Nickel-induced transcriptional memory in lung epithelial cells promotes interferon signaling upon nicotine exposure.

Exposure to nickel, an environmental respiratory toxicant, is associated with lung diseases including asthma, pulmonary fibrosis, bronchitis and cancers. Our previous studies have shown that a majority of the nickel-induced transcriptional changes are persistent and do not reverse even after the termination of exposure. This suggested transcriptional memory, wherein the cell 'remembers' past nickel exposure. Transcriptional memory, due to which the cells respond more robustly to a previously encountered stimulus has been identified in a number of organisms. Therefore, transcriptional memory has been described as an adaptive mechanism. However, transcriptional memory caused by environmental toxicant exposures has not been well investigated. Moreover, how the transcriptional memory caused by an environmental toxicant might influence the outcome of exposure to a second toxicant has not been explored. In this study, we investigated whether nickel-induced transcriptional memory influences the outcome of the cell's response to a second respiratory toxicant, nicotine. Nicotine, an addictive compound in tobacco, is associated with the development of chronic lung diseases including chronic obstructive pulmonary disease (COPD) and pulmonary fibrosis. Our results show that nicotine exposure upregulated a subset of genes only in the cells previously exposed to nickel. Furthermore, our analyses indicate robust activation of interferon (IFN) signaling in these cells. IFN signaling is a driver of inflammation, which is associated with many chronic lung diseases. Therefore, our results suggest that nicotine exposure of lung cells that retain the transcriptional memory of previous nickel exposure could result in increased susceptibility to developing chronic inflammatory lung diseases.

ConnecTF: A platform to integrate transcription factor-gene interactions and validate regulatory networks.

Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.

WRKY1 Mediates Transcriptional Regulation of Light and Nitrogen Signaling Pathways.

Plant responses to multiple environmental stimuli must be integrated to enable them to adapt their metabolism and development. Light and nitrogen (N) are two such stimuli whose downstream signaling pathways must be intimately connected to each other to control plant energy status. Here, we describe the functional role of the WRKY1 transcription factor in controlling genome-wide transcriptional reprogramming of Arabidopsis (Arabidopsis thaliana) leaves in response to individual and combined light and N signals. This includes a cross-regulatory network consisting of 724 genes regulated by WRKY1 and involved in both N and light signaling pathways. The loss of WRKY1 gene function has marked effects on the light and N response of genes involved in N uptake and assimilation (primary metabolism) as well as stress response pathways (secondary metabolism). Our results at the transcriptome and at the metabolite analysis level support a model in which WRKY1 enables plants to activate genes involved in the recycling of cellular carbon resources when light is limiting but N is abundant and upregulate amino acid metabolism when both light and N are limiting. In this potential energy conservation mechanism, WRKY1 integrates information about cellular N and light energy resources to trigger changes in plant metabolism.

Cross-Species Network Analysis Uncovers Conserved Nitrogen-Regulated Network Modules in Rice.

In this study, we used a cross-species network approach to uncover nitrogen (N)-regulated network modules conserved across a model and a crop species. By translating gene network knowledge from the data-rich model Arabidopsis (Arabidopsis thaliana) to a crop, rice (Oryza sativa), we identified evolutionarily conserved N-regulatory modules as targets for translational studies to improve N use efficiency in transgenic plants. To uncover such conserved N-regulatory network modules, we first generated an N-regulatory network based solely on rice transcriptome and gene interaction data. Next, we enhanced the network knowledge in the rice N-regulatory network using transcriptome and gene interaction data from Arabidopsis and new data from Arabidopsis and rice plants exposed to the same N treatment conditions. This cross-species network analysis uncovered a set of N-regulated transcription factors (TFs) predicted to target the same genes and network modules in both species. Supernode analysis of the TFs and their targets in these conserved network modules uncovered genes directly related to N use (e.g. N assimilation) and to other shared biological processes indirectly related to N. This cross-species network approach was validated with members of two TF families in the supernode network, BASIC-LEUCINE ZIPPER TRANSCRIPTION FACTOR1-TGA and HYPERSENSITIVITY TO LOW PI-ELICITED PRIMARY ROOT SHORTENING1 (HRS1)/HRS1 Homolog family, which have recently been experimentally validated to mediate the N response in Arabidopsis.

Plasticity regulators modulate specific root traits in discrete nitrogen environments.

Plant development is remarkably plastic but how precisely can the plant customize its form to specific environments? When the plant adjusts its development to different environments, related traits can change in a coordinated fashion, such that two traits co-vary across many genotypes. Alternatively, traits can vary independently, such that a change in one trait has little predictive value for the change in a second trait. To characterize such "tunability" in developmental plasticity, we carried out a detailed phenotypic characterization of complex root traits among 96 accessions of the model Arabidopsis thaliana in two nitrogen environments. The results revealed a surprising level of independence in the control of traits to environment - a highly tunable form of plasticity. We mapped genetic architecture of plasticity using genome-wide association studies and further used gene expression analysis to narrow down gene candidates in mapped regions. Mutants in genes implicated by association and expression analysis showed precise defects in the predicted traits in the predicted environment, corroborating the independent control of plasticity traits. The overall results suggest that there is a pool of genetic variability in plants that controls traits in specific environments, with opportunity to tune crop plants to a given environment.

A functional phylogenomic view of the seed plants.

A novel result of the current research is the development and implementation of a unique functional phylogenomic approach that explores the genomic origins of seed plant diversification. We first use 22,833 sets of orthologs from the nuclear genomes of 101 genera across land plants to reconstruct their phylogenetic relationships. One of the more salient results is the resolution of some enigmatic relationships in seed plant phylogeny, such as the placement of Gnetales as sister to the rest of the gymnosperms. In using this novel phylogenomic approach, we were also able to identify overrepresented functional gene ontology categories in genes that provide positive branch support for major nodes prompting new hypotheses for genes associated with the diversification of angiosperms. For example, RNA interference (RNAi) has played a significant role in the divergence of monocots from other angiosperms, which has experimental support in Arabidopsis and rice. This analysis also implied that the second largest subunit of RNA polymerase IV and V (NRPD2) played a prominent role in the divergence of gymnosperms. This hypothesis is supported by the lack of 24nt siRNA in conifers, the maternal control of small RNA in the seeds of flowering plants, and the emergence of double fertilization in angiosperms. Our approach takes advantage of genomic data to define orthologs, reconstruct relationships, and narrow down candidate genes involved in plant evolution within a phylogenomic view of species' diversification.

Integrated RNA-seq and sRNA-seq analysis identifies novel nitrate-responsive genes in Arabidopsis thaliana roots.

BACKGROUND: Nitrate and other nitrogen metabolites can act as signals that regulate global gene expression in plants. Adaptive changes in plant morphology and physiology triggered by changes in nitrate availability are partly explained by these changes in gene expression. Despite several genome-wide efforts to identify nitrate-regulated genes, no comprehensive study of the Arabidopsis root transcriptome under contrasting nitrate conditions has been carried out. RESULTS: In this work, we employed the Illumina high throughput sequencing technology to perform an integrated analysis of the poly-A + enriched and the small RNA fractions of the Arabidopsis thaliana root transcriptome in response to nitrate treatments. Our sequencing strategy identified new nitrate-regulated genes including 40 genes not represented in the ATH1 Affymetrix GeneChip, a novel nitrate-responsive antisense transcript and a new nitrate responsive miRNA/TARGET module consisting of a novel microRNA, miR5640 and its target, AtPPC3. CONCLUSIONS: Sequencing of small RNAs and mRNAs uncovered new genes, and enabled us to develop new hypotheses for nitrate regulation and coordination of carbon and nitrogen metabolism.

Building High-Confidence Gene Regulatory Networks by Integrating Validated TF-Target Gene Interactions Using ConnecTF.

Many methods are now available to identify or predict the target genes of transcription factors (TFs) in plants. These include experimental approaches such as in vivo or in vitro TF-target gene-binding assays and various methods for identifying regulated targets in mutants, transgenics, or isolated plant cells. In addition, computational approaches are used to infer TF-target gene interactions from the regulatory elements or gene expression changes across treatments. While each of these approaches has now been applied to a large number of TFs from many species, each method has its own limitations which necessitates that multiple data types are integrated to build the most accurate representation of the gene regulatory networks operating in plants. To make the analyses of TF-target interaction datasets available to the broader research community, we have developed the ConnecTF web platform ( https://connectf.org/ ). In this chapter, we describe how ConnecTF can be used to integrate validated and predicted TF-target gene interactions in order to dissect the regulatory role of TFs in developmental and stress response pathways. Using as our examples KN1 and RA1, two well-characterized maize TFs involved in developing floral tissue, we demonstrate how ConnecTF can be used to (1) compare the target genes between TFs, (2) identify direct vs. indirect targets by combining TF-binding and TF-regulation datasets, (3) chart and visualize network paths between TFs and their downstream targets, and (4) prune inferred user networks for high-confidence predicted interactions using validated TF-target gene data. Finally, we provide instructions for setting up a private version of ConnecTF that enables research groups to store and analyze their own TF-target gene interaction datasets.

The genome of the Wollemi pine, a critically endangered "living fossil" unchanged since the Cretaceous, reveals extensive ancient transposon activity.

We present the genome of the living fossil, Wollemia nobilis, a southern hemisphere conifer morphologically unchanged since the Cretaceous. Presumed extinct until rediscovery in 1994, the Wollemi pine is critically endangered with less than 60 wild adults threatened by intensifying bushfires in the Blue Mountains of Australia. The 12 Gb genome is among the most contiguous large plant genomes assembled, with extremely low heterozygosity and unusual abundance of DNA transposons. Reduced representation and genome re-sequencing of individuals confirms a relictual population since the last major glacial/drying period in Australia, 120 ky BP. Small RNA and methylome sequencing reveal conservation of ancient silencing mechanisms despite the presence of thousands of active and abundant transposons, including some transferred horizontally to conifers from arthropods in the Jurassic. A retrotransposon burst 8-6 my BP coincided with population decline, possibly as an adaptation enhancing epigenetic diversity. Wollemia, like other conifers, is susceptible to Phytophthora, and a suite of defense genes, similar to those in loblolly pine, are targeted for silencing by sRNAs in leaves. The genome provides insight into the earliest seed plants, while enabling conservation efforts.

An Optimized Procedure for Proteomic Analysis of Extracellular Vesicles Using In-Stage Tip Digestion and DIA LC-MS/MS: Application to Liquid Biopsy in Cancer.

Utilizing biofluids to identify cancer biomarkers has received considerable attention in the past decade. In this regard, serum and urine are convenient biofluids to noninvasively recapitulate information usually indicated by traditional tissue biopsies. In particular, we are interested in exploring the extracellular vesicle (ECV)-containing compartment of these fluids as a targeted source for cancer biomarker discovery. ECVs are membrane-enclosed particles, comprising of various fractions including exosomes, microvesicles, and apoptotic bodies. In both physiological and pathological states such as cancer, ECVs carry a rich load of molecular and protein cargoes, which aid in mediating intercellular communication between cells from various tissue types. Here we successfully enriched ECVs using a simple, low-cost, optimized method that we have developed; it is generalizable for the analysis of ECVs from multiple sample types. Such procedures are necessary as ECVs are nanoparticles that contain a treasure trove of large numbers of biomarkers each present at very low levels. Sample processing procedures can enrich for these vesicles and allow for the enhanced detection of proteins in downstream applications such as comprehensive proteomics methods using data-independent acquisition (DIA) and LC-MS/MS.

Adjuvant crizotinib in high-risk uveal melanoma following definitive therapy.

Introduction: Approximately 40% of patients with uveal melanoma (UM) will develop metastatic disease. Tumors measuring at least 12mm in basal diameter with a class 2 signature, as defined by a widely used gene expression-profiling test, are associated with significantly higher risk of metastasis, with a median time to recurrence of 32 months. No therapy has been shown to reduce this risk. Materials and Methods: This was a single-arm, multicenter study in patients with high-risk UM who received definitive treatment of primary disease and had no evidence of metastasis. Patients were consecutively enrolled to receive 12 four-week cycles of adjuvant crizotinib at a starting dose of 250mg twice daily and were subsequently monitored for 36 months. The primary outcome of this study was to assess recurrence-free survival (RFS) of patients with high-risk UM who received adjuvant crizotinib. Results: 34 patients enrolled and received at least one dose of crizotinib. Two patients were unevaluable due to early withdrawal and loss to follow-up, leaving 32 patients evaluable for efficacy. Eight patients (25%) did not complete the planned 48-week course of treatment due to disease recurrence (n=5) or toxicity (n=3). All patients experienced at least one adverse event (AE), with 11/34 (32%) experiencing a Common Terminology Criteria for Adverse Events (CTCAE) grade 3 or 4 AE. After a median duration of follow up of 47.1 months, 21 patients developed distant recurrent disease. The median RFS was 34.9 months (95% CI (Confidence Interval), 23-55 months), with a 32-month recurrence rate of 50% (95% CI, 33-67%). Analysis of protein contents from peripheral blood extracellular vesicles in a subset of patient samples from baseline, on-treatment, and off-treatment, revealed a change in protein content associated with crizotinib exposure, however without a clear association with disease outcome. Conclusions: The use of adjuvant crizotinib in patients with high-risk UM did not result in improved RFS when compared to historical controls. Analysis of blood extracellular vesicles revealed changes in protein content associated with treatment, raising the possibility of future use as a biomarker. Further investigation of adjuvant treatment options are necessary for this challenging disease.

VirtualPlant: a software platform to support systems biology research.

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org.

Systems approach identifies an organic nitrogen-responsive gene network that is regulated by the master clock control gene CCA1.

Understanding how nutrients affect gene expression will help us to understand the mechanisms controlling plant growth and development as a function of nutrient availability. Nitrate has been shown to serve as a signal for the control of gene expression in Arabidopsis. There is also evidence, on a gene-by-gene basis, that downstream products of nitrogen (N) assimilation such as glutamate (Glu) or glutamine (Gln) might serve as signals of organic N status that in turn regulate gene expression. To identify genome-wide responses to such organic N signals, Arabidopsis seedlings were transiently treated with ammonium nitrate in the presence or absence of MSX, an inhibitor of glutamine synthetase, resulting in a block of Glu/Gln synthesis. Genes that responded to organic N were identified as those whose response to ammonium nitrate treatment was blocked in the presence of MSX. We showed that some genes previously identified to be regulated by nitrate are under the control of an organic N-metabolite. Using an integrated network model of molecular interactions, we uncovered a subnetwork regulated by organic N that included CCA1 and target genes involved in N-assimilation. We validated some of the predicted interactions and showed that regulation of the master clock control gene CCA1 by Glu or a Glu-derived metabolite in turn regulates the expression of key N-assimilatory genes. Phase response curve analysis shows that distinct N-metabolites can advance or delay the CCA1 phase. Regulation of CCA1 by organic N signals may represent a novel input mechanism for N-nutrients to affect plant circadian clock function.

Using the Integrated Genome Viewer to reveal amplicon-derived polymorphism enriched at the phenylthiocarbamide locus in the teaching lab.

Due to its distinct phenotype and relatively simple inheritance pattern, the phenylthiocarbamide (PTC) loci is frequently utilized in teaching laboratories to demonstrate genetic concepts such as Mendelian inheritance and population genetics. We have developed a next-generation sequencing and bioinformatics approach to analyze the PTC gene locus to reveal single nucleotide polymorphism (SNP) variation at nucleotide position 785 that predicts tasting ability in humans. Here students purify DNA from their own cheek cells, perform polymerase chain reaction (PCR) amplification of the PTC gene followed by cleaved amplified polymorphic sequence (CAPS) testing. Students perform a second PCR on the PTC loci using high-fidelity Taq to create bar-coded amplicons for next-generation sequencing on the Ion Torrent Personal Genome Machine. Bioinformatic verification reveals polymorphic variation by aligning the entire class PTC PCR fragment sequence to the human gene using Bowtie2 and visualizing the results in the Integrated Genome Viewer. This exercise presents a learning opportunity for students to use next-generation sequencing to predict their own PTC taste sensitivity phenotype coupled with the standard CAPS method. This approach brings the PTC teaching method into the genomics era.

Genetic analysis and QTL mapping for multiple biotic stress resistance in cassava.

In Sub-Saharan Africa cassava (Manihot esculenta Crantz) is one of the most important food crops where more than 40% of the population relies on it as their staple carbohydrate source. Biotic constraints such as viral diseases, mainly Cassava Mosaic Disease (CMD) and Cassava Brown Streak Disease (CBSD), and arthropod pests, particularly Cassava Green Mite (CGM), are major constraints to the realization of cassava's full production potential in Africa. To address these problems, we aimed to map the quantitative trait loci (QTL) associated with resistance to CBSD foliar and root necrosis symptoms, foliar CMD and CGM symptoms in a full-sib mapping population derived from the genotypes AR40-6 and Albert. A high-density linkage map was constructed with 2,125 SNP markers using a genotyping-by-sequencing approach. For phenotyping, clonal evaluation trials were conducted with 120 F1 individuals for two consecutive field seasons using an alpha-lattice design at Chambezi and Naliendele, Tanzania. Previously identified QTL for resistance to CBSD foliar symptoms were corroborated, and a new putative QTL for CBSD root necrosis identified (qCBSDRNc14AR) from AR40-6. Two QTL were identified within the region of the previously recognized CMD2 locus from this population in which both parents are thought to possess the CMD2 locus. Interestingly, a minor but consistent QTL, qCGM18AR, for CGM resistance at 3 months after planting stage was also detected and co-localized with a previously identified SSR marker, NS346, linked with CGM resistance. Markers underlying these QTL may be used to increase efficiencies in cassava breeding programs.

In silico evaluation of predicted regulatory interactions in Arabidopsis thaliana.

BACKGROUND: Prediction of transcriptional regulatory mechanisms in Arabidopsis has become increasingly critical with the explosion of genomic data now available for both gene expression and gene sequence composition. We have shown in previous work 1, that a combination of correlation measurements and cis-regulatory element (CRE) detection methods are effective in predicting targets for candidate transcription factors for specific case studies which were validated. However, to date there has been no quantitative assessment as to which correlation measures or CRE detection methods used alone or in combination are most effective in predicting TF-->target relationships on a genome-wide scale. RESULTS: We tested several widely used methods, based on correlation (Pearson and Spearman Rank correlation) and cis-regulatory element (CRE) detection (>or=1 CRE or CRE over-representation), to determine which of these methods individually or in combination is the most effective by various measures for making regulatory predictions. To predict the regulatory targets of a transcription factor (TF) of interest, we applied these methods to microarray expression data for genes that were regulated over treatment and control conditions in wild type (WT) plants. Because the chosen data sets included identical experimental conditions used on TF over-expressor or T-DNA knockout plants, we were able to test the TF-->target predictions made using microarray data from WT plants, with microarray data from mutant/transgenic plants. For each method, or combination of methods, we computed sensitivity, specificity, positive and negative predictive value and the F-measure of balance between sensitivity and positive predictive value (precision). This analysis revealed that the >or=1 CRE and Spearman correlation (used alone or in combination) were the most balanced CRE detection and correlation methods, respectively with regard to their power to accurately predict regulatory-target interactions. CONCLUSION: These findings provide an approach and guidance for researchers interested in predicting transcriptional regulatory mechanisms using microarray data that they generate (or microarray data that is publically available) combined with CRE detection in promoter sequence data.

Molecular Basis of Disease Resistance in Banana Progenitor Musa balbisiana against Xanthomonas campestris pv. musacearum.

Banana Xanthomonas wilt disease, caused by Xanthomonas campestris pv. musacearum (Xcm), is a major threat to banana production in east Africa. All cultivated varieties of banana are susceptible to Xcm and only the progenitor species Musa balbisiana was found to be resistant. The molecular basis of susceptibility and resistance of banana genotypes to Xcm is currently unknown. Transcriptome analysis of disease resistant genotype Musa balbisiana and highly susceptible banana cultivar Pisang Awak challenged with Xcm was performed to understand the disease response. The number of differentially expressed genes (DEGs) was higher in Musa balbisiana in comparison to Pisang Awak. Genes associated with response to biotic stress were up-regulated in Musa balbisiana. The DEGs were further mapped to the biotic stress pathways. Our results suggested activation of both PAMP-triggered basal defense and disease resistance (R) protein-mediated defense in Musa balbisiana as early response to Xcm infection. This study reports the first comparative transcriptome profile of the susceptible and resistant genotype of banana during early infection with Xcm and provide insights on the defense mechanism in Musa balbisiana, which can be used for genetic improvement of commonly cultivated banana varieties.

Sungear: interactive visualization and functional analysis of genomic datasets.

UNLABELLED: Sungear is a software system that supports a rapid, visually interactive and biologist-driven comparison of large datasets. The datasets can come from microarray experiments (e.g. genes induced in each experiment), from comparative genomics (e.g. genes present in each genome) or even from non-biological applications (e.g. demographics or baseball statistics). Sungear represents multiple datasets as vertices in a polygon. Each possible intersection among the sets is represented as a circle inside the polygon. The position of the circle is determined by the position of the vertices represented in the intersection and the area of the circle is determined by the number of elements in the intersection. Sungear shows which Gene Ontology terms are over-represented in a subset of circles or anchors. The intuitive Sungear interface has enabled biologists to determine quickly which dataset or groups of datasets play a role in a biological function of interest. AVAILABILITY: A live online version of Sungear can be found at http://virtualplant-prod.bio.nyu.edu/cgi-bin/sungear/index.cgi

QTL Mapping for Pest and Disease Resistance in Cassava and Coincidence of Some QTL with Introgression Regions Derived from Manihot glaziovii.

Genetic mapping of quantitative trait loci (QTL) for resistance to cassava brown streak disease (CBSD), cassava mosaic disease (CMD), and cassava green mite (CGM) was performed using an F1 cross developed between the Tanzanian landrace, Kiroba, and a breeding line, AR37-80. The population was evaluated for two consecutive years in two sites in Tanzania. A genetic linkage map was derived from 106 F1 progeny and 1,974 SNP markers and spanned 18 chromosomes covering a distance of 1,698 cM. Fifteen significant QTL were identified; two are associated with CBSD root necrosis only, and were detected on chromosomes V and XII, while seven were associated with CBSD foliar symptoms only and were detected on chromosomes IV, VI, XVII, and XVIII. QTL on chromosomes 11 and 15 were associated with both CBSD foliar and root necrosis symptoms. Two QTL were found to be associated with CMD and were detected on chromosomes XII and XIV, while two were associated with CGM and were identified on chromosomes V and X. There are large Manihot glaziovii introgression regions in Kiroba on chromosomes I, XVII, and XVIII. The introgression segments on chromosomes XVII and XVIII overlap with QTL associated with CBSD foliar symptoms. The introgression region on chromosome I is of a different haplotype to the characteristic "Amani haplotype" found in the landrace Namikonga and others, and unlike some other genotypes, Kiroba does not have a large introgression block on chromosome IV. Kiroba is closely related to a sampled Tanzanian "tree cassava." This supports the observation that some of the QTL associated with CBSD resistance in Kiroba are different to those observed in another variety, Namikonga.

ESTimating plant phylogeny: lessons from partitioning.

BACKGROUND: While Expressed Sequence Tags (ESTs) have proven a viable and efficient way to sample genomes, particularly those for which whole-genome sequencing is impractical, phylogenetic analysis using ESTs remains difficult. Sequencing errors and orthology determination are the major problems when using ESTs as a source of characters for systematics. Here we develop methods to incorporate EST sequence information in a simultaneous analysis framework to address controversial phylogenetic questions regarding the relationships among the major groups of seed plants. We use an automated, phylogenetically derived approach to orthology determination called OrthologID generate a phylogeny based on 43 process partitions, many of which are derived from ESTs, and examine several measures of support to assess the utility of EST data for phylogenies. RESULTS: A maximum parsimony (MP) analysis resulted in a single tree with relatively high support at all nodes in the tree despite rampant conflict among trees generated from the separate analysis of individual partitions. In a comparison of broader-scale groupings based on cellular compartment (ie: chloroplast, mitochondrial or nuclear) or function, only the nuclear partition tree (based largely on EST data) was found to be topologically identical to the tree based on the simultaneous analysis of all data. Despite topological conflict among the broader-scale groupings examined, only the tree based on morphological data showed statistically significant differences. CONCLUSION: Based on the amount of character support contributed by EST data which make up a majority of the nuclear data set, and the lack of conflict of the nuclear data set with the simultaneous analysis tree, we conclude that the inclusion of EST data does provide a viable and efficient approach to address phylogenetic questions within a parsimony framework on a genomic scale, if problems of orthology determination and potential sequencing errors can be overcome. In addition, approaches that examine conflict and support in a simultaneous analysis framework allow for a more precise understanding of the evolutionary history of individual process partitions and may be a novel way to understand functional aspects of different kinds of cellular classes of gene products.