The Chlamydomonas reinhardtii Organellar Genomes Respond Transcriptionally and Post-Transcriptionally to Abiotic Stimuli

Jason W Lilly; Jude E Maul; David B Stern

doi:10.1105/tpc.005595

This article has been retracted.

Retraction in: Plant Cell. 2004 Mar;16(3):785 See also: PMC Retraction Policy

. 2002 Nov;14(11):2681–2706. doi: 10.1105/tpc.005595

The Chlamydomonas reinhardtii Organellar Genomes Respond Transcriptionally and Post-Transcriptionally to Abiotic Stimuli^W⃞

Jason W Lilly ¹, Jude E Maul ¹, David B Stern ^1,¹

PMCID: PMC152721 PMID: 12417695

Abstract

The Chlamydomonas reinhardtii plastid and mitochondrial transcriptomes were surveyed for changes in RNA profiles resulting from growth in 12 culture conditions representing 8 abiotic stimuli. Organellar RNA abundance exhibited marked changes during nutrient stress and exposure to UV light, as revealed by both RNA gel blot and DNA microarray analyses. Of particular note were large increases in tufA and clpP transcript abundance during nutrient limitation. Phosphate and sulfur limitation resulted in the most global, yet opposite, effects on organellar RNA abundance, changes that were dissected further using run-on transcription assays. Removal of sulfate from the culture medium, which is known to reduce photosynthesis, resulted in 2-fold to 10-fold decreases in transcription rates, which were reflected in lower RNA abundance. The decrease in transcriptional activity was completely reversible and recovered to twice the control level after sulfate replenishment. Conversely, phosphate limitation resulted in a twofold to threefold increase in RNA abundance that was found to be a post-transcriptional effect, because it could be accounted for by increased RNA stability. This finding is consistent with the known metabolic slowdown under phosphate stress. Additionally, inhibitor studies suggested that unlike those in higher plants, Chlamydomonas chloroplasts lack a nucleus-encoded plastid RNA polymerase. The apparently single type of polymerase could contribute to the rapid and genome-wide transcriptional responses observed within the chloroplast.

INTRODUCTION

Plant cells partition photosynthesis and respiration into two semiautonomous compartments: chloroplasts and mitochondria. These organelles contain up to several thousand proteins, of which ∼95% are nucleus encoded; however, the remaining organelle-encoded proteins generally are essential for energetic functions and, in some cases, for viability (Bowsher and Tobin, 2001). In addition to photosynthesis, chloroplasts store carbohydrates and are the site of synthesis for key metabolites, such as heme precursors, amino acids, purines, pyrimidines, and fatty acids (Buchanan et al., 2000). Plant mitochondrial functions are conserved (ATP production and organic and amino acid synthesis), but the mitochondrial genomes of photosynthetic species are highly variable in size, sequence, and composition (Gray, 1999; Knoop and Brennicke, 2002). The regulation of photosynthesis and respiration requires myriad factors to coordinate the coexpression of nucleus- and organelle-encoded partners in response to environmental cues, developmental signals, and other processes (Gruissem and Tonkyn, 1993; Surpin and Chory, 1997; Leon et al., 1998; Yu et al., 2001). Although nuclear gene regulation often is transcriptional, organellar gene regulation has been documented at both the transcriptional (Mulligan et al., 1991; Rapp et al., 1992; Mullet, 1993; Kristal et al., 1994; Binder et al., 1996) and post-transcriptional (Finnegan and Brown, 1990; Rochaix, 1992; Gillham et al., 1994; Drager and Stern, 1998; Menassa et al., 1999; Giege et al., 2000) levels. However, these individual studies remain to be integrated into a global analysis of organellar responses to abiotic stimuli.

In vascular plants, in which chloroplast maturation is a highly visible developmental process, plastid gene regulation has been examined thoroughly (Mayfield et al., 1995; Barkan, 1998). During the transition from proplastid to mature chloroplast, plastid-encoded genes are transcribed by two temporally regulated RNA polymerases (RNAPs) (Allison, 2000). One RNAP is encoded by the nuclear genomes of many streptophytes and is related to those of phages such as T7; it has been termed NEP for nucleus-encoded polymerase. NEP is highly expressed in nonphotosynthetic plastids and is responsible for transcribing the genes that specify the Escherichia coli–like plastid-encoded RNA polymerase (PEP), whose transcriptional targets are primarily the photosynthetic genes (Hajdukiewicz et al., 1997). Early in development, NEP activity predominates, but PEP activity eventually overtakes it (Baumgartner et al., 1993; Dubell and Mullet, 1995). Within the plastid, the primary determinant of polymerase utilization is promoter type, of which at least two are recognized by NEP and one by PEP (Hajdukiewicz et al., 1997; Liere and Maliga, 1999). However, data from tobacco plants engineered to lack PEP suggest that NEP specificity may be diminished in the absence of PEP, although PEP is essential for photosynthesis (Legen et al., 2002). In the unicellular green alga Chlamydomonas reinhardtii, which possesses only chloroplasts, promoters cannot be organized into wholly distinct classes (Klein et al., 1992), and attempts to disrupt genes encoding PEP have been unsuccessful (Goldschmidt-Clermont, 1991; Fischer et al., 1996). This fact suggested that, unlike higher plants, Chlamydomonas might require PEP even when growing heterotrophically.

Although the extent of transcriptional regulation in plastids is still somewhat enigmatic, post-transcriptional regulation is well known (Goldschmidt-Clermont, 1998). These processes can be divided into three classes: cleavage/splicing of primary transcripts, control of mRNA stability, and translational regulation. In angiosperms, the importance of these mechanisms can be illustrated by the psbB gene cluster, which has a single promoter yet encodes numerous RNAs that specify components of two major photosynthetic complexes (photosystem [PS] II and cytochrome b₆/f), whose ratio is dependent on light intensity (Herrmann et al., 1985) and cell type (Schuster et al., 1986). Both maize (Barkan et al., 1994) and Arabidopsis (Felder et al., 2001) nuclear mutants have been isolated that cause photosynthesis defects as a result of blocks in post-transcriptional steps of psbB gene cluster expression.

In Chlamydomonas, nuclear mutants that affect the accumulation but not the transcription of specific plastid RNAs have been isolated (Rochaix, 1996). These mutants typically are defective in proteins that protect the 5′ end from degradation but that also may function in RNA processing and translation (Drager et al., 1998; Boudreau et al., 2000; Vaistij et al., 2000). Together with these stability factors, enzymes act to degrade the processed transcripts—for example, through a polyadenylation-mediated pathway (Schuster et al., 1999; Komine et al., 2002)—thus modulating the translatable RNA pool within the plastid.

The transcriptional and post-transcriptional processes described above can be modulated by environmental stimuli (Goldschmidt-Clermont, 1998), most notably light quantity and quality and temperature extremes (Mayfield et al., 1995). Light, beyond inducing the proplastid/etioplast-to-chloroplast conversion, has been shown to alter transcription rates and increase the degradation of specific photosynthetic gene transcripts (Glick et al., 1986; Deng et al., 1989). In addition, chloroplast mRNA levels in Chlamydomonas were shown to fluctuate with diurnal change, mediated through transcription and RNA degradation (Leu et al., 1990); in another study, a sampling of seven chloroplastic RNAs showed that they were subject to higher degradation rates in the light (Salvador et al., 1993). It has been reported that heat stress in maize correlates with increases in transcription rates for five plastid genes and reduces the levels of cytosine-to-uridine editing (Nakajima and Mulligan, 2001). Linked to environmental stimuli are changes in reducing potential and ATP concentration, broadly categorized as the redox state (Levings and Siedow, 1995; Surpin et al., 2002). It has been reported that by inducing changes in electron transport (e.g., in photosynthetic mutants or by the addition of electron transport chain inhibitors), alterations in plastid transcription and RNA degradation rates occur (Allen, 1993; Pfannschmidt et al., 1999; Salvador and Klein, 1999). Redox state also has been reported to regulate plastid translation (Trebitsh et al., 2000). There is limited information, however, regarding whether and how other environmental factors contribute to redox changes or affect plastid gene expression in other ways.

Although Chlamydomonas has been a model organism for the dissection of photosynthetic processes (Dent et al., 2001; Raven and Girard-Bascou, 2001), the lack of a complete chloroplast genome sequence and the associated genomic tools have been obstacles to global investigations into chloroplast gene regulation. The Chlamydomonas genome project (http://www.biology.duke.edu/chlamy_genome/) recently resulted in a complete chloroplast genome sequence (Maul et al., 2002), and two arrays have been developed (see the genome World Wide Web site and below). In this study, we used the complete chloroplast and mitochondrial genome sequences to analyze organellar RNA abundance and transcription rates under 11 nonoptimum culture conditions. The results show sometimes surprisingly strong responses to abiotic stimuli, which occur at both the transcriptional and post-transcriptional levels.

RESULTS

Chloroplast Transcript Accumulation Changes with Culture Conditions

With the full Chlamydomonas chloroplast genome sequence newly available, we sought to examine how the plastid transcriptome responds to various environmental stimuli while simultaneously searching for newly expressed regions. Such new genes might correspond to some of the many apparently unique open reading frames whose expression had not been examined previously. A whole-genome “northern walk” was chosen as the approach most likely to reveal small or low-abundance RNAs. Because repetitive DNA represents ∼20% of the entire sequence and is endemic within the intergenic regions (Maul et al., 2002), we used 45 gene-specific probes and 6 intergenic fragments amplified by PCR rather than simply using overlapping clones. These same probes served as the foundation of an organelle-based DNA microarray. Because some chloroplast genes have been shown to be expressed differentially (e.g., in light versus dark [Salvador et al., 1993]), 11 culture conditions were explored in RNA gel blot analyses. These included five variations in light intensity, quality, and duration, four media lacking specific nutrients, and two altered temperatures (12 and 37°C). Each of these conditions was compared with the standard laboratory Chlamydomonas culture medium (Tris-acetate-phosphate [TAP]) (Harris, 1989), which includes acetate as a reduced carbon source, incubated under continuous medium light (75 μmol·m⁻²·s⁻¹) at 25°C.

Representative RNA gel blots for seven chloroplast genes, as well as nuclear (psaG and PSI) and mitochondrial (cob and apocytochrome b) examples, are shown in Figure 1. The chloroplast genes for which results are shown include one from each of the six categories of plastid transcripts (the ATP synthase complex [atpB]; the cytochrome b₆/f complex [petB]; PSI [psaB]; PSII [psbF], gene expression proteins [tufA and rpl2]; and other functions [cemA, an envelope transporter]) (Rolland et al., 1997). The total RNA samples were pooled from three independent cultures, and after blot hybridization with the probes listed above, the radioactivity was allowed to decay and rehybridization was performed with the chloroplast 16S rRNA gene as a loading control. Because it was possible that 16S rRNA levels might themselves fluctuate, hybridizations also were performed with a cytosolic 25S rRNA probe. The results showed <5% variation between the 25S rRNA and the 16S rRNA signals across all conditions, consistent with the typically high stability of rRNAs (data not shown). Therefore, all results were normalized to the 16S rRNA signal.

The normalized results showed that for the genes represented in Figure 1A, among others, there was significant variation in the accumulation of transcripts encoding components of the photosynthetic machinery (see also Figure 2 and supplemental data online). Relative to TAP medium (Figure 1A, lanes C), some of the largest decreases were in the dark (D; e.g., petB, −90%; psbF, −73%) and under heat stress (H; e.g., atpB, −47%; psaB, −62%; tufA, −67%). Conversely, tufA mRNA increased threefold relative to 16S rRNA in the dark. In terms of increased RNA accumulation, the greatest changes were noted after phosphate deprivation (−P; e.g., atpB, +294%; tufA, +455%). The mRNA of the tufA gene, which is involved in translation elongation (Krab and Parmeggiani, 2002), showed the most variation in abundance, ranging from a 4.5-fold phosphate-related increase to an 87% decline when cells were grown at 12°C (L). Other conditions that elicited major responses were sulfate deprivation (−S) and 24-h exposure to 254-nm UV light (UV). These results are discussed in more detail below. In one case a novel transcript was seen: a higher molecular mass species was identified with the petB probe in the 37°C (H) sample. Whether this is a 5′ or 3′ extension has not been determined, but in either case, it could signify the existence of a heat-sensitive RNA processing factor.

Figure 2. — Transcript Accumulation for Six Classes of Genes.

The x axis is labeled by culture condition, using the same symbols used in Figure 1. The y axis shows the percentage change in transcript accumulation relative to cells grown in TAP medium (0 = no change relative to TAP). Each value was the result of three independent RNA isolations pooled into one sample. The genes are color coded, as designated within each panel, and arranged along the z axis for each growth condition.

**(A)** Genes encoding proteins of the ATP synthase complex.

**(B)** Genes encoding proteins of the cytochrome b₆/f complex.

**(C)** Genes encoding PSI proteins.

**(D)** Genes encoding PSII proteins.

**(E)** Genes encoding conserved proteins (*ycf*).

**(F)** Genes encoding miscellaneous proteins. Values exceeding the y axis are shown along the top and are color coded according to the gene they represent.

Chlamydomonas chloroplastic DNA contains several complex transcription units, and although qualitative changes in banding patterns were rare, band intensity did shift under some conditions. Figure 1B shows data for cemA, a member of the atpA gene cluster (Drapier et al., 1998), which includes four genes (atpA, psbI, cemA, and atpH). Transcripts 1 and 2 were very minor in TAP medium but abundant in high light (BL) and low temperature (L), consistent with the temperature sensitivity of the RNA processing machinery. In particular for BL, transcripts 3 and 4, which are putative processing products of transcripts 1 and 2, were virtually absent. The rpl2 panel shows a more consistent pattern for this gene cluster, which encodes four ribosomal proteins (rpl36, rpl23, rpl2, and rps19), and chlL, apart from one novel transcript (marked with an asterisk) that accumulates only during high temperature. Other polycistrons exhibiting altered transcript ratios included atpI-psaJ-rps12 and psbZ-psbM (see supplemental data online). Although the functional significance of such variation remains to be determined, the results indicate a sensitivity of post-transcriptional factors to the growth environment.

Genes Encoding Members of a Complex Can Have Concerted Responses

To gain a more complete understanding of transcript abundance under different growth conditions, we used the graphic representation shown in Figure 2. In this figure, transcript accumulation is plotted on the y axis as the percentage change from the control (TAP medium), the x axis shows the different culture conditions, and the color-coded genes are arranged along the z axis. The purpose of this depiction is threefold. First, it reveals any coregulation of transcripts encoding subunits of the same complex, because the results are shown by functional groups of genes (e.g., ATP synthase in Figure 2A). Second, it illustrates whether any functional groups of genes are subject to greater variability (e.g., cytochrome b₆/f complex RNAs appear to vary more than PSII genes). Third, to determine whether changes in plastid transcript levels correlated with changes in their nucleus-encoded counterparts, we examined at least two nuclear genes encoding components of each complex (for a complete list of genes, see Table 1). These data are displayed in Figure 2 for ATP synthase, cytochrome b₆/f, PSI, and PSII (Figures 2A to 2D, respectively) along with genes encoding three conserved chloroplast proteins (Figure 2E) and eight miscellaneous chloroplast proteins (Figure 2F).

Table 1.

Expression Summary for Microarray and Run-On Transcription Experiments

Gene (Accession No.)	Complex^a	Array ID^b	Run-On ID^c	Rate Versus Control (%)^d	Conditions^e	Transcription Inhibitor Effect (%) ^f
atpA	ATPase	1-31	1-C9	0	N, P	1	4
atpB	ATPase	1-27	1-A3	16	D, N, S, U	0	3
AtpC	ATPase	2-31	1-C10	ND	D, N, P	37	41
AtpD	ATPase	4-8	1-D8	ND	P, S	40	16
atpE	ATPase	4-7	1-F8	−95	P, S	4	0
atpF	ATPase	1-20	1-G11	−66	P, S	1	4
atpH	ATPase	4-6	1-H8	−27	N	1	1
atpI	ATPase	4-26	1-D4	−67	N	24	11
petA	b₆/f	3-26	1-D3	13	N, S	0	−47
petB	b₆/f	4-21	1-F12	−30	P	2	62
PetC	b₆/f	4-16	1-F6	−66	P, S	26	6
petD	b₆/f	4-25	1-F4	−38	D, N, P	2	31
PetE	b₆/f	3-17	1-D5	ND	D, P, S, U	24	6
petG	b₆/f	4-39	1-H10	−64	P	5	8
petL	b₆/f	3-29	1-H9	−81	N, P, S	2	20
psaA	PSI	1-33	1-G1	−53	U	0	0
psaB	PSI	1-23	1-A11	−87	*	2	7
psaC	PSI	1-7	1-E7	−84	D	18	1
PsaD	PSI	4-22	1-D12	ND	U	91	8
PsaF	PSI	3-24	1-H3	−90	N, S, U	2	11
PsaG	PSI	1-33	1-G2	ND	S, U	15	3
psaJ	PSI	1-5	2-A3	ND	N	13	0
tscA	PSI	2-24	1-G4	−81	N, P	7	4
ycf3	PSI	2-29	1-G10	−71	N, P	10	11
ycf4	PSI	2-21	1-E12	−28	*	8	4
oee1	PSII	1-17	1-C5	ND	D, S, U	13	0
oee2	PSII	3-30	1-F9	ND	D, N	69	16
oee3	PSII	2-3	2-E4	ND	P, U	15	6
psbA-exon 2	PSII	1-35	1-C1	21	D, P	0	69
psbA-exon 4	PSII	3-13	2-D1	25	D, P	3	71
psbB	PSII	2-36	1-A2	126	P	1	18
psbC	PSII	2-18	1-A6	121	D	0	15
psbD	PSII	4-32	1-B10	120	P	0	23
psbE	PSII	2-30	1-E10	−33	*	1	18
psbF	PSII	3-20	1-H11	−67	N, S	4	6
psbH	PSII	4-33	1-H2	−73	D, N	0	39
psbI	PSII	2-13	2-C2	−94	P	5	19
psbJ	PSII	1-16	1-E5	−95	S	0	4
psbK	PSII	1-8	1-C7	67	*	1	24
psbL	PSII	2-20	1-G12	−72	P	9	0
psbM	PSII	3-33	1-H1	−84	N	0	13
psbN	PSII	3-15	1-H5	ND	*	5	2
psbT	PSII	4-13	2-D2	−95	P	13	3
psbZ	PSII	1-6	1-G7	−59	D, P	19	1
rpl14	T/T	1-29	1-G9	−75	N, P	8	6
rpl16	T/T	1-30	1-E9	−78	N, P	8	11
rpl2	T/T	1-26	1-C3	−45	*	1	18
rpl20	T/T	4-34	1-F2	−89	N, P, S	8	6
rpl5	T/T	3-14	2-B1	ND	*	19	3
rps2	T/T	3-7	1-F7	−87	N, P, U	4	2
rpoA	T/T	1-21	1-E11	−8	N	15	16
rpoB1	T/T	2-22	1-C12	−13	D, P	0	4
rpoB2	T/T	3-23	1-B11	−47	P, U	20	41
rpoC1a	T/T	2-34	1-E2	−57	P	2	41
Gene (Accession No.)	Complex^a	Array ID^b	Run-on ID^c	Rate versus control (%)^d	Conditions^e	Transcription Inhibitor Effect (%)^f
rpoC1b	T/T	2-27	1-A4	−94	U	1	16
rpoC2	T/T	4-36	1-B2	−84	U	0	0
rps18	T/T	1-15	1-G5	−75	N	0	7
rps12	T/T	1-11	2-G1	−39	N, P, U	3	15
rps3	T/T	3-11	2-H1	−61	N	2	13
rps7	T/T	2-7	1-E8	−81	N	6	15
rps9	T/T	3-16	1-F5	−59	N	1	20
16S rRNA	T/T	1-24	1-G3	147	*	22	19
23S rRNA	T/T	2-9	1-A8	160	*	21	15
5S rRNA	T/T	2-16	1-E6	−45	P, S	6	5
tufA	T/T	1-36	1-A1	100	P, U	0	4
trnE	tRNA	3-21	1-F11	−61	N, P	2	0
trnfMet	tRNA	4-20	1-H12	−59	N, U	8	2
trnR	tRNA	1-13	2-C1	−62	*	2	18
trnS	tRNA	1-34	1-E1	−38	*	0	3
trnT	tRNA	4-14	2-B2	−88	*	4	1
trnW	tRNA	2-25	1-E4	−35	N, U	1	17
ccsA	cp	3-9	1-B7	−44	N, S, U	0	1
chlB	cp	3-32	1-B9	−20	N	0	16
chlL	cp	3-35	1-D1	−65	P, U	20	4
chlN	cp	2-35	1-C2	−18	*	0	13
clpP	cp	1-25	1-E3	−90	N	3	4
ORF-112	cp	3-12	2-F1	−90	N	1	10
ORF-140	cp	1-12	2-E1	−78	N	3	3
ORF-1995	cp	2-23	1-A12	−68	*	1	0
ORF-271	cp	2-14	2-A2	−48	N, S	0	22
ORF-2971A	cp	3-27	1-B3	−78	N, P, U	9	13
ORF-2971B	cp	4-9	1-B8	−69	P, U	0	3
ORF-50	cp	4-18	1-B6	ND	*	0	28
rbcL	cp	1-32	1-A9	230	*	0	8
ycf10	cp	1-18	1-A5	5	N	1	20
ycf12	cp	4-24	1-H4	−70	N, P, S, U	0	1
cob	mito	4-17	1-D6	0	N	38	11
cox1	mito	3-18	1-B5	−24	N	45	6
nad1	mito	2-8	1-C8	−82	*	38	17
nad2	mito	4-23	1-B12	−21	N, P	116	2
nad4	mito	2-32	1-A10	14	N, S	37	6
nad5	mito	3-36	1-B1	18	N	108	20
nad6	mito	2-6	1-G8	−92	N	146	4
rrnL7	mito	3-25	1-F3	14	P	15	5
rrnS4	mito	3-34	1-F1	−76	P	21	6
rt1	mito	4-35	1-D2	ND	S	16	8
ac115 (AF045467)	nucl	3-19	2-D5	ND	N, S	ND	ND
Actin (D50838)	nucl	3-8	1-D7	ND	*	51	12
Adp (X65194)	nucl	1-14	2-A1	ND	P, S	ND	ND
Aox1 (AF047832)	nucl	2-11	2-G2	ND	N, P	ND	ND
Aox2 (AF285187)	nucl	4-11	2-H2	ND	N, P	ND	ND
Ars1 (X16180)	nucl	4-27	1-B4	ND	S	ND	ND
α-tubulin-1 (M11447)	nucl	3-10	2-F5	ND	P, S	ND	ND
β-ca-1 (U80804)	nucl	1-1	2-G5	ND	N, P	ND	ND
Cab2 (AF330793)	nucl	4-31	1-D10	ND	D, S	43	12
Calmodulin (M20729)	nucl	1-28	2-A5	ND	U	41	11
Cao1 (AB015139)	nucl	2-26	1-C4	ND	N	ND	ND
cox3 (AB046570)	nucl	4-29	2-D6	ND	N, S, U	37	3
Gene (Accession No.)	Complex^a	Array ID^b	Run-on ID^c	Rate versus control (%)^d	Conditions^e	Transcription Inhibitor Effect (%)^f
cp peroxidase (AB009084)	nucl	1-10	2-E5	ND	P	95	23
Eye2 (AF233430)	nucl	3-28	2-B5	ND	*	ND	ND
Fed (L10349)	nucl	2-2	2-G4	ND	N, U	ND	ND
Tbg1 (AF013109)	nucl	2-4	2-C4	ND	*	ND	ND
Gdch (AF387366)	nucl	3-3	2-F3	ND	*	ND	ND
Gtr (AF305613)	nucl	3-4	2-D3	ND	*	ND	ND
Lhca1 (AF104633)	nucl	4-10	2-F6	ND	S, U	ND	ND
Lhca2 (AF104632)	nucl	3-3	2-F4	ND	S	ND	ND
Lci5 (AF394230)	nucl	2-28	2-A6	ND	*	ND	ND
Lhcb5 (AB050007)	nucl	1-2	2-G6	ND	S	ND	ND
Mat3 (AF375824)	nucl	4-28	2-B6	ND	*	ND	ND
Mbb1 (AJ296291)	nucl	4-12	2-F2	ND	N	ND	ND
Mdh2 (U40465)	nucl	1-3	2-E3	ND	S	ND	ND
Mut6 (AF305070)	nucl	2-19	2-C6	ND	*	ND	ND
NDc2 (AJ271460)	nucl	3-5	2-B3	ND	P	ND	ND
NDr1 (AF149737)	nucl	3-31	1-D9	ND	N	ND	ND
Nrt2 (AJ223296)	nucl	4-15	1-H6	ND	*	ND	ND
25S (AF183463)	nucl	2-5	2-A4	ND	*	64	9
PNPase (AV644809)	nucl	4-1	2-H6	ND	*	ND	ND
Psr1 (AF174532)	nucl	4-30	1-F10	ND	P	ND	ND
Raa3 (AF310674)	nucl	1-19	2-C5	ND	*	ND	ND
rbcS1 (X04471)	nucl	3-6	1-H7	ND	P	ND	ND
rbcS2 (X04472)	nucl	2-17	1-C6	ND	D	ND	ND
RecA (AB048829)	nucl	1-9	1-A7	ND	*	ND	ND
Sac1 (U47541)	nucl	1-4	2-C3	ND	S	ND	ND
SF assemblin (U56982)	nucl	3-1	2-H5	ND	*	ND	ND
Sig1 (AB049220)	nucl	2-15	1-G6	ND	P, U	44	10
Sod1 (AF280056)	nucl	4-5	2-B4	ND	S	ND	ND
Sta2 (AF433156)	nucl	2-20	2-E6	ND	*	ND	ND
UbcX (AY062935)	nucl	2-12	2-E2	ND	*	ND	ND
Vfl1 (AF154916)	nucl	4-4	2-D4	ND	N	ND	ND
Vsp1 (L16461)	nucl	1-2	2-G3	ND	*	ND	ND
aadA	ND	1-22	1-C11	ND	*	ND	ND
GFP	ND	3-22	1-D11	ND	*	ND	ND

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Genes are categorized according to photosynthetic complex or genome location: cp, chloroplast-encoded protein; b₆/f, cytochrome b₆/f complex; mito, mitochondrially encoded protein; nucl, nucleus-encoded protein; T/T, chloroplast transcription or translation; tRNA, chloroplast tRNAs.

Location of the first of three replicate spots on microarrays, where the first number indicates the column and the second number indicates the row.

Locations of PCR products on nylon filters used for run-on transcription assays, where the first number indicates filter 1 or 2, followed by location according to the 96-well plate format.

Transcription rate of each chloroplast gene in TAP medium normalized to the transcription rate of atpA for plastid transcripts or to cob for mitochondrial transcripts (e.g., +100% = a doubling of transcription rate compared with the control). ND, not detected.

Culture conditions and media that affected transcript accumulation by twofold or greater as measured using microarrays. D, dark grown; N, medium lacking nitrogen; P, medium lacking phosphate; S, medium lacking sulfur; U, UV light. Boldface type indicates an increase in transcript abundance, and plain type represents a decrease in transcript abundance. Asterisks are used where there was no twofold change or data were not available.

Percent transcription inhibition during run-on transcription in the presence of tagetitoxin (first column) or actinomycin D (second column). Boldface type is used for mitochondrial and nuclear gene values to distinguish them from chloroplast genes. ND, not detected.

Figure 2 reveals that in many cases, Chlamydomonas cells grown under standard laboratory conditions (TAP medium and continuous light at 25°C) accumulate higher levels of chloroplast transcripts compared with the other conditions tested (in other words, many RNA levels fall below the x-z plane). Removal of major nutrients (nitrogen [−N], phosphate [−P], and sulfate [−S]), as well as temperature stress (L and H) and UV light exposure, accounted for the majority of the observed changes, although each condition could be correlated with at least some variation. With respect to nutrient stress, under nitrogen limitation, nearly all of the chloroplast mRNAs encoding photosynthetic proteins exhibited lower accumulation, whereas phosphate limitation resulted in an increase, especially for PSII, in which all genes were affected except psbJ (Figure 2D). Removal of sulfate from the culture medium resulted in a nearly genome-wide reduction in RNA accumulation (e.g., uniformly affecting genes encoding PSI components). Two notable exceptions under −S conditions were tufA and clpP (Figures 1A and 2F). The only other condition that affected transcript accumulation across the genome was exposure to damaging UV light, which resulted in twofold to fourfold increases for most mRNAs.

Another way to look at these data is to ask whether genes that encode a particular complex exhibit similar profiles. For ATP synthase (Figure 2A), data were scattered except for bright light (BL), for which modest decreases were seen for all mRNAs compared with the control. For the cytochrome b₆/f complex (Figure 2B), nearly uniform increases were seen under three conditions: middle of the light period in synchronized cells (ML), low temperature (L), and exposure to UV light. PSI transcripts (Figure 2C) were particularly concerted, showing decreases under bright light (BL), in the dark (D), in −S, and under heat stress (H). PSI transcripts increased in −P. PSII transcripts (Figure 2D) also increased uniformly in −P and decreased nearly uniformly in the dark (D), in −N and −S, in minimal medium (M), and in heat stress (H). This result is consistent with regulatory elements or factors that recognize classes of genes or RNAs and has an evident functional logic.

Under the conditions tested, we never observed the complete disappearance of any chloroplast transcript, and values usually were within twofold to threefold of the control. On the other hand, 15 chloroplast transcripts exhibited at least a two-thirds reduction under at least one condition, and 4 chloroplast transcripts (cemA, psbC, psbK, and rpl23) exhibited at least a two-thirds reduction under at least four conditions. Exceptional increases were shown by clpP and tufA, particularly under nutrient stress. The tufA mRNA also exhibited increased abundance in the dark, as reported previously (Silk and Wu, 1988). Because clpP and tufA are involved in proteolysis and translation, respectively (Hwang et al., 1996; Majeran et al., 2000), these may be symptomatic of a post-transcriptional plastid stress response mechanism.

To determine whether mitochondrial transcripts also were responsive to culture conditions, we used probes for two genes, cob and coxI. We limited the survey to these two genes because available data suggest that the linear genome consists of only two transcription units (Boer and Gray, 1988; Duby and Matagne, 1999). Interestingly, mitochondrial and chloroplast mRNAs showed opposite effects during nitrogen limitation, in which abundance was significantly higher than in TAP medium (101 and 142%) for cob and coxI, respectively (Figure 1; see also supplemental data online). The mitochondrial transcripts also changed sharply after exposure to 12°C for 24 h, and their response to phosphate limitation was substantial as well (67 and 210% increases). These results suggest that Chlamydomonas chloroplasts and mitochondria have similar yet organelle-specific controls on RNA abundance under abiotic stress conditions.

Having observed large differences in mRNA abundance between control cells and those grown under other conditions, it was important to confirm that these observations did not simply reflect an altered amount of RNA per individual cell. To do so, we obtained growth curves under five culture conditions, as shown in Figure 3A. As expected, cells grown under ideal conditions (TAP [C]) doubled most rapidly, every 5 to 6 h. Cells grown in low-phosphate medium (−P) doubled approximately as fast as dark-grown cells (D), whereas those grown in medium lacking acetate (M) or limited for sulfur (−S) divided very slowly. RNAs were isolated from 1 × 10⁷ cells at selected points during a 2-day time course, in accordance with the 48-h time point used for the samples analyzed in Figures 1 and 2. RNAs from equal numbers of cells were loaded in a gel and either stained for 25S rRNA or probed to identify the 16S rRNA. As shown in Figure 3B, little variation was seen for 16S rRNA over the time course between TAP, −P, and −S cells. 25S rRNA may have decreased slightly; however, the differences are within the range expected from inevitably variable yields in RNA preparations. Because the data in Figure 2 were based on normalization to 16S rRNA, the important conclusion from Figure 3 is that this was an appropriate basis for examining mRNA abundance.

Figure 3. — Growth Curves and RNA Levels on a per Cell Basis.

**(A)** Graph illustrating cell concentration (y axis) versus time (x axis) for the five different culture conditions shown at right, using the designations used in Figure 1. Values and standard errors are from three replicates.

**(B)** Gel blot analysis of RNAs isolated from the time points shown at top during growth curve experiments in complete (C), −P, and −S media. rRNA panels show the ethidium bromide–stained 25S band; the 16S panels show hybridization of the plastid 16S rRNA. Each lane contained RNA from an equal number of cells.

Expression Profiling Confirms That Nutrient Availability Is a Key Regulator of Plastid Transcript Levels

The RNA gel blot data presented above reveal transcript abundance after a given period in a specific culture condition. To examine changes occurring over time during phosphate and sulfate limitation, a microarray approach was used, as shown in Figure 4. We used this opportunity to expand our analysis of mitochondrial genes and to include additional nuclear genes. In all, 41 chloroplast and 8 mitochondrial genes, 45 nuclear cDNAs, plus 2 negative controls (gfp and aadA) were added to the existing probe set, resulting in 143 genes (a complete list appears in Table 1), which were arrayed onto glass slides (see Methods). Expression profiling experiments were replicated a minimum of three times and included a dye-swapping replicate. Overall, of the 143 genes arrayed, 98 consistently generated high-quality signals. To ensure that each replicate was not skewed as a result of variation in reverse transcription efficiency, the combined values for 23S and 16S chloroplast rRNAs and 25S nuclear rRNA in the control sample were used to normalize the experimental channel. Any replicate that required normalization greater than twofold was discarded as a failed experiment. The results from the original and normalized values were compared for those genes for which RNA gel blot data were available. Because the normalized values correlated qualitatively with the RNA gel blots (Figure 4, insets; see also supplemental data online), the normalization was considered to be valid.

Figure 4. — Differential RNA Accumulation in −S and −P Media, as Measured by Microarrays.

**(A)** The top shows a hierarchical cluster analysis showing averages of three independent experiments in which cells were grown in medium lacking sulfate and harvested at 8, 16, or 48 h. After 48 h, cells were transferred back into complete medium for 1 h (R). Green represents mRNAs that exhibited lower abundance than the control (TAP medium), and red represents mRNAs that had higher abundance than the control. A scale is shown at bottom. Enlarged clusters show mRNAs that increased under sulfur limitation (top) and those that rebounded during the replenishment period (bottom). The inset shows RNA gel blot data for two selected genes. The bottom shows a scatterplot for the 48-h time point, where the x axis is the log of the signal intensity (three replicates of three spots each) for transcripts from TAP-grown cells and the y axis is the log of the signal intensity from transcripts from −S grown cells. Numbers (+5, +2, −2, −5) represent fold changes as measured by quantified microarray data, where a twofold change on the array is equal to a 100% difference on RNA gel blots. Black squares represent nuclear genes, blue diamonds represent chloroplast genes, and orange diamonds represent mitochondrial genes.

**(B)** The top shows a hierarchical cluster analysis showing averages of three independent experiments in which cells were grown in medium lacking phosphate and harvested at 8, 24, and 48 h. After 48 h, the culture was supplemented with 2 mM phosphate, and cells were harvested after an additional 1 h (R). The enlarged view is of the dominant cluster in which 33 of 35 members are plastid transcripts. The nuclear *Psr1* gene is marked with an asterisk. The bottom shows a scatterplot of the 48-h time point, with labeling as in **(A)**.

Using microarrays, we obtained data for 8 of the 11 culture conditions shown in Figures 1 and 2 (D, −N, −P, −S, M, H, L, and UV). Conditions that resulted in a twofold or greater change in transcript abundance are denoted for each gene in Table 1. These analyses confirmed that growing cells in media lacking sulfate (Figure 4A) or phosphate (Figure 4B) for 48 h had altered transcript abundance. This is shown in two ways: the top of each panel is a cluster diagram, discussed below in the context of a time course; the bottom of each panel features a scatterplot showing the results after 48 h of treatment. To confirm that the cells were experiencing nutrient stress, two sulfur-responsive genes (Ars1 and Sac1 [de Hostos et al., 1989; Davies et al., 1996]) and one phosphate-responsive gene (Psr1 [Wykoff et al., 1999]) were used, and upon stress, their respective transcript levels increased as expected (marked with asterisks in expanded clusters).

The overall effects of nutrient depletion can be seen in the scatterplots. The 45 nuclear genes (black squares) exhibited substantial variation under sulfur limitation (Figure 4A), whereas phosphate limitation resulted in nearly all of our selected nuclear genes (with the exceptions of Psr1 and PetC) remaining within twofold of the control (Figure 4B). Chloroplast genes (blue diamonds) mostly had neutral to decreased transcript levels in −S, but they had increased levels in −P. Mitochondrial transcripts (orange diamonds) exhibited occasional variation. These results confirm and extend those shown by RNA gel blot analysis, in particular the nearly global response of chloroplast transcripts to phosphate limitation.

To gain a more detailed understanding of the chloroplast response to sulfate or phosphate limitation, we repeated these experiments using additional time points and a replenishment period (restoration of P or S to the culture for 1 h). Cells were grown as described in Methods, and samples were removed at 8, 16, and 48 h for −S or at 4, 24, and 48 h for −P. After 48 h, cells were transferred to complete medium and allowed to recover for 1 h before RNA was extracted. Selected RNA gel blots (Figure 4, insets) show the correlation between microarray analyses and filter hybridizations. Data from the microarrays were collected, normalized as described above, and used to generate hierarchical cluster diagrams. Cluster analysis for −S revealed three primary expression profiles. One cluster included the majority of chloroplast genes, for which RNA abundance decreased over time and failed to recover fully to control levels during the replenishment period. A second cluster (expanded at the top of Figure 4A) included 10 nuclear genes and 2 chloroplast genes (atpH and ycf12) that were overabundant (compared with complete medium) during sulfur limitation and declined during replenishment. The third cluster consisted primarily of chloroplast transcription/translation RNAs, which exhibited a reduction during sulfur limitation but recovered upon replenishment (expanded at the bottom of Figure 4A).

The −P time course revealed a complex pattern. At 8 and 24 h, >30 chloroplast transcripts decreased relative to the control; however, by 48 h, these same RNAs had become overabundant (expanded in Figure 4B). This abundance decreased by various amounts during replenishment, by which time 15 chloroplast RNAs had returned to control levels. These rapid changes are consistent with post-transcriptional controls being overlaid on generally less responsive transcriptional mechanisms.

Run-On Assays Reveal Roles for Transcriptional and Post-Transcriptional Control

Because both RNA gel blots and DNA microarrays measure steady state transcript levels, differences observed between samples cannot be ascribed immediately to transcriptional versus post-transcriptional regulation. Therefore, we performed run-on transcription assays by permeabilizing cells using freeze-thaw cycles, followed by a short incubation in the presence of α-³²P-UTP to extend previously initiated transcripts (Gagne and Guertin, 1992). RNAs thus labeled are used as probes on DNA gel blots, giving a measure of transcription activity for that gene. In the case of nutrient limitation responses, we expected that if the RNA decreases during sulfur limitation were caused by reduced transcription, this effect would be mirrored in the run-on assay. Similarly, phosphate limitation–induced RNA accumulation would be reflected in increased transcription rates if it were controlled transcriptionally, whereas if the response were attributable to increased RNA stability, the run-on assays would resemble the control (cells grown in TAP).

For run-on assays, the same 143 probes arrayed onto slides were spotted onto nylon membranes. Because genes with low transcription rates might not give signals above background, we first determined how many of the 142 probes could be quantified under our experimental conditions; we found that 104 targets yielded signals above the negative control for several independent TAP-grown samples. The information from the TAP medium could be used immediately to estimate relative plastid promoter strengths by comparison with the well-characterized atpA promoter (Drapier et al., 1998), which is most often used for the aadA selectable marker cassette (Goldschmidt-Clermont, 1991). Mitochondrial values were related to cob. Table 1 shows deduced promoter strengths for the 102 genes that gave values above background. Values for plastid promoters ranged from >90% less than atpA to twofold greater (rbcL). Overall, atpA has a strong promoter, with few genes having transcription rates ≥125% of its level. Those that did included the ribosomal operon, four PSII genes (psbA to psbD), tufA, and rbcL. These results correlated with previous in vivo measurements obtained by Blowers et al. (1990). Mitochondrial transcription rates varied from >90% less than cob to 18% more than cob. As expected, cob, nad4, and nad5 had similar rates because they originate from the same transcription unit (Remacle and Matagne, 1998).

To address transcriptional versus post-transcriptional control during nutrient limitation, run-on assays were performed for cells grown in −N, −S, and −P for 48 h. Chloroplast transcription rates in −N cells were reduced compared with those in the control sample; however, mitochondrial rates were similar to those of the control (see supplemental data online). Figure 5A shows quantified data from −S and −P samples. When cells were harvested from −S medium (Figure 5A, top), a large decrease in chloroplast transcription rates was observed. Overall, 46% of the chloroplast genes had rates reduced by fivefold or more compared with the control (blue diamonds). By contrast, only three genes had rate increases of twofold or more than the control: the sulfur- responsive genes Ars1 and Sac1 and cab2 (black squares). Figure 5B shows representative primary data (cf. columns C and −S). These results support the hypothesis that the decreases in chloroplast transcript abundance during sulfur limitation result at least in part from lower transcription rates. To confirm that sulfate was the regulator of this response, cells that had been starved for 48 h and then returned to complete medium for 1 h were analyzed in the same manner (Figure 5B, column SR). These sulfur-replenished cells exhibited a dramatic transcriptional recovery, with rates averaging twice those of the control cells (Figure 5B, column C).

Figure 5. — Transcription Rates as Measured by Run-On Analysis.

**(A)** Scatterplots showing transcription rates for cells grown in TAP medium versus those grown for 48 h in medium lacking sulfate (top) or phosphate (bottom), averaged from two independent experiments. Labeling is as for the bottom of Figure 4.

**(B)** Selected primary data illustrating the decrease in transcription rates during limitation (−S and −P) and their revival after 1 h of nutrient replenishment (SR and PR). Complete data sets are available (see supplemental data online). Chloroplast genes are shown in blue, mitochondrial genes are shown in orange, and nuclear genes are shown in black.

A similar set of experiments was performed with cells grown in −P medium. Surprisingly, the large increases in transcript abundance observed in both microarrays and RNA gel blots were not accompanied by increased transcription rates. The scatterplot shown in Figure 5A (bottom) shows that 42 genes exhibited rates between twofold and fivefold less than the control and that no chloroplast genes exhibited significant increases. The restoration of phosphate for 1 h resulted in transcription rates similar to or in some cases higher than those of the control, with 10 genes exhibiting higher rates (Figure 4B, columns C, −P, and PR). The apparent lack of transcriptional responsiveness during phosphate limitation and a failure to increase transcription rates globally during the replete period lend support to the hypothesis that increased chloroplastic RNA accumulation during phosphate stress was attributable to increased RNA stability and thus that different nutrient stress events might modulate organelle gene expression in distinctive ways.

Phosphate Limitation Increases Chloroplast RNA Half-Lives

The combined microarray and run-on data suggested that phosphate limitation increased the stabilities of at least some chloroplast RNAs. To validate this hypothesis, selected RNA half-lives were measured in vivo. When Chlamydomonas is grown in the presence of the antibiotic rifampicin, the β-subunit of the PEP is altered irreversibly, inhibiting chloroplast transcription (Surzycki and Shellenbarger, 1976; Eberhard et al., 2002). Therefore, we cultured cells in complete or −P medium and added rifampicin after 48 h; then, we harvested 10 1-h aliquots for RNA extraction and gel blot analysis. As shown in Figure 6, we measured the half-lives of atpB and petB RNAs. The atpB mRNA exhibited a substantial increase during phosphate limitation (Figures 1A and 2A). Although petB mRNA abundance did not change to the same degree, the transcription rate for petB decreased 51% upon phosphate deprivation, suggesting that this RNA also was more stable under these growth conditions. To account for variations in RNA loading, a rifampicin-insensitive mitochondrial transcript, cob, was used as a loading control. After normalization to cob, the half-lives of atpB and petB transcripts in TAP were found to be 46 and 65 min, respectively, whereas in −P, the half lives increased to 85 and 105 min. These data further support greater RNA stability as a mechanism for increasing chloroplast RNA pools in phosphate-limited Chlamydomonas cells.

Figure 6. — Analysis of Chloroplast RNA Half-Lives in Phosphate-Limited Cells.

The gels at top show RNA gel blot hybridizations (10 μg/lane of total RNA) for cells grown in TAP or for 48 h in −P medium, followed by the addition of 300 μg/mL rifampicin and harvest at 0, 0.5, 1, 2, 4, 6, 8, or 10 h. The *atpB* and *petB* coding regions were used in initial hybridizations, followed by the mitochondrial *cob* gene, a rifampicin-insensitive transcript, as a loading control. The graphs at bottom illustrate the percentage of *atpB* or *petB* RNA remaining over the 10-h time course relative to that of *cob* RNA. Half-lives are shown for each gene and were calculated by linear regression analysis of the data points.

Chlamydomonas Chloroplasts Exclusively Use a Prokaryote-Like RNA Polymerase

Higher plant chloroplasts possess at least two RNAPs, the prokaryote-like PEP and the phage-like NEP (see Introduction; reviewed by Gray and Lang, 1998). In Chlamydomonas, there has been no success in creating homoplasmic PEP deletion strains (Goldschmidt-Clermont, 1991; Fischer et al., 1996), and to date, promoter classifications have found only eight sequences characteristic of PEP recognition sites (Klein et al., 1992). Across the 11 culture conditions examined here, only sulfur limitation revealed a response indicating substantial regulation at the transcriptional level. To begin to examine whether PEP or NEP might be implicated, we performed polymerase inhibitor studies.

Cells were incubated with two transcription inhibitors during run-on transcription assays. These were a PEP-specific inhibitor, tagetitoxin (Mathews and Durbin, 1990), and a whole-cell transcription inhibitor, actinomycin D. If chloroplast RNAs were transcribed by dual polymerases, tagetitoxin would fail to eliminate all chloroplast signals, because some genes still would be transcribed by the NEP; unfortunately, no NEP-specific inhibitors are available. Figure 7 shows the results of these experiments, along with a control reaction lacking inhibitors. It should be noted that because each experiment was performed with the same number of cells, the entire labeling reaction was added to the hybridization, and because each blot was treated (hybridized, washed, and exposed) in the same way, the images, which were exposed for equal amounts of time, give a fair representation of the relative transcription rates.

Figure 7. — Run-On Transcription Analysis in the Presence of Transcription Inhibitors.

**(A)** Cells were grown in liquid culture to mid-log phase, harvested and resuspended to 1 × 10⁷ cells/mL, and allowed to grow for 2 h under standard conditions. Cells were harvested and prepared for run-on transcription as described in Methods. Run-on transcription assays were performed with the addition of 30 μL of cells, 1 μL of 100% ethanol (control), 50 units of tagetitoxin, or 100 μg/mL actinomycin D and allowed to transcribe for 30 min at room temperature. Selected genes highlighted on the control and tagetitoxin filters are as follows: black triangles, chloroplast rRNA genes; gray triangles, other chloroplast genes; circles, mitochondrial genes; squares, nuclear genes; X, negative controls. The percentage inhibition for each gene is given in Table 1. Each spot contained ∼50 ng of DNA, and loading was confirmed by hybridization with a vector probe (data not shown).

**(B)** Gene-specific PCR products representing chloroplast (23S and 16S), mitochondrial (S4 and L7), or nuclear (18S) rRNAs were filter blotted and probed with run-on transcripts as described for **(A)**.

On the control filter, selected chloroplast (gray triangles), mitochondrial (circles), and nuclear (squares) genes are highlighted to facilitate comparisons between the control and tagetitoxin treatments. The center panel of Figure 7A shows that in the presence of tagetitoxin, signals corresponding to chloroplast transcripts were reduced to 10% or less of the control, with many reduced to background levels (Table 1). The only chloroplast genes with any apparent tagetitoxin-resistant transcriptional activity were the 23S and 16S rRNAs (black triangles), which can be attributed to cross-reaction with the mitochondrial rRNAs (see below). Mitochondrial and nuclear transcription were largely unaffected, because residual levels were within 25% of the control (Table 1). Unfortunately, growing cultures in the presence of tagetitoxin is prohibitively expensive, so complementary data were not obtained under these conditions. To ensure that reductions in transcription rates could be observed for all cellular compartments, we used actinomycin D (Figure 7A, bottom). As expected, virtually complete inhibition of RNA labeling was observed (Table 1).

To determine whether the 23S and 16S rRNA hybridization that occurred with RNAs labeled in the presence of tagetitoxin was the result of cross-reactivity with mitochondrial rRNAs, we used the same run-on products to probe filter blots of PCR products specific to the chloroplast or mitochondrial rRNAs; the full genes have regions of substantial similarity. In this experiment, only faint hybridization was visible with the 23S product, and no hybridization was seen with the 16S product (Figure 7B), whereas the mitochondrial and nuclear rRNAs hybridized with and without the inhibitor. Together, these data support a lack of NEP activity in Chlamydomonas chloroplasts.

Novobiocin Modifies Chloroplast RNA Abundance and Transcription Rates

A final question regarding the Chlamydomonas transcriptome involved the potential role of intergenic repetitive elements in influencing the transcription of flanking genes. It is well known that RNAP access to promoter regions can influence transcription initiation rates. In vascular plant plastid genomes, it was reported that the RNAP has a preference for supercoiled templates in vitro, suggesting that DNA structure may be a key factor in establishing basal transcription rates (Lam and Chua, 1987; Zaitlin et al., 1989). In Chlamydomonas, it was observed previously that the addition of a DNA gyrase inhibitor, novobiocin, resulted in altered transcription of petA (Thompson and Mosig, 1987). The putative control element characterized by Thompson and Mosig, the pA repeat, later was found to be ubiquitous throughout the plastid genome (Maul et al., 2002), although it lacks any obvious features such as promoter-like elements. With the availability of chloroplast gene arrays, we wished to determine if novobiocin had a global effect on chloroplast transcription or if it affected only selected regions (e.g., where pA repeats closely flanked known promoters).

For these experiments, cells were grown in TAP medium to log phase (2 × 10⁶ cells/mL), at which time novobiocin was added, and growth was continued for 24 h (Thompson and Mosig, 1987). These cultures were used for both DNA microarray and run-on transcription assays, as shown in Figure 8. Overall, the addition of novobiocin resulted in the most dramatic expression profile and run-on transcription variation observed for any experiment among those reported here. For many chloroplast genes, the increased abundance seen in arrays (Figure 8A) could be correlated with an increased transcription rate (e.g., atpB and chlB; Figure 8B); however, other chloroplast genes failed to respond in a similar manner (e.g., psbB showed decreased transcription without decreased abundance). Additionally, every mitochondrial gene on the array that exhibited an increase in RNA abundance failed to show higher transcription rates. A sample of the quantified values is presented in Figure 8C, where solid bars show RNA abundance changes and hatched bars show altered transcription rates. Only psaJ showed a transcription rate response akin to that of petA, although several genes did exhibit moderate changes in rate. Together, these data suggest that novobiocin can influence multiple regions in the chloroplast genome and also the mitochondrial genome; however, there is no obvious correlation between the presence of pA repeats, which are widespread, and transcriptional stimulation. The lack of complete correlation between RNA accumulation and transcription rates is not surprising and implies a certain amount of post-transcriptional control.

Figure 8. — Novobiocin Alters Organelle Transcript Accumulation and Transcription Rates.

**(A)** Pseudocolored DNA microarray showing changes in RNA abundance after exposure of cells to novobiocin (100 μg/mL) for 24 h. Control RNA was reverse transcribed in the presence of Cy3 (green); RNA from novobiocin-treated cells was reversed transcribed in the presence of Cy5 (red). Yellow ovals indicate rRNA genes.

**(B)** Run-on transcription assays of control cells (top) and novobiocin-treated cells (bottom). Ovals in **(A)** correspond to spots indicated on the novobiocin dot blot. Blue lines denote chloroplast genes with altered expression after novobiocin treatment. Both accumulation and transcription increased for *atpB* and *chlB*, whereas *atpA* exhibited decreased accumulation and transcription. Orange lines indicate two mitochondrial genes for which RNA abundance increased but transcription rates decreased.

**(C)** Fold changes in abundance (solid bars) versus transcription rates (hatched bars) for selected genes. The black bar at left represents the 25S control, blue bars represent chloroplast genes, and orange bars represent mitochondrial genes. See supplemental data online at http://bti.cornell.edu/bti2/chlamyweb/ for a complete data set.

DISCUSSION

The Chlamydomonas Plastid Chromosome Is Highly Transcribed but Modestly Translated

Here, we have investigated gene regulation across the Chlamydomonas chloroplast genome both at the transcription activity and RNA abundance levels, with comparison to mitochondrial and selected nuclear genes. Our results revealed significant responses to abiotic stress, which is consistent with the importance of photosynthesis and other chloroplast processes to cell viability.

The opportunity for a genome-wide transcription analysis arose after the completion of the plastid sequence in our laboratory (Maul et al., 2002) and allowed us to compare transcription statistics from Chlamydomonas with those of several other organisms with sequenced plastid genomes. Table 2 shows that relative to a representative group of organisms that includes red and green algae and lower and higher plants, Chlamydomonas possesses the largest plastid genome, yet it is relatively gene poor. This increased genome size is not the result of gene duplication or intron accumulation, as has occurred in Marchantia and tobacco; instead, it is caused by repetitive DNA accumulation (Maul et al., 2002). Like Chlorella, Chlamydomonas lacks the seven NADH dehydrogenase genes; it also lacks the accD gene encoding an acetyl CoA carboxylase subunit, which is present in all other sequenced chlorophyte and streptophyte plastid genomes, and at least five other genes present in Chlorella, resulting in the most gene-poor chlorophyte plastid reported to date. However, sequences homologous with five of these missing genes (AccD, CysA, FtsH, InfA, and MinD) have been identified in the Chlamydomonas EST database (http://www.biology.duke.edu/chlamy_genome/), indicating that they have been transferred functionally to the nuclear genome.

Table 2.

Chloroplast Transcription Statistics for Six Sequenced Genomes

Statistic	Porphyra	Chlorella	Chlamydomonas	Mesostigma	Marchantia	Nicotiana
Size^a	191,028	150,613	203,395	118,360	121,124	155,939
Gene number^b	251	111	100	135	122	113
Percent transcribed^c	86%	65%	63%	75%	75%	62%
Percent translated^d	64%	44%	43%	57%	46%	48%
Intron number	NA	3	6	NA	20	21
Polycistrons^e	NA	2/NA	12/37	NA	NA	9/25

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NA, information not available.

Genome size (bp) based on NCBI accession data. Accession numbers as follows: Chlamydomonas reinhardtii, AF396929; Chlorella vulgaris, NC_001865; Marchantia polymorpha, NC_001319; Mesostigma viride, NC_002186; Porphyra purpurea, NC_000925; Nicotiana tabacum, NC_001879.

Gene number includes genes and tRNAs that are known to be expressed or conserved among plastid genomes, and counts duplicated genes only once.

Percent of genome encoding unprocessed transcripts (including introns and polycistronic spacers).

Percent of genome translated, based on total base pairs encoding expressed proteins of known function or homologous with proteins in other organisms.

Number of unprocessed transcripts resulting in polycistronic RNAs/total number of genes encoded within polycistrons (defined as two or more cotranscribed genes).

The analysis reported in Figure 1 failed to reveal any new expressed regions (i.e., novel genes or noncoding RNAs). However, we were able to correctly locate or confirm rpoA and rpoC1 (Maul et al., 2002) and to identify the C-terminal portion of rps2 (classified previously as ORF570 [Leu, 1998]). The overall transcription of the genome is summarized in Figure 9 and detailed in Table 2. Two factors contribute to the large (65%) portion of the genome that is transcribed. The first is a large number of dicistrons or polycistrons compared with Chlorella (12 versus 2), which include >40% of the genes. This finding contradicts a general belief that Chlamydomonas has relatively few polycistronic gene clusters (Barkan and Goldschmidt-Clermont, 2000). The second factor contributing to the large transcriptome are three large open reading frames, rpoC2, ORF1995, and ORF2971 (Fong and Surzycki, 1992; Boudreau et al., 1997), although their transcript termini have not been defined. Overall, this global approach revealed a densely transcribed genome in which the extent of polycistronic units resulted in a 20% difference between the approximate amount of DNA transcribed and the DNA that encodes the amino acids of expressed proteins.

Figure 9. — The Chlamydomonas Plastid Transcriptome.

The outer circle diagrams the transcribed regions, with those in black corresponding to monocistronic RNAs and those in gray corresponding to polycistronic RNAs or complex transcription units. The extents of many transcripts are approximate because the 5′ and/or 3′ ends have not been mapped precisely. The *rpoC2* gene is shown as punctually transcribed based on reverse transcriptase PCR data presented elsewhere (Maul et al., 2002). Genes are color coded by function: yellow indicates cytochrome b₆/f, blue indicates PSI, green indicates PSII, red indicates ATP synthase, orange indicates ribosomal and RNA-associated genes, black indicates tRNAs, and purple indicates other classes and genes of unknown function.

Organelle RNAs Exhibit Environmental Sensitivity

We also documented significant changes in organellar transcription rates and RNA accumulation using traditional and genomic approaches (Figures 1, 2, 4, and 5, Table 1). The first revelation from these data was that standard culture conditions for Chlamydomonas allow the accumulation of a probably excessive plastid RNA pool, a phenomenon replicated to a lesser extent within the mitochondrion but not mirrored among the nuclear transcripts analyzed here. One explanation is simply that in rich medium, there is little physiological pressure to recycle nucleotides; another explanation is that decades of adaptation by Chlamydomonas to TAP medium have allowed it to optimize photosynthetic gene expression. This “excessive” pool (given that diminishing RNA in many cases does not affect protein synthesis [Eberhard et al., 2002]) was decreased by nearly all abiotic stresses with the exception of −P for the chloroplast and −N for the mitochondrion. Based on these data, future studies that focus on RNA accumulation might include comparisons of phenotypes in different media or under altered light or temperature regimes. The other major finding was that stresses caused both concerted and specific responses, consistent with hierarchical organellar gene regulation. We did not observe retrograde signaling (Rodermel, 2001), in which decreases in plastid photosynthetic transcript levels would have been reflected in decreases in nucleus-encoded transcripts encoding subunits of the same complex.

Chloroplast RNA responses to shifts in light quality and quantity have been reported previously (Salvador et al., 1993; Kloppstech, 1997). However, in angiosperms, these initial findings were shown to be attributable primarily to shifts in RNA polymerase activity in response to light-stimulated developmental changes (Pfannschmidt and Link, 1997; Oikawa et al., 1998; Krause et al., 2000; Singh and Singhal, 2001). If these effects are excluded, there are few published studies emphasizing a direct role of environmental stimuli on organelle gene regulation, although two of these have been conducted using Chlamydomonas (Salvador et al., 1993; Hwang et al., 1996). Here, we have demonstrated that under laboratory-induced stress, Chlamydomonas modulates transcript levels in both the plastid and the mitochondrion. Transcript abundance as measured by both RNA gel blot and DNA microarray analysis revealed that nutrient stress and exposure to UV light caused the most consistent changes across the genome as well as the largest overall changes in transcript levels.

To determine if chloroplast protein levels were affected by the observed changes in RNA levels, immunoblot analysis was performed for selected chloroplast proteins. These experiments showed only minor changes in protein levels (data not shown), with −S cells having a slight reduction in total protein; however, relative levels of individual protein were unaffected. In the future, proteomic methods would be valuable in showing how changes in transcript levels are reflected in the organellar proteomes under stress conditions. It also would be desirable to measure translation rates directly by pulse labeling, to determine whether changes in available RNA alter rates of protein synthesis. However, pulse labeling in Chlamydomonas has not been successful under stress conditions.

A Role for Nutrients in Organellar Gene Regulation

How can the physiological changes that occur during nutrient limitation be linked to our observations regarding chloroplast gene expression? In both sulfur and phosphate deficiency, cells deprived of the nutrient quickly induce a scavenging system and make metabolic adjustments. When sulfur is limited, proteins such as arylsulfatases are secreted, and there is an inhibition of protein synthesis that is correlated with an increase in the accumulation of organic nitrogen (reviewed by Marschner, 1995). This decrease in protein synthesis leads to chlorosis and a specific reduction in proteins that have higher proportions of Met and Cys. Under phosphate limitation, phosphatases are secreted, and there is a mobilization of internal stores, for example, through the activation of cytosolic ribonucleases (reviewed by Green, 1994). Phosphate-limited growth also is characterized by a depressed rate of starch synthesis; however, total carbon fixation is increased (reviewed by Marschner, 1995). Phosphate and sulfur limitation have similar effects on photosynthetic electron transport. Chlorophyll levels decline, PSII function is decreased, and light harvested by the light harvesting complex II is lost as heat or possibly directed to PSI (Wykoff et al., 1998).

The effects of these two stresses have been well studied in Chlamydomonas (reviewed by Grossman, 2000). For example, there is a marked decline in photosynthesis during suboptimal growth conditions, which is critical to cell viability (Wykoff et al., 1998). During sulfur limitation, the cells quickly reduce photosynthesis rates below that of respiration, and the levels of the photosynthetic proteins encoded by psbA, rbcL, and psaA are reduced greatly after 48 h (Zhang et al., 2002). During phosphate limitation, both Chlamydomonas and the green alga Selenastrum minutum exhibit shifts in photosynthetic activity toward the fixation of carbon for storage versus for respiratory metabolism (Ball et al., 1990; Theodorou et al., 1991). Our results showing marked changes in organellar RNA profiles during abiotic stress complement these previous studies, mainly in that physiological changes not only are correlated with gene regulation in the nucleus but also directly affect the energetic centers.

Run-on transcription analysis showed that sulfur and phosphate limitation had different effects on transcription rates (Figure 5). Sulfur limitation reduced rates by 2- to 10-fold for both chloroplast and mitochondrial genes, consistent with diminished RNA accumulation. When sulfate was replenished, transcription recovered; it was stimulated particularly for a group of chloroplast genes involved in transcription or translation. It would be interesting to determine whether the translational apparatus responds as rapidly as the transcriptional apparatus under these conditions. During phosphate limitation, rates decreased up to 80% of the control rate after 48 h. Because we concomitantly observed increased transcript accumulation, this suggested, and we later confirmed, that changes in at least some RNA levels were a result of increased stability (Figure 6). The mechanism for decreased transcription rates under sulfur limitation could be part of a general metabolic slowdown or could result from a specific signaling cascade. For example, it has been postulated that the mustard chloroplast RNAP can be regulated by reversible phosphorylation of its sigma factors (Tiller and Link, 1993).

A limited role for transcriptional regulation in the chloroplast has long been postulated (Deng and Gruissem, 1987), and we found that phosphate limitation nearly doubled the half-lives of atpB and petB mRNAs. Phosphate stress is a particularly interesting context in which to study chloroplast RNA regulation. Recent models for chloroplast RNA degradation suggest a two-step process in which initial endonucleolytic cleavages are followed by the addition of poly(A)-rich tails, which in turn lead to rapid decay (Schuster et al., 1999; Monde et al., 2000). Although the endonucleases have not been identified definitively, it is likely that polynucleotide phosphorylase (PNPase) is responsible for removing the poly(A) tail and upstream sequences. Polyadenylation-mediated chloroplast RNA decay has been demonstrated clearly in spinach in vitro (Kudla et al., 1996; Lisitsky et al., 1997) and in Chlamydomonas both in vitro and in vivo (Komine et al., 2000, 2002). Most interestingly, it was shown recently that like its bacterial counterpart, spinach chloroplast PNPase has both poly(A) polymerase and 3′ to 5′ exoribonuclease activities (Yehudai-Resheff et al., 2001). In these in vitro assays, the activity of PNPase could be directed to RNA degradation or polymerization by manipulating physiologically relevant concentrations of Pi and ADP. Pi significantly enhanced degradation, whereas ADP inhibited degradation and enhanced polymerization. Thus, phosphate limitation could cause a reduction of PNPase-mediated degradation activities.

Dependence on a Single RNA Polymerase May Be Reflected in Environmental Sensitivity

In surveying the transcriptional response, we used both a PEP-specific inhibitor, tagetitoxin, and a global transcriptional inhibitor, actinomycin D (Figure 7). Because tagetitoxin appeared to inhibit all transcription, we tentatively conclude that the Chlamydomonas chloroplast lacks a NEP. This conclusion is supported by a recent study in which the addition of another PEP inhibitor, rifampicin, resulted in the reduction of transcription rates for four genes when cells were cultured in TAP medium and for five genes when cells were grown in minimal medium (Eberhard et al., 2002). If Chlamydomonas chloroplasts lack a NEP, this would be a unique situation. Chloroplast-targeted NEPs are known in several land plants and in the moss Physcomitrella (Kabeya et al., 2002; Richter et al., 2002). It is possible that photosynthetic organisms that fail to undergo the complex process of plastid biogenesis, particularly unicellular organisms, generally lack the NEP, suggesting a putative evolutionary role in its acquisition or maintenance.

METHODS

Chlamydomonas Strains and Culture Conditions

Chlamydomonas reinhardtii (CC-125 mt⁺) was maintained on 0.8% Tris-acetate-phosphate (TAP) agar (Harris, 1989) at 25°C under constant light (70 μE·m⁻²·s⁻¹). For RNA isolation, a sample of cells was placed into 50 mL of liquid TAP medium and allowed to grow under continuous light at 25°C on a rotary shaker (125 rpm) to mid-log phase (1 × 10⁷ cells/mL). This starter culture then was harvested by centrifugation, washed with appropriate liquid medium, transferred to 500 mL of the desired culture medium, and placed in the appropriate culture condition (see figure legends). Five culture media were used: complete medium (TAP), minimal medium lacking acetate (M) (Harris, 1989), medium without nitrogen (−N), medium without phosphate (−P [Shimogawara et al., 1999]), and medium without sulfate (−S [Yildiz et al., 1996]). Cells were harvested at 24 h (UV light treatment), 48 h (−P, −N, −S, minimal medium, light treatment, and heat treatment), or 72 h (dark treatment and bright-light treatment) after transfer. For analysis of synchronized cells, 50 mL of starter culture was transferred to 500 mL of minimal medium and grown under a 14-h-light/10-h-dark cycle for 5 days. On day 6, half of the culture was collected during the middle of the light period, and the remaining half was collected during the middle of the dark period. For each culture condition, a minimum of three independent samples (each originating from a different starter culture) were tested for RNA quality via hybridization and pooled for subsequent analyses.

Growth curves were obtained by harvesting cells grown in TAP medium for 24 h under standard conditions, washing once with sterile water, and adding 2 × 10⁵ cells to 500 mL of each culture medium assayed. Cells were counted at seven time points (4, 8, 12, 16, 24, 48, and 72 h) using a hemacytometer, and the appropriate amount of culture was harvested to yield 1 × 10⁷ cells.

RNA Gel Blot Analysis

Total RNA isolation, electrophoresis, blotting, and hybridization were performed as described previously (Drager et al., 1998). RNA from growth curves was resuspended in 50 μL of sterile water, and 10 μL was loaded per lane. RNA for microarray analysis was processed further with the addition of 10 units of DNase (Promega, Madison, WI) per microgram of RNA and incubation at 37° for 0.5 h, followed by the addition of 1 volume of 4 M LiCl and incubation on ice for 30 min. RNA was collected by centrifugation and washed twice with 70% ethanol. For RNA gel blot hybridizations, the experimental probe was hybridized first, images were captured on a Storm 840 phosphorimaging system, and band intensity was quantified using Image Quant version 1.2 software (Molecular Dynamics, Sunnyvale, CA). Membranes were placed at −80°C for 2 months and then rehybridized with the chloroplast 16S rDNA, which also was quantified, and this information was used for normalization.

Gene Probes

Gene-specific probes were generated for 84 chloroplast genes, 10 mitochondrial genes, and 44 previously characterized nuclear genes. Additional chloroplast probes were generated in newly sequenced regions (Maul et al., 2002) for which expression data had not been collected previously. Primers were designed based on the chloroplast or mitochondrial genome sequence using Oligo 6.1 software (Molecular Biology Insights, Cascade, CO) with the following parameters: each resulting fragment was between 150 and 1500 bp and contained (for known genes) little if any noncoding sequence. Nuclear gene primers were designed based on previously deposited sequences (GenBank accession numbers are listed in Table 2). A complete list of primers is available (see supplemental data online). Probe generation for organelle genes was conducted using standard PCR conditions in the presence of Taq DNA polymerase (Fisher Scientific, Chicago, IL). Reverse transcriptase–mediated PCR was performed using the Access PCR system (Promega) to generate nuclear gene fragments. All PCR products were subjected to agarose gel electrophoresis, their sizes were confirmed using molecular mass standards, and single bands were excised and purified using Qiaex II (Qiagen, Valencia CA) for subsequent cloning into a T-tailed PCR vector (pGEM-T Easy [Promega] or PCR-II [Invitrogen, Carlsbad, CA]). Positive bacterial colonies were selected, and plasmid DNA was extracted. To ensure that the correct insert was cloned, a single pass sequence was performed with vector primers (M13 forward/reverse). Once the sequence was confirmed, inserts were extracted via restriction with EcoRI, gel purified, and used in RNA gel blot hybridizations.

Microarray Construction and Hybridization

DNA microarrays and low-density nylon membranes were generated using the plasmid DNA from the 142 clones described above and in Table 1. Standard PCR was performed for all clones in replicate using universal vector primers (M13 forward/reverse) and Fisher Taq DNA polymerase. Replicates were combined and purified using PCR filter plates (Millipore, Bedford, MA) in a 96-well format. Purified PCR products were resuspended in 20 μL of water, and 1 μL of each product was checked for successful amplification and purity by electrophoresis on a 1.0% agarose gel. PCR was repeated for any clones that failed to yield the desired size or gave weak amplification. Once the full set of 142 clones was determined to be of satisfactory quality, the purified products were diluted with either 20 μL of DMSO or 6 × SSC (1× SSC is 0.15 M NaCl and 0.015 M sodium citrate) and 1.5 M betaine (Sigma) and stored at −80°C until they were arrayed.

DNA microarrays were constructed at the Center for Gene Expression Profiling (CGEP), which is located at the Boyce Thompson Institute. Purified PCR products were spotted in triplicate onto GAPII glass slides (Corning, Corning, NY) using a BioRobotics Microgrid Pro arrayer (BioRobotics, Woburn, MA) with a 16-split-pin spotting tool. Because of the small size of the microarray (3 spots per gene, 426 spots total), it was possible to print two arrays per slide with a 20-mm gap between them. After both arrays were printed, slides were allowed to dry on the stage overnight, cross-linked with 300 μJ of 254-nm UV light, and baked at 80°C for 4 h. Slides then were blocked or stored under desiccation for up to 3 months. Before hybridization, slides were blocked with succinic anhydride according to the manufacturer's directions (Corning), prehybridized at 42°C for 1 h in prehybridization buffer (25% formamide, 5 × SSC, 0.25% SDS, and 0.25% filtered BSA), washed with water, and dried in isopropanol. A total of 20 μL of denatured cDNA probe in hybridization buffer (25% formamide, 5 × SSC, 0.1% SDS, 100 ng/μL sheared salmon sperm DNA, and 1 μL of poly[A] blocker [Invitrogen]) was added immediately after prehybridization, and arrays were covered with 22- × 22-mm Hybri-Slip cover slips (Grace Laboratories, Bend, OR) and allowed to hybridize in sealed chambers (Corning) at 42°C for a minimum of 20 h. Slides were prepared for scanning by removing the cover slip at room temperature in 2 × SSC and 0.25% SDS, followed by a 10-min wash in the same buffer at 42°C with vigorous agitation. The slides were transferred to 0.2 × SSC and 0.1% SDS for 10 min at room temperature, followed by four 2-min rinses in 0.1 × SSC. Slides then were plunged into sterile water for <5 s, rinsed in 100% ethanol, and spun dry.

cDNA Labeling

Fluorescently labeled Cy3 or Cy5 cDNA probes were generated from total RNA using a direct incorporation reverse transcription method. Each experiment (control versus sample) was replicated a minimum of three times, and each was completely independent (from starting material to hybridization), including at least one dye swap per experiment. DNase-treated total RNA (50 μg) was used in each labeling reaction using the method described by Hegde et al. (2000) with the addition of 6 μg of random primers (Invitrogen). Labeled cDNA was passed through PCR purification columns (Qiagen) and eluted with 40 μL of sterile water. Corresponding Cy3 and Cy5 samples were combined and then dried under vacuum. Pellets were resuspended in hybridization buffer, denatured at 95°C for 5 min, and allowed to cool at room temperature for 2 min before adding the probe to the slide.

Array Image Acquisition and Data Analysis

Microarrays were scanned at the CGEP using a confocal laser fluorescent scanner (ScanArray 5000; Packard Instruments) using the Cy3 channel (570 nm) and the Cy5 channel (660 nm) set to 10-μm resolution. TIFF images were saved, and spot intensities were quantified using the TIGR Spotfinder software package (www.tigr.org/softlab). Quantified data were imported into Excel XP (Microsoft, Redmond, WA), and spot replicates were averaged. Any poor spots (defined as spots exhibiting signal of <150,000 units) were removed from subsequent analyses. Based on RNA gel blot analyses, 25S, 23S, and 16S rRNAs failed to show significant accumulation differences under any conditions examined; therefore, the combination of these values was used in those cases in which the Cy3 and Cy5 channels needed to be normalized to account for nonuniform reverse transcription. Any experiment that required normalization greater than twofold was considered to have failed and was repeated. The three (or more) averaged data sets per experiment were merged into a final data set, resulting in a minimum of nine individual data points per gene per experiment. Microarray results also were compared with the previously collected RNA gel blot data and were qualitatively consistent.

Normalized and averaged microarray data were used to generate scatterplots and hierarchical clusters in the TIGR multiple-experiment viewer software package. Each replicate of each experiment (control versus experimental) was visualized in the single-experiment viewer and filtered to exclude spots equal to or less than the negative control spots, resulting in an average of 98 of 142 targets that yielded acceptable signals. Genes showing expression ratios greater than twofold were retained, and data were collected. Quantified data and resulting images are available on the Chlamydomonas chloroplast genome World Wide Web site (http://bti.cornell.edu/bti2/chlamyweb/). The averaged data sets of time points were used in the TIGR multiple-experiment viewer and to generate hierarchical cluster results using the average linkage clustering option, in which only genes were clustered, irrespective of the experiment.

Run-On Transcription Analysis

Nylon filters used in run-on transcription assays were generated using the same PCR products as those spotted onto glass slides. Purified PCR products were diluted to a final concentration of 2 ng/μL in water, and an equal volume of 0.8 M NaOH and 20 mM EDTA was added before denaturation at 98°C for 15 min and placement on ice. A total of 50 μL of each PCR product were transferred to Hybond N⁺ nylon membranes (Amersham Biotech, Uppsala, Sweden) using the Bio-Rad 96-well vacuum dot-blot apparatus according to the manufacturer's directions (Hercules, CA).

Chlamydomonas cells were grown as described above and harvested, and their density was determined. For all conditions, cells were adjusted to 1 × 10⁶ cells/mL, and 50 mL of this dilution was used to prepare cells for run-on transcription. Cells were permeabilized according to Gagne and Guertin (1992), and 30-μL aliquots were stored at −80°C until use. For generation of radiolabeled transcripts, cells were subjected to three freeze/thaw cycles, with some experiments requiring the addition of selected transcription inhibitors (50 units of Tagetin [Epicenter, Madison, WI], 100 μg/mL novobiocin, or 100 μg/mL actinomycin D [Sigma, St. Louis, MO]) during the freeze/thaw cycle. After the last thaw, cells were washed twice in run-on wash buffer. Each reaction contained 30 μL of permeabilized cells, 15 μL of 4 × run-on transcription buffer (Sakamoto et al., 1993), 2 μL of RNase Out RNase inhibitor (Invitrogen), 8 μL of α-³²P-UTP, and an inhibitor if required. Reactions proceeded at room temperature for 30 min and were stopped by the addition of 20 μL of 20% SDS and 1 mL of Tri-reagent (Molecular Research Center, Cincinnati, OH). Radiolabeled RNAs were extracted, and unincorporated nucleotides were removed. Hybridization, washing, and quantification were as described for RNA gel blots. Each quantified data set is an average of at least two independently replicated samples.

Transcription rates were measured by taking the average signal for each gene and subtracting the signal produced by negative control spots. Basal transcription rates in TAP medium were measured against the rate of atpA for chloroplast transcripts or cob for mitochondrial transcripts. Comparisons between treatments were made by dividing the average signal of each experimental sample by that of the TAP control sample. Complete data sets and primary data are available at http://bti.cornell.edu/bti2/chlamyweb/.

Upon request, all novel materials described in this article will be made available in a timely manner for noncommercial research purposes. No restrictions or conditions will be placed on the use of any materials described in this article that would limit their use for noncommercial research purposes.

Supplementary Material

[Supplemental Data]

plntcell_14_11_2681__index.html^{(1.8KB, html)}

Acknowledgments

We thank Carrie Breehl for providing technical assistance on the project, Paul Debbie for his assistance with microarray experiments performed at the CGEP, and the Cornell BioPL 641 class for enthusiastic participation in early microarray experiments. We also acknowledge Stephan Eberhard and Francis-André Wollman for communicating unpublished results. This work was supported by National Science Foundation Grant MCB 9975765 to D.B.S. and a National Institutes of Health–National Research Service Award postdoctoral fellowship to J.W.L.

Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.005595.

Footnotes

^W⃞

Online version contains Web-only data.

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PERMALINK

This article has been retracted.

The Chlamydomonas reinhardtii Organellar Genomes Respond Transcriptionally and Post-Transcriptionally to Abiotic StimuliW⃞

Jason W Lilly

Jude E Maul

David B Stern