Mutants responsive to non-photorespiratory conditions.

<p>High CO₂ is 0.3%, air is ambient atmosphere. Mutants presented are those with numbers of seedlings with germination, survival, or morphology differences significant at p = 0.05 or less (Student’s T-test). Homozygous genotypes for seedlings with abnormal morphologies were confirmed by PCR except SALK_008478.</p>a<p>see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone-0073291-g001" target="_blank">Figure 1</a>.</p>b<p>n = , number of discrete growouts conducted in indicated conditions.</p>c<p>percentage of seedlings that survived to three weeks old in indicated conditions.</p>d<p>percentage of seeds sown that yielded a seedling with described abnormal appearance.</p>e<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Wei1" target="_blank">[52]</a> for additional characterized alleles that could be screened.</p>f<p>Germinated albino seedlings did not survive to 3 weeks; stronger alleles are embryo lethal <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Asakura1" target="_blank">[53]</a>.</p>g<p>Additional alleles could be screened to confirm result.</p>h<p>See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Romani1" target="_blank">[86]</a>.</p>i<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Robles1" target="_blank">[81]</a>.</p>j<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Boldt1" target="_blank">[87]</a>.</p>k<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291-Peterhansel1" target="_blank">[48]</a>.</p

Analysis of Essential Arabidopsis Nuclear Genes Encoding Plastid-Targeted Proteins

<div><p>The Chloroplast 2010 Project (<a href="http://www.plastid.msu.edu/" target="_blank">http://www.plastid.msu.edu/</a>) identified and phenotypically characterized homozygous mutants in over three thousand genes, the majority of which encode plastid-targeted proteins. Despite extensive screening by the community, no homozygous mutant alleles were available for several hundred genes, suggesting that these might be enriched for genes of essential function. Attempts were made to generate homozygotes in ∼1200 of these lines and 521 of the homozygous viable lines obtained were deposited in the Arabidopsis Biological Resource Center (<a href="http://abrc.osu.edu/" target="_blank">http://abrc.osu.edu/</a>). Lines that did not yield a homozygote in soil were tested as potentially homozygous lethal due to defects either in seed or seedling development. Mutants were characterized at four stages of development: developing seed, mature seed, at germination, and developing seedlings. To distinguish seed development or seed pigment-defective mutants from seedling development mutants, development of seeds was assayed in siliques from heterozygous plants. Segregating seeds from heterozygous parents were sown on supplemented media in an attempt to rescue homozygous seedlings that could not germinate or survive in soil. Growth of segregating seeds in air and air enriched to 0.3% carbon dioxide was compared to discover mutants potentially impaired in photorespiration or otherwise responsive to CO₂ supplementation. Chlorophyll fluorescence measurements identified CO₂-responsive mutants with altered photosynthetic parameters. Examples of genes with a viable mutant allele and one or more putative homozygous-lethal alleles were documented. RT-PCR of homozygotes for potentially weak alleles revealed that essential genes may remain undiscovered because of the lack of a true null mutant allele. This work revealed 33 genes with two or more lethal alleles and 73 genes whose essentiality was not confirmed with an independent lethal mutation, although in some cases second leaky alleles were identified.</p></div

Chlorophyll fluorescence false color images for mutants with altered growth in 0.3% CO₂.

<p>Plants shown at left photographed after growth on enriched CO₂ and before analysis. The meanings of the column headings and cutoffs for ‘+’ and ‘−’ are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone-0073291-t003" target="_blank">Table 3</a>. Arrows indicate homozygous mutant plants. A, <i>shm1-1</i>. B, SALK_073728, an allele of glycerate kinase. C, <i>npq1-1</i>. D, allele FLAG_202E10 of At3g17930. E, allele SALK_088638 of At3g17930. F, allele SALK_032903 of At4g17620. G, allele SALK_097243 of At4g14605.</p

Seedling phenotypes found on supplemented media.

<p>Top, phenotype classes (x-axis) refer to portion of segregating seed progeny with phenotype consistently different than wild-type Col sown on the same plate. ‘1’, no rescue on any type of medium (germination ranged from 0 - 89%). ‘2’, germination ≥90% with all seedlings indistinguishable from wild-type Col on all media tested. ‘3’, very early seed development mutants, all seedlings shown or presumed to be heterozygous for the mutation or homozygous wild type. ‘4’, seedling growth with positive response to 0.5% sucrose. ‘4a’, completely albino on all media tested. ‘4b’, albino without 0.5% sucrose supplementation, green with sucrose. ‘4c’, green with or without sucrose supplementation. ‘5’, growth affected by amino acid supplementation. Examples shown: 4a, allele SALK_002470 of At1g63970 encoding 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase, 4b, allele SALK_056011 of At4g17740 encoding a peptidase S41 family protein, 4c, allele SALK_151530 of At1g50900, encoding <i>GDC1,</i> 5, allele SALK_090549 Chloroplast Calvin Cycle sedoheptulose-1,7-bisphosphatase gene At3g55800.</p

Chlorophyll fluorescence measurements for mutants affected by growth in nonphotorespiratory conditions.

<p>Controls are described further in text. Fv/Fm, +, measurement indicating reduced Fv/Fm, −, measurement indicating no difference compared to wild type Col. NPQ, +, measurement indicating increased NPQ, −, measurement indicating no difference compared to wild type Col. BHL, before high light treatment, AHL, after high light treatment, Recovery, after 2 days return to original growth conditions. ND, direct air vs. high CO₂ comparisons were not done for these mutant controls.</p

RT-PCR for selected T-DNA mutants, allele strength, and expression.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone-0073291-g001" target="_blank">Figure 1</a> for explanation of allele strength scores. Top (A – E), RT-PCR for selected T-DNA mutants, A, SALK_039213 (allele score 9), B, SALK_087168 (allele score 5), C, SAIL_373_H09 (allele score 8), D, WiscDsLox477-480L12 (allele score 9), E, SALK_004303 (allele score 3). For each panel, the lanes refer to amplified cDNA from: 1, wild-type Col RNA using gene-specific primers; 2, mutant using ef1α primers; 3, mutant RNA using gene-specific primers. A, C, E: expressed. B, D: partially expressed. Middle, summary of allele strengths for all mutants genotyped, lethal and viable phenotypes. Bottom, RT-PCR results for the tested subset of homozygous viable alleles categorized by allele strength score. No alleles of score 11 were tested.</p

Summary of developing seed phenotypes of homozygous lethal mutants.

<p>Summary numbers indicate all mutants from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone-0073291-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073291#pone.0073291.s005" target="_blank">S1</a> with no previous identification as homozygous lethal. Morphology classification descriptions are based on our observations, guided by tutorial tools at <a href="http://www.seedgenes.org" target="_blank">www.seedgenes.org</a>. Seedling lethal or severe seedling growth or survival defect: seeds appear normal throughout development and at maturity, but seeds do not germinate, or seedlings are not viable. Very early seed development defect: seed development appears arrested very early, sometimes similar to unfertilized ovules. Seed development defect: seed development arrested before full maturity. Seed pigment defect: pigment abnormalities in developing and mature seeds. Phenotype examples, yellow bar = 1 mm. Seedling lethal: SALK_011114 annotated as affecting At2g29630, encoding an enzyme of vitamin B1 (thiamine) biosynthesis; upper image, intermediate stage, lower image, late stage (see text). Bottom two images SAIL_1274_C03 annotated as affecting At4g34090, ‘unknown protein’; upper, intermediate stage, lower, late stage. Very early seed development defect, late stage siliques. Top, SALK_058595, annotated as affecting At2g20690, ‘riboflavin synthase’; bottom, SALK_094586, annotated as affecting At1g03910, ‘unknown protein with cactin-binding domain’. Seed development defect, late stage siliques, top, SALK_014008, annotated as affecting At1g21650, ‘Sec system component’; bottom, SALK_035460, annotated as affecting At5g60750, ‘CAAX amino terminal protease family protein’. Seed pigment defect: late stage siliques, top, FLAG_583E05, annotated as affecting At4g14870, ‘Secretory system component’; bottom, SAIL_810_G07, annotated as affecting At5g01590, ‘unknown protein’.</p

Utility and Limitations of Using Gene Expression Data to Identify Functional Associations

<div><p>Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using <i>Arabidopsis thaliana</i> as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.</p></div

Impact of datasets on pathway EC percentile.

<p><b>(A)</b> Relationship between pathway EC percentiles calculated using the combined stress gene expression dataset and those calculated based on one of the individual stress datasets, abiotic/shoot. <b>(B)</b> Relationship between pathway EC percentiles calculated using the light, development and stress combined dataset and those calculated based on individual dataset, stress. In (A) and (B) the dashed line represents <i>y</i> = <i>x</i>, and each dot represents a pathway. <b>(C)</b> Individual and combinations of datasets used to determine pathway EC Percentiles. *: NASCArray consisting of all the datasets listed here as well as additional datasets (~700 samples). The columns in (C) correspond to those in (D) and (E). <b>(D)</b> Bar plot of percent high EC pathways using different expression datasets <b>(E)</b> Heat map of pathway EC percentiles from 13 gene expression datasets. Dark red: EC percentiles≥ 95. Orange: 95 > EC percentiles < 75. Yellow: 75 > EC percentiles <50, Blue: 50 > EC percentiles < 0 <b>(F)</b> Histogram of the numbers of datasets leading to high EC values for each pathway. Example pathways are labeled with an arrow.</p

Relationship between pathway ECs, annotation quality and similarity measures.

<p><b>(A)</b> Relationship between the EC calculated for pathway genes that are annotated based on experimental evidence (ECexp) and EC calculated for pathway genes that are annotated only based on computational evidence (ECcomp). The genes used to calculate ECexp and ECcomp do not overlap. Each dot represents one pathway. Dashed line: <i>y</i> = <i>x</i> line. <b>(B)</b> Heatmap of correlations between pathway EC percentiles calculated with: partial correlations estimated with the corpcor method, Spearman’s rank correlation coefficient (Spearman), Pearson Correlation Coefficient (PCC), adjusted and normalized Mutual Information (MI), partial correlation calculated with the partialcorr method, and transformed <i>p-</i>values of Bayesian Network (BN) (<b>C)</b> Percent pathways that have high EC using different similarity measures. <b>(D)</b> Heatmap of pathway EC percentiles calculated using different similarity measures. Color represents EC percentiles. White dotted rectangles: high EC pathways that are specific to one measure.</p