Effectiveness of gene-expression connectivity analysis for evaluating treatment effects of frequent medicines on kidney in IgA nephropathy

doi:10.21203/rs.3.rs-2226299/v1

Download PDF

Research Article

Effectiveness of gene-expression connectivity analysis for evaluating treatment effects of frequent medicines on kidney in IgA nephropathy

https://doi.org/10.21203/rs.3.rs-2226299/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction IgA nephropathy (IgAN) is a leading cause of end-stage kidney disease worldwide. It is still challenging to assess the treatment effects of frequently used IgAN drugs.

Methods We collected gene-expression profiles of renal tissues from eligible IgAN studies in Gene Expression Omnibus (GEO) dataset. The IgAN drugs were identified by InChIKey from Connectivity Map (CMap) L1000 to construct a reference dataset of perturbational signatures. Dr Insight method of gene-expression connectivity analysis was performed to find effective drugs. Then, potential molecular mechanisms and target genes were explored using bioinformatic methods to assess the robustness of these results.

Results Six eligible IgAN studies were collected from GEO, and 92 compounds of drugs were identified from CMap L1000. Four types of calcium channel blockers and two glucocorticoids were identified as the most negatively connected to IgAN. The biological processes of mitosis and response to stimulus were enriched for involvement in the kidney injury of IgAN, and SMC4, TIPARP, TSC22D3, and ZFP36, were the important targets in the process.

Conclusions Our results indicated that gene-expression connectivity analysis was useful for evaluating the treatment effects of IgAN drugs, and more attention should be paid to SMC4, TIPARP, TSC22D3, and ZFP36 genes in IgAN.

IgAN

gene-expression profiles

connectivity analysis

cMap

IgA nephropathy (IgAN) is a dominant type of primary glomerulonephritis and is a leading cause of end-stage kidney disease (ESRD) worldwide. Approximately 20%-40% of IgAN patients progress to ESRD within 10-20 years after diagnosis [1,2]. Unfortunately, the pathogenesis of IgAN has not been fully clarified, and there is no specific treatment for IgAN. According to the Kidney Disease: Improving Global Outcomes (KDIGO) guideline [3], measures to delay the progress of renal function, including providing optimal supportive care and immunosuppressive therapy, are the focus and goal of IgA nephropathy management. Glucocorticoids have been thought to have protective effects on IgAN patients [4-6], but the recent supportive versus immunosuppressive therapy for the treatment of progressive IgA nephropathy (STOP-IgAN) randomized and controlled trial yielded different results [7]. Moreover, the renal effects of other immunosuppressants in IgAN, such as cytotoxic drugs, mycophenolate mofetil, tacrolimus, and cyclosporine, are still ambiguous [8]. Notably, compared with other types of glomerulonephritis, supportive therapy is essential for IgA nephropathy [9]. Control of blood pressure and reduction of proteinuria, as the core of supportive treatment, is usually achieved through renin-angiotensin system (RAS) blockades and calcium channel blocker (CCB) drugs.

In clinical practice, it is difficult to accurately and rapidly evaluate the effects of IgAN drugs. More importantly, individualized treatment according to the process of kidney injury would become a highlight of precise treatment. Complex and lengthy clinical trials could not meet these demands. In addition, same types of drugs were widely used in clinical to treat for IgAN, especially RAS blockades, CCBs, and glucocorticoids. Even though they might have similar pharmacological effects, the effects on target tissues would be different due to the diversity of molecular structures. Thus, appropriate drug evaluation methods would improve the precise management of IgAN and further reveal its pathogenesis.

Gene-expression profiles have recently been used to discover the pharmacological effects of compounds [10-12]. Exploring the relationship between drugs and diseases through the connectivity analysis of gene-expression profiles has become an essential method to obtain new drug targets, to evaluate drug efficacy and to find pathogenesis of diseases [13,14]. However, it is worth noting that necessary attention should be paid to the reference databases and statistical methods used for connectivity analysis, because they would significantly affect the accuracy of the results. Connectivity Map (CMap) [14,15] is one of the public reference databases for drug reuse. It uses transcriptomics to form unbiased connections among diseases, genes, and drugs. Several studies have confirmed the effectiveness of CMap in drug discovery [16-18]. Chan et al. [19] developed a connectivity analysis called Dr Insight and showed that the analysis has more convenient and accurate calculation results.

At present, no CMap database has been used to systematically investigate the effects of IgAN drugs, and no appropriate evaluation method has been established. In this study, we systematically collected gene-expression

studies of IgAN. Then we selected Dr Insight to evaluate the relationship between IgAN drugs and renal injury in order to find appropriate medicines and potential pathogenesis of IgAN.

(1) Searching strategy for IgAN gene-expression objects.

The Gene Expression Omnibus (GEO) database was systematically searched to find studies of IgAN for gene expression. The title, organism, type of experiment, summary, overall design, and citation(s) of candidate accessions in GEO were reviewed independently by two authors (M.P. and J.Z.). The criteria were as follows: 1. Studies for Homo sapiens. 2. Both IgAN and normal control kidney tissues were assessed in a study. The exclusion criteria were as follows: 1. Levels of mRNA were not evaluated. 2. Duplicated publications. 3. Including pooled samples.

(2) Data preparation and differentially expressed gene (DEG) analysis.

The metadata of eligible studies was downloaded from the GEO database using GEOquery. The probes of metadata were converted to gene symbols for further analysis, and the multiple probes corresponding to same genes were aggregated using median value. DEGs were calculated using Limma [20,21]. The volcano plots were plotted to display the points of DEGs. DEGs with a p-value > 0.05 were removed to reduce invalid signals. Principal component analysis (PCA) was performed to show the difference in gene-expression profiles between IgAN patients and controls. Kendall's concordance coefficients were calculated from the fold variation values of intersecting DEGs to display the similarity of gene-expression profiles among selected studies.

(3) Searching for the whole compounds of IgAN medicines.

We used terms "Angiotensin-Converting Enzyme Inhibitors", "Angiotensin Receptor Antagonists", "Calcium Channel Blockers", "glucocorticoids", "cyclophosphamide", "cyclosporine OR ciclosporin OR voclosporin OR tacrolimus OR pimecrolimus", "mycophenolic OR mycophenolate", "azathioprine", "hydroxychloroquine" and "Tripterygium wilfordii" as keywords for searching the compounds in the classes of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), calcium channel blockers (CCBs), glucocorticoids (GSs), cyclophosphamide (CTX), calcineurin inhibitors (CNIs), mycophenolate mofetil (MMF), azathioprine (AZA), hydroxychloroquine (HCQ), and Tripterygium wilfordii (GTW), respectively. InChIKey was recorded as the unique identification of these compounds.

(4) Preparing the reference dataset of compound perturbational gene-expression signatures.

The CMap L1000, which was released in 2017, was collected as reference dataset with perturbation signatures generated from gene-expression profiles for cell lines following million small molecule perturbations [14]. We downloaded the level 5 beta version of CMap L1000 from clue.io (https://clue.io/), updated in 2020, and contains 21870 unique compounds. InChikey is used for the unique identification of IgAN medicines to map CMAP compounds. Then, the perturbed gene-expression features of the matching combinations are extracted from the CMAP database for further analysis. In CMAP, the parameters of repetition correlation coefficient (cc_q75) >0.2 and signature strength (ss_ngene) >200 are used to filter robust and repeatable signatures.

(5) Disease–drug connectivity analysis.

The insight package of R was used to calculate the connection between the DRGs of IgAN in the kidney and the perturbational gene-expression signatures of matched compounds [19]. The compounds with a significant negative connection with IgAN in more than 4 studies were considered focused medicines. Then, the pathway enrichment analysis of focused medicines was also performed using Dr Insight, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and the significant genes called concordantly expressed genes (CEGs) of the enriched pathway were extracted for intersection analysis. The GO and KEGG datasets were downloaded from the GSEA database (https://www.gsea-msigdb.org/). In at least two studies, enriched pathways with FDR less than 0.05 were identified as significantly enriched pathways. The offspring of GO terms were acquired from GO.db of R, and the GO networks were graphed using igraph of R. The Venn diagrams were plotted to display the intersection of significantly enriched pathways and CEGs.

(6) Assessing the impact of cells lines or compounds on perturbational gene expression levels in CMap.

The perturbational gene expression levels of these focused medicines were extracted from the CMap dataset. The R package lumi was used to normalize the gene expression matrix. t-SNE was used to reduce the dimensions of the gene expression matrix. Then, the hierarchical cluster and kmean algorithm were used for clustering and displaying the similarity of different gene expressions among focused medicines and perturbated cells. Then, the gene expression levels of the most significant genes are displayed.

Six eligible IgAN studies were identified from GEO under systematic retrieval (Figure 1). Five studies employed microdissection technology for separating frozen renal tissue into glomerular or tubulointerstitial components, and three of these studies came from the European Renal cDNA Bank - Kröner-Fresenius Biopsy Bank (ERCB-KFB). The last study extracted mRNA from whole renal tissue with archival formalin fixation and paraffin embedding (Table A1).

Volcano plots (Figure 2A) and PCA displayed significantly different gene-expression profiles between IgAN and normal controls (Figure 2B). Kendall's correlation analysis indicated more similar gene-expression profiles in glomerular and tubulointerstitial tissues and were far from whole kidney tissue (Figure 2C).

A total of 806 compounds were identified from PubChem, 104 compounds were evaluated in CMAP L1000, and 92 of these eligible compounds were selected for connectivity analysis (Table A2). Our connectivity analysis showed that four types of drugs, Tripterygium wilfordii, glucocorticoids, CCBs, and cyclosporin-a, were significantly negatively connected with more than two studies of IgAN. Surprisingly, no RAS blockers were significantly negatively connected with these IgAN studies. We selected the most negative connective drugs, perhexiline, lacidipine, flunarizine, and dexniguldipine (a type of CCBs) and rimexolone and ciclesonide (a type of glucocorticoids), as focused medicines for further analysis (Figure 3 and Figure A1).

The numbers of enriched biological process (BP) terms of perhexiline, lacidipine, flunarizine, and dexniguldipine were 55, 29, 9, and 62, respectively. Six intersection BP terms existed for all four CCB drugs (Figure 4A). The BP networks for the four drugs (Figure 4B) showed that the eight intersection BP terms were involved in mitosis, from "mitotic sister chromatid segregation" (GO:0000070) to "chromosome segregation" (GO:0007059). Two glucocorticoids, rimexolone and ciclesonide, were significantly enriched in 6 and 8 BP terms, respectively, and had three intersection BP terms (Figure 4C). The BP networks (Figure 4D) showed the intersection of BP terms involved in response to stimulus, from "cellular response to corticosteroid stimulus" (GO:0048545) to "response to steroid hormone" (GO:0048545). The relationships between the intersection of BP terms and included studies are displayed in Figure A2. For both CCBs (Figure A2A) and glucocorticoids (Figure A2B), there was a significant association with IgAN raised from the glomerulus and tubulointerstitium, especially the glomerulus.

Six significantly upregulated intersection genes (Figure A3A) and seven significantly downregulated intersection genes (Figure A3B) were identified in the CCBs, and the expression levels of these genes in different studies are displayed in Figure A3C and Figure A3D. Thirteen significantly upregulated intersections (Figure A4A) and 25 significantly downregulated intersections (Figure A4B) were identified in glucocorticoids, and the expression levels of these genes are displayed in Figure A4C and Figure A4D. Among these genes, one upregulated intersection gene (Figure 5A) and three downregulated intersection genes (Figure 5B) between CCBs and glucocorticoids were identified. The expression levels of these genes are displayed in Figure 5C.

The hierarchical and kmean clusters indicated apparent clusters among the same types of medicines (Figure 6A and Figure 6B), and the distance of Lacidipine was closer to the GSs than other CCBs (Figure 6A). However, a few cell lines clustered into the same clusters (Figure 6C and Figure 6D), which may demonstrate that differences in cell lines were not the leading cause of differences in gene-expression profiles following small molecule intervention. Furthermore, these effects were also displayed in the four selected genes for similar expression levels among different cell lines (Figure 6E). Only HA1E was derived from the normal human renal epithelium in these cell lines, and its gene expression was similar to that in other cells (Figure 6D and Figure 6E).

The convenient use of genome-wide profiling by sequencing technologies makes transcriptome analysis an integral part of genome research on almost all diseases and biological processes [22]. However, there is still a long way to apply genomic information in evaluating drug efficacy. Referencing CMap L1000, the gene-expression profile connectivity analysis of the collected studies displayed some robust results. Glucocorticoids (Rimexolone and Ciclesonide) and CCBs (Perhexiline, Lacidipine, Flunarizine, and Dexniguldipine) are the two strongest classes of medicines for reversing the gene-expression profiles of the kidney with IgAN. According to gene set enrichment analysis, the pathways enriched by these two classes of drugs were consistent with the mechanism of action. Although the 4 intersection genes we focused on, SMC4, TIPARP, TSC22D3, and ZFP36, have not been thoroughly studied in IgAN, these genes are critical targets for the action of these two classes of drugs and are also essential factors in the progression of IgAN.

Glucocorticoids are the main immunosuppressants used in the treatment of IgAN [3]. Randomized controlled trials (RCTs) confirmed that glucocorticoid treatment could decrease proteinuria and delay renal progression in IgAN patients [6]. However, the oral or intravenous use of glucocorticoids had severe side effects and gradually increased with the focus on locally targeted drugs. In vivo and in vitro studies have shown that glucocorticoids have direct renal effects, which might protect podocytes from injuries by regulating multiple podocyte-specific targets, including nephrin, Rac1, RhoA, and actinin, while conditional knockout of the glucocorticoid receptor (GR) gene of podocytes makes mice more vulnerable to lipopolysaccharide (LPS) [23-25]. Colombo et al. developed a nanoparticle for targeting and enriching dexamethasone into glomeruli to repair podocytes [26]. Nefigan et al. showed that budesonide targeted to the ileocecal region could effectively reduce the side effects of glucocorticoids and improve the prognosis of IgAN patients [27]. Therefore, identifying the renal targets of glucocorticoids would be helpful for screening effective drugs and reducing side effects.

Although CCBs are the leading drugs for controlling blood pressure in patients with CKD, their effects on renal protection are still unclear [28]. Our study showed that CCBs mainly participated in mitosis in the renal tissue of IgAN. Therefore, inhibiting the process of mesangial cell proliferation, which is also a significant pathological feature in IgAN, might become one of the therapeutic measures for these patients. Mesangial cells play critical roles in cell cycle progression, proliferation, and division [29]. Previous studies have shown that mesangial cells express T-type and L-type calcium channels; in turn, L-type and T-type CCBs inhibit mesangial cell proliferation [30-33]. Three of four significantly affected CCBs were L-type, and only one was T-type. Moreover, compared with T-type CCB, L-type CCB could enrich more biological processes, which suggested that it had a more substantial effect on IgAN and participated in cell mitosis in combination with cav1 channels [34]. It is speculated that the L-type may significantly inhibit cell proliferation progression. However, clinical evidence shows that T-type could substantially reduce proteinuria and protect glomeruli from progression and might mitigate glomerular intrathecal pressure and improve protein filtration, similar to RAAS blockers [30].

SMC4, TIPARP, TSC22D3, and ZFP36 have been identified as significant genes in CCBs and glucocorticoids and are critical genes for IgAN. SMC4 can stabilize chromosome structure, participate in mitosis and is mainly expressed in proliferating cells. The expression of SMC4 in IgAN patients' kidney tissue was significantly increased, suggesting that cell proliferation was active, which also exacerbated the pathological changes of IgAN. The relationship between SMC4 and glucocorticoids is still unclear. Rainer's study found that glucocorticoids induced the expression of SMC4-related miRNAs and participated in apoptosis [35]. Other studies have shown that the expression of TIPARP, TSC22D3, and ZFP36 is regulated by glucocorticoids and is associated with the anti-inflammatory effects of glucocorticoids [36-38]. Knockout of TIPARP could lead to decreased tolerance to toxicants. TTP-deficient mice could develop a complex syndrome of inflammatory diseases [39]. In addition, TIPARP is a pattern recognition receptor involved in innate immunity. It might participate in inflammation regulation through negative regulation of AHR and positive regulation of LXRα and LXRβ to regulate lipid and glucose metabolism [40-42]. In recent years, many studies have shown that the GILZ protein encoded by TSC22D3 might be the leading molecule of the anti-inflammatory and immunosuppressive effects of glucocorticoids, and the TAT-GILZ protein could produce glucocorticoid-like effects but would not produce glucocorticoid-like side effects [37,43-46]. ZFP36 is also considered an essential gene for immune regulation. Researchers believe that the downregulation of ZFP36 expression might be related to some autoimmune diseases [38]. Leigh et al. found that budesonide could induce the expression of ZFP36 to inhibit inflammation [47]. However, few studies have shown that these four genes are related to the role of CCBs. We speculate that CCBs regulate mitosis by changing intracellular calcium homeostasis, which may affect the expression of SMC4, but whether CCBs can promote the expression of TIPARP, TSC22D3 and ZFP36 remains to be studied.

The treatment of IgAN remains a global problem. The KDIGO guideline only recommends supportive care and glucocorticoid therapy, but 20-40% of patients will enter uremia and require renal replacement therapy [3]. Drug selection for the treatment of IgAN is mainly based on clinical experience or RCT studies. However, the defects of RCTs are particularly prominent in chronic kidney diseases (such as IgAN) with high clinical variability and long duration [48]. Recently, gene-expression profiling based on the perturbation of small molecule compounds has also been used to study the potential therapeutic effects of small molecule compounds. Several studies have found that gene-expression connectivity analysis could identify the roles and targets of small molecules [49,50]. Our study shows that glucocorticoids and CCBs can reverse the gene-expression profile of renal tissues with IgAN, and the identified targets and effector pathways have been confirmed by previous studies. These results suggest that this drug exploration method may become an essential method for screening and evaluating drugs in the future. Based on the retrieval data of ClinicalTrials.gov, many clinical studies have used transcriptome analysis to evaluate drug efficacy. Combining these research results, we can further explore drug effects and evaluate the effectiveness of this method. However, it should be noted that our reference to CMap L1000 is a change in the expression profile of the cell lines, rather than a bulk tissue effect. The complex systemic interaction process of drugs will affect their effects, which cannot be identified by this method.

Our study has obvious limitations. 1. All gene-expression profiles were downloaded from GEO, not our experimental results, and there are possible interfering factors that we did not control. 2. These IgAN patients may have different disease stages and possible drug treatment effects, which significantly affect the IgA gene-expression profile. 3. These results are from microarray results, and the current more efficient and stable next-generation sequencing methods may bring more robust results. 4. The reference dataset CMap is based on cell lines, and only HA1E is a kidney-derived cell line. Although we found that cell lines are not the main factor causing transcriptome profile changes, this may still affect the results. In addition, we lack a reference system for bulk tissue; whether this can more accurately reflect drug effects still needs more data to clarify. However, it still needs to be pointed out that our study has important implications for exploring new IgAN drug discovery and selection.

Our study explored the potential effects of frequent medicines on IgAN based on a transcriptomic profiling-based approach to drug evaluation, which indicated that the gene-expression connectivity analysis was useful for evaluating the treatment effects of frequent medicines on the kidney within IgAN. However, rigorously designed clinical studies should be performed to explore this method for assessing the personal impact. Furthermore, more attention should be given to the four genes SMC4, TIPARP, TSC22D3, and ZFP36 in IgAN.

Funding

This research was funded by Lin He's Academician Workstation of New Medicine and Clinical Translation (grant No. 18331210), Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (grant No. 2020377868), and Wenzhou Science & Technology Bureau (grant No. Y20210150).

Acknowledgement

The authors would like to thank their colleagues at the Department of Nephrology, The First and Second Affiliated Hospitals of Wenzhou Medical University, for their support and assistance during the study period.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ji Zhang, and Qiongxiu Zhou. The first draft of the manuscript was written by Ji Zhang， and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethics approval

This is an observational study. The First Affiliated Hospital of Wenzhou Medical University Research Ethics Committee has confirmed that no ethical approval is required.

Rodrigues, J.C.; Haas, M.; Reich, H.N. IgA Nephropathy. Clin J Am Soc Nephrol 2017, 12, 677-686, doi:10.2215/CJN.07420716.
Hassler, J.R. IgA nephropathy: A brief review. Semin Diagn Pathol 2020, 37, 143-147, doi:10.1053/j.semdp.2020.03.001.
Rovin, B.H.; Adler, S.G.; Barratt, J.; Bridoux, F.; Burdge, K.A.; Chan, T.M.; Cook, H.T.; Fervenza, F.C.; Gibson, K.L.; Glassock, R.J. KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney International 2021, 100, S1-S276.
Ponticelli, C.; Glassock, R.J. IgA Nephritis with Declining Renal Function: Treatment with Corticosteroids May Be Worthwhile. J Am Soc Nephrol 2015, 26, 2071-2073, doi:10.1681/ASN.2015010030.
Tesar, V.; Troyanov, S.; Bellur, S.; Verhave, J.C.; Cook, H.T.; Feehally, J.; Roberts, I.S.; Cattran, D.; Coppo, R.; Group, V.s.o.t.E.-E.I.W. Corticosteroids in IgA Nephropathy: A Retrospective Analysis from the VALIGA Study. J Am Soc Nephrol 2015, 26, 2248-2258, doi:10.1681/ASN.2014070697.
Lv, J.; Wong, M.G.; Hladunewich, M.A.; Jha, V.; Hooi, L.S.; Monaghan, H.; Zhao, M.; Barbour, S.; Jardine, M.J.; Reich, H.N.; et al. Effect of Oral Methylprednisolone on Decline in Kidney Function or Kidney Failure in Patients With IgA Nephropathy: The TESTING Randomized Clinical Trial. Jama 2022, 327, 1888-1898, doi:10.1001/jama.2022.5368.
Rauen, T.; Eitner, F.; Fitzner, C.; Sommerer, C.; Zeier, M.; Otte, B.; Panzer, U.; Peters, H.; Benck, U.; Mertens, P.R.; et al. Intensive Supportive Care plus Immunosuppression in IgA Nephropathy. N Engl J Med 2015, 373, 2225-2236, doi:10.1056/NEJMoa1415463.
Vecchio, M.; Bonerba, B.; Palmer, S.C.; Craig, J.C.; Ruospo, M.; Samuels, J.A.; Molony, D.A.; Schena, F.P.; Strippoli, G.F. Immunosuppressive agents for treating IgA nephropathy. Cochrane Database Syst Rev 2015, CD003965, doi:10.1002/14651858.CD003965.pub2.
Pattrapornpisut, P.; Avila-Casado, C.; Reich, H.N. IgA Nephropathy: Core Curriculum 2021. Am J Kidney Dis 2021, 78, 429-441, doi:10.1053/j.ajkd.2021.01.024.
Chengalvala, M.V.; Chennathukuzhi, V.M.; Johnston, D.S.; Stevis, P.E.; Kopf, G.S. Gene expression profiling and its practice in drug development. Curr Genomics 2007, 8, 262-270, doi:10.2174/138920207781386942.
Yella, J.K.; Yaddanapudi, S.; Wang, Y.; Jegga, A.G. Changing trends in computational drug repositioning. Pharmaceuticals 2018, 11, 57.
Ganter, B.; Tugendreich, S.; Pearson, C.I.; Ayanoglu, E.; Baumhueter, S.; Bostian, K.A.; Brady, L.; Browne, L.J.; Calvin, J.T.; Day, G.J.; et al. Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol 2005, 119, 219-244, doi:10.1016/j.jbiotec.2005.03.022.
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, doi:10.1126/science.1132939.
Subramanian, A.; Narayan, R.; Corsello, S.M.; Peck, D.D.; Natoli, T.E.; Lu, X.; Gould, J.; Davis, J.F.; Tubelli, A.A.; Asiedu, J.K.; et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017, 171, 1437-1452 e1417, doi:10.1016/j.cell.2017.10.049.
Iorio, F.; Bosotti, R.; Scacheri, E.; Belcastro, V.; Mithbaokar, P.; Ferriero, R.; Murino, L.; Tagliaferri, R.; Brunetti-Pierri, N.; Isacchi, A.; et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci U S A 2010, 107, 14621-14626, doi:10.1073/pnas.1000138107.
Williams, V.R.; Konvalinka, A.; Song, X.; Zhou, X.; John, R.; Pei, Y.; Scholey, J.W. Connectivity mapping of a chronic kidney disease progression signature identified lysine deacetylases as novel therapeutic targets. Kidney Int 2020, 98, 116-132, doi:10.1016/j.kint.2020.01.029.
Li, S.; Jiang, S.; Zhang, Q.; Jin, B.; Lv, D.; Li, W.; Zhao, M.; Jiang, C.; Dai, C.; Liu, Z. Integrin β3 Induction Promotes Tubular Cell Senescence and Kidney Fibrosis. Front Cell Dev Biol 2021, 9, 733831, doi:10.3389/fcell.2021.733831.
Scott, A.T.; Weitz, M.; Breheny, P.J.; Ear, P.H.; Darbro, B.; Brown, B.J.; Braun, T.A.; Li, G.; Umesalma, S.; Kaemmer, C.A.; et al. Gene Expression Signatures Identify Novel Therapeutics for Metastatic Pancreatic Neuroendocrine Tumors. Clin Cancer Res 2020, 26, 2011-2021, doi:10.1158/1078-0432.Ccr-19-2884.
Chan, J.; Wang, X.; Turner, J.A.; Baldwin, N.E.; Gu, J. Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing. Bioinformatics 2019, 35, 2818-2826, doi:10.1093/bioinformatics/btz006.
Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3, Article3, doi:10.2202/1544-6115.1027.
Smyth, G.K.; Ritchie, M.; Thorne, N.; Wettenhall, J. LIMMA: linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. 2005.
Srivastava, A.; George, J.; Karuturi, R.K.M. Transcriptome Analysis. In Encyclopedia of Bioinformatics and Computational Biology, Ranganathan, S., Gribskov, M., Nakai, K., Schönbach, C., Eds.; Academic Press: Oxford, 2019; pp. 792-805.
Kidney Disease: Improving Global Outcomes Glomerular Diseases Work, G. KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney Int 2021, 100, S1-S276, doi:10.1016/j.kint.2021.05.021.
Goodwin, J.E. Role of the glucocorticoid receptor in glomerular disease. Am J Physiol Renal Physiol 2019, 317, F133-f136, doi:10.1152/ajprenal.00217.2019.
Zhou, H.; Tian, X.; Tufro, A.; Moeckel, G.; Ishibe, S.; Goodwin, J. Loss of the podocyte glucocorticoid receptor exacerbates proteinuria after injury. Sci Rep 2017, 7, 9833, doi:10.1038/s41598-017-10490-z.
Colombo, C.; Li, M.; Watanabe, S.; Messa, P.; Edefonti, A.; Montini, G.; Moscatelli, D.; Rastaldi, M.P.; Cellesi, F. Polymer Nanoparticle Engineering for Podocyte Repair: From in Vitro Models to New Nanotherapeutics in Kidney Diseases. ACS Omega 2017, 2, 599-610, doi:10.1021/acsomega.6b00423.
Fellstrom, B.C.; Barratt, J.; Cook, H.; Coppo, R.; Feehally, J.; de Fijter, J.W.; Floege, J.; Hetzel, G.; Jardine, A.G.; Locatelli, F.; et al. Targeted-release budesonide versus placebo in patients with IgA nephropathy (NEFIGAN): a double-blind, randomised, placebo-controlled phase 2b trial. Lancet 2017, 389, 2117-2127, doi:10.1016/S0140-6736(17)30550-0.
Robles, N.R.; Fici, F.; Grassi, G. Dihydropyridine calcium channel blockers and renal disease. Hypertens Res 2017, 40, 21-28, doi:10.1038/hr.2016.85.
Capiod, T. Cell proliferation, calcium influx and calcium channels. Biochimie 2011, 93, 2075-2079, doi:10.1016/j.biochi.2011.07.015.
Sugano, N.; Wakino, S.; Kanda, T.; Tatematsu, S.; Homma, K.; Yoshioka, K.; Hasegawa, K.; Hara, Y.; Suetsugu, Y.; Yoshizawa, T. T-type calcium channel blockade as a therapeutic strategy against renal injury in rats with subtotal nephrectomy. Kidney international 2008, 73, 826-834.
Orth, S.R.; Nobiling, R.; Bönisch, S.; Ritz, E. Inhibitory effect of calcium channel blockers on human mesangial cell growth: evidence for actions independent of L-type Ca2+ channels. Kidney international 1996, 49, 868-879.
Baylis, C.; Qiu, C.; Engels, K. Comparison of L-type and mixed L-and T-type calcium channel blockers on kidney injury caused by deoxycorticosterone-salt hypertension in rats. American journal of kidney diseases 2001, 38, 1292-1297.
Ohno, S.; Ishii, A.; Yanagita, M.; Yokoi, H. Calcium channel blocker in patients with chronic kidney disease. Clinical and Experimental Nephrology 2022, 26, 207-215, doi:10.1007/s10157-021-02153-1.
Loechner, K.J.; Salmon, W.C.; Fu, J.; Patel, S.; McLaughlin, J.T. Cell Cycle-Dependent Localization of Voltage-Dependent Calcium Channels and the Mitotic Apparatus in a Neuroendocrine Cell Line(AtT-20). Int J Cell Biol 2009, 2009, 487959, doi:10.1155/2009/487959.
Rainer, J.; Ploner, C.; Jesacher, S.; Ploner, A.; Eduardoff, M.; Mansha, M.; Wasim, M.; Panzer-Grümayer, R.; Trajanoski, Z.; Niederegger, H.; et al. Glucocorticoid-regulated microRNAs and mirtrons in acute lymphoblastic leukemia. Leukemia 2009, 23, 746-752, doi:10.1038/leu.2008.370.
Juszczak, G.R.; Stankiewicz, A.M. Glucocorticoids, genes and brain function. Prog Neuropsychopharmacol Biol Psychiatry 2018, 82, 136-168, doi:10.1016/j.pnpbp.2017.11.020.
Mozaffari, M.S. Role of GILZ in the Kidney and the Cardiovascular System: Relevance to Cardiorenal Complications of COVID-19. J Pharmacol Exp Ther 2020, 375, 398-405, doi:10.1124/jpet.120.000243.
Makita, S.; Takatori, H.; Nakajima, H. Post-Transcriptional Regulation of Immune Responses and Inflammatory Diseases by RNA-Binding ZFP36 Family Proteins. Front Immunol 2021, 12, 711633, doi:10.3389/fimmu.2021.711633.
Carballo, E.; Lai, W.S.; Blackshear, P.J. Feedback inhibition of macrophage tumor necrosis factor-alpha production by tristetraprolin. Science 1998, 281, 1001-1005, doi:10.1126/science.281.5379.1001.
Kozaki, T.; Komano, J.; Kanbayashi, D.; Takahama, M.; Misawa, T.; Satoh, T.; Takeuchi, O.; Kawai, T.; Shimizu, S.; Matsuura, Y.; et al. Mitochondrial damage elicits a TCDD-inducible poly(ADP-ribose) polymerase-mediated antiviral response. Proc Natl Acad Sci U S A 2017, 114, 2681-2686, doi:10.1073/pnas.1621508114.
Ahmed, S.; Bott, D.; Gomez, A.; Tamblyn, L.; Rasheed, A.; Cho, T.; MacPherson, L.; Sugamori, K.S.; Yang, Y.; Grant, D.M.; et al. Loss of the Mono-ADP-ribosyltransferase, Tiparp, Increases Sensitivity to Dioxin-induced Steatohepatitis and Lethality. J Biol Chem 2015, 290, 16824-16840, doi:10.1074/jbc.M115.660100.
Bindesbøll, C.; Tan, S.; Bott, D.; Cho, T.; Tamblyn, L.; MacPherson, L.; Grønning-Wang, L.; Nebb, H.I.; Matthews, J. TCDD-inducible poly-ADP-ribose polymerase (TIPARP/PARP7) mono-ADP-ribosylates and co-activates liver X receptors. Biochem J 2016, 473, 899-910, doi:10.1042/bj20151077.
Baban, B.; Khodadadi, H.; Vaibhav, K.; Marchetti, C.; Riccardi, C.; Mozaffari, M.S. Regulation of Innate Lymphoid Cells in Acute Kidney Injury: Crosstalk between Cannabidiol and GILZ. J Immunol Res 2020, 2020, 6056373, doi:10.1155/2020/6056373.
Bruscoli, S.; Riccardi, C.; Ronchetti, S. GILZ as a Regulator of Cell Fate and Inflammation. Cells 2021, 11, doi:10.3390/cells11010122.
Flamini, S.; Sergeev, P.; Viana de Barros, Z.; Mello, T.; Biagioli, M.; Paglialunga, M.; Fiorucci, C.; Prikazchikova, T.; Pagano, S.; Gagliardi, A.; et al. Glucocorticoid-induced leucine zipper regulates liver fibrosis by suppressing CCL2-mediated leukocyte recruitment. Cell Death Dis 2021, 12, 421, doi:10.1038/s41419-021-03704-w.
Nataraja, C.; Dankers, W.; Flynn, J.; Lee, J.P.W.; Zhu, W.; Vincent, F.B.; Gearing, L.J.; Ooi, J.; Pervin, M.; Cristofaro, M.A.; et al. GILZ Regulates the Expression of Pro-Inflammatory Cytokines and Protects Against End-Organ Damage in a Model of Lupus. Front Immunol 2021, 12, 652800, doi:10.3389/fimmu.2021.652800.
Leigh, R.; Mostafa, M.M.; King, E.M.; Rider, C.F.; Shah, S.; Dumonceaux, C.; Traves, S.L.; McWhae, A.; Kolisnik, T.; Kooi, C.; et al. An inhaled dose of budesonide induces genes involved in transcription and signaling in the human airways: enhancement of anti- and proinflammatory effector genes. Pharmacol Res Perspect 2016, 4, e00243, doi:10.1002/prp2.243.
Mielke, D.; Rohde, V. Randomized controlled trials-a critical re-appraisal. Neurosurg Rev 2021, 44, 2085-2089, doi:10.1007/s10143-020-01401-4.
Musa, A.; Ghoraie, L.S.; Zhang, S.D.; Glazko, G.; Yli-Harja, O.; Dehmer, M.; Haibe-Kains, B.; Emmert-Streib, F. A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 2018, 19, 506-523, doi:10.1093/bib/bbw112.
Mirzakhani, H.; Litonjua, A.A.; McElrath, T.F.; O'Connor, G.; Lee-Parritz, A.; Iverson, R.; Macones, G.; Strunk, R.C.; Bacharier, L.B.; Zeiger, R.; et al. Early pregnancy vitamin D status and risk of preeclampsia. J Clin Invest 2016, 126, 4702-4715, doi:10.1172/jci89031.

No competing interests reported.

Manuscriptssupplementary.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Effectiveness of gene-expression connectivity analysis for evaluating treatment effects of frequent medicines on kidney in IgA nephropathy

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusion

Declarations

Funding

This research was funded by Lin He's Academician Workstation of New Medicine and Clinical Translation (grant No. 18331210), Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (grant No. 2020377868), and Wenzhou Science & Technology Bureau (grant No. Y20210150).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

References

Additional Declarations

Supplementary Files

Status:

Version 1