El debate del artista callejero como constructor cultural

To generate a starting point for our experiments, in silico predictions were performed for transcripts coding for proteins known to be important for podocyte structure and function and miRNAs we identified to be enriched or highly expressed in podocytes. In the last years, several miRNA-mRNA interaction prediction programs have been published. Many of them can be run online one the respective websites. However, the amount of possible interaction partners obtained by these programs is rather overwhelming. Starting predictions with miRNAs, thousands of mRNAs that could potentially be regulated by these miRNAs are predicted. Vice versa, starting with a single mRNA, hundreds of miRNAs are predicted as possible regulators.

Prediction of interaction between podocyte miRNAs and genes important for

podocyte maintenance

The in silico predictions were performed for putative miRNA-mRNA pairs. Binding sites for miRNAs that were enriched or highly expressed in podocytes were searched in the 3’-UTRs of transcripts coding for proteins that are known to be important for podocyte structure and function. This approach excludes possible interactions in the promoter regions of the genes as well as in the 5’-UTRs and coding regions of the transcripts, which are still a small minority in the

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whole number of verified interactions. It also limits the predicted dataset to proteins that are already known to play an important role for podocyte structure and function. The first predictions were performed for 29 transcripts coding for proteins known to be important for podocyte structure and function using the miRWalk prediction platform. From this initial candidate set, four putative targets, CD2AP, FYN, NCK2 and NEPH1, all known to play an important role in the maintenance of the podocyte slit diaphragms, were selected as first candidates and analyzed in more detail using miRWalk2.

Boundaries of in silico predictions

To be able to concentrate on the most promising predictions in the further experiments, the prediction program miRWalk (Dweep et al. 2011) was used. miRWalk does not only predict interactions itself, but also obtains the predictions of up to eight different algorithms. Recently, the miRWalk2 program was released (Dweep et al. 2011), now also enabling prediction of binding sites in 3’-UTRs with twelve programs, in coding regions with seven programs and 5’- UTRs with six programs. Additionally, prediction of binding sites in the promoter regions is now possible with four programs. To shrink the enormous dataset obtained from the prediction programs, a score consisting of the number of programs that predict a certain interaction was introduced. An interaction was counted to be predicted when it was predicted by at least half of the used programs.

All in silico prediction programs use a kind of score or threshold to determine if a certain miRNA- mRNA pair is predicted for interaction. A second possible way to shrink the dataset, next to using different programs, could be a stricter adjustment of these thresholds in the prediction algorithms. These strategy harbors two possible disadvantages: Firstly, it is nearly impossible to determine which algorithm is the best in predicting miRNA-mRNA interaction pairs. They can be tested with miRNA-mRNA pairs that were proven to be functional, but that does not imply that they will be the best in predicting still unknown interaction pairs. Secondly, all programs work with models that are used to calculate the probability of binding, like the thermodynamic stability of the double stranded RNA to be built. Since these models may deviate from the situation in cells, physiological interactions might get rejected with a threshold that is set to strictly.

For the miRNAs enriched in podocytes, and some miRNAs that are not enriched, but highly expressed in podocytes, in silico target predictions were performed using the algorithms miRWalk and miRWalk2 (Dweep et al. 2011). For 20 candidate miRNAs, binding sites in the 3’- UTRs of human transcripts coding for podocyte proteins were predicted (Tab. 5.5).

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Tab. 5.5: Podocyte miRNAs and targets – prediction and known functions miRNAs identified in murine podocytes predicted human targets by miRWalk & miRWalk2

Expression in mammalian kidney Function in kidney

mmu-let-7e-3p Expressed in murine glomeruli (smirnadb database)

mmu-miR-23b-3p ACTR2, EZR, PARD6B, TRPC6, VEGFA, WASL, WT1

Expressed in murine glomeruli (smirnadb database)

mmu-miR-24-1-5p

mmu-miR-27b-5p

mmu-miR-30a-3p

mmu-miR-30a-5p CD2AP, IQGAP1, NCK2, TRPC6, WASL

Highly expressed in podocytes (Harvey et al. 2008)

Expressed in murine glomeruli (smirnadb database) Expressed in podocytes (Boerries et al. 2013)

Affected in podocyte Dicer knockout (Harvey et al. 2008, Shi et al. 2008) Important for Xenopus kidney development (Agrawal et al. 2009)

mir-30 family is involved in TGF- signaling during glomerulosclerosis (Shi et al. 2013) Downregulation of mir-30 family facilitates podocyte injury, miR-30s target Notch1 (Wu et al. 2014)

Upregulated in injured podocytes, prevents PAN induced podocyte apoptosis (Xie et a. 2015)

mmu-miR-30b-3p

mmu-miR-30b-5p Expressed in murine glomeruli (smirnadb database) Expressed in podocytes (Boerries et al. 2013)

mmu-miR-30c-2-3p

mmu-miR-30c-5p Expressed in murine glomeruli (smirnadb database) Expressed in podocytes (Boerries et al. 2013)

mmu-miR-30d-3p

mmu-miR-30d-5p Expressed in murine glomeruli (smirnadb database)

mmu-miR-99a-3p

mmu-miR-99a-5p Expressed in rat kidney cortex and medulla (Tian et al. 2008)

mmu-miR-107-3p ACTR2, CD2AP, CDC42, EZR, GRB2, NEPH1, NPHS1, PODXL, VEGFA, WASL

Putative tumor suppressor in renal clear cell carcinoma (Song et al. 2015)

mmu-miR-125b-2-3p

mmu-miR-125b-5p Expressed in rat kidney cortex and medulla (Tian et al. 2008) Marks juxtaglomerular cells (Medrano et al. 2012)

Balances juxtaglomerular cells' smooth muscle phenotype (Medrano et al. 2012)

mmu-miR-130a-3p CD2AP, EZR, WASL, FYN

Expressed in murine glomeruli (smirnadb database)

mmu-miR-146b-5p GRB2, FYN, IQGAP, PARD6B, RAC1, RHOA

Increased in renal cell carcinoma (Ha et al. 2010)

Expressed in podocytes (Boerries et al. 2013)

Attenuates renal fibrosis (Morishita et al. 2015) Targets mutated renalase (Kalyani et al. 2015)

mmu-miR-148a-3p EZR, PODXL, RAC1, WASL

mmu-miR-149-5p GRB2, LMX1B, NEPH1, NEPH3, PODXL

Expressed in murine glomeruli (smirnadb database)

When mutated, involved in renal clear cell carcinoma (Wang et al. 2014)

mmu-miR-196b-5p NOSTRIN,PARD6B, TLN1, WASL

Expressed in murine glomeruli (smirnadb database)

mmu-miR-210-3p LMX1B

mmu-miR-322-3p Expressed in murine glomeruli (smirnadb database) Expressed in podocytes (Boerries et al. 2013)

154 miRNAs identified in murine podocytes predicted human targets by miRWalk & miRWalk2

Expression in mammalian kidney Function in kidney

mmu-miR-330-3p Marks juxtaglomerular cells (Medrano et al. 2012)

Balances juxtaglomerular cells' smooth muscle phenotype (Medrano et al. 2012) Regulates VEGF (Ye et al. 2008)

mmu-miR-330-5p

mmu-miR-340-5p ACTR2, CD2AP, CLIC5, IQGAP1, NCK1, NCK2, PARD6B, PODXL, RAC1, TRPC6, WASL, WT1 mmu-miR-351-3p mmu-miR-351-5p

mmu-miR-450a-5p Expressed in murine glomeruli (smirnadb database) Enriched in rat kidney cortex (Tian et al. 2008)

Expressed in podocytes (Boerries et al. 2013)

Regulates Hnrpk (Tian et al. 2008)

mmu-miR-450b-5p ACTR2, CD2AP, EZR, GRB2, NCK2, NPHS2, RAC1

mmu-miR-503-3p

mmu-miR-503-5p ACTR2, CD2AP, PARD6B, TLN1, VEGFA, WASL, WT1

Expressed in murine glomeruli (smirnadb database) mmu-miR-542-3p IQGAP1, NCK1, NPHS2, PARD6B, RAC1

mmu-miR-574-3p RAC1 Expressed in murine glomeruli (smirnadb database) Expressed in podocytes (Boerries et al. 2013) mmu-miR-615-3p mmu-miR-652-3p mmu-miR-873a-5p

mmu-miR-22-3p ACTR2, EZR, GRB2, PODXL

Expressed in murine glomeruli (smirnadb database)

mmu-miR-24-3p IQGAP1, VEGFA Expressed in rat kidney cortex and medulla (Tian et al. 2008)

mmu-miR-26a-5p CD2AP, CLIC5, FAT1, TRPC6

Expressed in murine glomeruli (smirnadb database)

Lowered levels in post stenotic kidneys (Zhu et al. 2015)

Lowered levels in patients with lupus nephritis or IgA nephropathy (Ichii et al. 2014)

Targets CTGF, involved in nephropathy (Koga et al. 2015)

mmu-miR-27a/b-3p ACTR2, CD2AP,

CDC42, EZR, FYN, GRB2, PARD6B, WASL

Expressed in murine glomeruli (smirnadb database)

Expressed in rat kidney cortex and medulla (Tian et al. 2008)

mmu-miR-29a-c-3p CDC42, NEPH1, TRPC6, VEGFA

Expressed in murine glomeruli (smirnadb database)

Signature miRNA under high glucose conditions

(Long et al. 2011)

Regulates several collagens (Liu et al. 2010A)

Targets Spry1 (Long et al. 2011) Blocks progressive renal fibrosis (Qin et al. 2011)

Represses expression of collagens (Wang et al. 2012)

Modulates nephrin acetylation (HDAC4) (Lin et al. 2014)

mmu-miR-92a-3p CD2AP, CDC42, NCK2, PARD6B, WASL

Expressed in murine glomeruli (smirnadb database)

Expressed in rat kidney cortex and medulla (Tian et al. 2008)

miR-17~92 cluster retards cyst growth (Patel et al. 2013)

miR-17~92 cluster is crucial for kidney development (Marrone et al. 2014)

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