Oliver P. Windram

Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis

The authors generated a high-resolution time series of Arabidopsis thaliana gene expression following infection with the fungal pathogen Botrytis cinerea. Computational analysis of this large data set identified the timing of specific processes and regulatory events in the host plant and showed a role for the transcription factor TGA3 in the defense response against the fungal pathogen. Transcriptional reprogramming forms a major part of a plant’s response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.

Recombination, decreased host specificity and increased mobility may have driven the emergence of maize streak virus as an agricultural pathogen.

Maize streak virus (MSV; family Geminiviridae, genus Mastrevirus), the causal agent of maize streak disease, ranks amongst the most serious biological threats to food security in subSaharan Africa. Although five distinct MSV strains have been currently described, only one of these – MSV-A – causes severe disease in maize. Due primarily to their not being an obvious threat to agriculture, very little is known about the ‘grass-adapted’ MSV strains, MSV-B, -C, -D and -E. Since comparing the genetic diversities, geographical distributions and natural host ranges of MSV-A with the other MSV strains could provide valuable information on the epidemiology, evolution and emergence of MSV-A, we carried out a phylogeographical analysis of MSVs found in uncultivated indigenous African grasses. Amongst the 83 new MSV genomes presented here, we report the discovery of six new MSV strains (MSV-F to -K). The non-random recombination breakpoint distributions detectable with these and other available mastrevirus sequences partially mirror those seen in begomoviruses, implying that the forces shaping these breakpoint patterns have been largely conserved since the earliest geminivirus ancestors. We present evidence that the ancestor of all MSV-A variants was the recombinant progeny of ancestral MSV-B and MSV-G/-F variants. While it remains unknown whether recombination influenced the emergence of MSV-A in maize, our discovery that MSV-A variants may both move between and become established in different regions of Africa with greater ease, and infect more grass species than other MSV strains, goes some way towards explaining why MSV-A is such a successful maize pathogen.

Reconstructing the History of Maize Streak Virus Strain A Dispersal To Reveal Diversification Hot Spots and Its Origin in Southern Africa

ABSTRACT Maize streak virus strain A (MSV-A), the causal agent of maize streak disease, is today one of the most serious biotic threats to African food security. Determining where MSV-A originated and how it spread transcontinentally could yield valuable insights into its historical emergence as a crop pathogen. Similarly, determining where the major extant MSV-A lineages arose could identify geographical hot spots of MSV evolution. Here, we use model-based phylogeographic analyses of 353 fully sequenced MSV-A isolates to reconstruct a plausible history of MSV-A movements over the past 150 years. We show that since the probable emergence of MSV-A in southern Africa around 1863, the virus spread transcontinentally at an average rate of 32.5 km/year (95% highest probability density interval, 15.6 to 51.6 km/year). Using distinctive patterns of nucleotide variation caused by 20 unique intra-MSV-A recombination events, we tentatively classified the MSV-A isolates into 24 easily discernible lineages. Despite many of these lineages displaying distinct geographical distributions, it is apparent that almost all have emerged within the past 4 decades from either southern or east-central Africa. Collectively, our results suggest that regular analysis of MSV-A genomes within these diversification hot spots could be used to monitor the emergence of future MSV-A lineages that could affect maize cultivation in Africa.

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana

MOTIVATION Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here. RESULTS Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings. AVAILABILITY Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.

Dating the origins of the maize-adapted strain of maize streak virus, MSV-A.

Maize streak virus (MSV), which causes maize streak disease (MSD), is one of the most serious biotic threats to African food security. Here, we use whole MSV genomes sampled over 30 years to estimate the dates of key evolutionary events in the 500 year association of MSV and maize. The substitution rates implied by our analyses agree closely with those estimated previously in controlled MSV evolution experiments, and we use them to infer the date when the maize-adapted strain, MSV-A, was generated by recombination between two grass-adapted MSV strains. Our results indicate that this recombination event occurred in the mid-1800s, ∼20 years before the first credible reports of MSD in South Africa and centuries after the introduction of maize to the continent in the early 1500s. This suggests a causal link between MSV recombination and the emergence of MSV-A as a serious pathogen of maize.

Directed Partial Correlation: Inferring Large-Scale Gene Regulatory Network through Induced Topology Disruptions

Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC) method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/).

Panicum streak virus diversity is similar to that observed for maize streak virus

Panicum streak virus (PanSV; genus Mastrevirus, family Geminiviridae) is, together with maize streak virus (MSV), sugarcane streak virus (SSV), sugarcane streak Reunion virus (SSRV) and sugarcane streak Egypt virus (SSEV), one of the currently described “African streak virus” (AfSV) species [6]. As with all the other AfSV species other than MSV, very little is known about PanSV genomic sequence diversity across Africa. Only two PanSV full genome sequences have ever been reported: one from Kenya [2], and the other from South Africa [17]. Both these genomes were isolated from Panicum maximum plants, but share only approximately 90% sequence identity. The reason this is noteworthy is that throughout mainland Africa all MSV genomes ever sampled from maize have been found to share >97% sequence identity. Although other MSV strains sharing between 78 and 90% identity with the maize-adapted strain (MSV-A) have been described, these have all been isolated from different host species, indicating that host adaptation is probably the main force driving MSV diversification. MSV and PanSV share common vector species (leafhoppers in the genus Cicadulina) and probably also share some host species. Although the host range of PanSV is currently unknown, the MSV host range is extensive and includes P. maximum [3]. One might therefore expect that similar evolutionary forces acting on both species might result in their sharing similar patterns of both geographical and host-associated diversity. Here we describe the full genome sequences of five new PanSV isolates (including two new strains) sampled from southern and western Africa, and report that PanSV and MSV do indeed have similar patterns of diversity. We find, however, that unlike with MSV, geographical separation rather than host adaptation is possibly the dominant force driving PanSV diversification.

Network Modeling to Understand Plant Immunity

Deciphering the networks that underpin complex biological processes using experimental data remains a significant, but promising, challenge, a task made all the harder by the added complexity of host-pathogen interactions. The aim of this article is to review the progress in understanding plant immunity made so far by applying network modeling algorithms and to show how this computational/mathematical strategy is facilitating a systems view of plant defense. We review the different types of network modeling that have been used, the data required, and the type of insight that such modeling can provide. We discuss the current challenges in modeling the regulatory networks that underlie plant defense and the future developments that may help address these challenges.

Novel sugarcane streak and sugarcane streak Reunion mastreviruses from southern Africa and La Réunion

The sugarcane infecting streak viruses (SISVs) are mastreviruses (Family Geminiviridae) belonging to a group of “African streak viruses” (AfSVs) that includes the economically devastating Maize streak virus (MSV). Although there are three currently described SISV species (Sugarcane streak virus [SSV], Sugarcane streak Egypt virus [SSEV] and Sugarcane streak Réunion virus [SSRV]), only one strain variant has been fully sequenced for each of these species and as a result very little is known about the diversity and evolutionary origins of the SCISVs. Here we present annotated full genome sequences of four new SISV isolates, including a new strain of both SSRV and SSV, and one potentially new SISV species, sampled from wild grasses in La Réunion and Zimbabwe. For the first time, we report the finding of SSRV isolates in Zimbabwe and SSV isolates on the island of La Réunion. Phylogenetic and recombination analyses indicate continent-wide SSRV strain diversity and that our isolate potentially representing a new SISV species is a recombinant.

Modelling signaling networks underlying plant defence.

Transcriptional reprogramming plays a significant role in governing plant responses to pathogens. The underlying regulatory networks are complex and dynamic, responding to numerous input signals. Most network modelling studies to date have used large-scale expression data sets from public repositories but defence network models with predictive ability have also been inferred from single time series data sets, and sophisticated biological insights generated from focused experiments containing multiple network perturbations. Using multiple network inference methods, or combining network inference with additional data, such as promoter motifs, can enhance the ability of the model to predict gene function or regulatory relationships. Network topology can highlight key signaling components and provides a systems level understanding of plant defence.