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Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative condition. Despite significant advances in pre-clinical models that enhance understanding of disease pathobiology, translation of candidate drugs to effective human therapies has been disappointing. There is increasing recognition of the need for a precision medicine approach toward drug development, as many failures in translation can be attributed in part to disease heterogeneity in humans. PRECISION-ALS is an academic industry collaboration between clinicians, Computer Scientists, Information engineers, technologists, data scientists and industry partners that will address the key clinical, computational, data science and technology associated research questions to generate a sustainable precision medicine based approach toward new drug development. Using extant and prospectively collected population based clinical data across nine European sites, PRECISION-ALS provides a General Data Protection Regulation (GDPR) compliant framework that seamlessly collects, processes and analyses research-quality multimodal and multi-sourced clinical, patient and caregiver journey, digitally acquired data through remote monitoring, imaging, neuro-electric-signaling, genomic and biomarker datasets using machine learning and artificial intelligence. PRECISION-ALS represents a first-in-kind modular transferable pan-European ICT framework for ALS that can be easily adapted to other regions that face similar precision medicine related challenges in multimodal data collection and analysis. ispartof: AMYOTROPHIC LATERAL SCLEROSIS AND FRONTOTEMPORAL DEGENERATION vol:24 issue:5-6 ispartof: location:England status: Published online

Gene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Currentde novoHidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from otherde novotools. Helixer’s predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning.Helixer is open source and available athttps://github.com/weberlab-hhu/HelixerA web interface is available athttps://www.plabipd.de/helixer_main.html

Natural light environments are highly variable. Flexible adjustment between light energy utilization and photoprotection is therefore of vital importance for plant performance and fitness in the field. Short-term reactions to changing light intensity are triggered inside chloroplasts and leaves within seconds to minutes, whereas long-term adjustments proceed over hours and days, integrating multiple signals. While the mechanisms of long-term acclimation to light intensity have been studied by changing constant growth light intensity during the day, responses to fluctuating growth light intensity have rarely been inspected in detail. We performed transcriptome profiling in Arabidopsis (Arabidopsis thaliana) leaves to investigate long-term gene expression responses to fluctuating light (FL). In particular, we examined whether responses differ between young and mature leaves or between morning and the end of the day. Our results highlight global reprogramming of gene expression under FL, including that of genes related to photoprotection, photosynthesis, and photorespiration and to pigment, prenylquinone, and vitamin metabolism. The FL-induced changes in gene expression varied between young and mature leaves at the same time point and between the same leaves in the morning and at the end of the day, indicating interactions of FL acclimation with leaf development stage and time of day. Only 46 genes were up- or down-regulated in both young and mature leaves at both time points. Combined analyses of gene coexpression and cis-elements pointed to a role of the circadian clock and light in coordinating the acclimatory responses of functionally related genes. Our results also suggest a possible cross talk between FL acclimation and systemic acquired resistance-like gene expression in young leaves.

A pseudomonad isolated from garlic has multiple copies of genes on three genomic islands that confer resistance to allicin, a secondary plant product and defense substance that causes disulfide stress., The common foodstuff garlic produces the potent antibiotic defense substance allicin after tissue damage. Allicin is a redox toxin that oxidizes glutathione and cellular proteins and makes garlic a highly hostile environment for non-adapted microbes. Genomic clones from a highly allicin-resistant Pseudomonas fluorescens (PfAR-1), which was isolated from garlic, conferred allicin resistance to Pseudomonas syringae and even to Escherichia coli. Resistance-conferring genes had redox-related functions and were on core fragments from three similar genomic islands identified by sequencing and in silico analysis. Transposon mutagenesis and overexpression analyses revealed the contribution of individual candidate genes to allicin resistance. Taken together, our data define a multicomponent resistance mechanism against allicin in PfAR-1, achieved through horizontal gene transfer.

The antibiotic defense substance allicin (diallylthiosulfinate) is produced by garlic (Allium sativumL.) after tissue damage, giving garlic its characteristic odor. Allicin is a redox-toxin that oxidizes thiols in glutathione and cellular proteins. A highly allicin-resistantPseudomonas fluorescensstrain (PfAR-1) was isolated from garlic, and genomic clones were shotgun electroporated into an allicin-susceptibleP. syringaestrain (Ps4612). Recipients showing allicin-resistance had all inherited a group of genes from one of three similar genomic islands (GI), that had been identified in anin silicoanalysis of thePfAR-1 genome. A core fragment of 8-10 congruent genes with redox-related functions, present in each GI, was shown to confer allicin-specific resistance toP. syringae, and even to an unrelatedE. colistrain. Transposon mutagenesis and overexpression analyses revealed the contribution of individual candidate genes to allicin-resistance. Moreover,PfAR-1 was unusual in having 3glutathione reductase(glr) genes, two copies in two of the GIs, but outside of the core group, and one copy in thePfAR-1 genome. Glr activity was approximately 2-fold higher inPfAR-1 than in related susceptiblePf0-1, with only a singleglrgene. Moreover, anE. coliΔglrmutant showed increased susceptibility to allicin, which was complemented byPfAR-1glr1. Taken together, our data support a multi-component resistance mechanism against allicin, achieved through horizontal gene transfer during coevolution, and allowing exploitation of the garlic ecological niche. GI regions syntenic withPfAR-1 GIs are present in other plant-associated bacterial species, perhaps suggesting a wider role in adaptation to plantsper se.

Upon local infection, plants activate a systemic immune response called systemic acquired resistance (SAR). During SAR, systemic leaves become primed for the superinduction of defense genes upon reinfection. We used formaldehyde-assisted isolation of regulatory DNA elements coupled to next-generation sequencing to identify SAR regulators. Our bioinformatic analysis produced 10,129 priming-associated open chromatin sites in the 5′ region of 3,025 genes in the systemic leaves of Arabidopsis (Arabidopsis thaliana) plants locally infected with Pseudomonas syringae pv. maculicola. Whole transcriptome shotgun sequencing analysis of the systemic leaves after challenge enabled the identification of genes with priming-linked open chromatin before (contained in the formaldehyde-assisted isolation of regulatory DNA elements sequencing dataset) and enhanced expression after (included in the whole transcriptome shotgun sequencing dataset) the systemic challenge. Among them, Arabidopsis MILDEW RESISTANCE LOCUS O3 (MLO3) was identified as a previously unidentified positive regulator of SAR. Further in silico analysis disclosed two yet unknown cis-regulatory DNA elements in the 5′ region of genes. The P-box was mainly associated with priming-responsive genes, whereas the C-box was mostly linked to challenge. We found that the P- or W-box, the latter recruiting WRKY transcription factors, or combinations of these boxes, characterize the 5′ region of most primed genes. Therefore, this study provides a genome-wide record of genes with open and accessible chromatin during SAR and identifies MLO3 and two previously unidentified DNA boxes as likely regulators of this immune response.

18 páginas, 6 figuras, 2 tablas. -- The first publication is by Frontiers Media, Global warming is becoming a significant problem for food security, particularly in the Mediterranean basin. The use of molecular techniques to study gene-level responses to environmental changes in non-model organisms is increasing and may help to improve the mechanistic understanding of durum wheat response to elevated CO2 and high temperature. With this purpose, we performed transcriptome RNA sequencing (RNA-Seq) analyses combined with physiological and biochemical studies in the flag leaf of plants grown in field chambers at ear emergence. Enhanced photosynthesis by elevated CO2 was accompanied by an increase in biomass and starch and fructan content, and a decrease in N compounds, as chlorophyll, soluble proteins, and Rubisco content, in association with a decline of nitrate reductase and initial and total Rubisco activities. While high temperature led to a decline of chlorophyll, Rubisco activity, and protein content, the glucose content increased and starch decreased. Furthermore, elevated CO2 induced several genes involved in mitochondrial electron transport, a few genes for photosynthesis and fructan synthesis, and most of the genes involved in secondarymetabolism and gibberellin and jasmonate metabolism, whereas those related to light harvesting,N assimilation, and other hormone pathways were repressed. High temperature repressed genes for C, energy, N, lipid, secondary, and hormone metabolisms. Under the combined increases in atmospheric CO2 and temperature, the transcript profile resembled that previously reported for high temperature, although elevated CO2 partly alleviated the downregulation of primary and secondary metabolism genes. The results suggest that there was a reprogramming of primary and secondary metabolism under the future climatic scenario, leading to coordinated regulation of C-N metabolism towards C-rich metabolites at elevatedCO2 and a shift away from C-rich secondarymetabolites at high temperature. Several candidate genes differentially expressed were identified, including protein kinases, receptor kinases, and transcription factors., This research was supported by the Spanish National R&D&I Plan of the Ministry of Economy and Competitiveness [grants AGL2009-11987, AGL2013-41363-R (ERDF), and AGL2016-79589-R (ERDF)

BackgroundGiven its tolerance to stress and its richness in particular secondary metabolites, the tobacco tree, Nicotiana glauca, has been considered a promising biorefinery feedstock that would not be competitive with food and fodder crops.ResultsHere we present a 3.5 Gbp draft sequence and annotation of the genome of N. glauca spanning 731,465 scaffold sequences, with an N50 size of approximately 92 kbases. Furthermore, we supply a comprehensive transcriptome and metabolome analysis of leaf development comprising multiple techniques and platforms.The genome sequence is predicted to cover nearly 80% of the estimated total genome size of N. glauca. With 73,799 genes predicted and a BUSCO score of 94.9%, we have assembled the majority of gene-rich regions successfully. RNA-Seq data revealed stage-and/or tissue-specific expression of genes, and we determined a general trend of a decrease of tricarboxylic acid cycle metabolites and an increase of terpenoids as well as some of their corresponding transcripts during leaf development.ConclusionThe N. glauca draft genome and its detailed transcriptome, together with paired metabolite data, constitute a resource for future studies of valuable compound analysis in tobacco species and present the first steps towards a further resolution of phylogenetic, whole genome studies in tobacco.

Phenolics are antioxidants present in tomato fruit that confer healthy benefits and exhibit crucial roles for plant metabolism and response to environmental stimuli. An approach based on two genomics platforms was undertaken to identify candidate genes associated to higher phenolics content in tomato fruit. A comparative transcriptomic analysis between the S. pennellii Introgression Line 7-3, which produced an average higher level of fruit phenolics, and the cultivated variety M82, revealed that their differences are attributed to genes involved in phenolics accumulation into the vacuole. The up-regulation of genes coding for one MATE-transporter, one vacuolar sorting protein and three GSTs supported this hypothesis. The observed balancing effect between two ethylene responsive factors (ERF1 and ERF4) was also hypothesized to drive the transcriptional regulation of these transport genes. In order to confirm such model a TILLING platform was explored. A mutant was isolated harbouring a point mutation in the ERF1 cds that affects the protein sequence and its expected function. Fruits of the mutant exhibited a significant reduced level of phenolics than the control variety. Changes in the expression of genes involved in sequestration of phenolics in vacuole also supported the hypothesized key-role of ERF1 in orchestrating these genes.

Recent massive growth in the production of sequencing data necessitates matching improve-ments in bioinformatics tools to effectively utilize it. Existing tools suffer from limitations in both scalability and applicability which are inherent to their underlying algorithms and data structures. We identify the key requirements for the ideal data structure for sequence analy-ses: it should be informationally lossless, locally updatable, and memory efficient; requirements which are not met by data structures underlying the major assembly strategies Overlap Layout Consensus and De Bruijn Graphs. We therefore propose a new data structure, the LOGAN graph, which is based on a memory efficient Sparse De Bruijn Graph with routing information. Innovations in storing routing information and careful implementation allow sequence datasets for Escherichia coli (4.6Mbp, 117x coverage), Arabidopsis thaliana (135Mbp, 17.5x coverage) and Solanum pennellii (1.2Gbp, 47x coverage) to be loaded into memory on a desktop computer in seconds, minutes, and hours respectively. Memory consumption is competitive with state of the art alternatives, while losslessly representing the reads in an indexed and updatable form. Both Second and Third Generation Sequencing reads are supported. Thus, the LOGAN graph is positioned to be the backbone for major breakthroughs in sequence analysis such as integrated hybrid assembly, assembly of exceptionally large and repetitive genomes, as well as assembly and representation of pan-genomes.

Recent updates in sequencing technology have made it possible to obtain Gigabases of sequence data from one single flowcell. Prior to this update, the nanopore sequencing technology was mainly used to analyze and assemble microbial samples1-3. Here, we describe the generation of a comprehensive nanopore sequencing dataset with a median fragment size of 11,979 bp for the wild tomato speciesSolanum pennelliifeaturing an estimated genome size of ca 1.0 to 1.1 Gbases. We describe its genome assembly to a contig N50 of 2.5 MB using a pipeline comprising a Canu4pre-processing and a subsequent assembly using SMARTdenovo. We show that the obtained nanopore basedde novogenome reconstruction is structurally highly similar to that of the referenceS. pennelliiLA7165genome but has a high error rate caused mostly by deletions in homopolymers. After polishing the assembly with Illumina short read data we obtained an error rate of S. pennelliigenome5. Taken together our data indicate such long read sequencing data can be used to affordably sequence and assemble Gbase sized diploid plant genomes.Raw data is available athttp://www.plabipd.de/portal/solanum-pennelliiand has been deposited as PRJEB19787.

Large-scale biology article; International audience; Updates in nanopore technology have made it possible to obtain gigabases of sequence data. Prior to this, nanopore sequencing technology was mainly used to analyze microbial samples. Here, we describe the generation of a comprehensive nanopore sequencing data set with a median read length of 11,979 bp for a self-compatible accession of the wild tomato species Solanum pennellii. We describe the assembly of its genome to a contig N50 of 2.5 MB. The assembly pipeline comprised initial read correction with Canu and assembly with SMARTdenovo. The resulting raw nanopore-based de novo genome is structurally highly similar to that of the reference S. pennellii LA716 accession but has a high error rate and was rich in homopolymer deletions. After polishing the assembly with Illumina reads, we obtained an error rate of

Plant biotechnology journal 15(5), 634 - 647 (2017). doi:10.1111/pbi.12662, Published by Wiley-Blackwell, Oxford

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Functional gene clusters, containing two or more genes encoding different enzymes for the same pathway, are sometimes observed in plant genomes, most often when the genes specify the synthesis of specialized defensive metabolites. Here, we show that a cluster of genes in tomato (Solanum lycopersicum; Solanaceae) contains genes for terpene synthases (TPSs) that specify the synthesis of monoterpenes and diterpenes from cis-prenyl diphosphates, substrates that are synthesized by enzymes encoded by cis-prenyl transferase (CPT) genes also located within the same cluster. The monoterpene synthase genes in the cluster likely evolved from a diterpene synthase gene in the cluster by duplication and divergence. In the orthologous cluster in Solanum habrochaites, a new sesquiterpene synthase gene was created by a duplication event of a monoterpene synthase followed by a localized gene conversion event directed by a diterpene synthase gene. The TPS genes in the Solanum cluster encoding cis-prenyl diphosphate–utilizing enzymes are closely related to a tobacco (Nicotiana tabacum; Solanaceae) diterpene synthase encoding Z-abienol synthase (Nt-ABS). Nt-ABS uses the substrate copal-8-ol diphosphate, which is made from the all-trans geranylgeranyl diphosphate by copal-8-ol diphosphate synthase (Nt-CPS2). The Solanum gene cluster also contains an ortholog of Nt-CPS2, but it appears to encode a nonfunctional protein. Thus, the Solanum functional gene cluster evolved by duplication and divergence of TPS genes, together with alterations in substrate specificity to utilize cis-prenyl diphosphates and through the acquisition of CPT genes.

Sustainable agriculture demands reduced input of man-made nitrogen (N) fertilizer, yet N2 fixation limits the productivity of crops with heterotrophic diazotrophic bacterial symbionts. We investigated floating ferns from the genus Azolla that host phototrophic diazotrophic Nostoc azollae in leaf pockets and belong to the fastest growing plants. Experimental production reported here demonstrated N-fertilizer independent production of nitrogen-rich biomass with an annual yield potential per ha of 1200 kg−1 N fixed and 35 t dry biomass. 15N2 fixation peaked at noon, reaching 0.4 mg N g−1 dry weight h−1. Azolla ferns therefore merit consideration as protein crops in spite of the fact that little is known about the fern’s physiology to enable domestication. To gain an understanding of their nitrogen physiology, analyses of fern diel transcript profiles under differing nitrogen fertilizer regimes were combined with microscopic observations. Results established that the ferns adapted to the phototrophic N2-fixing symbionts N. azollae by (1) adjusting metabolically to nightly absence of N supply using responses ancestral to ferns and seed plants; (2) developing a specialized xylem-rich vasculature surrounding the leaf-pocket organ; (3) responding to N-supply by controlling transcripts of genes mediating nutrient transport, allocation and vasculature development. Unlike other non-seed plants, the Azolla fern clock is shown to contain both the morning and evening loops; the evening loop is known to control rhythmic gene expression in the vasculature of seed plants and therefore may have evolved along with the vasculature in the ancestor of ferns and seed plants.

Next-generation sequencing techniques reduced the cost of sequencing a genome rapidly, but came with a relatively high error rate. Therefore, error correction of this data is a necessary task before assembly can take place. Since the input data is huge and error correction is compute intensive, parallelizing this work on a modern shared-memory system can help to keep the runtime feasible. In this work we present PAGANtec, a tool for error correction of next-generation sequencing data, based on the novel PAGAN graph structure. PAGANtec was parallelized with OpenMP and a performance analysis and tuning was done. The analysis led to the awareness, that OpenMP tasks are a more suitable paradigm for this work than traditional work-sharing.

Next generation RNA sequencing provides one with the opportunity to gain comprehensive insights into transcriptomes. However these data sets pose new challenges: the raw sequence data need to be quality checked and filtered, and sophisticated statistical methods have to be applied to find changed genes. Given the large amount of data involved, this is far from trivial. Here we present RobiNA as an integrated solution that consolidates all these steps in one user-friendly graphical platform.

Björn Usadel and colleagues report the genome sequence of the wild tomato species Solanum pennellii. The authors identify genes important for stress tolerance, metabolism and fruit maturation and suggest that transposable elements have had an important role in the evolution of the S. penellii stress response. Supplementary information The online version of this article (doi:10.1038/ng.3046) contains supplementary material, which is available to authorized users., Solanum pennellii is a wild tomato species endemic to Andean regions in South America, where it has evolved to thrive in arid habitats. Because of its extreme stress tolerance and unusual morphology, it is an important donor of germplasm for the cultivated tomato Solanum lycopersicum1. Introgression lines (ILs) in which large genomic regions of S. lycopersicum are replaced with the corresponding segments from S. pennellii can show remarkably superior agronomic performance2. Here we describe a high-quality genome assembly of the parents of the IL population. By anchoring the S. pennellii genome to the genetic map, we define candidate genes for stress tolerance and provide evidence that transposable elements had a role in the evolution of these traits. Our work paves a path toward further tomato improvement and for deciphering the mechanisms underlying the myriad other agronomic traits that can be improved with S. pennellii germplasm. Supplementary information The online version of this article (doi:10.1038/ng.3046) contains supplementary material, which is available to authorized users.

International audience; Although applied over extremely short timescales, artificial selection has dramatically altered the form, physiology, and life history of cultivated plants. We have used RNAseq to define both gene sequence and expression divergence between cultivated tomato and five related wild species. Based on sequence differences, we detect footprints of positive selection in over 50 genes. We also document thousands of shifts in gene-expression level, many of which resulted from changes in selection pressure. These rapidly evolving genes are commonly associated with environmental response and stress tolerance. The importance of environmental inputs during evolution of gene expression is further highlighted by large-scale alteration of the light response coexpression network between wild and cultivated accessions. Human manipulation of the genome has heavily impacted the tomato transcriptome through directed admixture and by indirectly favoring nonsynonymous over synonymous substitutions. Taken together, our results shed light on the pervasive effects artificial and natural selection have had on the transcriptomes of tomato and its wild relatives.

Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/ robin.

The data explosion in the biological sciences has led to many novel challenges for the individual researcher. One of these is to interpret the sheer mass of data at hand. Typical high-throughput data sets from transcriptomic data can easily comprise hundred thousand data points. It is thus necessary to provide tools to interactively visualize these data sets in a way that aids in their interpretation. Thus we have developed the MAPMAN application. This application renders individual data points from different domains as different glyphs that are color coded to reflect underlying changes in the magnitude/abundance of the underlying data. In order to augment the human comprehensibility of the biologist domain experts these data are organized on meaningful pathway diagrams that the biologist has encountered numerous times. Using these representations together with a high level organization thus helps to quickly realize the main outcome of such a high throughput study and to further decide on additional tasks that should be performed to explore the data.