David A. Rand

High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation

This work presents a high-resolution time-course analysis of gene expression during development of a leaf from expansion through senescence. Enrichment in ontologies, sequence motifs, and transcription factor families within genes showing altered expression over time identified both metabolic pathways and potential regulators active at different stages of leaf development and senescence. Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a high-resolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence.

Pulsatile Stimulation Determines Timing and Specificity of NF-κB-Dependent Transcription

The nuclear factor κB (NF-κB) transcription factor regulates cellular stress responses and the immune response to infection. NF-κB activation results in oscillations in nuclear NF-κB abundance. To define the function of these oscillations, we treated cells with repeated short pulses of tumor necrosis factor–α at various intervals to mimic pulsatile inflammatory signals. At all pulse intervals that were analyzed, we observed synchronous cycles of NF-κB nuclear translocation. Lower frequency stimulations gave repeated full-amplitude translocations, whereas higher frequency pulses gave reduced translocation, indicating a failure to reset. Deterministic and stochastic mathematical models predicted how negative feedback loops regulate both the resetting of the system and cellular heterogeneity. Altering the stimulation intervals gave different patterns of NF-κB–dependent gene expression, which supports the idea that oscillation frequency has a functional role.

Measurement of single-cell dynamics

Populations of cells are almost always heterogeneous in function and fate. To understand the plasticity of cells, it is vital to measure quantitatively and dynamically the molecular processes that underlie cell-fate decisions in single cells. Early events in cell signalling often occur within seconds of the stimulus, whereas intracellular signalling processes and transcriptional changes can take minutes or hours. By contrast, cell-fate decisions, such as whether a cell divides, differentiates or dies, can take many hours or days. Multiparameter experimental and computational methods that integrate quantitative measurement and mathematical simulation of these noisy and complex processes are required to understand the highly dynamic mechanisms that control cell plasticity and fate.

Invasion, Stability and Evolution to Criticality in Spatially Extended, Artificial Host-Pathogen Ecologies

We consider an individual-based spatial model of a generic host—pathogen system and explore the differences between such models and mean-field systems. We find a range of new dynamical and evolutionary phenomena, in particular: (i) in this system, selective pressure is substantially reduced compared with the corresponding mean-field models, and artificial suppression of the pathogen population leads to faster evolution and reduces evolutionary stability; (ii) unlike the mean-field models, there exists a critical transmissibility тcabove which the pathogen dies out; and (iii) the system displays self-evolved criticality. If the transmissibility т is allowed to mutate, it evolves to the critical value тc. Thus the system evolves to put itself at the boundary at which it can exist. Observations of the individual-based spatial model motivate an explanation for these phenomena in terms of the dynamics of host patches involving their connections and disconnections. We therefore construct a patch model of this and show that this simplified model behaves in a similar way to the individual-based spatial model.

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.

High pre-treatment serum hepatitis B virus titre predicts failure of lamivudine prophylaxis and graft re-infection after liver transplantation

BACKGROUND/AIMS Orthotopic liver transplantation has an established role for the treatment of patients with chronic liver failure secondary to hepatitis B virus (HBV) infection. Unfortunately, recurrent infection of the graft can be associated with aggressive disease, and with diminished graft and patient survival. Currently, the role of nucleoside analogues for prevention of graft re-infection is being evaluated. Preliminary results are encouraging, but treatment failure has been associated with emergence of drug-resistant virus. METHODS We have studied ten consecutive patients who received lamivudine prophylaxis for prevention of HBV graft reinfection. Sequential sera, collected prelamivudine then during treatment before and after liver transplantation, were examined. Conventional serological markers were measured, as were serum viral DNA levels with a sensitive quantitative polymerase chain reaction assay. RESULTS Lamivudine treatment effected a reduction in serum HBV levels, but six patients still had measurable viral DNA at the time of transplantation. Five patients developed graft re-infection with lamivudine-resistant virus. Resistant virus emerged 8 to 15 months post-transplant. The likelihood of emergence of resistant virus was related to the pre-treatment serum HBV titre. Persistent serum viral DNA positivity and evidence of graft re-infection during the early post-transplant period did not predict the subsequent emergence of resistant virus. CONCLUSIONS Our observations suggest that the resistant species may be present in the viral quasispecies in the serum and liver of patients with high-level replication prior to lamivudine exposure. The resistant species can persist during lamivudine treatment prior to transplantation, and emerge following transplantation. These observations suggest strategies which might prevent the emergence of drug-resistant species, and imply that graft re-infection may be a preventable phenomenon.

Correlation models for childhood epidemics

One of the simplest set of equations for the description of epidemics (the SEIR equations) has been much studied, and produces reasonable approximations to the dynamics of communicable disease. However, it has long been recognized that spatial and social structure are important if we are to understand the long–term persistence and detailed behaviour of disease. We will introduce three pair models which attempt to capture the underlying heterogeneous structure by studying the connections and correlations between individuals. Although modelling the correlations necessarily leads to more complex equations, this pair formulation naturally incorporates the local dynamical behaviour generating more realistic persistence. In common with other studies on childhood diseases we will focus our attention on measles, for which the case returns are particularly well documented and long running.

The dynamic architecture of the metabolic switch in Streptomyces coelicolor

BackgroundDuring the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples.ResultsSurprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis.ConclusionsOur study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting.

Dynamic Analysis of Stochastic Transcription Cycles

Cycling of gene expression in individual cells is controlled by dynamic chromatin remodeling.

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models

We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.