Distinct expression patterns of natural antisense ... · Henz et al.: Expression of natural...

Distinct expression patterns of natural antisense

transcripts in Arabidopsis

Stefan R. Henz, Jason S. Cumbie, Kristin D. Kasschau, Jan U. Lohmann, James C.

Carrington, Detlef Weigel, Markus Schmid*

Max Planck Institute for Developmental Biology, Department of Molecular Biology,

Spemannstrasse 37-39, D-72076 Tübingen, Germany (S.R.H., J.U.L., D.W., M.S.); and

Center for Genome Research and Biocomputing and Department of Botany and Plant

Pathology, Oregon State University, Corvallis, Oregon 97331, USA (J.S.C., K.D.K., J.C.C.)

*To whom correspondence should be addressed: [email protected]

RUNNING TITLE: Antisense transcripts in Arabidopsis thaliana

Key words: Arabidopsis, natural antisense transcript, cis-NATs, microRNA, small RNA

Mailing address of corresponding author:

Markus Schmid

MPI for Developmental Biology

Spemannstrasse 37-39/VI

D-72076 Tübingen

GERMANY

Phone: +49 7071-601-1416

Fax: +49 7071-601-1412

Email: [email protected]

Plant Physiology Preview. Published on May 11, 2007, as DOI:10.1104/pp.107.100396

Copyright 2007 by the American Society of Plant Biologists

www.plantphysiol.orgon June 13, 2020 - Published by Downloaded from Copyright © 2007 American Society of Plant Biologists. All rights reserved.

http://www.plantphysiol.org

Henz et al.: Expression of natural antisense transcripts in Arabidopsis

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Abstract

It has been shown that overlapping cis-natural antisense transcripts (cis-NATs) can

form a regulatory circuit, in which small RNAs derived from one transcript regulate

stability of the other transcript, which manifests itself as anti-correlated expression.

However, little is known about how widespread antagonistic expression of cis-NATs

is. We have determined how frequently cis-NAT pairs, which make up 7.4% of

annotated transcription units in the Arabidopsis thaliana genome, show anti-

correlated expression patterns. Indeed, global expression profiles of pairs of cis-

NATs on average have significantly lower pairwise Pearson correlation coefficients

(PCC) than other pairs of neighboring genes whose transcripts do not overlap.

However, anti-correlated expression that is greater than expected by chance is only

found in a small number of cis-NAT pairs. The degree of anti-correlation does not

depend on the length of the overlap or on the distance of the 5’ ends of the

transcripts. Consistent with earlier findings, cis-NATs do not exhibit an increased

likelihood to give rise to small RNAs, as determined from available small RNA

sequences and MPSS tags. However, the overlapping regions of cis-NATs appeared

to be enriched for small RNA loci compared to non-overlapping regions.

Furthermore, expression of cis-NATs was not disproportionately affected in various

RNA silencing mutants. Our results demonstrate that there is a trend towards anti-

correlated expression of cis-NAT pairs in Arabidopsis, but currently available data do

not produce a strong signature of small RNA mediated silencing for this process.




3

Introduction

Much of gene expression is primarily regulated at the level of transcription. Over the

last few years, however, it has become increasingly apparent that post-transcriptional

regulation at the RNA level is more widespread and important than previously

assumed (Behm-Ansmant and Izaurralde, 2006; Brodersen and Voinnet, 2006;

Newbury, 2006). While various types of regulatory RNA molecules have been shown

to exist, arguably the most prominent ones are micro-RNAs (miRNAs) (Bartel, 2004;

Jones-Rhoades et al., 2006; Vazquez, 2006). MiRNAs are derived from larger

transcripts generated by RNA polymerase II, and found in both animals and plants.

The primary transcript is processed to give rise to a short 20 to 24 nucleotide long

RNA molecule, the miRNA, which by annealing to partially complementary sites of

mRNAs, can lead to either cleavage of the mRNA or translational inhibition. Another

type of small RNAs that regulate the stability of transcripts are short interfering RNAs

(siRNAs). In contrast to miRNAs, siRNAs are always perfectly complementary to

their targets. One source of siRNAs are double-stranded RNAs generated by

transcription of a locus in both the sense and antisense orientation (Kumar and

Carmichael, 1998; Vanhee-Brossollet and Vaquero, 1998). Such antisense

transcripts were first observed in transgenic experiments, but natural antisense

transcripts (NATs) also occur. There are two classes of NATs: cis-NATs, which are

formed by antisense transcription at the same genomic locus, and trans-NATs,

where sense and antisense transcripts are derived from different loci.

Large-scale genome projects have revealed the common occurrence of

overlapping gene pairs in most species analyzed (Lehner et al., 2002; Shendure and

Church, 2002; Osato et al., 2003; Yelin et al., 2003; Wang et al., 2005). The reported

frequencies for overlapping gene pairs found in different species vary, depending on

sample size and other search parameters, but usually range between 5% to 10% of

all neighboring gene pairs. In the human genome, 4% to 9% of all transcript pairs

overlap, while in the murine genome 1.7% to 14% have been identified as

overlapping. A particularly extreme case is Drosophila, where up to 22% of all

neighboring genes have been reported to overlap. Across the various species, the

majority of overlapping gene pairs is transcribed in convergent orientation, thus

representing true cis-NAT pairs.

NATs have been implicated in such diverse processes as transcription

occlusion, RNA interference, alternative splicing, RNA editing, DNA methylation and

genomic imprinting (Farrell and Lukens, 1995; Sureau et al., 1997; Billy et al., 2001;

Tufarelli et al., 2003; Kim et al., 2004; Jen et al., 2005; Wang et al., 2005). In the

plant kingdom, cis-NATs have been analyzed in rice and in Arabidopsis (Osato et al.,




4

2003; Yamada et al., 2003; Jen et al., 2005; Wang et al., 2005). Analysis of the

Arabidopsis transcriptome by means of whole genome tiling arrays has revealed

antisense expression of 7,600 transcripts, corresponding to roughly 25% of all

annotated genes (Yamada et al., 2003). A few additional studies have addressed the

question of antisense gene pairs in Arabidopsis in detail. Wang and colleagues

(Wang et al., 2005) identified 1,340 potential cis-NAT pairs in Arabidopsis and

confirmed expression of sense and antisense transcripts of 957 cis-NAT pairs using

sequence information of Arabidopsis full-length cDNAs and Massively Parallel

Signature Sequencing (MPSS) data (Meyers et al., 2004; Meyers et al., 2004). Using

qualitative criteria, these authors concluded that the majority of cis-NATs showed

highly anti-correlated expression. In an independent study, Jen and colleagues (Jen

et al., 2005) reported the existence of 1,083 transcript pairs that overlapped in

antisense orientation. They further uncovered a possible role of convergent

overlapping gene pairs in alternative splicing and polyadenylation, but did not find

any evidence for anti-correlated expression greater than expected by chance, which

is in disagreement with the findings of Wang and colleagues (Wang et al., 2005).

Finally, in an elegant set of experiments, SRO5 and P5CDH, a pair of cis-NATs,

were shown to have antagonistic functions in the regulation of salt tolerance in

Arabidopsis (Borsani et al., 2005). In response to salt stress, SRO5 mRNA is

induced, and a 24 nucleotide long siRNA is formed from the region of overlap with

P5CDH, dependent on components that are also involved in the generation of

siRNAs from transgene-derived dsRNA, such as DICER-LIKE 2 (DCL2) and RNA-

DEPENDENT RNA POLYMERASE 6 (RDR6). Subsequently, 21 nucleotide siRNAs

are formed by DCL1-dependent processing of P5CDH transcripts. Finally, 1,320

putative trans-NATs have been recently identified in the Arabidopsis genome (Wang

et al., 2006). Interestingly, a large number of transcripts was predicted to have both

trans- and cis-NATs, suggesting that antisense transcripts can form a complex

regulatory network.

Making use of large collections of microarray data, we have analyzed the

extent to which cis-NATs in Arabidopsis show anti-correlated expression, as reported

under salt stress for the SRO5 and P5CDH paradigm. We find that cis-NATs on

average are significantly more anti-correlated than non-overlapping neighboring

genes, but clear global anti-correlated expression is restricted to a small subset of

cis-NAT pairs, solving conflicting results that had previously been published (Jen et

al., 2005; Wang et al., 2005). Available data sets do not point to small RNAs being

increased in cis-NATs nor is expression of cis-NATs typically affected by mutations in




5

genes necessary for the biogenesis of small RNAs, suggesting that cis-NATs do not

always enter the RNA silencing pathway.

Results and Discussion

Antisense transcript pairs in Arabidopsis thaliana

As a first step towards analyzing the transcriptional regulation of natural antisense

transcripts derived from the same or adjacent loci, we categorized the transcription

units of the Arabidopsis genome, as annotated by The Arabidopsis Information

Resource (TAIR), release 6 (Haas et al., 2005). The Arabidopsis genome contains

30,359 transcription units that can be grouped into 30,354 transcript-pairs (Table 1).

Transcript pairs were further broken down into four major categories, depending on

which strand neighboring transcripts were located on, and whether transcripts were

overlapping or not. The majority of transcript pairs were found to be non-overlapping,

with 15,926 pairs transcribed from the same strand (category 1) and 13,249 from

opposite strands (category 2). We found only 53 overlapping transcript pairs where

both transcripts originated from the same strand (category 3). In contrast, we

identified 2,243 overlapping transcripts originating from opposite strands forming

1,126 natural antisense transcript pairs (cis-NAT; category 4), equaling 3.7% of all

transcript pairs. The majority of the cis-NAT pairs were simple pairs; with only eight

triplets and a single quadruplet identified.

To investigate the expression profiles of the cis-NATs, we mapped the

TAIR6.0 transcription units onto the Affymetrix ATH1 microarray. We found that

21,021 (out of 30,359) transcripts were represented by the array. Of these, 16,014

were arranged in adjacent pairs, which correspond to about half of all transcript pairs

encoded by the Arabidopsis genome. There was no substantial difference between

adjacent non-overlapping transcripts transcribed from the same strand (8,258;

51.8%) or from opposite strands (7,022; 53.0%). In contrast, overlapping transcripts

derived from the same strand were slightly underrepresented (20; 37.7%), while cis-

NATs were slightly overrepresented (714; 63.4%). The latter make up 4.4% of all

transcript pairs mapped onto the ATH1 array. Because of the low number of

transcript pairs in category 3, these were dropped from further analysis. Mapping

information of the four different transcript pair categories onto the Arabidopsis

genome and the ATH1 array can be found in Supplementary Tables 1 and 2,

respectively.

One concern with cis-NAT predictions is that the transcript ends reported in

the TAIR annotation might not be necessarily correct (Haas et al., 2005). We




6

therefore manually inspected all 714 potential cis-NATs that are present on the

Affymetrix ATH1 array for support by cDNA and/or EST clones that include either

spliced introns or large (at least 100 codons long) open reading frames. We found

that of the 714 potential cis-NATs, only 515 (72.1%; 1,027 transcripts in total) are

currently supported by cloned mRNAs with an overlap of at least one base

(Supplementary Table 3). Subsequent analysis focused primarily on this set of cis-

NATs.

The number of cis-NATs identified is slightly higher than what had previously

been reported (Jen et al., 2005; Wang et al., 2005). The discrepancies are likely due

to the different methods used to map cis-NATs onto the genome and changes in

gene annotation introduced with the latest genome release. A limitation of this

analysis one has to keep in mind is that the current annotation of the Arabidopsis

genome may still lack the extreme 5’ and 3’ ends for many transcripts. As a

consequence, our analysis might underestimate the number of Arabidopsis cis-

NATs. Even so, the ATH1 array is a fair representation of the different transcript pair

categories in Arabidopsis, allowing us to use expression data sets based on this

array to examine the expression profiles of cis-NATs in detail.

An excess of negative correlation coefficients of cis-NAT expression

To examine if there is a general difference between the expression profiles of cis-

NATs and non-overlapping transcript pairs, we calculated the pairwise Pearson

correlation coefficient (PCC) for these transcript pairs from four publicly available

data sets generated by the AtGenExpress initiative. The first set comprised data from

234 arrays that capture expression of 78 different tissue samples assayed in

triplicate throughout development (Schmid et al., 2005). The original data set

included also pollen samples, but because many genes show either very high or very

low expression levels in this tissue type, and pairs are therefore more likely to be

perfectly correlated or anti-correlated by chance than in other samples, we omitted

the pollen samples for this analysis. The second set of 236 arrays, from duplicate

samples, reflects responses to hormones and related substances (mostly created by

RIKEN) (Kiba et al., 2005; Nakabayashi et al., 2005; Nemhauser et al., 2006). The

two final sets, of 136 arrays each, had been used to measure the response to

various abiotic stresses in shoots and roots, respectively, with duplicate samples

(Kilian et al., 2007). We analyzed the shoot and or root data separately, to minimize

effects of tissue-specific expression.

In all four data sets, the pairwise PCCs of cis-NATs are skewed towards

negative values (Fig. 1), when compared to non-overlapping transcript pairs located




7

on either the same or opposite strands. This shift in distribution was statistically

significant in all four data sets using a two-sided, two sample Welch t-test (Table 2)

regardless whether all cis-NATs supported by the TAIR annotation (714; Table 1,

category 4), or only the manually curated set (515; Table 1, category 4*) were used.

Similar results were obtained using pairwise Spearman’s rank correlation coefficients

(SCCs), which are less sensitive to outliers (Supplementary Table 4). Comparisons

of the PCC and SCC values by scatter plot analysis revealed a high degree of

similarity, with R2 values ranging between 0.71 an 0.83, indicating the robustness of

the anti-correlation we observed (Supplementary Fig. 1).

In contrast, distributions between non-overlapping transcript pairs located on

either the same or the opposite strand were not significantly different in any of the

data sets. Fig. 2 shows the expression profiles of the cis-NATs with the lowest PCCs

for the individual microarray experiments.

One limitation of the AtGenExpress data sets is that they lack cellular

resolution. We therefore analyzed microarray data Birnbaum and colleagues

obtained from various cell types and regions of the root after cell sorting (Birnbaum et

al., 2003). We found that the distribution of PCC and SCC values of cis-NATs was

skewed towards negative values when compared to non-overlapping transcripts

(Supplementary Fig. 2). As was the case for the AtGenExpress data sets, this shift

towards negative correlation was found to be statistically significant (Table 2 and

Supplementary Table 4), suggesting that the bias towards anti-correlation we

observed in the AtGEnExpress data set reflects true anti-correlation of cis-NATs

within the same cells, as would be expected for direct regulatory effects.

The fact that we found on average statistically significant lower PCCs for cis-

NATs suggests that expression of one of the transcripts in these pairs can influence

expression of the other. However, the PCCs for the majority of cis-NATs fell in the

same range as non-overlapping transcript pairs, suggesting strong mutual regulation

for only a subset of cis-NATs. Thus, anti-correlated expression is much less

widespread than previously suggested based on MPSS expression data from 14

cDNA libraries, in which for the majority of cis-NATs, coexpression in the same tissue

was rarely found (Wang et al., 2005).

It has experimentally been demonstrated that SRO5 and P5CDH, a pair of

cis-NATs, have antagonistic functions in the regulation of salt tolerance in

Arabidopsis (Borsani et al., 2005). We therefore examined the expression profiles of

these two genes in greater detail and found that global expression of P5CDH and

SRO5 is not highly anti-correlated (Supplementary Fig. 3). The strongest anti-

correlation was found in the hormone data set with a PCC of -0.546, while in the




8

development and the abiotic stress data sets derived from shoots, anti-correlation

was weaker with PCC values of -0.171 and -0.178, respectively. In roots, the

expression of the two genes actually is positively correlated (PCC = +0.208) across

the various stress treatments, suggesting that mutual regulation of these two genes

is restricted to specific conditions.

Anti-correlation across the different microarray data sets

We next analyzed whether always the same cis-NAT pairs displayed strong negative

anti-correlation in the various data sets. We found that across the different data sets,

the most strongly anti-correlated cis-NATs varied (Fig. 3), and that there was only

weak overall correlation between the individual experiments. The highest correlation

was found between the development and hormone data sets with R2=0.25. For the

remaining comparisons the R2 value ranged from 0.05 to 0.14. Of the 515 manually

curated cis-NAT pairs analyzed, only six showed an average PCC of less than -0.5 in

all four microarray experiments. Of these, only two had PCC values lower than -0.5

in every individual experiment (Supplementary Table 3). These findings are

consistent with the idea that gene expression is primarily regulated at the

transcriptional level by factors, such as tissue identity, hormone status or stress, and

that only under specific conditions clear anti-correlation is seen. This finding also

implies that the simple presence of an antisense transcript is not sufficient for the

negative cross regulation, suggesting that the effectiveness of posttranscriptional

RNA regulation by RNA interference greatly varies.

Anti-correlation of antisense transcripts is not predicted by extent of overlap

or promoter distance

One obvious parameter that might influence the degree of mutual regulation could be

the length of the overlapping region. We therefore analyzed whether the PCC for a

given cis-NAT pair was correlated with the length of the overlap, but found no

evidence for such a relationship (Fig. 4). We next determined whether the distance

between the 5’ ends of the transcripts of cis-NATs were indicative for the degree of

negative correlation found, with the idea that proximity of promoters could cause

positive correlation in expression. However, similar to the length of the overlap, the

distance of 5’ ends of cis-NAT pairs had no effects on their PCCs (data not shown),

indicating that varying promoter distance is unlikely to confound the conclusions

about transcript overlap and anti-correlated expression.




9

cis-NAT transcripts and RNA silencing

One possible mechanism that might cause negative correlation of cis-NAT RNA

accumulation could be the formation of double-stranded RNAs from the overlapping

mRNA regions and subsequent processing to siRNAs by DICER-LIKE proteins. The

resulting siRNAs could in turn lead to the destruction of one of the transcripts by an

RNA interference-like mechanism, as demonstrated for P5CDH and SRO5 (Borsani

et al., 2005).

To analyze the contribution of siRNAs to anti-correlated expression of cis-

NATS, we examined the distribution of small RNA loci across the genome

(Gustafson et al., 2005; Lu et al., 2005). Specifically we asked whether MPSS tags or

small RNA sequences from several source tissues are enriched in the overlapping

regions of cis-NATs, as would be expected if double-stranded RNAs were the cause

for downregulation of one of the transcripts (Rajagopalan et al., 2006; Kasschau et

al., 2007). Analysis was carried out for all cis-NATs based on the TAIR 6 annotation

(1,136 gene-pairs), as well as for those cis-NATs that are present on the ATH1 array

before (714 gene pairs) and after manual curation (515 gene-pairs). Results are

summarized in Table 3. For detailed information on the mapping of unique small

RNA loci to the Arabidopsis transcriptome see supplementary information.

We found that over all 1,126 cis-NAT pairs predicted by the TAIR 6

annotation, small RNAs were not enriched in cis-NATs when compared to non-

overlapping neighboring genes pairs (Table 3, top half). For example, we observed

1.467 small RNA loci per kb genomic sequence in non-overlapping gene pairs, but

we found only 0.388 loci/kb in the cis-NATs. However, if small RNAs were present in

cis-NATs at all, they appeared to be enriched in the overlapping region of cis-NAT

pairs (1.126 loci/kb) when compared to the non-overlapping region (0.315 loci/kb).

Similar results were obtained when we restricted the analysis to those cis-NATs that

are present on the ATH1 arrays (714) and were confirmed by manual curation (515).

In all instances, no enrichment of small RNAs in cis-NATs was observed. If one takes

into account that not all gene-pairs in a given category do contain small RNA loci, the

outcome differs in that, small RNAs were found to be enriched in the overlapping

region of cis-NATs (4.949 loci/kb) compared to non-overlapping gene-pairs (2.523

loci/kb) by a factor of approx. 2 (Table 3, lower half). Together, these findings point to

the fact that siRNA mediated silencing does not play a major role in the global

regulation of cis-NAT expression, at least not under those conditions examined in

published small RNA sequencing projects (Gustafson et al., 2005; Lu et al., 2005;

Rajagopalan et al., 2006; Kasschau et al., 2007)




10

Further support for this notion came from analyzing microarray data of

mutants affected in the biogenesis of small RNAs (Allen et al., 2005). We found that

cis-NATs accounted for 2.1% to 5.7% of all transcripts that changed significantly

between wild type and the different mutants (Table 4). Given that cis-NATs

supported by mRNAs make up 4.5% of all probe sets present on the ATH1 array

(1,027 / 22,810 probe sets), this is approximately what one would expect by chance,

indicating that cis-NATs are not more likely to be regulated by small RNAs than non-

overlapping transcripts. Taken together, we could not find positive evidence for a

pervasive role of small RNAs in the regulation of antisense transcripts.




11

Conclusion

Our results paint the most detailed picture of the global regulation of cis-NATS in

plants so far. While we could show that cis-NAT pairs tend to have more anti-

correlated expression patterns than non-overlapping neighboring transcripts, we

found that pronounced anti-correlation across many samples can only be found in a

small subset of cis-NATs. Along these lines we found that discrete cis-NAT pairs

show anti-correlated expression in different experiments, suggesting that

independent transcriptional regulation of both members of a pair have a strong

influence on cis-NAT expression. The negative correlation of cis-NATs was also

observed in a cell-type specific data set, indicating that cis-NATs affect each others’

expression in individual cells. The observation that small RNA loci, representing

mainly siRNAs, were underrepresented in cis-NATs along with the fact that mutations

in the RNA silencing machinery did not have a significant effect on cis-NAT

expression confirm this notion and complement previous suggestions that small

RNAs and RNA interference are important for only a subset of cis-NATs (Lu et al.,

2005).

However, there is at least one known example in which small RNAs derived

from cis-NATs have been shown to be important in mutually antagonistic expression,

namely the SRO5 and P5CDH pair of cis-NATs involved in Arabidopsis salt tolerance

(Borsani et al., 2005). When exposed to salt stress, SRO5 message is induced,

leading to formation of small RNAs and activation of an RNA silencing pathway that

ultimately leads to downregulation of the P5CDH transcript. As pointed out before, no

small RNA MPSS tag from wild-type tissue maps to the overlapping region of the two

transcripts, consistent with the inducible nature of this particular siRNA. Borsani and

colleagues (Borsani et al., 2005) have also suggested that microarrays are imperfect

for assessing mutually antagonistic effects, if 3’ products are largely stable. Indeed,

SRO5 and P5CDH are only weakly anti-correlated in our data sets and are not

significantly different from non-overlapping transcripts. Nevertheless, the significant

shift in correlation coefficients of cis-NATs towards negative values when compared

with non-overlapping transcripts indicates that coordinated expression of cis-NAT

can be detected by microarrays, even if the mechanism by which this is achieved is

still unclear. These strongly anti-correlated cis-NATs will be attractive targets for

further mechanistic studies.




12

Material and Methods

Mapping of transcript pairs

The XML file containing the latest annotation (version 6) of Arabidopsis

pseudochromosomes was downloaded from the TAIR FTP server

(ftp://ftp.arabidopsis.org/home/tair/). Start and stop position of the transcription units

along with information on the strand that encodes a mRNA and the gene description

were extracted. We used Perl scripts to categorize pairs of adjacent transcripts,

depending on overlap and whether they were transcribed from the same strand. In a

first step we defined all antisense transcripts that overlapped for at least one base as

predicted by the TAIR 6 annotation as potential cis-NATs. In a second step, all

predicted cis-NATs were manually inspected and only those that were supported by

spliced cDNA and/or EST clones were analyzed further. Single exon genes and gene

models not supported by any mRNA were required to be clearly coding (≥100 codon

open reading frame) in order to be included in the final cis-NATs list.

Determining correlation coefficients

Mapping information of transcripts onto the Affymetrix ATH1 array was obtained from

TAIR as well. We only used those probe sets that mapped to a single transcription

unit. In those few cases where a transcription unit was represented by more than one

specific probe set, we retained for further analysis only one of the probe sets at

random. Pairwise Pearson correlation coefficients (PCC) and pairwise Spearman’s

rank correlation coefficients (SCC) were calculated using programs written in Java.

Histograms (bin size 0.1), ranking and comparisons of PCCs between individual

microarray data sets were created in Microsoft Excel.

Microarray analysis

All microarray data used are publicly available. Data for correlation analysis were

from the AtGenExpress initiative (available from TAIR). Microarray data of small RNA

biogenesis mutants (Allen et al., 2005) were obtained from NCBI-GEO (GSE2473).

Microarray data were normalized using gcRMA (Wu et al., 2004) implemented in

GeneSpring 7.1 (Agilent Technologies). Genes that were differentially expressed

between controls and mutants affected in the biogenesis of small RNAs were

identified using the ‘Rank Product’ (Breitling et al., 2004) package implemented in ‘R’

(http://www.R-project.org). Percentage false positives (pfp) were calculated based on

100 permutations. Only probe sets with a pfp<0.05 for a given comparison were

carried forward. In addition to a pfp<0.05 we required a minimum of 2-fold change in




13

expression estimate for a probe set to be considered to be robustly differentially

expressed.

Mapping MPSS tags and small RNA sequences to cis-NATs

All MPSS tags and small RNA sequences used are publicly available. MPSS tags

were downloaded from the Arabidopsis MPSS database (http://mpss.udel.edu/at/)

(Meyers et al., 2004; Lu et al., 2005). Small RNAs sequences (Col-0) from several

source tissues were described previously and are accessible at NCBI-GEO

[GSE5228 and GSE6682] or the ASRP Database (ASRP,

http://asrp.cgrb.oregonstate.edu/db/) (Gustafson et al., 2005; Rajagopalan et al.,

2006; Kasschau et al., 2007). All tags and sequences were blasted against the

Arabidopsis genome to identify positions of perfect matches. MPSS tags or small

RNA sequences mapping to a single locus were analyzed for position information in

relation to cis-NATs using PERL scripts. MPSS tags or small RNA sequences were

counted if any portion of the locus overlapped the region of interest.




14

Acknowledgements

We are indebted to Blake Meyers for making the MPSS data of small RNAs available

as a database dump. The initial generation of AtGenExpress microarray data was

supported by the Deutsche Forschungsgemeinschaft through a grant to L. Nover, T.

Altmann and DW, and by the Max Planck Society. We acknowledge support for this

work from the Max Planck Society, and by grants from the National Science

Foundation (MCB-0618433) and the United States Department of Agriculture (2005-

35319-15280) to JCC. JUL is an EMBO Young Investigator, and DW is a director of

the Max Planck Institute.




15

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Figure Legends

Figure 1

Distribution of pairwise Pearson correlation coefficients (PCC) for expression of pairs

of adjacent genes. PCCs of transcript-pairs in categories 1 (same strand, no overlap;

black), 2 (opposite strand, no overlap; grey), and 4* (opposite strand, overlap; red)

was calculated for four Affymetrix ATH1 microarray data sets (development,

hormones, abiotic stress/root, and abiotic stress/shoot) created by the AtGenExpress

initiative.

Figure 2

Expression profiles of selected cis-NATs. The NATs with the strongest anti-

correlation (lowest PCC) in a particular data set are shown as examples.

Figure 3

Correlation of the PCCs for 515 cis-NATs between different microarray data sets.

Figure 4

Scatter plot showing independence of PCCs for 515 cis-NATs and length of

transcript overlap.




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Tables

Table 1 Categories of adjacent transcript pairs in Arabidopsis

TU = transcribed unit; categories: 1) neighboring genes on the same strand, no overlap; 2)

neighboring genes on opposite strands, no overlap; 3) neighboring genes on the same

strand, overlap; 4) neighboring genes on opposite strands, overlap; TAIR6 genome

annotation and TAIR ATH1 probe set mapping were used; 4*) gene pairs in category 4 whose

overlap is supported by spliced or long ORF cDNA and/or EST clones.

Arabidopsis genome

transcripts transcript-pair category

Chr TU pairs

1

5'

5'3'

3'

5'5'3'3'

2

5'5'3'3'

5'5'3'3'

3

5'5'3'3'

5'5'3'3'

4

5'5'3'3'

5'

5'3'

3'

I 7593 7592 3936 3345 14 297

II 4994 4993 2641 2175 8 168

III 6129 6128 3254 2661 10 199

IV 4715 4714 2438 2085 13 178

V 6928 6927 3657 2983 8 279

total 30359 30354 15926 13249 53 1126

Affymetrix ATH1 array

transcripts transcript-pair category

Chr TU pairs 1 2 3 4 4*

I 5452 4196 2123 1865 6 202 142

II 3401 2544 1314 1128 4 98 75

III 4223 3215 1705 1381 1 128 77

IV 3110 2318 1170 1030 5 113 77

V 4835 3741 1946 1618 4 173 144

total 21021 16014 8258 7022 20 714 515




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Table 2 Statistical analysis of PCC distributions

p-value data set

Category 1 vs. 2 Category 1 vs. 4* Category 2 vs. 4* +

development 0.5662 1.00 -05 1.97 -05

hormones 0.5536 8.06 -06 1.02 -05

abiotic stress, root 0.6453 1.43 -04 2.45 -04

abiotic stress, shoot 0.6727 3.40 -04 6.02 -04

Birnbaum 0.3581 9.00 -09 2..63 -08

p-values for differences in the distribution of PCC were calculated using two-sided, two

sample Welch t-test. +) PCC values for those 199 gene pairs that originally belonged to

category 4 (see Table 1), but whose overlap is not by manual curation, were included in

category 2 for this analysis.




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Table 3 Density of small RNA loci in cis-NATs and non-overlapping gene pairs

cis-NATs Annotation Source Non-overlapping

gene pairs Total Non-overlapping Overlapping

small RNA 1.467 0.388 0.315 1.126 TAIR6

MPSS 0.234 0.065 0.058 0.137

small RNA 0.808 0.295 0.290 0.448 ATH1

MPSS 0.139 0.061 0.0059 0.096

small RNA 0.800 0.304 0.305 0.337 curated

MPSS 0.138 0.061 0.062 0.064

TAIR6 small RNA 2.523 0.711 0.630 4.949

MPSS 0.772 0.321 0.347 1.904

ATH1 small RNA 1.455 0.552 0.575 2.777

MPSS 0.585 0.304 0.331 1.799

curated small RNA 1.442 0.571 0.579 3.970

MPSS 0.581 0.313 0.322 3.781

Density of small RNAs [loci/kb] according to the TAIR6 annotation and those cis-NATs

present on the ATH1 array before (ATH1) and after manual curation (curated) of the

overlapping region for MPSS (Meyers et al., 2004; Lu et al., 2005) and small RNA data sets

(Gustafson et al., 2005; Rajagopalan et al., 2006; Kasschau et al., 2007). Calculations were

preformed based on all gene pairs (top) and those gene-pairs that actually contain small RNA

loci (bottom). See Supplementary Material for detailed information.




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Table 4 Analysis of differentially cis-NAT expression in RNA silencing mutants

genotype All transcripts cis-NATs (4) cis-NATs (4*)

dcl1-7 981 56 (5.7%) 43 (4.4%)

dcl2-1 145 7 (4.8%) 3 (2.1%)

dcl3-1 221 14 (6.3%) 8 (3.6%)

hen1-1 893 44 (4.9%) 34 (3.8%)

hst-15 895 55 (6.1%) 45 (5.0%)

hyl1-2 291 22 (7.6%) 16 (5.5%)

rdr1-1 105 8 (7.6%) 6 (5.7%)

rdr2-1 166 9 (5.4%) 5 (3.0%)

rdr6-15 397 22 (5.5%) 17 (4.3%)

Transcripts that changed significantly in a given genotype relative to the wild-type control are

indicated.











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