Search

Regulation of gene expression plays a crucial role in the development and adaptation of organisms to changing environments. A population-based procedure used to investigate the genetic regulation of gene expression is eQTL mapping. Typically, the age of the population used for eQTL mapping at the time of sampling is strictly controlled. This is necessary because the developmental process causes changes in gene expression, complicating the interpretation of eQTL mapping experiments. However, organisms can differ in their “developmental age”, even if they are of the same chronological age. As a result, eQTL patterns are affected by uncontrolled developmental variation in gene expression. The model organismC. elegansis particularly suited for studying the effect of developmental variation on eQTL mapping patterns. In a span of daysC. eleganstransitions from embryo through four larval stages to adult while undergoing massive changes to its transcriptome. Here we useC. elegansto investigate the effect of developmental age variation on eQTL patterns and an available normalization procedure. We used dynamical eQTL mapping, which includes developmental age as a cofactor, to separate the variation in development from genotypic variation and explain variation in gene expression levels. We compare classical single marker eQTL mapping and dynamical eQTL mapping using RNA-seq data of ∼200 multi-parental recombinant inbred lines ofC. elegans. The results show that many eQTLs are actually caused by developmental variation, that most trans-bands are associated with developmental age and that dynamical eQTL mapping detects additional eQTLs not found with classical eQTL mapping.

The quality of seeds contributes to plant performance, especially during germination and in the young seedling stage, and hence affects the economic value of seed crops. A seed’s innate quality is determined during seed development and the following seed maturation phase. It is tightly controlled by the genetic make-up of the mother plant and further shaped by the environmental conditions of the mother plant. The interaction between genotype and environment can result in substantial quantitative variation in seed traits like dormancy and viability.Making use of naturally occurring variation within theArabidopsis thalianagermplasm, we studied the interaction between seed production environments and the genetic architecture of mother plants on diverse seed quality traits. An Arabidopsis Bayreuth-0 x Shahdara recombinant inbred line (RIL) population was grown in four different seed production environments: high temperature, high light, low phosphate, and control conditions. The seeds harvested from the mother plants that were exposed to these environments from flowering until seed harvest were subsequently subjected to germination assays under standard and mild stress conditions (cold, heat, osmotic stress and added phytohormone ABA). Quantitative trait locus (QTL) analysis identified many environmental-sensitive QTLs (QTL x E) as well as several interactions between the maternal and germination environments. Variation in the number and position of the QTLs was largely determined by the germination conditions, however effects of the maternal environment were clearly present regarding the genomic location as well as significance of the individual QTLs.Together, our findings uncover the extensive environmental modulation of the genetic influence on seed performance and how this is shaped by the genetic make-up of the mother plant. Our data provides a systems-view of the complex genetic basis of genotype-by-environment interactions determining seed quality.

Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (RIL) population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments, i.e., on high phosphorus or low nitrogen. The obtained single-nucleotide polymorphisms (SNPs) were subsequently used to construct a genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.

Expression quantitative trait locus mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of expression quantitative trait locus data has been generated for this model plant; however, only a few causal expression quantitative trait locus genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal expression quantitative trait locus genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype quantitative trait loci to be used for expression quantitative trait loci by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize 16 out of 25 potential expression quantitative trait locus causal genes within the top 20% rank. Several new features are important in prioritizing causal expression quantitative trait locus genes, including the number of protein–protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate expression quantitative trait locus causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results show that ML methods are a better choice for nonlinear phenotypes and still comparable to Bayesian methods for linear phenotypes in the case of large effect QTNs. Furthermore, we find that ML methods are susceptible to confounding due to population structure and less sensitive to low linkage disequilibrium than linear parametric methods. Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

Background The nematode Caenorhabditis elegans has been extensively used to explore the relationships between complex traits, genotypes, and environments. Complex traits can vary across different genotypes of a species, and the genetic regulators of trait variation can be mapped on the genome using quantitative trait locus (QTL) analysis of recombinant inbred lines (RILs) derived from genetically and phenotypically divergent parents. Most RILs have been derived from crossing two parents from globally distant locations. However, the genetic diversity between local C. elegans populations can be as diverse as between global populations and could thus provide means of identifying genetic variation associated with complex traits relevant on a broader scale. Results To investigate the effect of local genetic variation on heritable traits, we developed a new RIL population derived from 4 parental wild isolates collected from 2 closely located sites in France: Orsay and Santeuil. We crossed these 4 genetically diverse parental isolates to generate a population of 200 multi-parental RILs and used RNA-seq to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the 4 genotypes with an average spacing of 11 kb, doubling the mapping resolution relative to currently available RIL panels for many loci. The SNPs were used to construct a genetic map to facilitate QTL analysis. We measured life history traits such as lifespan, stress resistance, developmental speed, and population growth in different environments, and found substantial variation for most traits. We detected multiple QTLs for most traits, including novel QTLs not found in previous QTL analysis, including those for lifespan and pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping. Conclusion Taken together, we show that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates facilitates the detection of QTLs. The use of multi-parent RIL populations can further enhance our understanding of local adaptation and life history trade-offs. Electronic supplementary material The online version of this article (10.1186/s12915-019-0642-8) contains supplementary material, which is available to authorized users.

Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depends on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments.In this study, we applied RNA-sequencing to measure gene expression of seeds of a tomato RIL population derived from a cross betweenS. lycopersicum(cv. Moneymaker) andS. pimpinellifolium(G1.1554). The seeds matured on plants cultivated under different nutritional environments; i.e. on high phosphorus or low nitrogen. The obtained SNPs were subsequently used to construct a high-density genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.

Studying genetic variation of gene expression provides a powerful way to unravel the molecular components underlying complex traits. Expression QTL studies have been performed in several different model species, yet most of these linkage studies have been based on genetic segregation of two parental alleles. Recently we developed a multi-parental segregating population of 200 recombinant inbred lines (mpRILs) derived from four wild isolates (JU1511, JU1926, JU1931 and JU1941) in the nematode Caenorhabditis elegans. We used RNA-seq to investigate how multiple alleles affect gene expression in these mpRILs. We found 1,789 genes differentially expressed between the parental lines. Transgression, expression beyond any of the parental lines in the mpRILs, was found for 7,896 genes. For expression QTL mapping almost 9,000 SNPs were available. By combining these SNPs and the RNA-seq profiles of the mpRILs, we detected almost 6,800 eQTLs. Most trans-eQTLs (63%) co-locate in six newly identified trans-bands. The trans-eQTLs found in previous 2-parental allele eQTL experiments and this study showed some overlap (17.5%- 46.8%), highlighting on the one hand that a large group of genes is affected by polymorphic regulators across populations and conditions, on the other hand it shows that the mpRIL population allows identification of novel gene expression regulatory loci. Taken together, the analysis of our mpRIL population provides a more refined insight into C. elegans complex trait genetics and eQTLs in general, as well as a starting point to further test and develop advanced statistical models for detection of multi-allelic eQTLs and systems genetics studying the genotype-phenotype relationship.

Studying genetic variation of gene expression provides a powerful way to unravel the molecular components underlying complex traits. Expression quantitative trait locus (eQTL) studies have been performed in several different model species, yet most of these linkage studies have been based on the genetic segregation of two parental alleles. Recently, we developed a multiparental segregating population of 200 recombinant inbred lines (mpRILs) derived from four wild isolates (JU1511, JU1926, JU1931, and JU1941) in the nematode Caenorhabditis elegans. We used RNA-seq to investigate how multiple alleles affect gene expression in these mpRILs. We found 1789 genes differentially expressed between the parental lines. Transgression, expression beyond any of the parental lines in the mpRILs, was found for 7896 genes. For expression QTL mapping almost 9000 SNPs were available. By combining these SNPs and the RNA-seq profiles of the mpRILs, we detected almost 6800 eQTLs. Most trans-eQTLs (63%) co-locate in six newly identified trans-bands. The trans-eQTLs found in previous two-parental allele eQTL experiments and this study showed some overlap (17.5–46.8%), highlighting on the one hand that a large group of genes is affected by polymorphic regulators across populations and conditions, on the other hand, it shows that the mpRIL population allows identification of novel gene expression regulatory loci. Taken together, the analysis of our mpRIL population provides a more refined insight into C. elegans complex trait genetics and eQTLs in general, as well as a starting point to further test and develop advanced statistical models for detection of multiallelic eQTLs and systems genetics studying the genotype–phenotype relationship.

Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we performed a quantitative trait locus mapping of gene expression (eQTL) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs). The mapping displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Interestingly, an eQTL hotspot on chromosome five collocates with hotspots for phenotypic and metabolic QTL in the same population. Finally, we constructed a gene co-expression network to prioritize the regulatory genes for two major eQTL hotspots. The network analysis prioritizes transcription factors DEWAX and ICE1 as the most likely regulatory genes for the hotspot. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination.

Summary Primary seed dormancy is a mechanism that orchestrates the timing of seed germination in order to prevent out‐of‐season germination. Secondary dormancy can be induced in imbibed seeds when they encounter prolonged unfavourable conditions. Secondary dormancy is not induced during dry storage, and therefore the mechanisms underlying this process have remained largely unexplored. Here, a 2‐year seed burial experiment in which dormancy cycling was studied at the physiological and transcriptional level is presented. For these analyses six different Arabidopsis thaliana genotypes were used: Landsberg erecta (Ler) and the dormancy associated DELAY OF GERMINATION (DOG) near‐isogenic lines 1, 2, 3, 6 and 22 (NILDOG1, 2, 3, 6 and 22). The germination potential of seeds exhumed from the field showed that these seeds go through dormancy cycling and that the dynamics of this cycling is genotype dependent. RNA‐seq analysis revealed large transcriptional changes during dormancy cycling, especially at the time points preceding shifts in dormancy status. Dormancy cycling is driven by soil temperature and the endosperm is important in the perception of the environment. Genes that are upregulated in the low‐ to non‐dormant stages are enriched for genes involved in translation, indicating that the non‐dormant seeds are prepared for rapid seed germination., Significance Statement Little is known about seed dormancy under natural conditions. This field study provides an in‐depth insight, at the physiological and transcriptional level, of how Arabidopsis seeds cycle through dormancy. We show that dormancy is strongly regulated by the environment, both by seasonal changes as well as by sudden changes. We identified large transcriptional changes and show that in particular the translational machinery is upregulated when seeds are non‐dormant.

Floral induction in Tulipa gesneriana and Lilium longiflorum is triggered by contrasting temperature conditions, high and low temperature, respectively. In Arabidopsis, the floral integrator FLOWERING LOCUS T (FT), a member of the PEBP (phosphatidyl ethanolamine-binding protein) gene family, is a key player in flowering time control. In this study, one PEBP gene was identified and characterized in lily (LlFT) and three PEBP genes were isolated from tulip (TgFT1, TgFT2 and TgFT3). Overexpression of these genes in Arabidopsis thaliana resulted in an early flowering phenotype for LlFT and TgFT2, but a late flowering phenotype for TgFT1 and TgFT3. Overexpression of LlFT in L. longiflorum also resulted in an early flowering phenotype, confirming its proposed role as a flowering time-controlling gene. The tulip PEBP genes TgFT2 and TgFT3 have a similar expression pattern in tulip, but show opposite effects on the timing of flowering in Arabidopsis. Therefore, the difference between these two proteins was further investigated by interchanging amino acids thought to be important for the FT function. This resulted in the conversion of phenotypes in Arabidopsis upon overexpressing the substituted TgFT2 and TgFT3 genes, revealing the importance of these interchanged amino acid residues. Based on all obtained results, we hypothesize that LlFT is involved in creating meristem competence to flowering-related cues in lily, and TgFT2 is considered to act as a florigen involved in the floral induction in tulip. The function of TgFT3 remains unclear, but, based on our observations and phylogenetic analysis, we propose a bulb-specific function for this gene.

Objective Plants produce a plethora of specialized metabolites to defend themselves against pathogens and insects, to attract pollinators and to communicate with other organisms. Many of these are also applied in the clinic and in agriculture. Genes encoding the enzymes that drive the biosynthesis of these metabolites are sometimes physically grouped on the chromosome, in regions called biosynthetic gene clusters (BGCs). Several algorithms have been developed to identify plant BGCs, but a large percentage of predicted gene clusters upon further inspection do not show coexpression or do not encode a single functional biosynthetic pathway. Hence, further prioritization is needed. Results Here, we introduce a strategy to systematically evaluate potential functions of predicted BGCs by superimposing their locations on metabolite quantitative trait loci (mQTLs). We show the feasibility of such an approach by integrating automated BGC prediction with mQTL datasets originating from a recombinant inbred line (RIL) population of Oryza sativa and a genome-wide association study (GWAS) of Arabidopsis thaliana. In these data, we identified several links for which the enzyme content of the BGCs matches well with the chemical features observed in the metabolite structure, suggesting that this method can effectively guide bioprospecting of plant BGCs. Electronic supplementary material The online version of this article (10.1186/s13104-019-4222-3) contains supplementary material, which is available to authorized users.

Genome sequencing remains a challenge for species with large and complex genomes containing extensive repetitive sequences, of which the bulbous and monocotyledonous plants tulip and lily are examples. In such a case, sequencing of only the active part of the genome, represented by the transcriptome, is a good alternative to obtain information about gene content. In this study we aimed to generate a high quality transcriptome of tulip and lily and to make this data available as an open-access resource via a user-friendly web-based interface. The Illumina HiSeq 2000 platform was applied and the transcribed RNA was sequenced from a collection of different lily and tulip tissues, respectively. In order to obtain good transcriptome coverage and to facilitate effective data mining, assembly was done using different filtering parameters for clearing out contamination and noise of the RNAseq datasets. This analysis revealed limitations of commonly applied methods and parameter settings used in de novo transcriptome assembly. The final created transcriptomes are publicly available via a user friendly Transcriptome browser (http://www.bioinformatics.nl/bulbs/db/species/index). The usefulness of this resource has been exemplified by a search for all potential transcription factors in lily and tulip, with special focus on the TCP transcription factor family. This analysis and other quality parameters point out the quality of the transcriptomes, which can serve as a basis for further genomics studies in lily, tulip, and bulbous plants in general. Electronic supplementary material The online version of this article (doi:10.1007/s11103-016-0508-1) contains supplementary material, which is available to authorized users.

Many economically important perennial species bear recalcitrant seeds, including tea, coffee, cocoa, mango, citrus, rubber, oil palm and coconut. Orthodox seeds can be dried almost completely without losing viability, but so‐called recalcitrant seeds have a very limited storage life and die upon drying below a higher critical moisture content than orthodox seeds. As a result, the development of long‐term storage methods for recalcitrant seeds is compromised. Lowering this critical moisture content would be very valuable since dry seed storage is the safest, most convenient and cheapest method for conserving plant genetic resources. Therefore, we have attempted to induce desiccation tolerance (DT) in the desiccation sensitive seeds of Citrus limon. We show that DT can be induced by paclobutrazol (an inhibitor of gibberellin biosynthesis) and we studied its associated transcriptome to delineate the molecular mechanisms underlying this induction of DT. Paclobutrazol not only interfered with gibberellin related gene expression but also caused extensive changes in expression of genes involved in the biosynthesis and signaling of other hormones. Paclobutrazol induced a transcriptomic switch encompassing suppression of biotic‐ and induction of abiotic responses. We hypothesize that this is the main driver of the induction of DT by paclobutrazol in C. limon seeds.

Local populations of the bacterivorous nematode Caenorhabditis elegans can be genetically almost as diverse as global populations. To investigate the effect of local genetic variation on heritable traits, we developed a new recombinant inbred line (RIL) population derived from four wild isolates. The wild isolates were collected from two closely located sites in France: Orsay and Santeuil. By crossing these four genetically diverse parental isolates a population of 200 RILs was constructed. RNA-seq was used to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the four genotypes with an average spacing of 11 kb, possibly doubling the mapping resolution relative to currently available RIL panels. The SNPs were used to construct a genetic map to facilitate QTL analysis. Life history traits, such as lifespan, stress resistance, developmental speed and population growth were measured in different environments. For most traits substantial variation was found, and multiple QTLs could be detected, including novel QTLs not found in previous QTL analysis, for example for lifespan or pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping. Taken together, we show that RNA-seq can be used for genotyping, that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates permits analysis of local adaptation and life history trade-offs.

The Hawaiian strain (CB4856) of Caenorhabditis elegans is one of the most divergent from the canonical laboratory strain N2 and has been widely used in developmental, population, and evolutionary studies. To enhance the utility of the strain, we have generated a draft sequence of the CB4856 genome, exploiting a variety of resources and strategies. When compared against the N2 reference, the CB4856 genome has 327,050 single nucleotide variants (SNVs) and 79,529 insertion–deletion events that result in a total of 3.3 Mb of N2 sequence missing from CB4856 and 1.4 Mb of sequence present in CB4856 but not present in N2. As previously reported, the density of SNVs varies along the chromosomes, with the arms of chromosomes showing greater average variation than the centers. In addition, we find 61 regions totaling 2.8 Mb, distributed across all six chromosomes, which have a greatly elevated SNV density, ranging from 2 to 16% SNVs. A survey of other wild isolates show that the two alternative haplotypes for each region are widely distributed, suggesting they have been maintained by balancing selection over long evolutionary times. These divergent regions contain an abundance of genes from large rapidly evolving families encoding F-box, MATH, BATH, seven-transmembrane G-coupled receptors, and nuclear hormone receptors, suggesting that they provide selective advantages in natural environments. The draft sequence makes available a comprehensive catalog of sequence differences between the CB4856 and N2 strains that will facilitate the molecular dissection of their phenotypic differences. Our work also emphasizes the importance of going beyond simple alignment of reads to a reference genome when assessing differences between genomes.

Desiccation tolerance is common in seeds and various other organisms, but only a few angiosperm species possess vegetative desiccation tolerance. These 'resurrection species' may serve as ideal models for the ultimate design of crops with enhanced drought tolerance. To understand the molecular and genetic mechanisms enabling vegetative desiccation tolerance, we produced a high-quality whole-genome sequence for the resurrection plant Xerophyta viscosa and assessed transcriptome changes during its dehydration. Data revealed induction of transcripts typically associated with desiccation tolerance in seeds and involvement of orthologues of ABI3 and ABI5, both key regulators of seed maturation. Dehydration resulted in both increased, but predominantly reduced, transcript abundance of genomic 'clusters of desiccation-associated genes' (CoDAGs), reflecting the cessation of growth that allows for the expression of desiccation tolerance. Vegetative desiccation tolerance in X. viscosa was found to be uncoupled from drought-induced senescence. We provide strong support for the hypothesis that vegetative desiccation tolerance arose by redirection of genetic information from desiccation-tolerant seeds.

The vegetative-to-reproductive phase change in tulip (Tulipa gesneriana) is promoted by increasing temperatures during spring. The warm winters of recent years interfere with this process and are calling for new adapted cultivars. A better understanding of the underlying molecular mechanisms would be of help, but unlike the model plant Arabidopsis (Arabidopsis thaliana), very little is known about floral induction in tulip. To shed light on the gene regulatory network controlling flowering in tulip, RNA sequencing was performed on meristem-enriched tissue collected under two contrasting temperature conditions, low and high. The start of reproductive development correlated with rounding of the shoot apical meristem and induction of TGSQA expression, a tulip gene with a high similarity to Arabidopsis APETALA1. Gene Ontology enrichment analysis of differentially expressed genes showed the overrepresentation of genes potentially involved in floral induction, bulb maturation, and dormancy establishment. Expression analysis revealed that TERMINAL FLOWER1 (TgTFL1) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1-like1 (TgSOC1-like1) might be repressors, whereas TgSOC1-like2 likely is an activator, of flowering. Subsequently, the flowering time-associated expression of eight potential flowering time genes was confirmed in three tulip cultivars grown in the field. Additionally, heterologous functional analyses in Arabidopsis resulted in flowering time phenotypes in line with TgTFL1 being a floral repressor and TgSOC1-like2 being a floral activator in tulip. Taken together, we have shown that long before morphological changes occur in the shoot apical meristem, the expression of floral repressors in tulip is suppressed by increased ambient temperatures, leading either directly or indirectly to the activation of potential flowering activators shortly before the commencement of the phase change.

Highlight text The genotype-by-environment interactions of five parental environments with seed and plant performance are mediated by distinct genetic and molecular pathways, and the selective pressures that have shaped their natural variation., Seed performance after dispersal is highly dependent on parental environmental cues, especially during seed formation and maturation. Here we examine which environmental factors are the most dominant in this respect and whether their effects are dependent on the genotypes under investigation. We studied the influence of light intensity, photoperiod, temperature, nitrate, and phosphate during seed development on five plant attributes and thirteen seed attributes, using 12 Arabidopsis genotypes that have been reported to be affected in seed traits. As expected, the various environments during seed development resulted in changed plant and/or seed performances. Comparative analysis clearly indicated that, overall, temperature plays the most dominant role in both plant and seed performance, whereas light has a prominent impact on plant traits. In comparison to temperature and light, nitrate mildly affected some of the plant and seed traits while phosphate had even less influence on those traits. Moreover, clear genotype-by-environment interactions were identified. This was shown by the fact that individual genotypes responded differentially to the environmental conditions. Low temperature significantly increased seed dormancy and decreased seed longevity of NILDOG1 and cyp707a1-1, whereas low light intensity increased seed dormancy and decreased seed longevity of NILDOG3 and NILDOG6. This also indicates that different genetic and molecular pathways are involved in the plant and seed responses. By identifying environmental conditions that affect the dormancy vs longevity correlation in the same way as previously identified naturally occurring loci, we have identified selective forces that probably shaped evolution for these important seed traits.

Amino acid repeats (AARs) are abundant in protein sequences. They have particular roles in protein function and evolution. Simple repeat patterns generated by DNA slippage tend to introduce length variations and point mutations in repeat regions. Loss of normal and gain of abnormal function owing to their variable length are potential risks leading to diseases. Repeats with complex patterns mostly refer to the functional domain repeats, such as the well-known leucine-rich repeat and WD repeat, which are frequently involved in protein–protein interaction. They are mainly derived from internal gene duplication events and stabilized by ‘gate-keeper’ residues, which play crucial roles in preventing inter-domain aggregation. AARs are widely distributed in different proteomes across a variety of taxonomic ranges, and especially abundant in eukaryotic proteins. However, their specific evolutionary and functional scenarios are still poorly understood. Identifying AARs in protein sequences is the first step for the further investigation of their biological function and evolutionary mechanism. In principle, this is an NP-hard problem, as most of the repeat fragments are shaped by a series of sophisticated evolutionary events and become latent periodical patterns. It is not possible to define a uniform criterion for detecting and verifying various repeat patterns. Instead, different algorithms based on different strategies have been developed to cope with different repeat patterns. In this review, we attempt to describe the amino acid repeat-detection algorithms currently available and compare their strategies based on an in-depth analysis of the biological significance of protein repeats.

Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations has enabled investigation of the genetic architecture of variation in gene expression. This has the potential to enrich our understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. A number of genetical genomics studies have been published for Arabidopsis thaliana, uncovering many expression quantitative trait loci (eQTLs). However, these complex data are not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this problem with AraQTL (http://www.bioinformatics.nl/Ara QTL/), an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic QTLs, or detect gene-by-environment interactions by comparing eQTLs under different conditions.

CyLineUp is a Cytoscape 3 app for the projection of high-throughput measurement data from multiple experiments/samples on a network or pathway map using “small multiples”. This visualization method allows for easy comparison of different experiments in the context of the network or pathway. The user can import various kinds of measurement data and select any appropriate Cytoscape network or WikiPathways pathway map. CyLineUp creates small multiples by replicating the loaded network as many times as there are experiments/samples (e.g. time points, stress conditions, tissues, etc.). The measurement data for each experiment are then mapped onto the nodes (genes, proteins etc.) of the corresponding network using a color gradient. Each step of creating the visualization can be customized to the user’s needs. The results can be exported as a high quality vector image.

Studies of the defence capacity of ancient hominins against toxic substances may contribute importantly to the reconstruction of their niche, including their diets and use of fire. Fire usage implies frequent exposure to hazardous compounds from smoke and heated food, known to affect general health and fertility, probably resulting in genetic selection for improved detoxification. To investigate whether such genetic selection occurred, we investigated the alleles in Neanderthals, Denisovans and modern humans at gene polymorphisms well-known to be relevant from modern human epidemiological studies of habitual tobacco smoke exposure and mechanistic evidence. We compared these with the alleles in chimpanzees and gorillas. Neanderthal and Denisovan hominins predominantly possess gene variants conferring increased resistance to these toxic compounds. Surprisingly, we observed the same in chimpanzees and gorillas, implying that less efficient variants are derived and mainly evolved in modern humans. Less efficient variants are observable from the first early Upper Palaeolithic hunter-gatherers onwards. While not clarifying the deep history of fire use, our results highlight the long-term stability of the genes under consideration despite major changes in the hominin dietary niche. Specifically for detoxification gene variants characterised as deleterious by epidemiological studies, our results confirm the predominantly recent appearance reported for deleterious human gene variants, suggesting substantial impact of recent human population history, including pre-Holocene expansions.

GeneNetwork (GN) is a free and open source (FOSS) framework for web-based genetics that can be deployed anywhere. GN allows biologists to upload high-throughput experimental data, such as expression data from microarrays and RNA-seq, and also `classic' phenotypes, such as disease phenotypes. These phenotypes can be mapped interactively against genotypes using embedded tools, such as R/QTL (Arends et al. 2010) mapping, interval mapping for model organisms and pylmm; an implementation of FaST-LMM (Lippert et al. 2011) which is more suitable for human populations and outbred crosses, such as the mouse diversity outcross. Interactive D3 graphics are included from R/qtlcharts and presentation-ready figures can be generated. Recently we have added functionality for phenotype correlation (Wang et al. 2016) and network analysis (Langfelder and Horvath 2008).

ProRepeat (http://prorepeat.bioinformatics.nl/) is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats’ corresponding codons.

Current protein sequence databases employ different classification schemes that often provide conflicting annotations, especially for poorly characterized proteins. ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap) is a web-tool designed to help researchers and database annotators to assess the coherence of protein groups defined in various databases and thereby facilitate the annotation of newly sequenced proteins. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240 000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF. ProGMap combines the underlying classification schemes via a network of links constructed by a fast and fully automated mapping approach originally developed for document classification. The web interface enables queries to be made using sequence identifiers, gene symbols, protein functions or amino acid and nucleotide sequences. For the latter query type BLAST similarity search and QuickMatch identity search services have been incorporated, for finding sequences similar (or identical) to a query sequence. ProGMap is meant to help users of high throughput methodologies who deal with partially annotated genomic data.

Here we present Primer3Plus, a new web interface to the popular Primer3 primer design program as an enhanced alternative for the CGI- scripts that come with Primer3. Primer3 consists of a command line program and a web interface. The web interface is one large form showing all of the possible options. This makes the interface powerful, but at the same time confusing for occasional users. Primer3Plus provides an intuitive user interface using present-day web technologies and has been developed in close collaboration with molecular biologists and technicians regularly designing primers. It focuses on the task at hand, and hides detailed settings from the user until these are needed. We also added functionality to automate specific tasks like designing primers for cloning or step-wise sequencing. Settings and designed primer sequences can be stored locally for later use. Primer3Plus supports a range of common sequence formats, such as FASTA. Finally, primers selected by Primer3Plus can be sent to an order form, allowing tight integration into laboratory ordering systems. Moreover, the open architecture of Primer3Plus allows easy expansion or integration of external software packages. The Primer3Plus Perl source code is available under GPL license from SourceForge. Primer3Plus is available at http://www.bioinformatics.nl/primer3plus.

Main conclusion During re-establishment of desiccation tolerance (DT), early events promote initial protection and growth arrest, while late events promote stress adaptation and contribute to survival in the dry state. Mature seeds of Arabidopsis thaliana are desiccation tolerant, but they lose desiccation tolerance (DT) while progressing to germination. Yet, there is a small developmental window during which DT can be rescued by treatment with abscisic acid (ABA). To gain temporal resolution and identify relevant genes in this process, data from a time series of microarrays were used to build a gene co-expression network. The network has two regions, namely early response (ER) and late response (LR). Genes in the ER region are related to biological processes, such as dormancy, acquisition of DT and drought, amplification of signals, growth arrest and induction of protection mechanisms (such as LEA proteins). Genes in the LR region lead to inhibition of photosynthesis and primary metabolism, promote adaptation to stress conditions and contribute to seed longevity. Phenotyping of 12 hubs in relation to re-establishment of DT with T-DNA insertion lines indicated a significant increase in the ability to re-establish DT compared with the wild-type in the lines cbsx4, at3g53040 and at4g25580, suggesting the operation of redundant and compensatory mechanisms. Moreover, we show that re-establishment of DT by polyethylene glycol and ABA occurs through partially overlapping mechanisms. Our data confirm that co-expression network analysis is a valid approach to examine data from time series of transcriptome analysis, as it provides promising insights into biologically relevant relations that help to generate new information about the roles of certain genes for DT. Electronic supplementary material The online version of this article (doi:10.1007/s00425-015-2283-7) contains supplementary material, which is available to authorized users.

International audience; Expression analyses of time series have become a very popular method for studying the dynamics of a wide range of biological processes. Here, we present expression analysis of a time series with the help of microarrays used to study the re-establishment of desiccation tolerance (DT) in germinated Arabidopsis thaliana seeds. Mature seeds of A. thaliana are desiccation tolerant (survive the loss of most of their water content), but they become desiccation sensitive while progressing to germination. Yet, there is a small developmental window during which DT can be re-established by treatment with the plant hormone abscisic acid (ABA). We studied germinated A. thaliana seeds at the stage of radicle protrusion during ABA incubation for 0h, 2h, 12h, 24h and 72h. We describe in detail the methodology applied for generating and analyzing this expression data of time series. The microarray raw data (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62876) may be valuable for further studies on this experimental system, such as the construction of a gene co-expression network [1].

Phylogenetic analysis and examination of protein domains allow accurate genome annotation and are invaluable to study proteins and protein complex evolution. However, two sequences can be homologous without sharing statistically significant amino acid or nucleotide identity, presenting a challenging bioinformatics problem. We present TreeDomViewer, a visualization tool available as a web-based interface that combines phylogenetic tree description, multiple sequence alignment and InterProScan data of sequences and generates a phylogenetic tree projecting the corresponding protein domain information onto the multiple sequence alignment. Thereby it makes use of existing domain prediction tools such as InterProScan. TreeDomViewer adopts an evolutionary perspective on how domain structure of two or more sequences can be aligned and compared, to subsequently infer the function of an unknown homolog. This provides insight into the function assignment of, in terms of amino acid substitution, very divergent but yet closely related family members. Our tool produces an interactive scalar vector graphics image that provides orthological relationship and domain content of proteins of interest at one glance. In addition, PDF, JPEG or PNG formatted output is also provided. These features make TreeDomViewer a valuable addition to the annotation pipeline of unknown genes or gene products. TreeDomViewer is available at http://www.bioinformatics.nl/tools/treedom/.

Anecdotal evidence shows promoters being reused separate from their downstream gene, thus providing a mechanism for the efficient and rapid rewiring of a gene’s transcriptional regulation. We have identified over 4000 groups of highly similar promoters using a conservative sequence similarity search in all fully sequenced prokaryotic genomes. About 6% of those groups are shared between bacteria from different taxonomic depth, including different genera, families, orders, classes and even phyla. Database searches against known mobile elements and RNA motifs have indicated that regulatory motifs such as riboswitches could be moved around on putative mobile promoters.

Background Brassica rapa is an economically important crop species. During its long breeding history, a large number of morphotypes have been generated, including leafy vegetables such as Chinese cabbage and pakchoi, turnip tuber crops and oil crops. Results To investigate the genetic variation underlying this morphological variation, we re-sequenced, assembled and annotated the genomes of two B. rapa subspecies, turnip crops (turnip) and a rapid cycling. We then analysed the two resulting genomes together with the Chinese cabbage Chiifu reference genome to obtain an impression of the B. rapa pan-genome. The number of genes with protein-coding changes between the three genotypes was lower than that among different accessions of Arabidopsis thaliana, which can be explained by the smaller effective population size of B. rapa due to its domestication. Based on orthology to a number of non-brassica species, we estimated the date of divergence among the three B. rapa morphotypes at approximately 250,000 YA, far predating Brassica domestication (5,000-10,000 YA). Conclusions By analysing genes unique to turnip we found evidence for copy number differences in peroxidases, pointing to a role for the phenylpropanoid biosynthesis pathway in the generation of morphological variation. The estimated date of divergence among three B. rapa morphotypes implies that prior to domestication there was already considerably divergence among B. rapa genotypes. Our study thus provides two new B. rapa reference genomes, delivers a set of computer tools to analyse the resulting pan-genome and uses these to shed light on genetic drivers behind the rich morphological variation found in B. rapa.

A previous study of prokaryotic genomes identified large reservoirs of putative mobile promoters (PMPs), that is, homologous promoter sequences associated with nonhomologous coding sequences. Here we extend this data set to identify the full complement of mobile promoters in sequenced prokaryotic genomes. The expanded search identifies nearly 40,000 PMP sequences, 90% of which occur in noncoding regions of the genome. To gain further insight from this data set, we develop a birth–death–diversification model for mobile genetic elements subject to sequence diversification; applying the model to PMPs we are able to quantify the relative importance of duplication, loss, horizontal gene transfer (HGT), and diversification to the maintenance of the PMP reservoir. The model predicts low rates of HGT relative to the duplication and loss of PMP copies, rapid dynamics of PMP families, and a pool of PMPs that exist as a single copy in a genome at any given time, despite their mobility. We report evidence of these “singletons” at high frequencies in prokaryotic genomes. We also demonstrate that including selection, either for or against PMPs, was not necessary to describe the observed data.