Jitender Cheema

Algal ancestor of land plants was preadapted for symbiosis

Significance Colonization of land by plants was a critical event for the emergence of extant ecosystems. The innovations that allowed the algal ancestor of land plants to succeed in such a transition remain unknown. Beneficial interaction with symbiotic fungi has been proposed as one of these innovations. Here we show that the genes required for this interaction appeared in a stepwise manner: Some evolved before the colonization of land by plants and others first appeared in land plants. We thus propose that the algal ancestor of land plants was preadapted for interaction with beneficial fungi and employed these gene networks to colonize land successfully. Colonization of land by plants was a major transition on Earth, but the developmental and genetic innovations required for this transition remain unknown. Physiological studies and the fossil record strongly suggest that the ability of the first land plants to form symbiotic associations with beneficial fungi was one of these critical innovations. In angiosperms, genes required for the perception and transduction of diffusible fungal signals for root colonization and for nutrient exchange have been characterized. However, the origin of these genes and their potential correlation with land colonization remain elusive. A comprehensive phylogenetic analysis of 259 transcriptomes and 10 green algal and basal land plant genomes, coupled with the characterization of the evolutionary path leading to the appearance of a key regulator, a calcium- and calmodulin-dependent protein kinase, showed that the symbiotic signaling pathway predated the first land plants. In contrast, downstream genes required for root colonization and their specific expression pattern probably appeared subsequent to the colonization of land. We conclude that the most recent common ancestor of extant land plants and green algae was preadapted for symbiotic associations. Subsequent improvement of this precursor stage in early land plants through rounds of gene duplication led to the acquisition of additional pathways and the ability to form a fully functional arbuscular mycorrhizal symbiosis.

Computational approaches and software tools for genetic linkage map estimation in plants

Genetic maps are an important component within the plant biologists toolkit, underpinning crop plant improvement programs. The estimation of plant genetic maps is a conceptually simple yet computationally complex problem, growing ever more so with the development of inexpensive, high-throughput DNA markers. The challenge for bioinformaticians is to develop analytical methods and accompanying software tools that can cope with datasets of differing sizes, from tens to thousands of markers, that can incorporate the expert knowledge that plant biologists typically use when developing their maps, and that facilitate user-friendly approaches to achieving these goals. Here, we aim to give a flavour of computational approaches for genetic map estimation, discussing briefly many of the key concepts involved, and describing a selection of software tools that employ them. This review is intended both for plant geneticists as an introduction to software tools with which to estimate genetic maps, and for bioinformaticians as an introduction to the underlying computational approaches.

Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition

We examined succession of the rhizosphere microbiota of three model plants (Arabidopsis, Medicago and Brachypodium) in compost and sand and three crops (Brassica, Pisum and Triticum) in compost alone. We used serial inoculation of 24 independent replicate microcosms over three plant generations for each plant/soil combination. Stochastic variation between replicates was surprisingly weak and by the third generation, replicate microcosms for each plant had communities that were very similar to each other but different to those of other plants or unplanted soil. Microbiota diversity remained high in compost, but declined drastically in sand, with bacterial opportunists and putative autotrophs becoming dominant. These dramatic differences indicate that many microbes cannot thrive on plant exudates alone and presumably also require carbon sources and/or nutrients from soil. Arabidopsis had the weakest influence on its microbiota and in compost replicate microcosms converged on three alternative community compositions rather than a single distinctive community. Organisms selected in rhizospheres can have positive or negative effects. Two abundant bacteria are shown to promote plant growth, but in Brassica the pathogen Olpidium brassicae came to dominate the fungal community. So plants exert strong selection on the rhizosphere microbiota but soil composition is critical to its stability. microbial succession/ plant–microbe interactions/rhizosphere microbiota/selection.

Emergence of terpene cyclization in Artemisia annua

The emergence of terpene cyclization was critical to the evolutionary expansion of chemical diversity yet remains unexplored. Here we report the first discovery of an epistatic network of residues that controls the onset of terpene cyclization in Artemisia annua. We begin with amorpha-4,11-diene synthase (ADS) and (E)-β-farnesene synthase (BFS), a pair of terpene synthases that produce cyclic or linear terpenes, respectively. A library of ~27,000 enzymes is generated by breeding combinations of natural amino-acid substitutions from the cyclic into the linear producer. We discover one dominant mutation is sufficient to activate cyclization, and together with two additional residues comprise a network of strongly epistatic interactions that activate, suppress or reactivate cyclization. Remarkably, this epistatic network of equivalent residues also controls cyclization in a BFS homologue from Citrus junos. Fitness landscape analysis of mutational trajectories provides quantitative insights into a major epoch in specialized metabolism.

THREaD Mapper Studio: a novel, visual web server for the estimation of genetic linkage maps

The estimation of genetic linkage maps is a key component in plant and animal research, providing both an indication of the genetic structure of an organism and a mechanism for identifying candidate genes associated with traits of interest. Because of this importance, several computational solutions to genetic map estimation exist, mostly implemented as stand-alone software packages. However, the estimation process is often largely hidden from the user. Consequently, problems such as a program crashing may occur that leave a user baffled. THREaD Mapper Studio (http://cbr.jic.ac.uk/threadmapper) is a new web site that implements a novel, visual and interactive method for the estimation of genetic linkage maps from DNA markers. The rationale behind the web site is to make the estimation process as transparent and robust as possible, while also allowing users to use their expert knowledge during analysis. Indeed, the 3D visual nature of the tool allows users to spot features in a data set, such as outlying markers and potential structural rearrangements that could cause problems with the estimation procedure and to account for them in their analysis. Furthermore, THREaD Mapper Studio facilitates the visual comparison of genetic map solutions from third party software, aiding users in developing robust solutions for their data sets.

Resistance gene discovery and cloning by sequence capture and association genetics

Genetic resistance is the most economic and environmentally sustainable approach for crop disease protection. Disease resistance (R) genes from wild relatives are a valuable resource for breeding resistant crops. However, introgression of R genes into crops is a lengthy process often associated with co-integration of deleterious linked genes1, 2 and pathogens can rapidly evolve to overcome R genes when deployed singly3. Introducing multiple cloned R genes into crops as a stack would avoid linkage drag and delay emergence of resistance-breaking pathogen races4. However, current R gene cloning methods require segregating or mutant progenies5–10, which are difficult to generate for many wild relatives due to poor agronomic traits. We exploited natural pan-genome variation in a wild diploid wheat by combining association genetics with R gene enrichment sequencing (AgRenSeq) to clone four stem rust resistance genes in <6 months. RenSeq combined with diversity panels is therefore a major advance in isolating R genes for engineering broad-spectrum resistance in crops.

Rice In Vivo RNA Structurome Reveals RNA Secondary Structure Conservation and Divergence in Plants

RNA secondary structure plays a critical role in gene regulation. Rice (Oryza sativa) is one of the most important food crops in the world. However, RNA structure in rice has scarcely been studied. Here, we have successfully generated in vivo Structure-seq libraries in rice. We found that the structural flexibility of mRNAs might associate with the dynamics of biological function. Higher N6-methyladenosine (m6A) modification tends to have less RNA structure in 3′ UTR, whereas GC content does not significantly affect in vivo mRNA structure to maintain efficient biological processes such as translation. Comparative analysis of RNA structurome between rice and Arabidopsis revealed that higher GC content does not lead to stronger structure and less RNA structural flexibility. Moreover, we found a weak correlation between sequence and structure conservation of the orthologs between rice and Arabidopsis. The conservation and divergence of both sequence and in vivo RNA structure corresponds to diverse and specific biological processes. Our results indicate that RNA secondary structure might offer a separate layer of selection to the sequence between monocot and dicot. Therefore, our study implies that RNA structure evolves differently in various biological processes to maintain robustness in development and adaptational flexibility during angiosperm evolution.

FoldAtlas: a repository for genome-wide RNA structure probing data.

Summary: Most RNA molecules form internal base pairs, leading to a folded secondary structure. Some of these structures have been demonstrated to be functionally significant. High-throughput RNA structure chemical probing methods generate millions of sequencing reads to provide structural constraints for RNA secondary structure prediction. At present, processed data from these experiments are difficult to access without computational expertise. Here we present FoldAtlas, a web interface for accessing raw and processed structural data across thousands of transcripts. FoldAtlas allows a researcher to easily locate, view, and retrieve probing data for a given RNA molecule. We also provide in silico and in vivo secondary structure predictions for comparison, visualized in the browser as circle plots and topology diagrams. Data currently integrated into FoldAtlas are from a new high-depth Structure-seq data analysis in Arabidopsis thaliana, released with this work. Availability and Implementation: The FoldAtlas website can be accessed at www.foldatlas.com. Source code is freely available at github.com/mnori/foldatlas under the MIT license. Raw reads data are available under the NCBI SRA accession SRP066985. Contact: yiliang.ding@jic.ac.uk or matthew.norris@jic.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

REVIEW: Epistasis and dominance in the emergence of catalytic function as exemplified by the evolution of plant terpene synthases.

Epistasis, the interaction between mutations and the genetic background, is a pervasive force in evolution that is difficult to predict yet derives from a simple principle - biological systems are interconnected. Therefore, one effect may be intimately linked to another, hence interdependent. Untangling epistatic interactions between and within genes is a vibrant area of research. Deriving a mechanistic understanding of epistasis is a major challenge. Particularly, elucidating how epistasis can attenuate the effects of otherwise dominant mutations that control phenotypes. Using the emergence of terpene cyclization in specialized metabolism as an excellent example, this review describes the process of discovery and interpretation of dominance and epistasis in relation to current efforts. Specifically, we outline experimental approaches to isolating epistatic networks of mutations in protein structure, formally quantifying epistatic interactions, then building biochemical models with chemical mechanisms in efforts to achieve an understanding of the physical basis for epistasis. From these models we describe informed conjectures about past evolutionary events that underlie the emergence, divergence and specialization of terpene synthases to illustrate key principles of the constraining forces of epistasis in enzyme function.

NMR metabolomics defining genetic variation in pea seed metabolites

Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value.