Yoon et al, page 1

SUPPLEMENTARY MATERIALS

Supplementary Figure 1. Comparison of HuR PAR-CLIP and AUF1 PAR-CLIP. (a) Summary of

annotation and mapping of the PAR-CLIP libraries for the four different AUF1 isoforms and the combined

dataset using PARalyzer. (b) Number of reads of AUF1 PAR-CLIP tags per transcript. (c) Percentage of

AUF1 PAR-CLIP tags in mature mRNAs, introns, and ncRNAs. (d) Shared target mRNAs and 3’UTRs in

AUF1 and HuR PAR-CLIP (e) Relative distribution of PAR-CLIP tags upstream (‘up’) and downstream

(‘dn’) of the start and stop (‘end’) codons. Density of AUF1, HuR32 and Ago29 PAR-CLIP tags across the

5’UTR, coding region, and 3’UTR of mRNAs. (f) Representative (top three) RNA Recognition Elements

(RREs) from each AUF1 isoform.

Total Common target mRNAs

target mRNAs p37 p40 p42 p45 HuR

p37 (%) 100.0 --- 46.8 48.1 58.8 71.8

Gene (#) 308 144 148 181 221

p40 (%) 100.0 8.7 --- 46.0 59.0 83.0

Gene (#) 1,663 144 765 981 1,380

p42 (%) 100.0 5.3 27.6 --- 37.2 19.9

Gene (#) 2,768 148 765 1,029 552

p45 (%) 100.0 5.2 28.3 29.7 --- 19.9

Gene (#) 3,468 181 981 1,029 690

HuR (%) 100.0 3.6 22.4 9.0 11.2 ---

Gene (#) 6,154 221 1,380 552 690

Total Common target 3`UTR

target 3’UTR p37 p40 p42 p45 HuR

p37 (%) 100.0 --- 49.7 52.5 69.1 84.0

Gene (#) 181 90 95 125 152

p40 (%) 100.0 6.5 --- 44.8 61.0 86.5

Gene (#) 1380 90 618 842 1194

p42 (%) 100.0 6.2 40.2 --- 57.4 82.7

Gene (#) 1535 95 618 881 1269

p45 (%) 100.0 6.5 43.7 45.7 --- 87.9

Gene (#) 1926 125 842 881 1692

HuR (%) 100.0 2.6 20.3 21.5 28.7 ---

Gene (#) 5895 152 1194 1269 1692

Shared target 3’UTRShared target mRNAs

Yoon et al, Supplementary Fig. 1

AUF1 p37

AUF1 p40

AUF1 p42

AUF1 p45

e

Ave

rag

e t

ag

s

pe

r 1

00

-b w

ind

ow

pe

r m

illio

n r

ea

ds

d

f

1000b_up

900b_up

800b_up

700b_up

600b_up

500b_up

400b_up

300b_up

200b_up

100b_up

Coding_Start

100b_dn_start

200b_dn_start

300b_dn_start

400b_dn_start

500b_dn_start

600b_dn_start

700b_dn_start

800b_dn_start

900b_dn_start

1000b_dn_start

1000b_up_end

900b_up_end

800b_up_end

700b_up_end

600b_up_end

500b_up_end

400b_up_end

300b_up_end

200b_up_end

100b_up_end

Coding_End

100b_dn

200b_dn

300b_dn

400b_dn

500b_dn

600b_dn

700b_dn

800b_dn

900b_dn

1000b_dn

Compiled Binding Profile over mRNA

AUF1_p37

AUF1_p40

AUF1_p42

AUF1_p45

AUF1_ALL

HuR

Ago

0.60

0.48

0.36

0.24

0.12

0

5’UTR 3’UTRCoding Region

Distance from Start or Stop codons

p37 p40 p42 p45 Combined

Raw sequence reads 39333766 41501812 34200306 32579514 147615398

Adapter extracted reads 26590962 23849090 17541376 20245876 88227304

Sequence reads mapping to the genome with 0 and 1 mismatch 2250794 2496539 3073255 5017101 12837689

Sequence reads uniquely mapping to genome with 0 and 1 mismatch 467084 502618 643639 1274269 2870020

Sequence reads utilized for group assembly 118367 485028 329163 333140 1392847

Number of groups 2744 11385 35567 33587 86789

% P

AR

-CL

IP t

ag

s

0

2500

2000

1500

1000

500

0

p37

p40

p42

p45

1 2 2.5 3 3.5 4 4.5 5 5.5 6

Log2 (AUF1 sites per transcript)

Co

un

ts

b c

0

10

20

30

40

50

60

70

80

90

p37 p40 p42 p45

5’UTR

CDS

3’UTR

Intron

ncRNA

a

Yoon et al, page 2

Supplementary Figure 2. RNA recognition motifs in AUF1 PAR-CLIP. Representative (top three) RNA

Recognition Elements (RREs) for all four AUF1 isoforms according to the binding region on the mRNA:

5’UTR, coding region (CDS), and 3’UTR.

5’UTR

CDS

3’UTR

Yoon et al, Supplementary Fig. 2

Yoon et al, page 3

Supplementary Figure 3. AUF1 target pre-mRNAs, influence on alternatively spliced transcripts. (a)

Top, relative distribution of tags within each target pre-mRNA. Bottom, shared pre-mRNAs between

combined AUF1 and HuR PAR-CLIP libraries. (b) Number of target pre-mRNAs shared among the four

AUF1 isoforms and HuR. (c) Left, number of PAR-CLIP target pre-mRNAs differentially expressed after

overexpression of each AUF1 isoform, as determined by RNA-Seq. Right, specific relative levels of

different splicing variants (#1, #2, #3) for each alternatively spliced, shared pre-mRNA.

#2#1#1 #2#2#1#2

a

p40(11 transcripts)

p42(291 transcripts)

p37(24 transcripts)

p45(6 transcripts)

ATRX

CLCC1

DENND5A

ESYT2

KIAA0586

ZEB1

EXD2

RTTN

UGGT1

RAPGEF2

53081395 2380

# target pre-mRNAs

in PAR-CLIPs

AUF1 PAR-CLIP HuR PAR-CLIP

75

60

45

30

15

0

b

c # target pre-mRNAs with differential expression

after AUF1 isoform overexpression (RNA-seq)

6

5

4

3

2

1

0

vector

p37

p42

ATRX

Rela

tive levels

CLCC1 DENND5A ESYT2 KIAA0586 ZEB1

#1 #2#1#3#2#1Isoform

#2#1#2

6

5

4

3

2

1

0

vector

p40

p42

EXD2

Rela

tive levels

RTTN UGGT1

#1 #2#1Isoform #2

4

3

2

1

0

vector

p42

p45

RAPGEF2

Rela

tive levels

#1Isoform

% d

istr

ibu

tio

n a

mo

ng

RN

As

wit

h P

AR

-CL

IP t

ag

s

Total Shared target pre-mRNAs

target pre mRNAs p37 p40 p42 p45 HuR

p37 (%) 100.0 - 66.0 84.1 79.4 85.9

Gene (#) 870 574 732 691 747

p40 (%) 100.0 18.3 - 82.4 77.8 85.9

Gene (#) 3,142 574 2,589 2,443 2,698

p42 (%) 100.0 13.6 47.9 - 68.9 82.5

Gene (#) 5,402 732 2,589 3,722 4,455

p45 (%) 100.0 15.1 53.4 81.4 - 86.8

Gene (#) 4,572 691 2,443 3,722 3,968

HuR (%) 100.0 9.7 35.1 57.9 51.6 -

Gene (#) 7,688 747 2,698 4,455 3,968

Shared target Pre-mRNAs

p37 p40 p42 p45 AUF1

(all)

HuR

5’UTR

CDS

3’UTR

Intron

Yoon et al, Supplementary Fig. 3

Yoon et al, page 4

Supplementary Figure 4. Comparison of transcriptomes among HEK293, HeLa, and WI-38 cells. (a)

Pairwise correlations of transcript levels in each cell type (HeLa, HEK293 and WI-38 cells) were calculated

and plotted with log2-transformed FPKM values (fragments per kilobase of exon per million fragments

mapped). The plots show high degree of correlation (0.80 to 0.85), indicating that the transcript abundances

in the three cell types were very highly similar. (b) Western blot analysis of AUF1 levels in HeLa cells,

HEK293 cells, and WI-38 fibroblasts (PDL 15 and PDL 55); 10 μg whole-cell lysate was loaded in each

lane.

-9

-5

-1

3

7

11

15

-9

-5

-1

3

7

11

15

-9

-5

-1

3

7

11

15

-9 -5 -1 3 7 11 15 -9 -5 -1 3 7 11 15 -9 -5 -1 3 7 11 15

HeLa FPKM

(Log2)

HEK293 FPKM

(Log2)

WI38 FPKM

(Log2)

HeLa FPKM (Log2) HEK293 FPKM (Log2) WI38 FPKM (Log2)

HeLa FPKM (Log2) 1.0000 0.8511 0.8069

HEK293 FPKM (Log2) 0.8511 1.0000 0.8312

WI38 FPKM (Log2) 0.8069 0.8312 1.0000

Scatterplot Matrix

Multivariate Correlations

HeLa

HEK293

WI-38

HeLa HEK293 WI-38

AUF1

TUBULIN

a

b

HeLa HEK293

WI-38

PDL15

WI-38

PDL55

Yoon et al, Supplementary Fig. 4

Yoon et al, page 5

Supplementary Figure 5. AUF1 does not affect global poly(A)+ RNA distribution, U2 snRNA, XIST

and MALAT1 function. (a) PCR analysis to analyze the exon inclusion ratio of pre-mRNAs targeted by

MALAT1. (b,c) RNA-FISH analysis to detect the distribution of U2 snRNA (red) (b), poly(A)+ RNA (green)

(b) in HeLa cells and XIST RNA (red) in WI-38 cells (c). The DNA is counterstained with DAPI (blue).

(d,e) In cells expressing normal (Ctrl siRNA) or reduced HuR or AUF1 levels, the steady-state levels of

PAICS, PCCB, and NUP43 mRNAs (normalized to GAPDH mRNA) (d), and the relative abundance of

NEAT1 RNA and GAPDH mRNA in the nucleus and cytoplasm (e) were measured by RT-qPCR. (f) In vitro

assays to test the binding of recombinant His-AUF1 and MBP-HuR to purified biotinylated HOTAIR,

lincRNA-p21, MALAT1, and NEAT1 lncRNAs. Input, recombinant protein used in binding assays; MBP,

negative control protein. (g) Forty-eight hours after silencing AUF1 and HuR, the levels of TOP2A, USP1,

and APP mRNAs in AUF1 IP and in HuR IP were measured by RIP followed by RT-qPCR analysis. Data

were normalized to GAPDH mRNA levels in each IP sample.

Hu

R s

iRN

A

AU

F1

siR

NA

Ctrl s

iRN

A

aC

on

tro

l s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

Co

ntr

ol s

iRN

A

SAT1

Exon 4

ARHGEF1

Exons 23-24

HMGXB4

Exon 2

HMGXB4

Exon 5

CDK7

Exons 2-3

CDK7

Exons 3-4

CTAGE5

Exon 2

CTAGE5

Exon 19MYBL2

Exon 3

92 8813 14 24 25 25 30 2 2 7 7 93 93 56 56 98 98 % exon

inclusion

Co

ntr

ol s

iRN

AA

UF

1 s

iRN

AH

uR

siR

NA

HeLa

U2 snRNA poly A+ RNA Merge Merge + DAPI XIST

WI-38

XIST + DAPI

Control siRNA AUF1 siRNA HuR siRNAb c

NEAT1 RNA

d

PAICS

Ctrl siRNA

AUF1 siRNA

HuR siRNA

NUP43PCCB

1.25

1.00

0.75

0.50

0.25

0

mR

NA

le

ve

ls (

fold

)

125

100

75

50

25

0

Re

lati

ve

le

ve

ls

GAPDH mRNA

eCytoplasmic

Nuclear

p37

p40

p42

p45

MBP-HuR

MBP

HOTAIR - - - - - +

lincRNA-p21 - - - - + -

MALAT1 - - - + - -

NEAT1 - - + - - -

Input + - - - - -

HOTAIR - - - +

lincRNA-p21 - - + -

MALAT1 - + - -

NEAT1 + - - -

f

Yoon et al, Supplementary Fig. 5

TO

P2

Am

RN

A e

nri

ch

me

nt

0

1

2

3

4

5

6

7

8

US

P1

mR

NA

en

ric

hm

en

t

0

1

2

3

4

5

6

7

8

AP

Pm

RN

A e

nri

ch

me

nt

0

1

2

3

4

5

6

IgG IP AUF1 IP

CtrlsiRNA

AUF1siRNA

+HuR

siRNA

IgG IP HuR IP

CtrlsiRNA

AUF1siRNA

+HuR

siRNA

g

Yoon et al, page 6

Supplementary Figure 6. AUF1 binds to TOP2A mRNA and promotes TOP2A translation. (a) RNP IP

analysis of the interaction of TOP2A mRNA with AUF1 using anti-AUF1 and rabbit IgG antibodies; TOP2A

mRNA was detected by RT-qPCR and normalized to GAPDH mRNA levels. Forty-eight h after transfecting

HeLa cells with Ctrl or AUF1 siRNA, Western blot analysis of the expression levels of TOP2A, AUF1, and

control β-ACTIN was performed and the levels of TOP2A mRNA relative to GAPDH mRNA were

calculated by RT-qPCR analysis. (b) Twenty-four h after transfecting cells with control plasmid pcDNA or

with four plasmids to overexpress each of the isoforms of AUF1 (pcDNA-AUF1), the levels of TOP2A,

AUF1, and control β-tubulin were assessed by Western blot analysis, and the abundance of TOP2A

mRNA/GAPDH mRNA by RT-qPCR analysis. (c) Schematic of TOP2A mRNA, depicting the 5’UTR, CR,

and 3’UTR. The TOP2A 3’UTR was cloned into plasmid pEGFP to generate pEGFP-TOP2A(3’). Cells

were transfected with the plasmids shown in the schematic together with either pcDNA or pooled pcDNA-

AUF1 plasmids; 16 h later, EGFP protein levels were assessed by Western blot analysis (left), quantified by

densitometry, normalized to α-tubulin levels, and plotted (right). (d) In cells that were transfected with the

EGFP reporter plasmids shown in (d), the association of AUF1 with the expressed transcripts (EGFP and

EGFP-TOP2A(3’) mRNAs) was measured 16 h later by RNP IP analysis.

TOP2A (Fold) 1.00 0.27 (0.2)

TOP2A (Fold) 1.00 4.0 (1.5)

a

b

TO

P2

Am

RN

A l

ev

els

pcDNA

AUF1

pcDNA

0.00

0.25

0.50

0.75

1.00

1.25

0.00

0.25

0.50

0.75

1.00

1.25

TO

P2

Am

RN

A l

ev

els

AUF1

siRNA

Ctrl

siRNA

AUF1

siRNA

Ctrl

siRNA

TOP2A (190 kDa)

b-ACTIN (45 kDa)

AUF1

c

pcDNA

AUF1pcDNA

TOP2A (190 kDa)

-TUBULIN (52 kDa)

AUF1

d

0

2

4

6

8

TO

P2

Am

RN

A e

nri

ch

me

nt

AUF1IgGIP:

p45

p42

p40

p37

**

pEGFP-TOP2A(3’)

pEGFP EGFP

EGFP

EGFP (27 kDa)

b-ACTIN (45 kDa)

pcDNAAUF1pcDNA

pcDNAAUF1pcDNA

pEGFP-TOP2A(3’)pEGFP

2.0

pEGFP-TOP2A(3’)

pEGFP

0.5

1.0

1.5

2.0

2.5

0.0

EG

FP

pro

tein

le

ve

ls

pcDNA

pcDNA-AUF1

pEGFP-TOP2A(3’)

0

3

6

9

12

15

EG

FP

mR

NA

in

AU

F1

IP

pEGFP

4722 5698

3’UTR

* **

p45

p42

p40

p37

AUF1

p45

p42

p40

p37

Yoon et al, Supplementary Fig. 6

Yoon et al, page 7

Supplementary Figure 7. Uncropped images for Western blots shown in the main article.

MWM

(kDa)

- 50

- 40

1B Tubulin

MWM

(kDa)

- 70

- 50

1B AUF1

MWM

(kDa)

- 50

- 40

1B Flag

MWM

(kDa)

- 50

- 40

3F Actin

MWM

(kDa)

- 70

- 50

- 40

3F AUF1

MWM

(kDa)

- 40

- 35

3F HuR

MWM

(kDa)

- 50

- 40

5D Tubulin

MWM

(kDa)

- 70

- 50

- 40

5D AUF1

MWM

(kDa)

- 40

- 35

5D HuR

MWM

(kDa)

- 140

- 100

5D APP5D USP1

MWM

(kDa)

- 260

- 140

5D Top2a

MWM

(kDa)

- 260

- 140

Yoon et al, Supplementary Fig. 7

Yoon et al, page 8

Supplementary Figure 7. Uncropped images for Western blots, and protein and DNA gels shown in the

main article.

6B EtBr

MWM

(kbp)

- 3

- 1

6A Coomassie

MWM

(kDa)

- 70

- 50

- 40

6B Coomassie MWM

(kDa)

- 70

- 40

6B Coomassie

6B Coomassie 6B Coomassie

6B Coomassie 6B Coomassie

6B Coomassie 6B Coomassie

MWM

(kDa)

- 70

- 40

MWM

(kDa)

- 70

- 50

- 40

MWM

(kDa)

- 70

- 50

- 40

MWM

(kDa)

- 70

- 50

- 40

MWM

(kDa)

- 70

- 40

MWM

(kDa)

- 70

- 50

- 40

MWM

(kDa)

- 70

- 50

- 40

Yoon et al, Supplementary Fig. 7 (continued)

Yoon et al, page 9

Supplementary Figure 7. Uncropped images for Western blots and protein gels shown in the main

article.

6D CoomassieMWM

(kDa)

-

- 50

- 40

6E Tubulin and RL MWM

(kDa)

- 70

- 50

- 40

- 35

6E Tubulin and RL

6E Tubulin and RL 6E Tubulin and RL

6E Tubulin and RL

6E Tubulin and RL

6D Coomassie

MWM

(kDa)

- 70

- 50

6E Tubulin and RL

MWM

(kDa)

- 70

- 50

- 40

- 35

MWM

(kDa)

- 70

- 50

- 40

- 35

MWM

(kDa)

- 70

- 50

- 40

- 35

MWM

(kDa)

- 70

- 50

- 40

- 35

MWM

(kDa)

- 70

- 50

- 40

- 35

MWM

(kDa)

- 70

- 50

- 40

- 35

Yoon et al, Supplementary Fig. 7 (continued)

Yoon et al, page 10

LEGENDS FOR SUPPLEMENTARY TABLES

Supplementary Table 1 – Analysis of AUF1 target RNAs identified by PAR-CLIP. Table lists AUF1

target RNAs overlapping with HuR PAR-CLIP in HEK293 cells.

Supplementary Table 2 – Analysis of HuR target RNAs identified by PAR-CLIP. Table lists

HuR target RNAs overlapping with AUF1 PAR-CLIP in HEK293 cells.

Supplementary Table 3 – Analysis of transcript abundance after AUF1 overexpression. Table lists

differentially expressed target RNAs after overexpressing AUF1 isoforms in HEK293 cells.

Supplementary Table 4 – Influence of AUF1 and HuR on ribosome profiles. Ribosome profile analyses

after silencing AUF1 or HuR in HeLa cells in HeLa cells.

Supplementary Table 5 – Analysis of transcript abundance after AUF1 silencing. Table lists

differentially expressed target RNAs after silencing AUF1 in HEK293 cells.

Supplementary Table 6 – Analysis of transcript abundance in WI-38 cells. Table lists differentially

expressed target RNAs after AUF1 or HuR silencing in proliferating WI-38 and senescent WI-38 fibroblasts.

Supplementary Table 7 – List of primers used in this study.

Supplementary Table 8 – Influence of AUF1 p37 on the levels of alternative transcripts. Table lists the

levels of alternative transcripts after overexpressing AUF1 p37 in HEK293 cells.

Supplementary Table 9 – Influence of AUF1 p40 on the levels of alternative transcripts. Table lists the

levels of alternative transcripts after overexpressing AUF1 40 in HEK293 cells.

Supplementary Table 10 – Influence of AUF1 p42 on the levels of alternative transcripts. Table lists

the levels of alternative transcripts after overexpressing AUF1 p42 in HEK293 cells.

Supplementary Table 11 – Influence of AUF1 p45 on the levels of alternative transcripts. Table lists

the levels of alternative transcripts after overexpressing AUF1 p45 in HEK293 cells.

Yoon et al, page 11

SUPPLEMENTARY NOTES

SUPPLEMENTARY RESULTS

Analysis of significance of the overlap among AUF1 and HuR sites

After applying tight criteria for inclusion of PAR-CLIP sites (>0.25 T-to-C conversions, >5 crosslinked reads,

and >2 crosslinking sites), 83,289 HuR binding sites and 27,981 AUF1 binding sites were identified. Of

these, 6,550 sites were common to both HuR and AUF1. To test if this overlap occurred by chance, we used

a hypergeometric test employing phyper in R, which returned a highly significant overlap p-value of <10e-22.

Briefly, the observed number of binding sites (6,550) was compared to the total possible binding sites

(57,161,827), which was calculated from the total transcriptome size (1,657,692,975) and median binding

size of 29 nt.

AUF1 binding sites=27981

HuR binding sites=83289

Overlap between AUF1 and HuR binding sites=6550

Total possible binding sites= 57161827

This gives a p-value of < 1.0e-22

Influence of AUF1 on splicing

AUF1 PAR-CLIP tags are most frequently found in introns. Depending on the isoform, intron tags comprise

53-85% independent tags, in the range seen for HuR (59%), which correspond to 64% to73% of all AUF1

target transcripts compared to 52% from HuR target transcripts (Supplementary Fig. 3a,b; pre-mRNAs

shared between AUF1 and HuR are listed in Supplementary Fig. 3b). Since other RBPs analyzed similarly

modulated the abundance of alternative transcripts [e.g., PTB, TDP43, Hu proteins]55-60, we studied if AUF1

affected the production of alternative transcripts from target pre-mRNAs. We overexpressed each AUF1

isoforms in HEK293 cells separately and compared the relative abundance of transcripts on a global scale by

RNA-Seq (Fig. 3c; Supplementary Tables 8-11). This analysis identified 24, 11, 291, and 6 transcripts

alternatively expressed when AUF1 p37, p40, p42, and p45 isoforms were overexpressed, respectively.

These results suggest that AUF1 might alter the abundance of select groups of transcripts, possibly by

affecting the splicing and/or stability of alternative transcripts. Future work to investigate a possible role of

AUF1 in splicing is warranted.

Yoon et al, page 12

SUPPLEMENTARY DISCUSSION

Comparison of transcriptomes and AUF1 levels among three cell types

Pairwise correlations among the three cell types in the study (HeLa, HEK293 and WI-38) were

calculated and plotted with log2-transformed FPKM (fragments per kilobase of exon per million

fragments mapped) values. As shown in Supplementary Fig. 4a, there was very high degree of

correlation (0.80 to 0.85) among all pairs compared, indicating that the transcript abundance in the three

cell types were very highly similar. The levels of AUF1 in HeLa and HEK293 were relatively similar,

as shown in Supplementary Fig. 4b; AUF1 was substantially higher in early-passage (PDL 15) WI-38

cells than in senescent (PDL 55) WI-38 cells, as earlier reported39

.

Limitations of PAR-CLIP

The method used here, PAR-CLIP, also has important limitations that restrict its use. For example, the

procedure is lengthy and has multiple steps requiring optimization and controls, and the sequencing is

costly and requires substantive bioinformatic expertise. The methodology also carries some bias

associated with the choice of crosslinking wavelength, the RNA linkers used in the preparation of the

small-RNA libraries, and the conditions of RNase digestion27,42

. However, as the PAR-CLIP technology

matures, a greater number of bioinformatic tools, including many tools that are more user-friendly, are

emerging (discussed in the main text).

SUPPLEMENTARY REFERENCES

55. Polymenidou, M., et al. Long pre-mRNA depletion and RNA missplicing contribute to neuronal

vulnerability from loss of TDP-43. Nat. Neurosci. 14, 459-468 (2011).

56. Lebedeva, S., et al. Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein

HuR. Mol. Cell 43, 340-352 (2011).

57. Zhou, H. L., et al. Hu proteins regulate alternative splicing by inducing localized histone

hyperacetylation in an RNA-dependent manner. Proc. Natl. Acad. Sci. USA 108, E627-E635 (2011).

58. Tollervey, J. R., et al. Characterizing the RNA targets and position-dependent splicing regulation by

TDP-43. Nat. Neurosci. 14, 452-458 (2011).

59. Ince-Dunn, G., et al. Neuronal Elav-like (Hu) proteins regulate RNA splicing and abundance to control

glutamate levels and neuronal excitability. Neuron 75, 1067-1080 (2012).

Yoon et al, page 13

60. Xue, Y., et al. Direct Conversion of Fibroblasts to Neurons by Reprogramming PTB-Regulated

MicroRNA Circuits. Cell 152, 82-96 (2013).