Search

In Arabidopsis, DNA damage-induced programmed cell death is limited to the meristematic stem cell niche and its early descendants. The significance of this cell-type-specific programmed cell death is unclear. Here, we demonstrate in roots that it is the programmed destruction of the mitotically compromised stem cell niche that triggers its regeneration, enabling growth recovery. In contrast to wild-type plants, sog1 plants, which are defective in damage-induced programmed cell death, maintain the cell identities and stereotypical structure of the stem cell niche after irradiation, but these cells fail to undergo cell division, terminating root growth. We propose DNA damage-induced programmed cell death is employed by plants as a developmental response, contrasting with its role as an anticarcinogenic response in animals. This role in plants may have evolved to restore the growth of embryos after the accumulation of DNA damage in seeds.

Targeting Induced Local Lesions in Genomes (TILLING) provides a nontransgenic method for reverse genetics that is widely applicable, even in species where other functional resources are missing or expensive to build. The efficiency of TILLING, however, is greatly facilitated by high mutation density. Species vary in the number of mutations induced by comparable mutagenic treatments, suggesting that genetic background may affect the response. Allopolyploid species have often yielded higher mutation density than diploids. To examine the effect of ploidy, we autotetraploidized the Arabidopsis (Arabidopsis thaliana) ecotype Columbia, whose diploid has been used for TILLING extensively, and mutagenized it with 50 mm ethylmethane sulfonate. While the same treatment sterilized diploid Columbia, the tetraploid M1 plants produced good seed. To determine the mutation density, we searched 528 individuals for induced mutations in 15 genes for which few or no knockout alleles were previously available. We constructed tridimensional pools from the genomic DNA of M2 plants, amplified target DNA, and subjected them to Illumina sequencing. The results were analyzed with an improved version of the mutation detection software CAMBa that accepts any pooling scheme. This small population provided a rich resource with approximately 25 mutations per queried 1.5-kb fragment, including on average four severe missense and 1.3 truncation mutations. The overall mutation density of 19.4 mutations Mb–1 is 4 times that achieved in the corresponding diploid accession, indicating that genomic redundancy engenders tolerance to high mutation density. Polyploidization of diploids will allow the production of small populations, such as less than 2,000, that provide allelic series from knockout to mild loss of function for virtually all genes.

TILLING is a method to find mutations in a gene of interest by scanning amplicons from a mutagenized population for sequence changes, commonly a single nucleotide. In the past 5 years, mutation detection by sequencing has become increasingly popular. This chapter details the experimental flow for TILLING-by-Sequencing, highlighting the critical steps involved in tridimensional pooling of genomic DNA templates, preparation of libraries for high-throughput sequencing, and bioinformatic processing of the sequence data.

TILLING is a method to find mutations in a gene of interest by scanning amplicons from a mutagenized population for sequence changes, commonly a single nucleotide. In the past 5 years, mutation detection by sequencing has become increasingly popular. This chapter details the experimental flow for TILLING-by-Sequencing, highlighting the critical steps involved in tridimensional pooling of genomic DNA templates, preparation of libraries for high-throughput sequencing, and bioinformatic processing of the sequence data.

Plants exhibit a robust transcriptional response to gamma radiation which includes the induction of transcripts required for homologous recombination and the suppression of transcripts that promote cell cycle progression. Various DNA damaging agents induce different spectra of DNA damage as well as collateral damage to other cellular components and therefore are not expected to provoke identical responses by the cell. Here we study the effects of two different types of ionizing radiation (IR) treatment, HZE (1 GeV Fe26+ high mass, high charge, and high energy relativistic particles) and gamma photons, on the transcriptome of Arabidopsis thaliana seedlings. Both types of IR induce small clusters of radicals that can result in the formation of double strand breaks (DSBs), but HZE also produces linear arrays of extremely clustered damage. We performed these experiments across a range of time points (1.5 to 24 hours after irradiation) in both wild-type plants and in mutants defective in the DSB-sensing protein kinase ATM. The two types of IR exhibit a shared double strand break-repair-related damage response, although they differ slightly in the timing, degree, and ATM-dependence of the response. The ATM-dependent, DNA metabolism-related transcripts of the DSB response were also induced by other DNA damaging agents, but were not induced by conventional stresses. Both Gamma and HZE irradiation induced, at 24 hours post-irradiation, ATM-dependent transcripts associated with a variety of conventional stresses; these were overrepresented for pathogen response, rather than DNA metabolism. In contrast, only HZE-irradiated plants, at 1.5 hr after irradiation, exhibited an additional and very extensive transcriptional response, shared with plants experiencing extended night. This response was not apparent in gamma-irradiated plants.

Background TILLING (Targeting induced local lesions IN genomes) is an efficient reverse genetics approach for detecting induced mutations in pools of individuals. Combined with the high-throughput of next-generation sequencing technologies, and the resolving power of overlapping pool design, TILLING provides an efficient and economical platform for functional genomics across thousands of organisms. Results We propose a probabilistic method for calling TILLING-induced mutations, and their carriers, from high throughput sequencing data of overlapping population pools, where each individual occurs in two pools. We assign a probability score to each sequence position by applying Bayes' Theorem to a simplified binomial model of sequencing error and expected mutations, taking into account the coverage level. We test the performance of our method on variable quality, high-throughput sequences from wheat and rice mutagenized populations. Conclusions We show that our method effectively discovers mutations in large populations with sensitivity of 92.5% and specificity of 99.8%. It also outperforms existing SNP detection methods in detecting real mutations, especially at higher levels of coverage variability across sequenced pools, and in lower quality short reads sequence data. The implementation of our method is available from: http://www.cs.ucdavis.edu/~filkov/CAMBa/.

Discovery of rare mutations in populations requires methods, such as TILLING (for Targeting Induced Local Lesions in Genomes), for processing and analyzing many individuals in parallel. Previous TILLING protocols employed enzymatic or physical discrimination of heteroduplexed from homoduplexed target DNA. Using mutant populations of rice (Oryza sativa) and wheat (Triticum durum), we developed a method based on Illumina sequencing of target genes amplified from multidimensionally pooled templates representing 768 individuals per experiment. Parallel processing of sequencing libraries was aided by unique tracer sequences and barcodes allowing flexibility in the number and pooling arrangement of targeted genes, species, and pooling scheme. Sequencing reads were processed and aligned to the reference to identify possible single-nucleotide changes, which were then evaluated for frequency, sequencing quality, intersection pattern in pools, and statistical relevance to produce a Bayesian score with an associated confidence threshold. Discovery was robust both in rice and wheat using either bidimensional or tridimensional pooling schemes. The method compared favorably with other molecular and computational approaches, providing high sensitivity and specificity.

Background One of the great advantages of next generation sequencing is the ability to generate large genomic datasets for virtually all species, including non-model organisms. It should be possible, in turn, to apply advanced computational approaches to these datasets to develop models of biological processes. In a practical sense, working with non-model organisms presents unique challenges. In this paper we discuss some of these challenges for ChIP-seq and RNA-seq experiments using the undomesticated tree species of the genus Populus. Results We describe specific challenges associated with experimental design in Populus, including selection of optimal genotypes for different technical approaches and development of antibodies against Populus transcription factors. Execution of the experimental design included the generation and analysis of Chromatin immunoprecipitation-sequencing (ChIP-seq) data for RNA polymerase II and transcription factors involved in wood formation. We discuss criteria for analyzing the resulting datasets, determination of appropriate control sequencing libraries, evaluation of sequencing coverage needs, and optimization of parameters. We also describe the evaluation of ChIP-seq data from Populus, and discuss the comparison between ChIP-seq and RNA-seq data and biological interpretations of these comparisons. Conclusions These and other "lessons learned" highlight the challenges but also the potential insights to be gained from extending next generation sequencing-supported network analyses to undomesticated non-model species.