James Hartwell is a lecturer in plant metabolism. His interests span the fields of plant molecular biology and biochemistry, and whole plant physiology. The focus of his research is understanding the molecular and biochemical basis for the circadian control of primary metabolic pathways in plants. In particular, he studies the molecular basis for the circadian regulation of Crassulacean acid metabolism and has interests in plant responses to abiotic stress. He also has interests in the development of novel non-food crops as biofuel feedstocks suited to marginal lands and more broadly in the role of plant science in the response to the global food security crisis.
Our research aims to elucidate the molecular basis for the circadian regulation of a metabolic adaptation of photosynthesis called Crassulacean acid metabolism (CAM), which is found in plants that inhabit arid and semi-arid regions of the world. We are leading a large DNA sequencing project to decode the transcriptome and genome of a new model CAM species, Kalanchoe fedtschenkoi. We are combining the powers of the Roche 454 GS-FLX Titanium, Applied Biosystems SOLiD4 and Illumina GAii sequencing systems for this sequencing work. K. fedtschenkoi was selected for sequencing because it has the attributes required for an amenable model plant system. In particular, we have developed a simple stable transformation system that allows us to test gene function in planta. Our goal is to identify and characterise the CAMome, the genes required for efficient operation of CAM, including gaining a detailed understanding of which genes mediate the strict circadian control of CAM. To transition this work from model to crop, we are collaborating with Dr. Anne Borland (University of Newcastle), Prof. Andrew Smith (University of Oxford), Prof. Howard Griffiths (University of Cambridge), and Dr. Joe Holtum (James Cook University, Australia) to explore the potential of high-productivity CAM Agaves as new non-food biofuel feedstock crops that can grow on seasonally dry land. We are performing transcriptome sequencing on Agaves in order to identify molecular markers that are linked to high productivity and high sugar content. In a separate collaborative project with Dr. Colin Osborne, University of Sheffield, we are sequencing the C3 and C4 subspecies of the grass, Alloteropsis semialata in order to identify the genes required for C4 photosynthesis. This is the only grass known to possess C3 and C4 species with such a close relationship.
John Cushman is a professor in the College of Agriculture, Biotechnology and Natural Resources at the University of Nevada, Reno. He is project leader on a $14 million study, funded by the Department of Energy Genonomic Sciences division, to move the water-use-efficient photosynthesis characteristics from drought-tolerant plants like agave and cactus into woody biomass plants like poplar, which can hedge against predicted long-term increases in temperatures and reduced precipitation. The team, including the Oak Ridge National Laboratory, will develop novel technologies to re-design bioenergy crops to grow on marginal agricultural lands and produce yields of biomass that can readily be converted to biofuels.
Functional Genomics of Crassulacean Acid Metabolism (CAM).CAM is water-conserving photosynthetic pathway that helps plants survive in seasonally arid climates or those with intermittent water supply (e.g. epiphytic habitats). Our research objectives are to understand how the expression of CAM is controlled by environmental stress (salinity, water deficit) and the circadian clock. Our approach is to conduct integrated transcriptome, proteome, and metabolome analyses.
Mechanisms of the Evolutionary Origins of Crassulacean Acid Metabolism in Tropical Orchids. Crassulacean acid metabolism (CAM) has evolved multiple times in 33 families and 328 genera comprising more than 6% of all vascular plant species making it the second most common mode of photosynthesis among vascular plants. Our goal is to understand the molecular mechanisms responsible for the evolution of this important photosynthetic adaptation. Our approach is to survey foliar carbon isotopic composition (delta13C) to map the occurrence of CAM in closely related species within the Oncidiinae, a subtribe within Orchidaceae, and then identify molecular genetic changes specific to plants that exhibit CAM.
Statistical Methods of Analyzing Gene Expression Data
Expression data are generated by hybridizing transcripts to microarrays or gene chips from tissues under controlled conditions. If one gene regulates (up or down) another gene, or both are involved in a biochemical pathway, the profile of their expressions over time will correlate. Expression data are often analyzed using clustering procedures: clusters represent sets of genes displaying coordinately regulated expression profiles. As expression data contain significant amounts of random variation, and as clusters are dependent on the procedure applied, the assignment of confidence measures to clusters is useful. Specifically, we have implemented an algorithm in the statistical programming language R that assigns confidence measures to groupings of genes obtained by clustering routines. By the use of permutation testing and convex hull methods to simulate pseudo-random gene expression data sets, statistics are obtained from these randomly generated sets to provide a basis for comparison to the original data.
My contribution to the GeneX OpenSource gene expression database and software system [http://sourceforge.net/projects/genex] consists of several gene expression normalization and analysis programs, two of which presents a novel approach to clustering techniques. These methods are being generalized for applications to microarray data generated on different technology platforms (Affymetrix, NimbleGen®, and custom two-color cDNA arrays). Enhancements are being made to include metrics that provide the researcher with (more) biologically meaningful results.
Experimental Design and Normalization Methods
As the accumulation of genetic data continues to grow at a rapid speed, there is a need for immediate data analysis methods to assess experiments as they are in progress. Properties of the experimental design, which provide control and understanding of the source of variation in both signal and noise, affect the manner in which data should be analyzed and appropriate models constructed. The ultimate aim of any gene expression data analyst is to be involved in the experimental design of the microarray. Too often, experiments are placed in the analysts' hands without proper design. Poorly designed experiments most often result in meaningless analysis results, and always increase the efforts (and creativity) of the analyst. I am currently developing several experimental designs of plant and human array experiments, with several sets of both positive and negative controls, and am assessing their performance within different experiments.
Dr. Borland is a plant physiologist. Her research is directed at understanding the metabolic and molecular basis of photosynthetic plasticity across diverse CAM genera and in determining the ecophysiological implications of this plasticity. Research approaches span from whole plant physiology through to biochemistry, gene expression and transcriptomics. She holds a joint appointment at Newcastle University (UK) and the Oak Ridge National Laboratory (TN, US).
Departmental page ncl.ac.uk/.../anne.borland
Understanding how plants adjust their physiology and metabolism in response to environmental perturbations is crucial for developing effective strategies to enhance growth and productivity in potentially limiting and unpredictable environments and for predicting the possible impacts of climate change on plant performance. The overarching aims of my research are to identify genes and proteins that underpin the acclimation of photosynthetic metabolism to extreme environments (in terms of drought, salinity, oxidative stress).
A focus for my research is the study of crassulacean acid metabolism. CAM is a relatively widespread adaptation to drought stress which has evolved in up to 7% of higher plants and permits the uptake of CO2 at night. Since expression of the pathway is readily modulated by the environment, CAM serves as a model system for establishing the functional significance of genes and enzymes that optimize physiological performance in arid, resource-limited habitats. Moreover, the day/night separation of carboxylation processes in the photosynthetic cells of CAM plants, poses fundamental questions in terms of metabolic control and synchronization of metabolism. We are exploiting CAM as a means of investigating the workings and evolutionary significance of circadian clocks in plants.
Our research is motivated by a desire to further our understanding of the underling genetics driving plant traits critical to organismal performance and terrestrial C cycle dynamics under changing climatic conditions. Our lab takes an integrated approach using high-throughput genomic technologies, biochemistry and genetics, all placed within a physiological framework to answer our research questions. Unique to our approach is the use of network and systems biology modeling approaches to correlate groups of genes and metabolites to physiological process (e.g., photosynthesis, respiration, microbial associates)
Current projects include the Department of Energy BER sponsored plant-microbe interaction project pmi.ornl.gov, two ecological projects targeting high-latitude ecosystems (SPRUCE; mnspruce.ornl.gov, NGEE; ngee.ornl.gov), and a computational project (KBase; kbase.us to further bioinformatic workflows and statistical model development aimed at harnessing next-generation sequencing technology.
The moss Sphagnum is a new study organism in our lab as it is a key member in peatland and arctic ecosystems that account for vast stores in terrestrial carbon. Whether these ecosystems continue to store carbon in response to changing climatic conditions remains an open question. This makes Sphagnum arguably the most important plant genus governing terrestrial carbon cycling. Our lab is pioneering the use of Sphagnum for ecological genomics studies and we are currently funded to generate a neutron-based mutagenesis population, a QTL mapping pedigree, and conduct RNA-Seq and physiological investigations.
My research direction is to investigate mechanisms of enzyme-catalyzed reactions and inhibitor binding processes by use of state-of-the art computational approaches. We are interested in understanding the origin of high catalytic efficiency and selectivity for enzymes. These studies would, in addition to being of fundamental scientific importance, also improve the basis for designing inhibitors, efficient drugs and enzyme mimics. We use molecular dynamics (MD) simulations, free energy calculations, mixed quantum mechanical/molecular mechanical (QM/MM) methods and other computational approaches to address the questions in these research areas. Several systems are currently under investigations in our laboratory, including protein lysine methyltransferases, RNA polymerases, serine-carboxyl peptidases, chorismate mutase, cytidine/adenosine deaminase, merB and merA. We also study structural and dynamic features of proteins and try to understand the forces that stabilize proteins.