian, N.; Shafi, A.; Donato, M.; Draghici, S. Systems biology: A novel computational approach for drug repurposing using systems biology. Bioinformatics 2018, 34, 2817–2825. [Google Scholar] [CrossRef] [Green Version]
- Duan, Q.; Reid, S.P.; Clark, N.R.; Wang, Z.; Fernandez, N.F.; Rouillard, A.D.; Readhead, B.; Tritsch, S.R.; Hodos, R.; Hafner, M.; et al. L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst. Biol. Appl. 2016, 2, 16015. [Google Scholar] [CrossRef]
- Zhang, L.; Wang, Z.; Liu, R.; Li, Z.; Lin, J.; Wojciechowicz, M.L.; Huang, J.; Lee, K.; Ma’Ayan, A.; He, J.C. Connectivity Mapping Identifies BI-2536 as a Potential Drug to Treat Diabetic Kidney Disease. Diabetes 2021, 70, 589–602. [Google Scholar] [CrossRef] [PubMed]
- Rowland, J.; Akbarov, A.; Eales, J.; Xu, X.; Dormer, J.; Guo, H.; Denniff, M.; Jiang, X.; Ranjzad, P.; Nazgiewicz, A.; et al. Uncovering genetic mechanisms of kidney aging through transcriptomics, genomics, and epigenomics. Kidney Int. 2019, 95, 624–635. [Google Scholar] [CrossRef] [Green Version]
- Oliveros, J.C. VENNY. An Interactive Tool for Comparing Lists with Venn Diagrams. Available online: http://bioinfogp.cnb.csic.es/tools/venny/index.html (accessed on 1 March 2021).
- Heberle, H.; Meirelles, G.V.; Da Silva, F.R.; Telles, G.P.; Minghim, R. InteractiVenn: A web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform. 2015, 16, 1–7. [Google Scholar] [CrossRef]
- Raudvere, U.; Kolberg, L.; Kuzmin, I.; Arak, T.; Adler, P.; Peterson, H.; Vilo, J. g:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019, 47, W191–W198. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Motulsky, H. GraphPad Prism Version 5.0 Regression Guide; GraphPad Software Inc.: San Diego, CA, USA, 2007. [Google Scholar]
- Wang, Z.; Lachmann, A.; Keenan, A.B.; Ma’Ayan, A. L1000FWD: Fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics 2018, 34, 2150–2152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wishart, D.S.; Knox, C.; Guo, A.C.; Shrivastava, S.; Hassanali, M.; Stothard, P.; Chang, Z.; Woolsey, J. DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006, 34, D668–D672. [Google Scholar] [CrossRef]
- Hodgin, J.B.; Borczuk, A.C.; Nasr, S.H.; Markowitz, G.S.; Nair, V.; Martini, S.; Eichinger, F.; Vining, C.; Berthier, C.C.; Kretzler, M.; et al. A Molecular Profile of Focal Segmental Glomerulosclerosis from Formalin-Fixed, Paraffin-Embedded Tissue. Am. J. Pathol. 2010, 177, 1674–1686. [Google Scholar] [CrossRef]
- Ju, W.; Greene, C.S.; Eichinger, F.; Nair, V.; Hodgin, J.B.; Bitzer, M.; Lee, Y.-S.; Zhu, Q.; Kehata, M.; Li, M.; et al. Defining cell-type specificity at the transcriptional level in human disease. Genome Res. 2013, 23, 1862–1873. [Google Scholar] [CrossRef] [Green Version]
- Reich, H.N.; Tritchler, D.; Cattran, D.C.; Herzenberg, A.M.; Eichinger, F.; Boucherot, A.; Henger, A.; Berthier, C.C.; Nair, V.; Cohen, C.D.; et al. A Molecular Signature of Proteinuria in Glomerulonephritis. PLoS ONE 2010, 5, e13451. [Google Scholar] [CrossRef] [Green Version]
- Bian, Y.; Han, J.; Kannabiran, V.; Mohan, S.; Cheng, H.; Friedman, J.; Zhang, L.; VanWaes, C.; Chen, Z. MEK Inhibitor PD-0325901 Overcomes Resistance to CK2 Inhibitor CX-4945 and Exhibits Anti-Tumor Activity in Head and Neck Cancer. Int. J. Biol. Sci. 2015, 11, 411–422. [Google Scholar] [CrossRef] [Green Version]
- Delaney, A.M.; Printen, J.A.; Chen, H.; Fauman, E.B.; Dudley, D.T. Identification of a Novel Mitogen-Activated Protein Kinase Kinase Activation Domain Recognized by the Inhibitor PD 184352. Mol. Cell. Biol. 2002, 22, 7593–7602. [Google Scholar] [CrossRef] [Green Version]
- Radhakrishnan, J.; Cattran, D.C. The KDIGO practice guideline on glomerulonephritis: Reading between the (guide)lines—application to the individual patient. Kidney Int. 2012, 82, 840–856. [Google Scholar] [CrossRef] [Green Version]
- Rosenberg, A.Z.; Kopp, J.B. Focal Segmental Glomerulosclerosis. Clin. J. Am. Soc. Nephrol. 2017, 12, 502–517. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Maas, R.J.; Deegens, J.K.; Wetzels, J.F. Permeability factors in idiopathic nephrotic syndrome: Historical perspectives and lessons for the future. Nephrol. Dial. Transplant. 2014, 29, 2207–2216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- De Vriese, A.S.; Sethi, S.; Nath, K.A.; Glassock, R.J.; Fervenza, F.C. Differentiating Primary, Genetic, and Secondary FSGS in Adults: A Clinicopathologic Approach. J. Am. Soc. Nephrol. 2018, 29, 759–774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Maas, R.J.; Deegens, J.K.; Smeets, B.; Moeller, M.J.; Wetzels, J.F. Minimal change disease and idiopathic FSGS: Manifestations of the same disease. Nat. Rev. Nephrol. 2016, 12, 768–776. [Google Scholar] [CrossRef] [PubMed]
- Fogo, A.B. Causes and pathogenesis of focal segmental glomerulosclerosis. Nat. Rev. Nephrol. 2015, 11, 76–87. [Google Scholar] [CrossRef]
- McCarthy, E.T.; Sharma, M.; Savin, V.J. Circulating Permeability Factors in Idiopathic Nephrotic Syndrome and Focal Segmental Glomerulosclerosis. Clin. J. Am. Soc. Nephrol. 2010, 5, 2115–2121. [Google Scholar] [CrossRef] [Green Version]
- Wei, C.; El Hindi, S.; Li, J.; Fornoni, A.; Goes, N.; Sageshima, J.; Maiguel, D.; Karumanchi, S.A.; Yap, H.-K.; Saleem, M.; et al. Circulating urokinase receptor as a cause of focal segmental glomerulosclerosis. Nat. Med. 2011, 17, 952–960. [Google Scholar] [CrossRef] [Green Version]
- Audard, V.; Zhang, S.-Y.; Copie-Bergman, C.; Rucker-Martin, C.; Ory, V.; Candelier, M.; Baia, M.; Lang, P.; Pawlak, A.; Sahali, D. Occurrence of minimal change nephrotic syndrome in classical Hodgkin lymphoma is closely related to the induction of c-mip in Hodgkin-Reed Sternberg cells and podocytes. Blood 2010, 115, 3756–3762. [Google Scholar] [CrossRef] [Green Version]
- Shalhoub, R. Pathogenesis of Lipoid Nephrosis: A Disorder of T-Cell Function. Lancet 1974, 304, 556–560. [Google Scholar] [CrossRef]
- Vivarelli, M.; Massella, L.; Ruggiero, B.; Emma, F. Minimal Change Disease. Clin. J. Am. Soc. Nephrol. 2017, 12, 332–345. [Google Scholar] [CrossRef] [Green Version]
- Audard, V.; Pawlak, A.; Candelier, M.; Lang, P.; Sahali, D. Upregulation of Nuclear Factor-Related Kappa B Suggests a Disorder of Transcriptional Regulation in Minimal Change Nephrotic Syndrome. PLoS ONE 2012, 7, e30523. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Aviles, D.H.; Vehaskari, V.M.; Manning, J.; Ochoa, A.C.; Zea, A.H. Decreased expression of T-cell NF-κB p65 subunit in steroid-resistant nephrotic syndrome. Kidney Int. 2004, 66, 60–67. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lai, K.N.; Tang, S.C.W.; Schena, F.P.; Novak, J.; Tomino, Y.; Fogo, A.B.; Glassock, R.J. IgA nephropathy. Nat. Rev. Dis. Prim. 2016, 2, 16001. [Google Scholar] [CrossRef]
- Gutiérrez, E.; Carvaca-Fontán, F.; Luzardo, L.; Morales, E.; Alonso, M.; Praga, M. A Personalized Update on IgA Nephropathy: A New Vision and New Future Challenges. Nephron 2020, 144, 555–571. [Google Scholar] [CrossRef]
- Ronco, P.; Debiec, H. Molecular Pathogenesis of Membranous Nephropathy. Annu. Rev. Pathol. Mech. Dis. 2020, 15, 287–313. [Google Scholar] [CrossRef] [Green Version]
- Behnert, A.; Fritzler, M.J.; Teng, B.; Zhang, M.; Bollig, F.; Haller, H.; Škoberne, A.; Mahler, M.; Schiffer, M. An Anti-Phospholipase A2 Receptor Quantitative Immunoassay and Epitope Analysis in Membranous Nephropathy Reveals Different Antigenic Domains of the Receptor. PLoS ONE 2013, 8, e61669. [Google Scholar] [CrossRef] [Green Version]
- Tomas, N.M.; Beck, L.H.; Meyer-Schwesinger, C.; Seitz-Polski, B.; Ma, H.; Zahner, G.; Dolla, G.; Hoxha, E.; Helmchen, U.; Dabert-Gay, A.-S.; et al. Thrombospondin Type-1 Domain-Containing 7A in Idiopathic Membranous Nephropathy. N. Engl. J. Med. 2014, 371, 2277–2287. [Google Scholar] [CrossRef] [Green Version]
- Debiec, H.; Guigonis, V.; Mougenot, B.; Decobert, F.; Haymann, J.-P.; Bensman, A.; Deschênes, G.; Ronco, P.M. Antenatal Membranous Glomerulonephritis Due to Anti–Neutral Endopeptidase Antibodies. N. Engl. J. Med. 2002, 346, 2053–2060. [Google Scholar] [CrossRef]
- Cybulsky, A.V.; Rennke, H.G.; Feintzeig, I.D.; Salant, D. Complement-induced glomerular epithelial cell injury. Role of the membrane attack complex in rat membranous nephropathy. J. Clin. Investig. 1986, 77, 1096–1107. [Google Scholar] [CrossRef] [Green Version]
- Reinhard, L.; Stahl, R.A.; Hoxha, E. Is primary membranous nephropathy a complement mediated disease? Mol. Immunol. 2020, 128, 195–204. [Google Scholar] [CrossRef] [PubMed]
- Motavalli, R.; Etemadi, J.; Soltani-Zangbar, M.S.; Ardalan, M.-R.; Kahroba, H.; Roshangar, L.; Nouri, M.; Aghebati-Maleki, L.; Khiavi, F.M.; Abediazar, S.; et al. Altered Th17/Treg ratio as a possible mechanism in pathogenesis of idiopathic membranous nephropathy. Cytokine 2021, 141, 155452. [Google Scholar] [CrossRef] [PubMed]
- Rana, K.; Yan, Y.W.; Buzza, M.; Tonna, S.; Ke, W.Z.; Lin, T.; Sin, L.; Padavarat, S.; Savige, J. The Genetics of Thin Basement Membrane Nephropathy. Semin. Nephrol. 2005, 25, 163–170. [Google Scholar] [CrossRef] [PubMed]
- Massaro, F.; Molica, M.; Breccia, M. Ponatinib: A Review of Efficacy and Safety. Curr. Cancer Drug Targets 2017, 18, 847–856. [Google Scholar] [CrossRef]
- Nishiya, N. JAK3 inhibitor VI is a mutant specific inhibitor for epidermal growth factor receptor with the gatekeeper mutation T790M. World J. Biol. Chem. 2015, 6, 409–418. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Li, N.; Liu, B.; Ling, J.; Yang, W.; Pang, X.; Li, T. Pracinostat (SB939), a histone deacetylase inhibitor, suppresses breast cancer metastasis and growth by inactivating the IL-6/STAT3 signalling pathways. Life Sci. 2020, 248, 117469. [Google Scholar] [CrossRef]
- Carboni, J.M.; Wittman, M.; Yang, Z.; Lee, F.; Greer, A.; Hurlburt, W.; Hillerman, S.; Cao, C.; Cantor, G.H.; Dell-John, J.; et al. BMS-754807, a small molecule inhibitor of insulin-like growth factor-1R/IR. Mol. Cancer Ther. 2009, 8, 3341–3349. [Google Scholar] [CrossRef] [Green Version]
- Nan, J.; Du, Y.; Chen, X.; Bai, Q.; Wang, Y.; Zhang, X.; Zhu, N.; Zhang, J.; Hou, J.; Wang, Q.; et al. TPCA-1 Is a Direct Dual Inhibitor of STAT3 and NF-κB and Regresses Mutant EGFR-Associated Human Non–Small Cell Lung Cancers. Mol. Cancer Ther. 2014, 13, 617–629. [Google Scholar] [CrossRef] [Green Version]
- Frangou, E.; Chrysanthopoulou, A.; Mitsios, A.; Kambas, K.; Arelaki, S.; Angelidou, I.; Arampatzioglou, A.; Gakiopoulou, H.; Bertsias, G.K.; Verginis, P.; et al. REDD1/autophagy pathway promotes thromboinflammation and fibrosis in human systemic lupus erythematosus (SLE) through NETs decorated with tissue factor (TF) and interleukin-17A (IL-17A). Ann. Rheum. Dis. 2019, 78, 238–248. [Google Scholar] [CrossRef]
Figure 1.
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.
Figure 1.
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.
Figure 2.
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.
Figure 2.
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.
Figure 3.
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.
Figure 3.
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.
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 |
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.
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 |
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.
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 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Garantziotis, P.; Doumas, S.A.P.; Boletis, I.; Frangou, E. Gene Expression as a Guide to the Development of Novel Therapies in Primary Glomerular Diseases. J. Clin. Med. 2021, 10, 2262. https://doi.org/10.3390/jcm10112262
AMA Style
Garantziotis P, Doumas SAP, Boletis I, Frangou E. Gene Expression as a Guide to the Development of Novel Therapies in Primary Glomerular Diseases. Journal of Clinical Medicine. 2021; 10(11):2262. https://doi.org/10.3390/jcm10112262
Chicago/Turabian StyleGarantziotis, Panagiotis, Stavros A. P. Doumas, Ioannis Boletis, and Eleni Frangou. 2021. "Gene Expression as a Guide to the Development of Novel Therapies in Primary Glomerular Diseases" Journal of Clinical Medicine 10, no. 11: 2262. https://doi.org/10.3390/jcm10112262
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
