Abstract

To test the inferences of spotted microarray technology against a biochemically well-studied process, we performed transcriptional profiling of conidial germination in the filamentous fungus, Neurospora crassa. We first constructed a 70 base oligomer microarray that assays 3366 predicted genes. To estimate the relative gene expression levels and changes in gene expression during conidial germination, we analyzed a circuit design of competitive hybridizations throughout a time course using a Bayesian analysis of gene expression level. Remarkable consistency of mRNA profiles with previously published northern data was observed. Genes were hierarchically clustered into groups with respect to their expression profiles over the time course of conidial germination. A functional classification database was employed to characterize the global picture of gene expression. Consensus motif searches identified a putative regulatory component associated with genes involved in ribosomal biogenesis. Our transcriptional profiling data correlate well with biochemical and physiological processes associated with conidial germination and will facilitate functional predictions of novel genes in N.crassa and other filamentous ascomycete species. Furthermore, our dataset on conidial germination allowed comparisons to transcriptional mechanisms associated with germination processes of diverse propagules, such as teliospores of the phytopathogenic fungus Ustilago maydis and spores of the social amoeba Dictyostelium discoideum.

INTRODUCTION

In the past 10 years, ∼75 eukaryotic genomes have been completed or assembled, yet molecular functions of a large number of genes are still unknown [e.g. 13% of genes in Homo sapiens (1) and 38% in Saccharomyces cerevisiae (Saccharomyces Genome Database, http://www.yeastgenome.org/]. Gene disruption, proteomics and mRNA transcription profiling have been extensively used in S.cerevisiae to characterize gene functions. The simple life cycle and small genome size (12 Mb) make S.cerevisiae an experimentally powerful model eukaryotic organism. However, S.cerevisiae provides limited insight into the complex developmental programs and intercellular communication observed in multicellular eukaryotic species. Neurospora crassa, a multicellular filamentous ascomycete fungus, has been used as a model laboratory organism for over 70 years and is a promising model system for addressing questions about multicellular development.

An abundance of tools and mutants for genetic, molecular genetics and biochemical analyses of N.crassa are available (2,3). N.crassa is characterized by a complex lifecycle; there are at least 28 distinct cell types (4,5). The DNA sequence of the 40 Mb genome of N.crassa encodes ∼10 000 genes (6). Functional annotation of these genes has succeeded largely on the basis of computer predictions using patterns recognized in characterized genes and homology searches to proteins in public protein databases (4,6,7). However, over 40% of the N.crassa genes have no counterpart in public databases and are listed as ‘hypothetical’.

The advent of microarray technology makes it possible to measure transcription in thousands of genes simultaneously by hybridizing labeled transcripts (targets) to probes attached to the surface of microscope slides (8,9). Transcriptional profiling data have been used to predict biological function of novel genes in S.cerevisiae (10). Gene expression data generated by microarray technology may be used to expedite the annotation process of the N.crassa genome and will aid in assigning putative functions for unique genes. However, most physiological and cellular phenomena are driven directly by proteins, rather than by genes or mRNAs. The total amount and/or activity of proteins in the cell is controlled through pre, co- and post-translational modification, in addition to mRNA abundance. Accordingly, it has been argued that mRNA abundance, discernable by microarrays, is a poor indicator of protein abundance or protein activity [reviewed in (11,12)]. If mRNA abundance had little effect upon protein or cellular activity, then mRNA profiling would provide little insight into underlying cellular mechanisms. To test this hypothesis of non-concordance, we chose to evaluate gene expression patterns associated with asexual spore (conidial) germination of N.crassa. This developmental process is ideal for testing this hypothesis, because morphological and biochemical aspects associated with conidial germination have been studied in great detail (13,14) and because conidial germination is a morphologically simple, rapid and a highly important developmental process in this fungus. If the hypothesis were to be correct, discordance between mRNA profiles and morphological/biochemical observations would be revealed.

N.crassa, similar to numerous other filamentous fungi, forms a mycelial colony via hyphal tip growth, branching and hyphal fusion. Upon exhaustion of nutrients, N.crassa produces numerous asexual multinucleate spores, called macroconidia, which bud from the tips of specialized aerial hyphae, or conidiophores (2,15). In addition to macroconidia, N.crassa also produces uninucleate microconidia, which are extruded directly from cells of microconidiophores (16) and arthroconidia, which are derived by segmentation (2). In this report, we use ‘conidia’ to refer exclusively to macroconidia. In other filamentous ascomycete species, conidia are often the infective propagules associated with both plant and animal mycoses, e.g. rice blast, caused by Magnaporthe grisea, and aspergillosis, caused by Aspergillus fumigatus, which is a common and frequently lethal infection of individuals with a compromised immune system. It has been proposed that the identification and targeting of conidial germination-specific processes provides an excellent strategy for drug and fungicide development in these pathogenic fungi (17).

Conidial germination in filamentous fungi is a highly regulated process that is triggered by environmental stimuli. Dehydrated conidia remain viable for many years, a resilience which is lost soon after germination begins (2). When N.crassa conidia are put in water with salts and a carbon source (13), the outer, hydrophobic rodlet layer of the cell wall is lost and the conidia swell (18). In the first 10 min of exposure to liquid medium, free glutamic acid is degraded; trehalose is mobilized; and aspartic acid and γ-aminobutyric acid are formed, followed by the initiation of protein and RNA synthesis (13,1922). The first morphological sign of conidial germination is the formation of the germ tube, which requires polarity establishment (23). Coincident with germ tube formation, DNA synthesis and nuclear division is initiated and mitochondrial oxidative phosphorylation can be detected. High levels of arginine and ornithine accumulate and synthesis of chitin (a component of the fungal cell wall) occurs. In N.crassa, the germ tube elongates ∼200 µm before the initiation of branching. Germ tubes or conidia in close proximity often undergo fusion via specialized hyphae, called conidial anastomosis tubes (14,24). In Vogel's medium (25), maximal growth rate is established soon after branching is initiated, ∼8 h post-inoculation.

Although biochemical aspects associated with conidial germination have been well documented in N.crassa, fundamental genetic mechanisms, such as those that drive the germination process and underlie the timing of gene expression and metabolic pathway activation, remain obscure. In this study, we tested two hypotheses that relate gene expression levels to their fundamental biological impact. The first hypothesis tested directly whether established timing of biochemical events and measurements of enzyme activity during conidial germination were consistent with our transcriptional profiling dataset. These analyses identified expression profiles of a large number of genes which lacked any previous functional annotation, proving the utility of microarray data for annotation of the Neurospora genome. The second hypothesis tested was whether transcriptional profiles associated with conidial germination shared similarities to transcriptional profiles obtained from spore germination from the phylogenetically distant species, such as Ustilago maydis, a basidiomycete fungus and Dictyostelium discoideum, an amoeboid protist species.

MATERIALS AND METHODS

Construction of N.crassa oligonucleotide microarrays

We chose to construct oligomer microarrays for transcriptional profiling experiments for N.crassa; with oligomer arrays, mistaken identity problems and failed PCR amplification are avoided (26,27). To construct a gene-specific microarray, we designed 70mer oligonucleotide immobilized probes using ArrayOligoSelector (27), and the ∼10 000 open reading frames (ORFs) derived mainly from the N.crassa databases at the Broad Institute (http://www.broad.mit.edu/annotation/fungi/neurospora_crassa_7/index.html) and MIPS (http://pedant.gsf.de/cgi-bin/wwwfly.pl?Set=Ncrassa_annotations&Page=index). ArrayOligoSelector selects a unique segment to identify each ORF considering the predicted sequences of all genes. The program avoids selecting internal self-annealing structures and internal repeat sequences, and preferentially chooses oligonucleotides within a narrow range of GC content, which are biased toward the 3′ terminal region of each gene. Oligomers were synthesized (Illumina, San Diego, CA) for 3366 ORFs, which represent ∼1/3 of the predicted full gene set (Supplementary Data S1). A large proportion of the gene set selected was located on linkage groups II and V (manually annotated by MIPS) and included previously characterized genes in N.crassa. Analysis of oligomer placement in predicted genes indicated the desired strong 3′ bias.

Oligomers were re-suspended in 3× SSC to a final concentration of 40 µM and spotted onto poly(L)-lysine-coated microscopic slides as described previously (9). Aliquots containing 8 of 40 µM ArrayControl Sense Oligo spots, which were complementary to the eight ArrayControl RNA spikes (Ambion, Austin, TX), were also included. Two spots were printed per slide of each synthesized oligomer.

Strain and culture conditions

To produce conidia, a wild-type laboratory strain, R1-01, was inoculated onto 25 ml solid Vogel's minimal medium (MM) with 1.5% agar and 2% sucrose in a 250 ml flask and grown for 8 days under constant light (∼300 lux) at 25°C. Growth under constant light conditions was performed to suppress gene expression associated with circadian rhythms (D. Bell-Pederson, personal communication). To harvest conidia free from hyphal fragments, they were suspended in Vogel's MM and passed through eight layers of cheese cloth. The conidial suspension was subsequently diluted to 107 conidia/ml in Vogel's MM. The ratio of hyphal fragments to conidia was no more than 1 to 50 by volume. To begin each experiment, 50 ml of the conidial suspension was introduced into each of a series of 250 ml flasks. After 30 min with no agitation at 25°C, one sample was harvested. The remaining flasks containing 50 ml conidial suspensions were incubated at 30°C with constant shaking (200 r.p.m., C25 Incubator-Shaker, New Brunswick Scientific, Edison NJ) and light (∼300 lux). Subsequent samples were harvested at 1, 2, 4, 8, 12 and 16 h. Samples were harvested by vacuum filtration onto a 0.45 µm nitrocellulose filter (Millipore, Bedford, MA) and immediately frozen in liquid nitrogen. The time 0 sample consists of conidia from an 8-day-old culture that were harvested by adding −80°C ethanol directly to the culture, filtered through cheese cloth and a 0.45 µm nitrocellulose filter, and frozen in liquid nitrogen.

Microscopy

Conidia and hyphae were examined using differential interference contrast optics with a Zeiss Axioskop II microscope. Desiccated and germinating conidia in Vogel's MM (25) were mounted on slides and viewed under a 100× oil immersion objective; 4-, 8- and 16-h-old hyphae were viewed with a 40× oil immersion objective. Micrographs were taken with a Hamamatsu digital camera and captured images were processed using Photoshop software (version 6.0, Adobe).

RNA extraction and cDNA labeling

Frozen samples were ground in liquid nitrogen with a mortar and pestle and total RNA was extracted using TRIzol (Invitrogen Life Technologies, Burlington, ON) following the manufacturer's suggested protocol. A 100 µg sample of total RNA from each sample was further purified using RNeasy Mini Protocol (Qiagen, Valencia, CA). For cDNA synthesis, 20 µg of total RNA was mixed with 5 µg of an anchored 17mer oligo(dT) [oligo(dT15) VN, where V is any nucleotide except thymidine and N is any nucleotide] and 3.3 ng of ArrayControl single RNA spike mixture (Ambion) in 15.5 µl H2O; the mixture was incubated at 70°C for 10 min. cDNA was synthesized in a final volume of 30 µl with 500 µM each of dATP, dCTP and dGTP, 200 µM of dTTP, 300 µM of aminoallyl-dUTP, 10 mM DTT and 100 U Stratascript reverse transcriptase (Stratagene, La Jolla, CA) in 1× reaction buffer. RNA was hydrolyzed by adding 7.5 µl of 0.5 M NaOH and 50 mM EDTA, incubated for 15 min at 65°C and subsequently neutralized by the addition of 37.5 µl of 1 M HEPES buffer, pH 7.5. The neutralized cDNA was purified using CyScribe GFX Purification Kit (Amersham Biosciences, Piscataway, NJ) as recommended by the manufacturer except that 60 µl of 0.05 M sodium bicarbonate was used for elution of the cDNA. The eluted cDNA was dried under vacuum and re-suspended in 25 µl H2O. For conjugation to fluorescent dyes, 10 µl of 0.05 M sodium bicarbonate was added to the monofunctional NHS-esters of Cy3 or Cy5 (CyDye Post-Labeling Reactive Dye, Amersham Bioscience, Piscataway, NJ) and 5 µl of the dye solution was added to the cDNA solution. Fluorescent dye was coupled in the dark at room temperature for 1 h; unbound dye was deactivated by adding 15 µl of 4 M hydroxylamine and incubating the mixture for 15 min in the dark. The labeled cDNA was purified with the CyScribe GFX Purification Kit (Amersham) and dried under vacuum.

Hybridization and image acquisition

Slides were pre-hybridized at 42°C for at least 1 h in a solution containing 50% formamide, 5× SSC, 0.1% SDS and 0.1 mg/ml BSA. The labeled cDNA was re-suspended in 28 µl of hybridization buffer containing 50% formamide, 5× SSC, 0.1% SDS, 0.1 mg/ml BSA, 0.1 mg/ml salmon sperm DNA and heated at 95°C for 3 min before it was pipetted into the space between a microarray slide and a LifterSlip cover glass (Erie Scientific, Portsmouth, NH). Hybridization was carried out for 16 h at 42°C and unbound DNA was washed off as described previously (28). An Axon GenePix 4000B scanner (Axon Instruments, CA) was used to acquire images, and GenePix Pro 4.1 software was used to quantify hybridization signals. Bad spots were flagged automatically by GenePix software and subsequently each slide was inspected manually.

Data analysis

Hybridized spots with at least one of the mean fluorescence intensities for Cy3 or Cy5 that were brighter than mean background intensity plus three standard deviations of background intensity were scored for further analysis if <2% of pixels were saturated. If both of the duplicated spots met these criteria, only the brighter spot was taken for analysis. This procedure was followed to avoid pseudoreplication, as duplicate spots on the microarray were highly correlated and did not constitute independent data [c.f. (29)]. Signal intensities from the eight spiked positive control cRNAs (Ambion) were used to perform linear normalization of the data. Normalized ratio data were then analyzed using Bayesian Analysis of Gene Expression Levels (BAGEL) software, with which we inferred a relative gene expression level and credible interval for each gene in each experimental time point (30). Estimated gene expression levels were clustered (31) using Hierarchical Clustering Explorer 2.0 (32), in which similarity in expression patterns between genes is measured as Pearson's correlation coefficient and the closest two genes or clusters are successively joined. Distances between clusters represent the average distances between genes in the clusters.

The Functional Catalogue (FunCat) created by MIPS (33,34) and tables from a N.crassa genome review (4) were used to group genes according to their cellular or molecular functions. Over- or under-representation of gene groups across expression gene clusters was evaluated against an expected hypergeometric distribution using the Fisher test function in the statistical software R 1.9 (http://bioconductor.org).

Consensus motif searches

Motif searches were conducted using three programs BioProspector (35), MDscan (36) and MEME (37) on segments 500 bp upstream of predicted translational start sites, which were downloaded from the Broad Institute N.crassa database release version 3 (http://www.broad.mit.edu/annotation/fungi/neurospora_crassa_3/). Predicted motifs were subsequently analyzed manually. To search for examples of candidate motifs, segments 1000 bp upstream were searched for sequences that matched either the consensus motif site or degenerate sites by using a string search with a PERL script. Enrichment of motifs (P < 0.001) was assessed using Fisher's exact test performed by the Fisher test function implemented in the R 1.9 program (http://bioconductor.org). A motif logo illustrating the consensus sequence was obtained using WebLogo program (http://weblogo.berkeley.edu).

Cross-species analyses of expression profiles

Bidirectional BLAST searches (blastx and tblastn) were performed between the N.crassa gene set and the predicted gene set of U.maydis (http://www.broad.mit.edu/annotation/fungi/ustilago_maydis/) and between the N.crassa gene set and the D.discoideum gene set (http://dictybase.org/) (38,39). A pair of protein coding genes was judged to be orthologous if each was each other's best bidirectional blast hit with values <10−5.

Expression profiles of N.crassa genes were cross-examined with gene orthologs belonging to expression gene groups obtained during teliospore germination in U.maydis (40) or those obtained during spore germination in D.discoideum (41). Over- or under-representation of expression profile groups across expression gene clusters was evaluated against an expected hypergeometric distribution using the Fisher test function in the statistical software R 1.9 (http://bioconductor.org).

RESULTS

Experimental design for conidial germination transcriptional profiling

Circuit design for microarray comparisons has been strongly endorsed by statisticians (29,42,43) and has demonstrated dramatically improved resolution in identifying differential gene regulation compared with designs using a universal reference (4448). The experimental design for transcriptional profiling of conidial germination is shown in Figure 1A. Each arrow represents one hybridization and the arrowhead points to Cy5-labeled cDNA. In this circuit of experimental comparisons, each sample was compared head-to-head with other samples, in a circular, and in some cases, multiple-pairwise fashion. This design maximized the ability to detect differences in expression because the comparisons are between transcripts from each of the developmental stages of greatest interest rather than between the stages and a reference sample.

RNA was isolated from eight time points during the germination process, time 0, time 30 min and times 1, 2, 4, 8, 12 and 16 h post-inoculation. Our microscopic observations on morphological aspects of conidial germination in N.crassa were similar to those reported previously [reviewed in (13)]. Briefly, dehydrated conidia harvested from time 0 showed irregular shapes (Figure 1A). When suspended in Vogel's MM, swelling of conidia was observed. For the first 2 h post-inoculation, microscopic changes were not observed, other than swelling. Germ tube emergence was first observed in a small fraction (∼2%) of conidia ∼2 h after hydration (Figure 1A). At 4 h post-inoculation, germ tubes had formed in the majority (∼98%) of conidia (Figure 1B). Numerous germling fusions were observed at this time point (Figure 1A). At 8 h, germ tubes had extended, branched and began to form a mycelial mat that is characteristic of an exponentially growing fungal colony (see 8 and 16 h time points; Figure 1A).

Dormant conidia have large differences in mRNA abundance compared with germinating conidia, which affected the choice of methods for data normalization

Sachs and Yanofsky (49) observed that the poly(A) mRNA fraction of total RNA increased during conidial germination. Consistent with this observation, competitive hybridization between cDNA from dormant conidia (0 h) and that after 1 h of germination showed a skewed distribution of transcription levels toward 1 h (Figure 2A). These data indicated that most (but not all) transcripts identified in the 1 h culture were much more abundant than in dormant conidia. Those few mRNAs at a measurable concentration in dormant conidia were often found at very high levels (Figure 2A). Between 1 and 2 h the data were dispersed diagonally, indicating that the overall difference in the transcriptional profiles of germinating conidia between the 1 and 2 h time points was small (Figure 2B).

To evaluate the ratio of mRNA from each of the 28 comparative hybridizations in this study (Figure 1A), we used control RNA spikes as internal standards. The control spikes consist of eight polyadenylated bacterial mRNAs at concentrations ranging from 50 to 1000 pg/µl, which are complementary to eight ArrayControl Sense oligonucleotides (Ambion). The ArrayControl oligonucleotides were added as duplicate spots to the N.crassa oligonucleotide microarrays. A total of 3.3 ng of each control mRNA spike was added to each of the 20 µg total RNA samples for each time point. The relative amount of total mRNA at each time point was then inferred using BAGEL software (Figure 2C). Although there was a 2-fold discrepancy between the poly(A) assay conducted by Sachs and Yanofsky (49) and control spike estimates in this study, both portray the mRNA content at its nadir in dormant conidia and at its peak between 3 and 4 h after hydration, with a subsequent gradual decline during the experimental time period. Because mRNA quantity changes so drastically in the early stages of germination, and because it forms such a small fraction of total RNA in dormant conidia, we used control RNA spikes to normalize the data. The other method of normalization, global normalization, assumes a constant number of transcripts per cell and is inappropriate when the number of transcripts per cell varies dramatically between samples.

Bayesian analysis of the microarray expression data

From the normalized ratios of Cy3 and Cy5 intensities, microarray expression data were analyzed using BAGEL (30). BAGEL uses information from both direct and transitive comparisons to infer relative gene expression levels and 95% confidence intervals across the replicated, interconnected experimental design. The lowest level of expression for a particular gene in the interconnected data is set as a unit of one. Expression levels of a particular gene in all other samples are scaled appropriately; all gene expression level measurements are positive. The inferred expression levels are of arbitrary unit scale, rather than in absolute counts of mRNA abundance per cell because of the inherent comparative nature of the two-color spotted microarray technology.

The dramatic difference in gene expression between dormant and germinated conidia (0, 0.5 and 1 h) makes the estimation of gene expression level across all time points difficult because genes expressed at near background will not provide measurements judged significant for subsequent analysis. With circuit designs (Figure 1A), BAGEL requires an interlinked measurement (arrow) for each comparison between two time points, i.e. nine interlinked measurements for eight interconnected time points. We detected hybridization to 3054 (out of a total of 3366) oligonucleotides in the raw data of at least one microarray comparison. Hybridization to 1061 oligonucleotides was detected in a minimum of nine interlinked measurements. Statistical support for relative expression level could not be established for genes that were insufficiently well measured at a number of time points during conidial germination. To use BAGEL to discover other genes with significantly variable relative expression levels, we used smaller circuits that either included only the early time points, or that excluded them. BAGEL analysis of the first small subcircuit, which comprised measurements among time points 0, 0.5, 1, 2 and 16 h (Figure 1A) revealed an additional 133 genes, many of which were abundant only in conidia. A second small subcircuit composed of measurements among time points 1, 2, 4, 8, 12 and 16 h revealed a further 93 genes, most of which had the lowest expression levels between time 0 and 1 h. Thus, significant data on a total of 1287 genes with relative expression levels and credible intervals were obtained during conidial germination up to 16 h of growth (Supplementary Data S2).

Comparison of microarray data to N.crassa expressed sequence tag (EST) databases

Of the 3366 oligos designed for predicted genes, 1748 (52%) have been labeled as ‘putative’, indicating an EST match, ‘hypothetical’, indicating no similarity to any protein in the database or ‘conserved hypothetical’, indicating a similarity to predicted proteins without experimental evidence (7) (Table 1). The genes for these 1748 oligos are termed ‘unannotated’ in this report, whereas the remaining 1618 oligos, which were designed to genes with predicted or known gene functions, are termed ‘annotated’. Relative gene expression levels for ∼50% of the annotated genes (814 out of 1618 genes) were obtained from the interlinked comparisons. In contrast, out of the total of 1748 oligos for unannotated genes, data were obtained for only 27% (473 genes). Unannotated genes for which no transcripts were found may represent genes for which expression is absent, transient or meager during conidial germination, or they may be artifacts of gene prediction algorithms.

EST libraries have previously been constructed and analyzed from germinating conidia, 4.5 h post-inoculation (the conidial library), from 24-h-old mycelium (the mycelial library) and from sexual reproductive structures, 5 days after fertilization (the perithecial library) (50,51). ESTs from a total of 4738 clones were analyzed and assigned to 1721 genetic loci (http://www.broad.mit.edu/annotation/fungi/neurospora_crassa_3/est2loci.html). Of these 1721 loci, 661 have corresponding oligomers on the partial genome microarray (Table 1). A relatively large portion of oligomers to genes on the microarray with matches in the EST libraries had functional annotations (see ‘Total ESTs’ versus ‘Total Oligos’ in Figure 3).

Relative expression profiles were obtained for 395 out of the 661 genes on the microarrays with EST matches (69%, Figure 3); 312 of the 395 genes have annotation, while 83 do not. Oligomers for genes with ESTs in the perithecial library showed the lowest percentage of hybridization results, 235 genes out of 413 (57%); the perithecial library is expected to contain ESTs for numerous genes that are specific to perithecial development and function. The mycelial library is expected to contain ESTs for genes that act late in conidial germination, during mycelial growth. Oligomers for genes with ESTs in the mycelial library showed an intermediate percentage of hybridization results, 152 out of 251 genes (61%). The mycelial EST library was constructed from a 24-h-old culture; it is likely that nutrients in the medium are exhausted at this time point. A similar phenomenon in S.cerevisiae is associated with large changes in transcriptional profile (52). These observations may explain the lower percentage of hybridization results recovered from the mycelial library. The conidial EST library is expected to contain ESTs for a large proportion of the genes that operate during conidial germination. Indeed, 174 out of 212 genes (82%) that had ESTs in the conidial library yielded hybridization results across the conidial germination time course. Thus, a greater number of oligomers for genes present in a particular EST library yield results when a process is transcriptionally profiled that relates closely to the EST sample conditions. One implication of this observation is that oligomers that lacked hybridization results may not be poorly designed, but rather may correspond to genes that are expressed at detectable levels only in conditions other than those we examined.

Consistency of microarray data with RNA blot analysis

Published northern blot results for several N.crassa genes, performed with a fixed amount of total RNA (3 µg), (49) were compared with the microarray results also performed at a fixed amount of total RNA (20 µg). In previous northern blot analyses, transcripts from a conidium-specific gene, con-8, were abundant in dormant conidia, but barely detectable at 0.5 h; our N.crassa microarray faithfully reproduced this result (Figure 4A). Other conidiation-specific genes on the microarrays including con-6, con-10 (53) and nop-1 (54) also showed highest expression levels in dormant conidia, with a subsequent decline (Supplementary Data S2), consistent with published northern data. Previously, it was shown that the expression of arg-2 (a gene for biosynthesis of arginine) increased with the onset of germination (maximum at 1–2 h post-inoculation) and decreased thereafter (49). Again, our microarray results faithfully reproduced these results as compared with technique of northern blotting (Figure 4B). A number of genes that are predicted to encode amino acid biosynthetic enzymes (Figure 6) showed similar expression profiles to that of arg-2 during conidial germination (Figure 4B). The transcriptional profiles for hH4-1 (encoding histone H4) and cox-5 (cytochrome-C oxidase chain V gene) obtained during conidial germination were likewise consistent with published northern data (49) (Figure 4C and D).

Gene clustering of microarray expression profiles during conidial germination

Hierarchical Clustering Explorer (32) was applied to the dataset to cluster genes according to their expression profiles (31) (Figure 5). For those genes whose expression levels were obtained from the small circuits (time points 0, 0.5, 1, 2 and 16 h or time points 1, 2, 4, 8, 12 and 16 h), it was inferred that the expression level at excluded time points was zero or extremely meager (Supplementary Data S2). For clustering purposes, an expression value of 0 was assigned to the excluded time points. Data were then merged with the larger set of results for genes with full time-course mRNA expression levels. The dendrogram was constructed from distances (1-correlation coefficient) between expression patterns of genes (see Materials and Methods). Seven representative clusters were chosen visually, each of which showed distinctive profiles (Figure 5). The majority of genes (1153 out of the total of 1287 genes) belonged to one of the seven chosen clusters. The genes in the Max0 cluster showed maximum expression levels in dormant conidia, with expression levels dropping quickly during conidial germination. The Max0.5 cluster included genes with a peak expression at 30 min. Two clusters, Max1A and Max1B, which showed maximum expression at 1 h, followed by a decrease in expression levels, clustered in different locations in the dendrogram. Genes within the Max1A and Max1B clusters were pooled and are referred to as Max1 cluster hereafter. Genes within the Max1∼4 cluster showed an expression plateau at 1–4 h post-inoculation. The Max8∼16 cluster contained genes that had low expression levels until 4 h, but abundant expression from 8 h onward. Genes within the Min0∼0.5 cluster showed the lowest expression levels in dormant conidia to 30 min post-inoculation, but higher and relatively constant expression levels thereafter.

Analysis of clustered genes via FunCat classification revealed major cellular events associated with conidial germination

A recent criticism against the use of mRNA profiling in characterizing cell phenotypes is based on observations in which transcriptional profile does not directly correlate with the proteomic profile, the abundance and activity of the components of which actively determine cell phenotype (11,12). However, the utility of mRNA profiling depends upon its correlation with and implications for functional measurements of cell biology, biochemistry and organismic development, not upon the bivariate correlation of high-throughput methodologies. We therefore cross-examined mRNA profiling data, gene function and cellular events in order to validate mRNA profiling.

The MIPS FunCat is an annotation scheme for the functional description of proteins (33,34). FunCat consists of 28 main functional categories, each of which contains hierarchically ordered subcategories with increasing levels of specificity. A single gene can belong to more than one functional category. Out of the 1618 genes with functional annotation which were included in the N.crassa oligonucleotide microarrays, 814 had time-course mRNA profile data (Table 1). The main functional categories populated by genes from each of the six clusters are summarized in Table 2.

Genes predicted to be in a particular functional category were not distributed evenly among the six clusters. For example, genes with transcriptional function were over-represented in Max1 and Max1∼4 clusters, but under-represented in Max0, Max8∼16 and Min0∼0.5 clusters (P = 0.009) (Table 2). Subsets of FunCat ‘Transcription’ were evaluated for their impact on the uneven distribution of genes within the clusters. Genes that function in RNA synthesis were particularly over-represented in the clusters Max1 and Max1∼4 (P = 0.021). These data are consistent with biochemical data indicating the RNA synthesis is activated soon after the induction of germination (13).

Genes that function in the cell cycle and DNA processing were over-represented in the Max0.5, Max1, Max1∼4 clusters, but were under-represented in Max0 and Min0∼0.5 clusters (P = 0.013). A subset of these genes putatively involved in DNA processing were over-represented in clusters Max0.5 and Max1∼4 (P = 0.039). These data are consistent with biochemical observations that the initiation of DNA replication occurs approximately 2 h post-inoculation (13), just before germ tube formation (between 1–2 h; Figure 1A).

Genes that function in protein synthesis were over-represented in Max1∼4 cluster (P < 0.001), as were genes within the ‘Subcellular localization’ MIPS category (P = 0.000). A subset of protein synthesis genes that function in ribosome biogenesis deviated remarkably from an even distribution among the gene clusters (P = 0.000) (Table 2). In fact, 49 out of 56 genes functioning in ribosome biogenesis, and all 24 of those that have been identified as ribosomal proteins, were members of the cluster Max1∼4 and showed strikingly similar expression patterns (Figure 6). The majority of genes categorized as functioning in ribosome biogenesis are also categorized within the ‘Subcellular Localization’ category, resulting in genes belonging to this category also being over-represented in Max1∼4. Ribosomes and mRNA are stored in dormant conidia and protein biosynthesis is detectable within the first few minutes after the conidia have been suspended in water, salts and a carbon source (13). A large fraction of macromolecular synthesis in germinating conidia is devoted to ribosomal RNA and the protein synthetic machinery (2,49). These data on increase in mRNA levels of genes encoding ribosomal protein genes are also consistent with previously published northern and biochemical data (13,49).

Transcriptional profiles for 17 heat shock proteins (HSPs) (4) were identified during conidial germination. Thirteen out of seventeen HSPs showed the highest level of expression between 0.5 and 1 h after the rehydration of conidia (Figure 6); eleven of these belong to either the Max0.5 or Max1 cluster. In another study, proteins levels of HSC70 (NCU09602.1, B18E6_040) were evaluated during development; high levels of HSC70 were associated with aerial hyphae formation and conidia (55). These observations have led to the speculation that HSPs may be important in correct folding of proteins of newly synthesized proteins during conidial germination. Our transcriptional profiling data supports such a hypothesis.

Biochemical analysis of amino acid pools in conidia and germinating conidia showed that conidial extracts contain detectable free pools of amino acids, with the exception of proline, methionine and cysteine. The amino acid pools present in conidia decrease rapidly at the initiation of germination, but rapidly increase ∼3 h post-inoculation, including arginine, ornithine and citrulline pools (56). Transcripts from 25 genes predicted to be involved in amino acid biosynthesis were detected as differentially expressed during conidial germination. Eighteen of these genes belonged to clusters for very early time points: one in Max0 (NCU01830.1, B23G1_170: probable 4-hydroxyphenylpyruvate dioxygenase), three in Max0.5 (NCU02333.1, 7nc525_140: arginase, NCU03748.1, 93G11_270: probable saccharopine reductase and NCU04856.1, xnc010_060: probable glutamine synthetase) and 14 in Max1.

Genes for ‘Cellular transport, transport facilities and transport routes’ were over-represented in the Max1∼4, Max8∼16 and Min0∼0.5 clusters, but under-represented in the Max0 and Max0.5 clusters (P = 0.003). This set of genes encodes diverse types of proteins, such as ATPases, ATP synthases, small molecule and ion transporters, mitochondrial translocators, cytochrome C-related enzymes and NADH-related enzymes. Subsets of these genes that function in ‘Transport facilities’ (P = 0.004) and in ‘Transported compounds’ (P = 0.024) explain the over-representation of genes in the Max1∼4, Max8∼16 and Min0∼0.5 clusters (i.e. vacuolar ATP synthase subunit F; NCU04387.1, 29e8_280). These data are also consistent with previous biochemical data, indicating that rate of transport of nucleosides, glucose and amino acid increases during conidial germination (13,57).

Genes within the ‘Energy’ functional category were also over-represented in the Max1∼4, Max8∼16 and Min0∼0.5 clusters, but under-represented in Max0, Max0.5 and Max1 clusters (P < 0.001). Subsets of these genes functioning in respiration (P = 0.010) and electron transport and membrane-associated energy conservation (P = 0.003) were partly responsible for this over-representation in Max8∼16 and Min0∼0.5 clusters. Eight NADH-related enzymes in this category, such as NADH dehydrogenases and NADH-ubiquinone oxidoreductase, showed low gene expression levels at early time points during conidial germination and belong to either Max8∼16 or Min0∼0.5 cluster. These data are consistent with data showing that conidia have a relatively low rate of oxygen consumption (13,58), which increases greatly 2 to 4 h post-inoculation. More than half of the genes represented on the microarray that function in respiration, electron transport and membrane-associated energy conservation have transcriptional profiles that belong to the Min0∼0.5 cluster. The function of N.crassa genes involved in the central metabolism (glycolysis, gluconeogenesis, TCA cycle, glyoxylate cycle, fermentation, pentose phosphate pathway) have been deduced from functions of orthologous genes in S.cerevisiae [Figure 2 in (59)]. The transcriptional profiles of 16 genes involved in central metabolism were identified in this study; most belonged to the Min0∼0.5 cluster.

Given that Neurospora hyphae contain chitin (∼1%) and abundant glucans and that conidia and conidial germ tubes and mature hyphae have different amounts of cell wall components (60,61), it is not surprising that transcriptional profiles of 10 genes predicted to be involved in glucan and chitin synthesis [Tables 51 and 52 in (4)] were identified during conidial germination. All of these genes have minimal expression in dormant conidia and reached peak expression levels at 0.5 h and afterwards.

Enrichment of FunCat categories in clusters enabled us to infer participation of genetic process during conidial germination. Correlation between gene expression and quantity of active protein during conidial germination notwithstanding, it was demonstrated that mRNA expression and major cellular events were highly associated.

Transcripts from hypothetical genes

Genes coding for unclassified proteins are described as ‘hypothetical’, ‘conserved hypothetical’ or ‘putative proteins’ (6,7). Transcriptional profiles for 473 unclassified genes were identified during conidial germination by the N.crassa microarrays. Of these 473 genes, 152 are listed as hypothetical, 252 are listed as conserved hypothetical and 69 are putative genes. These unannotated genes were over-represented in Max1 and Max1∼4, but otherwise were found abundantly in all the clusters (Table 2). For example, a hypothetical gene, NCU09614.1, showed maximum expression 30 min after the initiation of germination, followed by a steep decrease in expression levels, a profile identical to a number of the heat shock genes (Figure 6). The expression pattern of another putative gene, NCU05338.1 (B15B24_010), belongs to Max8∼16; its transcripts were almost undetectable until 8 h, but showed high expression at 8, 12 and 16 h. The Max8∼16 cluster contains many genes that function in fermentation and carbohydrate metabolism.

Promoter analysis of clustered genes reveals putative regulatory sequences

Transcriptional profiling has been useful in identifying co-regulated genes and predicting both function and consensus binding sites for transcription factors (35,6264). To find such nucleotide motifs, we evaluated genes within five clusters, Max0, Max0.5, Max1, Max1∼4 and Max8∼16, using three programs, BioProspector (65), MDscan (36) and MEME (37). We focused on genes within each cluster that showed defined expression profiles with statistical support. For most of the clusters, a statistically significant consensus sequence motif was not identified, probably because clusters based on similarity of expression pattern contained genes belonging to diverse functional categories with distinct transcriptional regulatory mechanisms. However, when 36 representative genes belonging to Max1∼4 were examined, a consensus motif (G/A)CCC(T/C)AA was identified in 26 genes (Figure 7). Subsequent analysis of these 26 genes showed that all were annotated as ribosomal or probable ribosomal protein genes (Table 3). A very similar sequence was previously identified by a purely computational approach that assessed putative cis-regulatory sequences in ribosomal protein genes in a number of euascomycete species (N.crassa, M.grisea and Aspergillus nidulans [Figure 3 in (62)]. This cis-regulatory sequence is not found in the promoter region of ribosomal protein genes in S.cerevisiae. The entire Max1∼4 cluster from N.crassa contains 211 genes. Of these 211 genes, 48 genes were annotated as (probable) ribosomal protein genes. The Max1∼4 cluster was searched for genes containing the (G/AC)CC(T/C)AA motif within 1000 bp of the predicted translational start site and an additional eight genes annotated as (probable) ribosomal protein genes were recovered (Table 3). Thus, out of the 48 ribosomal protein genes in N.crassa that showed peak expression 1–4 h post-inoculation, 34 have a consensus motif in their promoter region that may be involved in their transcriptional regulation during conidial germination. Coordinate expression of ribosomal protein genes that respond to altered nutritional conditions have also been reported (6668). Two consensus sequences in the promoter regions of crp-2 gene [encoding 40S ribosomal protein S17 (66)] were identified by deletion analysis of the crp-2 promoter region (68). The (G/A)CCC(T/C)AA consensus motif in the promoter of crp-2 is next to, but does not overlap, the −74 to −66 bp CG repeat, which was shown to be critical for transcriptional regulation based on carbon source (67).

The remaining genes within the Max1∼4 cluster did not contain the (G/A)CCC(T/C)AA consensus motif (Table 3), indicating that other regulatory mechanisms give a similar transcriptional profile to their target genes as those genes involved in ribosome biogenesis. Interestingly, NCU02075.1 (1nc340_120) and NCU03009.1 (B24P7_270) are putative homologs of S.cerevisiae heat shock proteins Ssb and Zuo (4); Ssb and Zuo constitute the S.cerevisiae ribosomal-associated chaperone machinery (6971). Similar to the putative ribosomal protein genes in Max1∼4 cluster, NCU02075.1 (1nc340_120) and NCU03009.1 (B24P7_270) showed peak expression levels between 1 and 4 h, although the (G/A)CCC(T/C)AA consensus motif was not identified in their promoter region.

Comparison of conidial germination transcriptional profiles with transcriptional profiles of germinating spores of U.maydis and D.discoideum

The ability to persist through harsh conditions and emerge from a dormant state, as is manifest in spore germination of bacteria, protozoans, ferns and fungi and seed germination of flowering plants, may rely on some universal genetic properties. Recently, a number of cDNA microarray studies have identified differentially expressed genes during spore germination in a basidiomycete fungus U.maydis (40), in a water fern Ceratopteris richardii (72), in the social amoeba D.discoideum (41) and during seed germination in Brassica oleracea (73). Among the diverse organisms above, the genome sequence and cDNA microarray data for U.maydis and D.discoideum were the most complete. We performed a cross-species comparison of mRNA profiles during spore germination between N.crassa and U.maydis and between N.crassa and D.discoideum to evaluate the hypothesis of conservation of processes associated with the breaking of dormancy and initiation of germination among phylogenetically diverse organisms. Of these three organisms, spore germination in N.crassa has been most well characterized on a biochemical and physiological level. Bidirectional BLAST searches identified 3117 U.maydis orthologs and 2115 D.discoideum orthologs to the predicted 10 032 N.crassa gene set. Expression profiles of these orthologous genes during germination were then cross-examined.

U.maydis is a phytopathogenic fungus and produces dormant propagules called teliospores on maize. Teliospores are diploid, and upon germination, undergo meiosis to form haploid basidiospores. These haploid basidiospores subsequently undergo mating with a cell of the opposite mating type to form the infectious dikaryotic hyphae. Via hybridization to a cDNA microarray representing ∼1/3 of the U.maydis genome, Zahiri et al. (40) identified a number of genes that were differentially expressed at 4 and 11 h after the induction of germination as compared with dormant teliospores. At 4 h post-inoculation, germ tubes of U.maydis start to emerge, a time point morphologically similar to N.crassa conidia at 2 h post-inoculation. At an 11 h time point, germ tubes of U.maydis are elongated, corresponding to a morphological time point of ∼4hr during conidial germination in N.crassa. A comparison of U.maydis clusters to the N.crassa conidial germination profiling dataset showed that orthologs of genes belonging to the ‘11 h up’ cluster in U.maydis were not distributed evenly in the N.crassa conidial germination clusters (Table 4). These orthologs were over-represented in the Max1∼4 cluster (P = 0.011). Gene orthologs found in the both Max1∼4 and ‘11 h up’ were mainly ribosomal proteins (Supplementary Data S3). Similarly, orthologous genes belonging to the ‘11 h down’ cluster in U.maydis were over-represented in the N.crassa Max0.5 and Max1 clusters and under-represented in the Max1∼4 cluster (P = 0.000). Four over-represented gene orthologs, which belonged to Max 0.5, Max1 and ‘11 h down’ showed peak expression at very early time points in both fungi, with expression declining upon germ tube emergence. These genes belonged to diverse FunCat categories (Supplementary Data S3).

The social soil amoeba, D.discoideum (41), produces asexual dormant spores in response to starvation. Two morphological transitions are associated with spore germination in D.discoideum. First, similar to conidial germination in N.crassa, spores swell and lose birefringence, which occurs in the first 1–3 h post-activation. The second transition occurs when spore cases break and amoebae emerge, which begins to occur at 2 h post-activation. Using a cDNA array to 7744 genes, Xu et al. (41) identified three expression clusters during spore germination in D.discoideum. N.crassa is evolutionarily distant from D.discoideum and a smaller number of gene orthologs were identified via a cross-species comparison, as compared with the N.crassa and U.maydis common gene set. Nevertheless, orthologs of genes belonging to the cluster III in D.discoideum were over-represented in the N.crassa Max1 cluster and under-represented in the Max1∼4 cluster (P = 0.030). These orthologs over-represented in Max1 cluster belonged to diverse FunCat categories (Supplementary Data S3). Both the D.discoideum cluster III and the N.crassa cluster Max1 comprised genes that showed an expression nadir in dormant spores, but maximum expression during spore swelling. Thus, a cross-species examination of gene expression patterns during spore germination in N.crassa, U.maydis and D.discoideum indicated that an evolutionarily conserved mechanism may underlie the breaking of dormancy and the initiation of transcription of certain sets of genes involved in early events of the germination process. Continued comparative analysis of transcriptional profiles of the germination processes in diverse organisms may further extend these observations.

CONCLUSION

This study describes the transcriptional program underlying the biochemical and physiological events associated with conidial germination in the filamentous fungus, N.crassa. To observe this transcriptional regulation on a genomic scale, we developed a 70mer oligonucleotide microarray for transcriptional profiling and the evaluated its performance based on the extensive biochemical literature on the conidial germination process (13,19,74). Our gene set for oligonucleotide design comprises 10 032 genes that were predicted using a combination of FGENESH, FGENESH+ and Genewise by the Broad Institute (6) and by annotation via FGENESH, plus manual corrections via GENEMARK, GENSCAN and GENEFINDER by MIPs (7). Out of 3366 oligonucleotides that we designed, synthesized and robotically deposited for microarray analysis, we obtained hybridization data for 3054 for at least one time point between conidial germination until 16 h of growth, indicating that gene predictions of the Neurospora genome are of high quality. We obtained strong statistical estimates of expression levels for 1287 genes during the process of conidial germination. Our estimates of gene expression levels from transcriptional profiling of conidial germination are remarkably consistent with previous data assessing transcript levels of a number of genes and with biochemical processes that have been associated with germination, even though different laboratory strains and conditions were used. These observations reject the hypothesis of non-concordance between mRNA abundance and protein or cellular activities.

N.crassa has an advantage over numerous other eukaryotic microorganisms because biochemical mechanisms associated with the germination process have been extensively studied [for review, see (13,19,74)]. Cross-species examination of expression profiles during spore germination in N.crassa, U.maydis and D.discoideum revealed that some of the same sets of orthologous genes were activated or deactivated under comparable developmental stages among these diverse organisms (40,41). These observations suggest that evolutionary conserved mechanisms are associated with spore germination in general. Orthologous genes with similar profiles during germination belonged to diverse functional categories. This may indicate that common regulatory features that affect diverse processes are conserved during spore germination across diverse species. A comparison between additional complete and robust transcriptional datasets on spore germination in diverse species will further test this hypothesis and may illuminate common regulatory processes.

Our power to determine the expression levels of genes relevant to conidial germination was maximized by a highly replicated, closed circuit experimental design. Closed loops of experimental comparisons within an experimental design have been advocated for a variety of reasons (29,30,42), but primarily because they allow the statistical integration of information from both transitive and direct comparisons (46,47) during estimation of gene expression levels from transcriptional profiling data. Expression levels at time points during conidial germination were estimated by a Bayesian analysis (30) that integrates all the ratio data from a replicated circuit design to yield gene expression level estimates, 95% credible intervals and Bayesian posterior probabilities for directional differential expression. The method is robust to small amounts of missing data, and with sufficient replication allows detection of both large and small differences in gene expression (45).

The N.crassa genome project revealed that 41% of predicted protein-coding sequences have no similarity to known sequences (6). Functional characterization of such genes is one of the most important tasks for the post genome area of N.crassa. Of the 1287 genes for which strong estimates of gene expression level were acquired, 473 have been described as hypothetical, conserved hypothetical or putative genes. Transcriptional profiles from these 473 genes were distributed among clusters associated with initiation (0.5–1 h), early (1–4 h), middle (4–8 h) and late (12–16 h) germination processes. The cascade of biochemical processes associated with conidial germination has been described in detail (13); transcriptional profiles of some of these unknown genes correlate well with biochemical processes associated with conidial germination. Thus, microarray data in N.crassa will guide future laboratory experiments with regard to functional annotation of hypothetical genes. The availability of full genome microarrays will allow expression patterns associated with developmental states in a filamentous fungus to be discerned and will aid in the functional analysis of deletion mutants generated by the Neurospora functional genomics project (http://www.dartmouth.edu/~neurosporagenome/).

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

Figure 1

(A) The closed loop experimental design used for transcriptional profiling during conidial germination. Each arrow represents one competitive hybridization and the arrowhead points to Cy5-labeled cDNA. Micrographs of dormant conidia (0) and of cultures 0.5, 2, 4, 8 and 16 h after hydration are shown in panels. (B) Fraction of conidia with germ tubes. For each time point, 200 spores were scored for presence or absence of germ tubes. Confidence of 95% intervals is shown.

Figure 1

(A) The closed loop experimental design used for transcriptional profiling during conidial germination. Each arrow represents one competitive hybridization and the arrowhead points to Cy5-labeled cDNA. Micrographs of dormant conidia (0) and of cultures 0.5, 2, 4, 8 and 16 h after hydration are shown in panels. (B) Fraction of conidia with germ tubes. For each time point, 200 spores were scored for presence or absence of germ tubes. Confidence of 95% intervals is shown.

Figure 2

(A) Scatter plot of competitive hybridizations. The median pixel intensities of Cy3-labeled cDNA at 0 h was plotted against Cy5-labeled cDNA from 1 h. (B) The median pixel intensities for Cy3-labeled cDNA from 1 h was plotted against Cy5-labeled cDNA from 2 h. Diagonal lines were drawn over probes for the eight bacterial spike controls. (C) A comparison of changes in mRNA quantity during conidial germination at fixed amount of total RNA is shown. Filled squares: mRNA quantified by poly(A) protection assay [data from (49)]. Open circles: mRNA quantity estimated from microarray data by use of cRNA control spikes and BAGEL software. For both experiments, mRNA quantities were smallest in the dormant conidia, and were set to 1 in relation to other time points.

Figure 2

(A) Scatter plot of competitive hybridizations. The median pixel intensities of Cy3-labeled cDNA at 0 h was plotted against Cy5-labeled cDNA from 1 h. (B) The median pixel intensities for Cy3-labeled cDNA from 1 h was plotted against Cy5-labeled cDNA from 2 h. Diagonal lines were drawn over probes for the eight bacterial spike controls. (C) A comparison of changes in mRNA quantity during conidial germination at fixed amount of total RNA is shown. Filled squares: mRNA quantified by poly(A) protection assay [data from (49)]. Open circles: mRNA quantity estimated from microarray data by use of cRNA control spikes and BAGEL software. For both experiments, mRNA quantities were smallest in the dormant conidia, and were set to 1 in relation to other time points.

Table 1

A summary of oligmers, mRNA profiles and EST matches

 Oligomers designed Oligomers yielded mRNA profilesa Oligomers having EST matchesb 
Annotated 1618 814 (50.3%) 452 (27.9%) 
Not annotated 1748 473 (27.0%) 209 (11.9%) 
Total 3366 1287 (38.2%) 661 (19.6%) 
 Oligomers designed Oligomers yielded mRNA profilesa Oligomers having EST matchesb 
Annotated 1618 814 (50.3%) 452 (27.9%) 
Not annotated 1748 473 (27.0%) 209 (11.9%) 
Total 3366 1287 (38.2%) 661 (19.6%) 

amRNA profiles estimated from interlinked experimental design of conidial germination.

bOligomers having matches to the New Mexico cDNA libraries (50) according to Broad Institute (http://www.broad.mit.edu/annotation/fungi/neurospora_crassa_3/est2loci.html).

Figure 3

Proportion of genes that yielded mRNA profiles and annotation status of genes to which oligomers were designed and synthesized. From the left, bars are for total genes, all of the predicted genes with EST matches, genes with perithecial EST matches, genes with mycelial EST matches and genes with conidial EST matches. The total number of predicted genes in each category is shown in parentheses. Below the line indicates annotated genes with (open box) or without (shaded box) mRNA profiles. Above the line indicates unannotated genes with (open box) or without (shaded box) mRNA profiles. Number of corresponding genes is shown for each area.

Figure 3

Proportion of genes that yielded mRNA profiles and annotation status of genes to which oligomers were designed and synthesized. From the left, bars are for total genes, all of the predicted genes with EST matches, genes with perithecial EST matches, genes with mycelial EST matches and genes with conidial EST matches. The total number of predicted genes in each category is shown in parentheses. Below the line indicates annotated genes with (open box) or without (shaded box) mRNA profiles. Above the line indicates unannotated genes with (open box) or without (shaded box) mRNA profiles. Number of corresponding genes is shown for each area.

Figure 4

Comparison of northern blot data (49) and microarray data for selected genes that are transcriptional regulated during conidial germination. Three µg/lane of total RNA was used for the northern data [from (49); used with permission*]. Bars are control cRNA-normalized estimates of relative expression level of mRNA transcripts obtained by BAGEL. An asymmetric 95% credible interval for each expression level is also shown. (A) con-8, a conidium-specific gene. (B) arg-2, a gene for arginine biosynthesis. (C) H4, a histone gene hH4-1. (D) cox-5, cytochrome-C oxidase chain V gene. Units for the ordinates are arbitrary. *Reprinted from (49) Page 121, Copyright (1991) with permission from Elsevier.

Figure 4

Comparison of northern blot data (49) and microarray data for selected genes that are transcriptional regulated during conidial germination. Three µg/lane of total RNA was used for the northern data [from (49); used with permission*]. Bars are control cRNA-normalized estimates of relative expression level of mRNA transcripts obtained by BAGEL. An asymmetric 95% credible interval for each expression level is also shown. (A) con-8, a conidium-specific gene. (B) arg-2, a gene for arginine biosynthesis. (C) H4, a histone gene hH4-1. (D) cox-5, cytochrome-C oxidase chain V gene. Units for the ordinates are arbitrary. *Reprinted from (49) Page 121, Copyright (1991) with permission from Elsevier.

Figure 5

Hierarchical clustering of microarray data and identification of genes with similar transcriptional profiles. (A) A total of 1287 genes were clustered based on their expression profiles across the eight time points. Each gene's expression values were standardized to have mean zero and standard deviation of one across the eight time points. The lighter color in the cluster dendrogram is correlated with a higher expression level. Seven distinct clusters were visually selected, and the node separating each cluster is shown in the distance tree. (B) An average expression profile of genes within each cluster. To obtain each profile, a sum of each gene's expression values across the eight time points was standardized to 1. Next, time course values for all genes in each cluster were summed, and the summed value for the eight time points for each cluster was scaled to 1. The name designation of each cluster and representative profile is shown.

Figure 5

Hierarchical clustering of microarray data and identification of genes with similar transcriptional profiles. (A) A total of 1287 genes were clustered based on their expression profiles across the eight time points. Each gene's expression values were standardized to have mean zero and standard deviation of one across the eight time points. The lighter color in the cluster dendrogram is correlated with a higher expression level. Seven distinct clusters were visually selected, and the node separating each cluster is shown in the distance tree. (B) An average expression profile of genes within each cluster. To obtain each profile, a sum of each gene's expression values across the eight time points was standardized to 1. Next, time course values for all genes in each cluster were summed, and the summed value for the eight time points for each cluster was scaled to 1. The name designation of each cluster and representative profile is shown.

Table 2

Distribution of genes belonging to specific functional categories among the six hierarchical clusters

Main category according to FunCat or (4FunCat sub categorya or table number Observed or expectedb Max0 Max0.5 Max1 Max1∼4 Max8∼16 Min0∼0.5 Other Totalc P-valued 
Biogenesis of cellular components  Obs 24 0.914 
  Exp 2.6 3.4 4.3 2.6 1.7 8.6 0.9   
Cell cycle and DNA processing  Obs 16 16 17 16 72 0.013 
  Exp 7.7 10.3 12.9 7.7 5.1 25.7 2.6   
Cell cycle and DNA processing DNA processing (2nd) Obs 25 0.039 
  Exp 0.5 2.7 6.1 4.2 1.7 7.2 2.6   
Cell fate  Obs 11 15 47 0.403 
  Exp 5.0 6.7 8.4 5.0 3.4 16.8 1.7   
Cell rescue, defense and virulence  Obs 12 18 57 0.307 
  Exp 6.1 8.1 10.2 6.1 4.1 20.4 2.0   
Cell type differentiation  Obs 10 12 44 0.247 
  Exp 4.7 6.3 7.9 4.7 3.1 15.7 1.6   
Cellular communication/signal transduction mechanism  Obs 13 22 48 0.128 
  Exp 5.1 6.9 8.6 5.1 3.4 17.1 1.7   
Cellular transport, transport facilities and transport routes  Obs 22 23 11 44 111 0.003 
  Exp 11.9 15.9 19.8 11.9 7.9 39.6 4.0   
Cellular transport, transport facilities and transport routes Transport facilities (2nd) Obs 12 22 53 0.004 
  Exp 1.1 5.7 12.9 8.9 3.5 15.4 5.5   
Cellular transport, transport facilities and transport routes Transported compounds (2nd) Obs 11 14 19 58 0.024 
  Exp 1.2 6.2 14.1 9.8 3.9 16.8 6.0   
Classification not yet clear-cut  Obs 14 10 39 0.210 
  Exp 4.2 5.6 7.0 4.2 2.8 13.9 1.4   
Energy  Obs 11 28 59 0.000 
  Exp 6.3 8.4 10.5 6.3 4.2 21.1 2.1   
Energy Respiration (2nd) Obs 12 21 0.010 
  Exp 0.4 2.3 5.1 3.5 1.4 6.1 2.2   
Energy Electron transport and membrane-associated energy conservation (2nd) Obs 10 19 0.003 
  Exp 0.4 2.0 4.6 3.2 1.3 5.5 2.0   
Interaction with the cellular environment  Obs 15 14 49 0.323 
  Exp 5.3 7.0 8.8 5.3 3.5 17.5 1.8   
Protein fate (folding, modification and destination)  Obs 11 28 13 38 11 109 0.758 
  Exp 11.7 15.6 19.5 11.7 7.8 38.9 3.9   
Protein synthesis  Obs 15 60 85 0.000f 
  Exp 9.1 12.1 15.2 9.1 6.1 30.4 3.0   
Protein synthesis Ribosome biogenesis (2nd) Obs 49 56 0.000 
  Exp 1.1 6.0 13.6 9.4 3.7 16.2 5.8   
Protein synthesis Ribosomal protein (3rd) Obs 24 24 0.000 
  Exp 0.5 2.6 5.8 4.0 1.6 7.0 2.5   
Protein with binding function or cofactor requirement (structural or catalytic)  Obs 12 15 42 0.177 
  Exp 4.5 6.0 7.5 4.5 3.0 15.0 1.5   
Subcellular localization  Obs 23 51 71 15 72 16 249 0.000f 
  Exp 26.7 35.6 44.5 26.7 17.8 88.9 8.9   
Subcellular localization Mithochondrion (2nd) Obs 16 28 21 82 0.002 
  Exp 0.8 4.1 9.3 6.5 2.6 11.1 4.0   
Subcellular localization Cytoplasm (2nd) Obs 11 27 13 60 0.000 
  Exp 0.8 4.1 9.3 6.5 2.6 11.1 4.0   
Metabolism  Obs 19 55 24 14 55 31 202 0.105 
  Exp 21.6 28.9 36.1 21.6 14.4 72.1 7.2   
Metabolism Amino acid biosynthesis (2nd)e Obs 14 25 0.004 
  Exp 0.5 2.7 6.1 4.2 1.7 7.2 2.6   
Transcription  Obs 13 34 18 25 96 0.009 
  Exp 10.3 13.7 17.1 10.3 6.9 34.3 3.4   
Transcription RNA synthesis (2nd) Obs 13 31 18 25 93 0.021 
  Exp 1.9 10.0 22.6 15.7 6.2 27.0 9.7   
Unclassified proteins  Obs 12 55 108 60 38 134 66 473 0.002f 
  Exp 50.7 67.6 84.5 50.7 33.8 168.9 16.9   
Transcription factors Tables 9–14 in (4Obs 27 0.017 
  Exp 0.5 2.9 6.6 4.6 1.8 7.8 2.8   
Heat shock proteins Table 50 in (4Obs 17 0.007 
  Exp 0.3 1.8 4.1 2.9 1.1 4.9 1.8   
Glucan and chitin synthesis Tables 51–52 in (4Obs 10 0.913 
  Exp 0.2 1.0 2.3 1.6 0.6 2.8 1.0   
 Totalg  26 138 313 217 86 373 134 1287  
Main category according to FunCat or (4FunCat sub categorya or table number Observed or expectedb Max0 Max0.5 Max1 Max1∼4 Max8∼16 Min0∼0.5 Other Totalc P-valued 
Biogenesis of cellular components  Obs 24 0.914 
  Exp 2.6 3.4 4.3 2.6 1.7 8.6 0.9   
Cell cycle and DNA processing  Obs 16 16 17 16 72 0.013 
  Exp 7.7 10.3 12.9 7.7 5.1 25.7 2.6   
Cell cycle and DNA processing DNA processing (2nd) Obs 25 0.039 
  Exp 0.5 2.7 6.1 4.2 1.7 7.2 2.6   
Cell fate  Obs 11 15 47 0.403 
  Exp 5.0 6.7 8.4 5.0 3.4 16.8 1.7   
Cell rescue, defense and virulence  Obs 12 18 57 0.307 
  Exp 6.1 8.1 10.2 6.1 4.1 20.4 2.0   
Cell type differentiation  Obs 10 12 44 0.247 
  Exp 4.7 6.3 7.9 4.7 3.1 15.7 1.6   
Cellular communication/signal transduction mechanism  Obs 13 22 48 0.128 
  Exp 5.1 6.9 8.6 5.1 3.4 17.1 1.7   
Cellular transport, transport facilities and transport routes  Obs 22 23 11 44 111 0.003 
  Exp 11.9 15.9 19.8 11.9 7.9 39.6 4.0   
Cellular transport, transport facilities and transport routes Transport facilities (2nd) Obs 12 22 53 0.004 
  Exp 1.1 5.7 12.9 8.9 3.5 15.4 5.5   
Cellular transport, transport facilities and transport routes Transported compounds (2nd) Obs 11 14 19 58 0.024 
  Exp 1.2 6.2 14.1 9.8 3.9 16.8 6.0   
Classification not yet clear-cut  Obs 14 10 39 0.210 
  Exp 4.2 5.6 7.0 4.2 2.8 13.9 1.4   
Energy  Obs 11 28 59 0.000 
  Exp 6.3 8.4 10.5 6.3 4.2 21.1 2.1   
Energy Respiration (2nd) Obs 12 21 0.010 
  Exp 0.4 2.3 5.1 3.5 1.4 6.1 2.2   
Energy Electron transport and membrane-associated energy conservation (2nd) Obs 10 19 0.003 
  Exp 0.4 2.0 4.6 3.2 1.3 5.5 2.0   
Interaction with the cellular environment  Obs 15 14 49 0.323 
  Exp 5.3 7.0 8.8 5.3 3.5 17.5 1.8   
Protein fate (folding, modification and destination)  Obs 11 28 13 38 11 109 0.758 
  Exp 11.7 15.6 19.5 11.7 7.8 38.9 3.9   
Protein synthesis  Obs 15 60 85 0.000f 
  Exp 9.1 12.1 15.2 9.1 6.1 30.4 3.0   
Protein synthesis Ribosome biogenesis (2nd) Obs 49 56 0.000 
  Exp 1.1 6.0 13.6 9.4 3.7 16.2 5.8   
Protein synthesis Ribosomal protein (3rd) Obs 24 24 0.000 
  Exp 0.5 2.6 5.8 4.0 1.6 7.0 2.5   
Protein with binding function or cofactor requirement (structural or catalytic)  Obs 12 15 42 0.177 
  Exp 4.5 6.0 7.5 4.5 3.0 15.0 1.5   
Subcellular localization  Obs 23 51 71 15 72 16 249 0.000f 
  Exp 26.7 35.6 44.5 26.7 17.8 88.9 8.9   
Subcellular localization Mithochondrion (2nd) Obs 16 28 21 82 0.002 
  Exp 0.8 4.1 9.3 6.5 2.6 11.1 4.0   
Subcellular localization Cytoplasm (2nd) Obs 11 27 13 60 0.000 
  Exp 0.8 4.1 9.3 6.5 2.6 11.1 4.0   
Metabolism  Obs 19 55 24 14 55 31 202 0.105 
  Exp 21.6 28.9 36.1 21.6 14.4 72.1 7.2   
Metabolism Amino acid biosynthesis (2nd)e Obs 14 25 0.004 
  Exp 0.5 2.7 6.1 4.2 1.7 7.2 2.6   
Transcription  Obs 13 34 18 25 96 0.009 
  Exp 10.3 13.7 17.1 10.3 6.9 34.3 3.4   
Transcription RNA synthesis (2nd) Obs 13 31 18 25 93 0.021 
  Exp 1.9 10.0 22.6 15.7 6.2 27.0 9.7   
Unclassified proteins  Obs 12 55 108 60 38 134 66 473 0.002f 
  Exp 50.7 67.6 84.5 50.7 33.8 168.9 16.9   
Transcription factors Tables 9–14 in (4Obs 27 0.017 
  Exp 0.5 2.9 6.6 4.6 1.8 7.8 2.8   
Heat shock proteins Table 50 in (4Obs 17 0.007 
  Exp 0.3 1.8 4.1 2.9 1.1 4.9 1.8   
Glucan and chitin synthesis Tables 51–52 in (4Obs 10 0.913 
  Exp 0.2 1.0 2.3 1.6 0.6 2.8 1.0   
 Totalg  26 138 313 217 86 373 134 1287  

aOrdinal numbers in the parentheses indicate levels of FunCat subcategories (33).

bObserved number of genes and expected number of genes if probabilities of each outcome are independent of the cluster.

cNumber of genes in each of the subcategories.

dHypergeometric probability was used to obtain the P-value using fisher.test( ) in R (bioconductor.org).

eGenes involving in amino acid biosynthesis were selected from a MIPS subcategory ‘amino acid metabolism (2nd)’.

fChi-square test was used to obtain the P-value using chisq.test( ) in R (bioconductor.org).

gNumber of genes in each of the hierarchical clusters.

Figure 6

The re-scaled expression profiles of genes from specified functional categories within a cluster. The averaged profile contours were obtained as in Figure 5B for each group of genes.

Figure 6

The re-scaled expression profiles of genes from specified functional categories within a cluster. The averaged profile contours were obtained as in Figure 5B for each group of genes.

Figure 7

Consensus sequence motif found in the promoter region of 34 putative ribosomal protein genes from the Max1∼4 cluster using BioProspector program (65) and WebLogo (http://weblogo.berkeley.edu).

Figure 7

Consensus sequence motif found in the promoter region of 34 putative ribosomal protein genes from the Max1∼4 cluster using BioProspector program (65) and WebLogo (http://weblogo.berkeley.edu).

Table 3

Motif enrichment analysis

Motif Selected genes from Max1∼4 clustera,b Ribosomal proteins in Max1∼4 clustera,c Total of 1257 genes with mRNA profiles 
GCCCTAA 14 (3.84 × 10−916 (1.07 × 10−955 
G/CCCCT/CAA 26 (4.17 × 10−334 (8.85 × 10−4414 
Motif Selected genes from Max1∼4 clustera,b Ribosomal proteins in Max1∼4 clustera,c Total of 1257 genes with mRNA profiles 
GCCCTAA 14 (3.84 × 10−916 (1.07 × 10−955 
G/CCCCT/CAA 26 (4.17 × 10−334 (8.85 × 10−4414 

P-values for enrichment of strict and degenerated motifs according to fisher's exact test were shown in parentheses.

aThere were 211 genes that belong to Max1∼4 cluster.

bThirty-seven genes were evaluated (see Materials and Methods).

cForty-eight genes are annotated as (putative) ribosomal protein genes out of 211 genes in the Max1∼4 cluster.

Table 4

Cross-species comparisons of spore-germination profiles comprising gene orthologs

Expression profile group Observed or expecteda N.crassa conidial germination profiles 
  Max0 Max0.5 Max1 Max1∼4 Max8∼16 Min0∼0.5 Other Totalb P-valuec 
U.maydis teliospore germination (40)d 
4 H up Obs 0.560 
 Exp 0.0 0.1 0.1 0.4 0.2 0.2 0.1   
4 H down Obs 1.000 
 Exp 0.0 0.1 0.1 0.4 0.2 0.2 0.1   
11 H up Obs 13 0.011 
 Exp 0.0 1.0 1.0 5.2 2.1 2.6 1.0   
11 H down Obs 10 0.000 
 Exp 0.0 0.8 0.8 4.0 1.6 2.0 0.8   
Totalf  10 25  
D.discoideum spore germination (41)e 
Cluster I Obs 0.769 
(Max in dormant spores) Exp 0.0 0.4 0.4 2.0 0.8 1.0 0.4   
Cluster II Obs 0.769 
(Max at 6 h) Exp 0.0 0.4 0.4 2.0 0.8 1.0 0.4   
Cluster III Obs 0.030 
(Max at 2 h) Exp 0.0 0.5 0.5 2.4 1.0 1.2 0.5   
Totalf  16  
Expression profile group Observed or expecteda N.crassa conidial germination profiles 
  Max0 Max0.5 Max1 Max1∼4 Max8∼16 Min0∼0.5 Other Totalb P-valuec 
U.maydis teliospore germination (40)d 
4 H up Obs 0.560 
 Exp 0.0 0.1 0.1 0.4 0.2 0.2 0.1   
4 H down Obs 1.000 
 Exp 0.0 0.1 0.1 0.4 0.2 0.2 0.1   
11 H up Obs 13 0.011 
 Exp 0.0 1.0 1.0 5.2 2.1 2.6 1.0   
11 H down Obs 10 0.000 
 Exp 0.0 0.8 0.8 4.0 1.6 2.0 0.8   
Totalf  10 25  
D.discoideum spore germination (41)e 
Cluster I Obs 0.769 
(Max in dormant spores) Exp 0.0 0.4 0.4 2.0 0.8 1.0 0.4   
Cluster II Obs 0.769 
(Max at 6 h) Exp 0.0 0.4 0.4 2.0 0.8 1.0 0.4   
Cluster III Obs 0.030 
(Max at 2 h) Exp 0.0 0.5 0.5 2.4 1.0 1.2 0.5   
Totalf  16  

aObserved number of genes and expected number of genes if probabilities of each outcome are independent of the cluster.

bNumber of gene orothologs in each of the expression profile groups.

cHypergeometric probability was used to obtain the P-value using fisher.test( ) in R (bioconducter.org).

dFour groups of differentially expressed genes in relation to dormant spores were reported.

eGenes were grouped into three clusters according to their expression profiles by K-means algorithm.

fNumber of gene orthologs in each of the hierarchical clusters.

We thank Sarah Calvo and James Galagan (The Broad Institute) for making genomic data available for oligonucleotide prediction, Qikai Xu and Gad Shaulsky for sharing Dictyostelium microarray data with us. We thank Audrey Gasch, Mike Eisen, Jing Zhu and Joe DeRisi for valuable discussions on the development of oligonucleotide microarrays for N.crassa. We thank Sarah Brown, Stu Brody and Matt Sachs for helpful suggestions and for reviewing our manuscript. The work was funded by a University of California Biological Sciences award to N.L.G, a National Institutes of Health multi-institutional program project grant (GM068087) to N.L.G (Core III: transcriptional profiling) and a National Science Foundation grant (DEB-0316710) to J.W.T. J.P.T. was supported by a Miller Post-doctoral Fellowship. Funding to pay the Open Access publication charges for this article was provided by NIH grant GM068087 to N.L.G.

Conflict of interest statement. None declared.

REFERENCES

1
Collins, F.S., Lander, E.S., Rogers, J., Waterston, R.H., Conso, I.H.G.S.
2004
Finishing the euchromatic sequence of the human genome
Nature
 
431
931
–945
2
Davis, R.H.
NEUROSPORA: Contributions of a Model Organism
 
2000
NY Oxford University Press
3
Davis, R.H. and Perkins, D.D.
2002
Neurospora: a model of model microbes
Nature Rev. Genet.
 
3
397
–403
4
Borkovich, K.A., Alex, L.A., Yarden, O., Freitag, M., Turner, G.E., Read, N.D., Seiler, S., Bell-Pedersen, D., Paietta, J., Plesofsky, N., et al.
2004
Lessons from the genome sequence of Neurospora crassa: tracing the path from genomic blueprint to multicellular organism
Microbiol. Molec. Biol. Rev.
 
68
1
–108
5
Bistis, G.N., Perkins, D.D., Read, N.D.
2003
Cell types of Neurospora crassa
Fungal Genet. Newslett.
 
50
17
–19
6
Galagan, J.E., Calvo, S.E., Borkovich, K.A., Selker, E.U., Read, N.D., Jaffe, D., FitzHugh, W., Ma, L.J., Smirnov, S., Purcell, S., et al.
2003
The genome sequence of the filamentous fungus Neurospora crassa
Nature
 
422
859
–868
7
Mannhaupt, G., Montrone, C., Haase, D., Mewes, H.W., Aign, V., Hoheisel, J.D., Fartmann, B., Nyakatura, G., Kempken, F., Maier, J., et al.
2003
What's in the genome of a filamentous fungus? Analysis of the Neurospora genome sequence
Nucleic Acids Res.
 
31
1944
–1954
8
DeRisi, J., Penland, L., Brown, P.O., Bittner, M.L., Meltzer, P.S., Ray, M., Chen, Y., Su, Y.A., Trent, J.M.
1996
Use of a cDNA microarray to analyze gene expression patterns in human cancer
Nature Genet.
 
14
457
–460
9
Eisen, M. and Brown, P.O.
1999
DNA arrays for analysis of gene expression In Weissman, S.M. (Ed.).
Methods in Enzymology
  San Diego, CA Academic Press Vol.
303
pp.
179
–205
10
Wu, L.F., Hughes, T.R., Davierwala, A.P., Robinson, M.D., Stoughton, R., Altschuler, S.J.
2002
Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters
Nature Genet.
 
31
255
–265
11
Pradet-Balade, B., Boulme, F., Beug, H., Mullner, E.W., Garcia-Sanz, J.A.
2001
Translation control: bridging the gap between genomics and proteomics?
Trends Biochem. Sci.
 
26
225
–229
12
Feder, M.E. and Walser, J.C.
2005
The biological limitations of transcriptomics in elucidating stress and stress responses
J. Evol. Biol.
 
18
901
–910
13
Schmit, J.C. and Brody, S.
1976
Biochemical Genetics of Neurospora crassa Conidial Germination
Bacteriol. Rev.
 
40
1
–41
14
Roca, M.G., Arlt, J., Jeffree, C.E., Read, N.D.
2005
Cell biology of conidial anastomosis tubes in Neurospora crassa
Eukaryotic Cell
 
4
911
–919
15
Springer, M.L.
1993
Genetic control of fungal differentiation—the 3 sporulation pathways of Neurospora crassa
Bioessays
 
15
365
–374
16
Lowry, R.J., Durkee, T.L., Sussman, A.
1967
Ultrastructural studies of microconidium formation in Neurospora crassa
J. Bacteriol.
 
94
1757
–1763
17
Denning, D.W., Anderson, M.J., Turner, G., Latge, J.P., Bennett, J.W.
2002
Sequencing the Aspergillus fumigatus genome
Lancet Infect. Dis.
 
2
251
–253
18
Griffin, D.H.
Fungal Physiology, 2nd edn.
 
1994
NY Wiley-Liss, Inc
19
d'Enfert, C.
1997
Fungal spore germination: Insights from the molecular genetics of Aspergillus nidulans and Neurospora crassa
Fungal Genet. Biol.
 
21
163
–172
20
d'Enfert, C., Bonini, B.M., Zapella, P.D.A., Fontaine, T., da Silva, A.M., Terenzi, H.F.
1999
Neutral trehalases catalyse intracellular trehalose breakdown in the filamentous fungi Aspergillus nidulans and Neurospora crassa
Molec. Microbiol.
 
32
471
–483
21
Mirkes, P.E. and McCalley, B.
1976
Synthesis of polyadenylic acid-containing ribonucleic acid during the germination of Neurospora crassa conidia
J. Bacteriol.
 
125
174
–180
22
Loo, M.
1975
Neurospora crassa temperature-sensitive mutant apparently defective in protein synthesis
J. Bacteriol.
 
121
286
–295
23
Momany, M.
2002
Polarity in filamentous fungi: establishment, maintenance and new axes
Curr. Opin. Microbiol.
 
5
580
–585
24
Pandey, A., Roca, M.G., Read, N.D., Glass, N.L.
2004
Role of a mitogen-activated protein kinase pathway during conidial germination and hyphal fusion in Neurospora crassa
Eukaryotic Cell
 
3
348
–358
25
Vogel, H.J.
1956
A convenient growth medium for Neurospora (Medium N)
Microbial. Genet. Bull.
 
13
42
–43
26
Wang, H.-Y., Malek, R.L., Kwitek, A.E., Greene, A.S., Luu, T.V., Behbahani, B., Frank, B., Quackenbush, J., Lee, N.H.
2003
Assessing unmodified 70-mer oligonucleotide probe performance on glass-slide microarrays
Genome Biol.
 
4
R5.1
–R5.13
27
Bozdech, Z., Zhu, J., Joachimiak, M.P., Cohen, F.E., Pulliam, B., DeRisi, J.L.
2003
Expression profiling of the schizont and trophozoite stages of Plasmodium falciparum with a long-oligonucleotide microarray
Genome Biol.
 
4
R9
28
Martinez, M.J., Aragon, A.D., Rodriguez, A.L., Weber, J.M., Timlin, J.A., Sinclair, M.B., Haaland, D.M., Werner-Washburne, M.
2003
Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays—art. no. e18
Nucleic Acids Res.
 
31
E18
29
Yang, Y.H. and Speed, T.
2002
Design issues for cDNA microarray experiments
Nature Rev. Genet.
 
3
579
–588
30
Townsend, J.P. and Hartl, D.L.
2002
Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments
Genome Biol.
 
3
research0071.0071–0071.0016
31
Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.
1998
Cluster analysis and display of genome-wide expression patterns
Proc. Natl Acad. Sci. USA
 
95
14863
–14868
32
Seo, J. and Shneiderman, B.
2002
Interactively exploring hierarchical clustering results
IEEE Comput.
 
35
80
–86
33
Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Guldener, U., Mannhaupt, G., Munsterkotter, M., et al.
2004
The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes
Nucleic Acids Res.
 
32
5539
–5545
34
Frishman, D., Albermann, K., Hani, J., Heumann, K., Metanomski, A., Zollner, A., Mewes, H.W.
2001
Functional and structural genomics using PEDANT
Bioinformatics
 
17
44
–57
35
McGuire, A.M., Hughes, J.D., Church, G.M.
2000
Conservation of DNA regulatory motifs and discovery of new motifs in microbial genomes
Genome Res.
 
10
744
–757
36
Liu, X.S., Brutlag, D.L., Liu, J.S.
2002
An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments
Nat. Biotechnol.
 
20
835
–839
37
Bailey, T.L. and Elkan, C.
Second International Conference on Intelligent Systems for Molecular Biology
 
1994
Menlo Park, CA AAAI Press
38
Kreppel, L., Fey, P., Gaudet, P., Just, E., Kibbe, W.A., Chisholm, R.L., Kimmel, A.R.
2004
dictyBase: a new Dictyostelium discoideum genome database
Nucleic Acids Res.
 
32
D332
–D333
39
Eichinger, L., Pachebat, J.A., Glockner, G., Rajandream, M.A., Sucgang, R., Berriman, M., Song, J., Olsen, R., Szafranski, K., Xu, Q., et al.
2005
The genome of the social amoeba Dictyostelium discoideum
Nature
 
435
43
–57
40
Zahiri, A.R., Babu, M.R., Saville, B.J.
2005
Differential gene expression during teliospore germination in Ustilago maydis
Mol. Genet. Genomics
 
273
394
–403
41
Xu, Q.K., Ibarra, M., Mahadeo, D., Shaw, C., Huang, E.Y., Kuspa, A., Cotter, D., Shaulsky, G.
2004
Transcriptional transitions during Dictyostelium spore germination
Eukaryotic Cell
 
3
1101
–1110
42
Kerr, M.K. and Churchill, G.A.
2001
Statistical design and the analysis of gene expression microarray data
Genet. Res.
 
77
123
–128
43
Wolfinger, R.D., Gibson, G., Wolfinger, E.D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C., Paules, R.S.
2001
Assessing gene significance from cDNA microarray expression data via mixed models
J. Comput. Biol.
 
8
625
–637
44
Jin, W., Riley, R.M., Wolfinger, R.D., White, K.P., Passador-Gurgel, G., Gibson, G.
2001
The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster
Nature Genet.
 
29
389
–395
45
Townsend, J.P.
2004
Resolution of large and small differences in gene expression using models for the Bayesian analysis of gene expression levels and spotted DNA microarrays
BMC Bioinformatics
 
5
54
46
Townsend, J.P.
2003
Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays
BMC Genomics
 
4
41
47
Vinciotti, V., Khanin, R., D'Alimonte, D., Liu, X., Cattini, N., Hotchkiss, G., Bucca, G., de Jesus, O., Rasaiyaah, J., Smith, C.P., et al.
2005
An experimental evaluation of a loop versus a reference design for two-channel microarrays
Bioinformatics
 
21
492
–501
48
Townsend, J.P. In Zimmer, E. and Roalson, E. (Eds.).
Methods in Enzymology: Molecular Evolution, Producing the Biochemical Data, Part B
 
2005
San Diego Academic Press Vol.
395
pp.
597
–617
49
Sachs, M.S. and Yanofsky, C.
1991
Developmental expression of genes involved in conidiation and amino acid biosynthesis in Neurospora crassa
Dev. Biol.
 
148
117
–128
50
Nelson, M.A., Kang, S.C., Braun, E.L., Crawford, M.E., Dolan, P.L., Leonard, P.M., Mitchell, J., Armijo, A.M., Bean, L., Blueyes, E., et al.
1997
Expressed sequences from conidial, mycelial, and sexual stages of Neurospora crassa
Fungal Genet. Biol.
 
21
348
–363
51
Dolan, P.L., Natvig, O.D., Nelson, M.A.
2000
Neurospora Proteome 2000
Fungal Genet. Newslett.
 
47
7
–24
52
DeRisi, J., Iyer, V.R., Brown, P.O.
1997
Exploring the metabolic and genetic control of gene expression on a genomic scale
Science
 
278
680
–686
53
Berlin, V. and Yanofsky, C.
1985
Isolation and characterization of genes differentially expressed during conidiation of Neurospora crassa
Mol. Cell. Biol.
 
5
849
–855
54
Bieszke, J.A., Braun, E.L., Bean, L.E., Kang, S.C., Natvig, D.O., Borkovich, K.A.
1999
The nop-1 gene of Neurospora crassa encodes a seven transmembrane helix retinal-binding protein homologous to archaeal rhodopsins
Proc. Natl Acad. Sci. USA
 
96
8034
–8039
55
Fracella, F., Scholle, C., Kallies, A., Haker, T., Schroder, T., Rensing, L.
1997
Differential HSC70 expression during asexual development of Neurospora crassa
Microbiology
 
143
3615
–3624
56
Schmit, J.C. and Brody, S.
1975
Neurospora crassa conidial germination: the role of endogenous amino acid pools
J. Bacteriol.
 
124
232
–242
57
Scarborough, G.A.
1973
Transcript in Neurospora
Int. Rev. Cytol.
 
34
103
–122
58
Colvin, H.J., Sauer, B.L., Munkres, K.D.
1973
Respiration of wild type and extrachromosomal mutants of Neurospora crassa
J. Bacteriol.
 
116
1314
–1321
59
Xie, X., Wilkinson, H.H., Correa, A., Lewis, Z.A., Bell-Pedersen, D., Ebbole, D.J.
2004
Transcriptional response to glucose starvation and functional analysis of a glucose transporter of Neurospora crassa
Fungal Genet. Biol.
 
41
1104
–1119
60
Schmit, J.C. and Brody, S.
1975
Changes in glucosamine and galactosamine levels during conidial germination in Neurospora crassa
J. Bacteriol.
 
122
1062
–1070
61
Ruiz-Herrera, J.
Fungal Cell Wall: Structure, Synthesis and Assembly
 
1992
Florida Boca Raton
62
Gasch, A.P., Moses, A.M., Chiang, D.Y., Fraser, H.B., Berardini, M., Eisen, M.B.
2004
Conservation and evolution of cis-regulatory systems in ascomycete fungi
Plos Biol.
 
2
2202
–2219
63
Sinha, S. and Tompa, M.
2002
Discovery of novel transcription factor binding sites by statistical overrepresentation
Nucleic Acids Res.
 
30
5549
–5560
64
Birnbaum, K., Benfey, P.N., Shasha, D.E.
2001
cis element/transcription factor analysis (cis/TF): a method for discovering transcription factor/cis element relationships
Genome Res.
 
11
1567
–1573
65
Liu, X., Brutlag, D.L., Liu, J.S.
2001
BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes
Pac. Symp. Biocomput.
 
127
–138
66
Shi, Y.G. and Tyler, B.M.
1991
Coordinate expression of ribosomal-protein genes in Neurospora crassa and identification of conserved upstream sequences
Nucleic Acids Res.
 
19
6511
–6517
67
Cujec, T.P. and Tyler, B.M.
1996
Functional promoter elements common to ribosomal protein and ribosomal RNA genes in Neurospora crassa
Mol. Gen. Genet.
 
253
205
–216
68
Cujec, T.P. and Tyler, B.M.
1996
Nutritional and growth control of ribosomal protein mRNA and rRNA in Neurospora crassa
Nucleic Acids Res.
 
24
943
–950
69
Yan, W., Schilke, B., Pfund, C., Walter, W., Kim, S.W., Craig, E.A.
1998
Zuotin, a ribosome-associated DnaJ molecular chaperone
EMBO J.
 
17
4809
–4817
70
Pfund, C., Lopez-Hoyo, N., Ziegelhoffer, T., Schilke, B.A., Lopez-Buesa, P., Walter, W.A., Wiedmann, M., Craig, E.A.
1998
The molecular chaperone Ssb from Saccharomyces cerevisiae is a component of the ribosome nascent chain complex
EMBO J.
 
17
3981
–3989
71
Hundley, H.A., Walter, W., Bairstow, S., Craig, E.A.
2005
Human Mpp11 J protein: ribosome-tethered molecular chaperones are ubiquitous
Science
 
308
1032
–1034
72
Salmi, M.L., Bushart, T.J., Stout, S.C., Roux, S.J.
2005
Profile and analysis of gene expression changes during early development in germinating spores of Ceratopteris richardii
Plant Physiol.
 
138
1734
–1745
73
Soeda, Y., Konings, M.C.J.M., Vorst, O., van Houwelingen, A.M.M.L., Stoopen, G.M., Maliepaard, C.A., Kodde, J., Bino, R.J., Groot, S.P.C., van der Geest, A.H.M.
2005
Gene expression programs during Brassica oleracea seed maturation, osmopriming, and germination are indicators of progression of the germination process and the stress tolerance level
Plant Physiol.
 
137
354
–368
74
Osherov, N. and May, G.S.
2001
The molecular mechanisms of conidial germination
Fems Microbiol. Lett.
 
199
153
–160

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