Involvement of Two Novel GRF-Type Zinc Finger Nodulins during Rhizobial Symbiotic Establishment in Medicago truncatula Nodules

In legumes, symbiotic nitrogen fixation (SNF) occurs through a cooperative relationship established with rhizobial diazotrophic bacteria. This association triggers the de novo development of a highly specialized organ from root, called nodule. In this dissertation, we explore the molecular functional genetics of nodulation in the model legume Medicago truncatula in the scope of knowledge. Followed by a linearization of the current knowledge about the roles of Transcription Factors in legume nodule symbiosis. In an attempt of understanding the orchestration dynamics of nodulation and processes that generate SNF. Legumes evolved novel genes related with nodule development, symbiosis establishment and nitrogen fixation, which are exclusively expressed at different stages of nodule development, and during nitrogen reduction per se. In the model legume Medicago truncatula, two genes encoding GRF-type zinc finger proteins present high and distinct expression pattern during symbiotic nitrogen fixation in root nodules. Both genes encode for very similar protein, part on a non-characterized protein family, GRF-type zinc finger, with putative function as transcriptional factor. The gene expression data present high and distinct transcription for both genes in specific zones of the indeterminate nodule: MtGRF1/MtN20 (Medtr7g086040.1) is highly expressed in an area below the nodule meristem (zone II, where endosymbiosis occurs), while MtGRF2 (Medtr1g064350.1) is expressed in the interzone, where bacteroid differentiation occurs. MtN20 is exclusively present in the distal-zone II of nodules, in tissue where the plant cells become colonized and bacteroids start to differentiate in SNF organelles . And a Tnt1-insertional line for MtN20 was genotyped and phenotype. Nodules of mtn20 present normal nodule formation but disruption in nitrogen fixation, showing early senescence, and loss of the rhizobial maturation, required in this process. MtGRF2 present expression pattern in the interzone of nodules, in a tissue where the and bacteroids pass through a singular differentiation stage to become a in SNF organelles . A genetic network for each gene of interest highlight high correlated genes that possible play essential roles on distinct stages of symbiotic nitrogen fixation and bacteroid differentiation, await to be functional characterized. This information contributes to understanding of symbiotic nitrogen fixation and emphasize important questions to be investigated and make possible to explore root nodulation in non-legumes

Characterization of nodule-specific, SLAC and MATE membrane transporters in Medicago truncatula

Legumes play a crucial role in sustainable agriculture because of their intrinsic ability to reduce atmospheric N2 into NH3 via symbiotic nitrogen fixation (SNF). SNF is carried out in the symbiosome, a quasi-organelle containing endosymbiotic rhizobial bacteria in the cytoplasm of infected cells of the nodule. Therefore, the bacteria are surrounded by a symbiosome membrane (SM), which is derived from the plant plasma membrane during infection. SNF requires constant nutritional exchanges between symbionts, including reduced carbon (dicarboxylates) from the plant for reduced nitrogen (NH4+) from the bacteroids. This exchange of nutrients and signals is fundamental to SNF and occurs through various transporters in several membranes, and critically through the SM. However, despite the fact that many nodule-specific or high-expressed putative transport genes have been identified in nodules at the genetic level, little is still known about their biochemical and physiological roles for SNF. Thus, we functionally characterized two transporter genes exclusively or highly expressed in the nodules of Medicago truncatula, a well-established legume model species: MtSLAH1 (Medtr4g049640, TCDB 2.A.16.5) of the Tellurite-resistance/Dicarboxylate transporter family, and MtMATE30 (Medtr7g082810, TCDB 2.A.66.1) of the Multidrug and toxic compound Extrusion family. There are seven SLAC (Slow Anion Channel associated) and 70 MATE (Multidrug and Toxic compound Extrusion) family members in the M. truncatula genome. MtSLAH1 is the SLAC transporter with highest expression value in nodules among seven members. Its expression is reduced 5-fold in 2 days after application of nitrate, a known SNF repressor. MtSLAH1 is highly expressed in infected cells and requires bacteroid differentiation for induction. MtSLAH1 channel permeability to dicarboxylates was tested by patch-clamp of Xenopus oocytes, but no current was detected. MtSLAH1 either requires a cofactor for activation or facilitates the efflux a different anion (e.g., nitrate, chloride). On the other hand, MtMATE30 is the MATE gene with highest expression in nodules. It is induced 100-fold by low nitrogen while strongly repressed by nitrate, suggesting a role in symbiotic nitrogen fixation. It starts to express in immature nodules (6 days post-inoculation, dpi), reaches its peak in young, mature nodules (10 dpi) and maintains consistent expression in older nodules. It is expressed in all nodule zones, except the meristem. MtMATE30 canonically belongs to a phylogenetic clade that includes transporters with affinity to alkaloids. Trigonelline is a widely distributed alkaloid and commonly found in legumes. Previous research showed that rhizobia are able to catabolize trigonelline by the trc gene located in the rhizobial pSym megaplasmid. We confirmed the presence of trigonelline in nodules of M. truncatula and showed MtMATE30 affinity to trigonelline in a heterologous bacteria system. Altogether, MtMATE30 may be participating in alkaloid metabolism of nodule cells, although its precise physiological role in symbiotic nitrogen fixation still requires further investigation. In the big picture, these two membrane transporters studied here are only two examples among thousands of transporters exclusively expressed in nodule. The legume research community needs to focus more efforts to understand how legumes and rhizobia communicate and cooperate to fix nitrogen in order to enhance SNF efficiency in legume crops, and possibly to eventually extend it into non-legume crops

Bradyrhizobium diazoefficiens USDA 110–glycine max interactome provides candidate proteins associated with symbiosis

Although the legume−rhizobium symbiosis is a most-important biological process, there is a limited knowledge about the protein interaction network between host and symbiont. Using interolog- and domain-based approaches, we constructed an interspecies protein interactome containing 5115 protein−protein interactions between 2291 Glycine max and 290 Bradyrhizobium diazoefficiens USDA 110 proteins. The interactome was further validated by the expression pattern analysis in nodules, gene ontology term semantic similarity, co-expression analysis, and luciferase complementation image assay. In the G. max−B. diazoefficiens interactome, bacterial proteins are mainly ion channel and transporters of carbohydrates and cations, while G. max proteins are mainly involved in the processes of metabolism, signal transduction, and transport. We also identified the top 10 highly interacting proteins (hubs) for each species. Kyoto Encyclopedia of Genes and Genomes pathway analysis for each hub showed that a pair of 14-3-3 proteins (SGF14g and SGF14k) and 5 heat shock proteins in G. max are possibly involved in symbiosis, and 10 hubs in B. diazoefficiens may be important symbiotic effectors. Subnetwork analysis showed that 18 symbiosis-related soluble N-ethylmaleimide sensitive factor attachment protein receptor proteins may play roles in regulating bacterial ion channels, and SGF14g and SGF14k possibly regulate the rhizobium dicarboxylate transport protein DctA. The predicted interactome provide a valuable basis for understanding the molecular mechanism of nodulation in soybean

Sequencing impact at the University of Missouri

Executive Summary: It would be an understatement to say that "next-generation" sequencing technology has been revolutionary. Over the last 10 years, sequencing has created a paradigm shift in biological sciences where more and more a component of research involves "just sequence it". This is because the types of data, applications and resulting insights are expanding every year. Further, the volume and speed of data generation are growing exponentially, while the costs to generate these data are decreasing exponentially. The Human Genome Project completed the first draft genome sequence in 2001 at an estimated cost of $3 billion. Next-generation sequencing became mainstream around 2007 and enabled the re-sequencing of a human genome at a cost of approximately$50,000. In late 2015, Illumina announced the availability of their X10 sequencer for use on non-human samples enabling the re-sequencing of a mammalian (human, cow, dog etc.) genome for approximately $1,500 and with an annual throughput of 10,000 genomes per year. The ease, rapidity and cost effectiveness of generating sequence data has created a computational analysis bottleneck. The growth of computational resources on the MU campus has not kept pace with the growth in data generation capability. In order for Mizzou to maintain a competitive research environment, we need to expand the computational resources available for bioinformatics analysis of large data which include sequence data. It will require an initial investment of$619,000 in early 2016 to build the needed core infrastructure and will require ongoing funding to maintain and expand this infrastructure. Initial investments (cost share of $231,000) made by Mizzou in 2005 to bring next-generation sequencing to this campus have been returned many-fold. Based on a survey sent to MU researchers in November 2015, a total of 66 grants have been awarded involving sequencing for a total of$87.5M. $7.6M of that is directly attributable to sequence data generation/analysis. In addition, another$7.9M in grant funding has been submitted and remains pending. This research has led to 173 refereed journal articles in top-tier journals producing over 6,000 citations. Additionally, 19 M.S., 62 Ph.D. and 21 postdocs have been trained as a result of these sequence related research projects. Plant and animal researchers at MU have been at the forefront of the next-generation sequencing revolution. However, based on the diversity of grants and papers gathered by the survey, sequence analysis provides a common foundation that ties together many disciplines on campus. As such, investment in computational capacity directed at sequence data analysis will serve the entire campus and provide technological ties between disciplines. The following is a detailed description of the history of sequencing/bioinformatics, a description of the computation resources required, and a model for sustainability and an analysis of the impacts of next-generation sequencing at Mizzou

Genetic and Epigenetic Control of Soybean Agronomic Traits

Greater soybean productivity depends on the genetic improvement of yield components, epigenetic effects and the interactions with surrounding environment. We explored the possibility of soybean improvement by conventional biparental crossing of soybean lines, differing by seed quality, yield, and resistance to a range of pathogens, including soybean cyst nematode (SCN). We investigated the role of Resistance to Heterodera glycines (Rhg) 1 and 4 loci in soybean resistance to SCN, race 2 and 3. Further, we evaluated the adaptability of temperate-origin soybean lines in tropical environments of Rwanda. Lastly, we examined gene expression, RNA splicing, DNA methylation and their interactions in three distinct developmental stages of soybean nodules. Briefly, compared to the parental lines, the recombinant inbred lines (RILs) generated from the biparental cross, represented varying phenotypes for seed yield, protein and oil contents, with high broad-sense heritability scores. This suggests a possibility of selection of best individuals with potentially high genetic gain for each trait. Analysis of gene expression, nucleotide sequences, and copy number variation of Rhg1 and Rhg4 in a set of RILs revealed that resistance to race 2 is mediated independently of Rhg1 and Rhg4. Importantly, a QTL on chromosome 17, associated with resistance to SCN race 2 was identified, a finding that provides the foundation for cloning the underlying SCN resistance gene. Our data suggested a possibility to stack favorable SCN resistance alleles in high yielding cultivars as the yield of RILs harboring SCN resistance alleles to race 2, compared to the susceptible RILs, was not negatively impacted. Some US-developed soybean lines could adapt and double the current local yield potential. However, our data revealed a significant GXE interaction, implying their fitness in specific micro-climates of Rwanda. Amino acid profile and consequently seed storage protein can be improved through the manipulation of soybean nodulation. Our results revealed dynamic changes in gene expression, alternative splicing events and extensive DNA methylation reprogramming in the developing nodules. The results also revealed novel insights to the associations between the transcriptome, spliceome, and methylome of the developing soybean nodules and improved our understanding of the genetic and epigenetic mechanisms controlling soybean nodulation

Modern Bioinformatics As A Tool To Understand Genomic And Transcriptopmic Variation In Legumes

University of Minnesota Ph.D. dissertation. July 2018. Major: Computer Science. Advisors: Robert Stupar, Chad Myers. 1 computer file (PDF); ix, 126 pages.The research presented here focuses on the deployment of modern bioinformatics to gain a greater understanding of legume genomes and gene functions. While improvement of legume crops still relies on conventional breeding approaches, transgenesis, the introduction of a foreign piece of DNA in a host genome, is becoming increasingly common. Using a transgenic approach, the integration of foreign DNA into the host genome using Agrobacterium-mediated transformation is almost always random and is known to induce mutations at the insertion site, but questions have been raised about the potential for mutagenesis at other loci. While genetic engineering has been widely used for crop improvement, few studies have addressed the genome-wide effects of transgenesis. Chapters two and three of this thesis address this question in the context of Glycine max, a major agricultural crop (soybean). Specifically, chapter two features a reanalysis of data from a previous study that reported a large number of mutations in soybean transgenic plants and describes several factors that led to an overestimation. Chapter three addresses the effects on the genome in a series of soybean plants transformed with CRISPR/Cas9, the most recently developed platform for genome editing. The findings of this work have implications on the frequency and transmission of novel variation resulting from soybean biotechnology. Chapter four focuses on applying transcriptome network analysis for predicting the genes that underlie nodule development variation in the Medicago-Ensifer symbiosis. Co-expression networks were constructed for Medicago truncatula and were integrated with data from genome-wide association analysis to prioritize candidate genes with a high likelihood of causal association with nodule development phenotypes. This approach sheds light on potential new genetic factors underlying an important phenotype, and more broadly, could be applied to understand genomic and phenotypic variation for a wide range of plant species and traits

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs

Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues