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Pisupati, R.

Publications and source records attributed to Pisupati, R..

8 recordsLinked to original sources

Life-history trade-offs explain local adaptation in Arabidopsis thaliana

Local adaptation has been demonstrated in many organisms, but the traits involved, and the temporal and spatial scales at which selection acts are generally unknown. We carried out a multi-year study of 200 accessions (natural inbred lines) of Swedish Arabidopsis thaliana using local field sites and a combination of common-garden experiments that measured adult survival and fecundity, and selection experiments that measured fitness over the full life cycle. We found evidence of strong and variable selection, with particular genotypes favored more than five-fold in certain years and locations. Fecundity showed evidence of classical local adaptation, with accessions generally performing better closer to their home. However, relative to other accessions, southern ones usually had the highest fecundity---but were far more sensitive to harsh winters and slug herbivory, which strongly decreased both survival and fecundity. Furthermore, accessions originally sampled on beaches had low fecundity in all common-garden experiments, but massively outperformed all other accessions in the selection experiments, presumably due to an advantage during seedling establishment associated with their very large seeds. We conclude that local adaptation in A. thaliana reflects strong temporally and spatially varying selection on multiple traits, generally involving trade-offs and different life-history strategies, making fitness difficult to predict and measure.

genetics↗

Bisulphite sequencing in the presence of cytosine-conversion errors

Bisulphite treatment of DNA converts unmethylated cytosines to thymine, and is a common method to infer the methylation status of cytosines when coupled with sequencing. Tagmentation approaches to bisulphite sequencing use a transposase to simultaneously make double-stranded breaks and ligate adaptors to the resulting fragments, allowing for higher throughput with less starting material. However, it has also been noted that certain tagmentation protocols have an unusually high number unmethylated cytosines that are not converted to thymine. Here we describe this phenomenon in detail, and find that results are consistent with single strand nicks by the transposase, followed by strand displacement of part or all of the DNA fragment, leading to erroneous incorporation of methylated cytosines. Nevertheless we show that these errors can be accounted for in downstream analysis and need not impede biological conclusions. We provide a Python package to allow users to implement this framework. Ultimately the additional effort of accounting for errors must be traded off against the scalability of the protocol in planning experiments.

genomics↗

Population-level annotation of lncRNAs in Arabidopsis thaliana reveals extensive expression and epigenetic variability associated with TE-like silencing

Long non-coding RNAs (lncRNAs) are understudied and underannotated in plants. In mammals, lncRNA loci are nearly as ubiquitous as protein-coding genes, and their expression is highly variable between individuals of the same species. Using Arabidopsis thaliana as a model, we aimed to understand the true scope of lncRNA transcription across plants from different regions and study its natural variation. We used transcriptome deep sequencing datasets spanning hundreds of natural accessions and several developmental stages to create a population-wide annotation of lncRNAs, revealing thousands of previously unannotated lncRNA loci. While lncRNA transcription is ubiquitous in the genome, most loci appear to be actively silenced and their expression is extremely variable between natural accessions. This high expression variability is largely caused by the high variability of repressive chromatin levels at lncRNA loci. High variability was particularly common for intergenic lncRNAs (lincRNAs), where pieces of transposable elements (TEs) present in 50% of these lincRNA loci are associated with increased silencing and variation, and such lncRNAs tend to be targeted by the TE silencing machinery. We create a population-wide lncRNA annotation in A. thaliana and improve our understanding of plant lncRNA genome biology, raising fundamental questions about what causes transcription and silencing across the genome. One-sentence summarylncRNA loci are plentiful in the A. thaliana genome, but their expression is extremely variable and largely repressed, with TE pieces enriched in intergenic lncRNAs aiding variability and silencing.

genomics↗

On the Causes of Gene-Body Methylation Variation in Arabidopsis thaliana

Gene-body methylation (gbM) refers to sparse CG methylation of coding regions, which is especially prominent in evolutionarily conserved house-keeping genes. It is found in both plants and animals, but is directly and stably (epigenetically) inherited over multiple generations in the former. Studies in Arabidopsis thaliana have demonstrated that plants originating from different parts of the world exhibit genome-wide differences in gbM, which could reflect direct selection on gbM, but which could also reflect an epigenetic memory of ancestral genetic and/or environmental factors. Here we look for evidence of such factors in F2 plants resulting from a cross between a southern Swedish line with low gbM and a northern Swedish line with high gbM, grown at two different temperatures. Using bisulfite-sequencing data with nucleotide-level resolution on hundreds of individuals, we confirm that CG sites are either methylated (nearly 100% methylation across sampled cells) or unmethylated (approximately 0% methylation across sampled cells), and show that the higher level of gbM in the northern line is due to more sites being methylated. Furthermore, methylation variants almost always show Mendelian segregation, consistent with their being directly and stably inherited through meiosis. To explore how the differences between the parental lines could have arisen, we focused on somatic deviations from the inherited state, distinguishing between gains (relative to the inherited 0% methylation) and losses (relative to the inherited 100% methylation) at each site in the F2 generation. We demonstrate that deviations predominantly affect sites that differ between the parental lines, consistent with these sites being more mutable. Gains and losses behave very differently in terms of the genomic distribution, and are influenced by the local chromatin state. We find clear evidence for different trans-acting genetic polymorphism affecting gains and losses, with those affecting gains showing strong environmental interactions (GxE). Direct effects of the environment were minimal. In conclusion, we show that genetic and environmental factors can change gbM at a cellular level, and hypothesize that these factors can also lead to transgenerational differences between individuals via the inclusion of such changes in the zygote. If true, this could explain genographic pattern of gbM with selection, and would cast doubt on estimates of epimutation rates from inbred lines in constant environments. Author summaryGene-body methylation, the sparse CG methylation that is associated with house-keeping genes, is found in both plants and animals, but can be directly inherited in the former. Recently, we discovered that Arabidopsis thaliana originating from different geographic regions exhibit different patterns of gbM, which could be due to direct selection on gbM, but could also reflect a transgenerational memory of genetic or environmental factors. Here we look for evidence of such factors using a genetic cross between two natural inbred lines: one with high, and one with low gbM. We confirm that methylation states are stably inherited, but also see large somatic deviations from the inherited state, in particular at sites that differ between the parental lines. We demonstrate that these deviations are affected by genetic variants in interaction with the environment, and hypothesize that geographic differences in gbM arise through the inclusion of such deviations in the zygote.

genetics↗

MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore.

plant biology↗

Genomic evidence that governmentally produced Cannabis sativa poorly represents genetic variation available in state markets

The National Institute on Drug Abuse (NIDA) is the sole producer of Cannabis for research purposes in the United States, including medical investigation. Previous research established that cannabinoid profiles in the NIDA varieties lacked diversity and potency relative to the Cannabis produced commercially. Additionally, microsatellite marker analyses have established that the NIDA varieties are genetically divergent form varieties produced in the private legal market. Here, we analyzed the genome of multiple Cannabis varieties from diverse lineages including two produced by NIDA, and we provide further support that NIDAs varieties differ from widely available medical, recreational, or industrial Cannabis. Furthermore, our results suggest that NIDAs varieties lack diversity in the single copy portion of the genome, the maternally inherited genomes, the cannabinoid genes, and in the repetitive content of the genome. Therefore, results based on NIDAs varieties are not generalizable regarding the effects of Cannabis after consumption. For medical research to be relevant, material that is more widely used would have to be studied. Clearly, having research to date dominated by a single, non-representative source of Cannabis has hindered scientific investigation.

genomics↗

Stage-specific transcriptomes and DNA methylomes indicate an early and transient loss of transposon control in Arabidopsis shoot stem cells

In contrast to animals, postembryonic development in plants is modular, and aerial organs originate from stem cells in the center of the shoot apical meristem (SAM) throughout life. Descendants of SAM stem cells in the subepidermal layer (L2) give also rise to male and female gametes (reviewed in 1) and are therefore considered primordial germ cells. In these cells, transmission of somatic mutations including virus and TE insertions must be avoided. Despite their essential role for plant development and intergenerational continuity, no comprehensive molecular analysis of SAM stem cells exists, due to their low number, deep embedding among non-stem cells, and difficult isolation. Here we present a comprehensive analysis of stage-specific gene expression and DNA methylation dynamics in Arabidopsis SAM stem cells. Stem cell expression signatures are mostly defined by development, but we also identified a core set of differentially expressed stemness genes. Surprisingly, vegetative SAM stem cells showed increased expression of transposable elements (TEs) relative to surrounding cells, despite high expression of genes connected to epigenetic silencing. We also find increasing methylation at CHG and a drop in CHH methylation at TEs before stem cells enter the reproductive lineage, indicating an onset of epigenetic reprogramming at an early stage. Transiently elevated TE expression is reminiscent of that in animal primordial germ cells (PGCs) 2 and demonstrates commonality of transposon biology. Our results connect SAM stem cells with germline development and transposon evolution and will allow future experiments to determine the degree of epigenetic heritability between generations.

developmental biology↗

Verification of Arabidopsis stock collections using SNPmatch - an algorithm for genotyping high-plexed samples

Large-scale studies such as the Arabidopsis thaliana 1001 Genomes Project aim to understand genetic variation in populations and link it to phenotypic variation. Such studies require routine genotyping of stocks to avoid sample contamination and mix-ups. To genotype samples efficiently and economically, sequencing must be inexpensive and data processing simple. Here we present SNPmatch, a tool which identifies the most likely strain (inbred line, or \"accession\") from a SNP database. We tested the tool by performing low-coverage sequencing of over 2000 strains. SNPmatch could readily genotype samples correctly from 1-fold coverage sequencing data, and could also identify the parents of F1 or F2 individuals. SNPmatch can be run either on the command line or through AraGeno (https://arageno.gmi.oeaw.ac.at), a web interface that permits sample genotyping from a user-uploaded VCF or BED file.\n\nAvailability and implementation: https://github.com/Gregor-Mendel-Institute/SNPmatch.git

genomics↗