Showing papers by "Douglas B. Kell published in 2012"

Large-scale sequestration of atmospheric carbon via plant roots in natural and agricultural ecosystems: why and how.

Abstract: The soil holds twice as much carbon as does the atmosphere, and most soil carbon is derived from recent photosynthesis that takes carbon into root structures and further into below-ground storage via exudates therefrom. Nonetheless, many natural and most agricultural crops have roots that extend only to about 1 m below ground. What determines the lifetime of below-ground C in various forms is not well understood, and understanding these processes is therefore key to optimising them for enhanced C sequestration. Most soils (and especially subsoils) are very far from being saturated with organic carbon, and calculations show that the amounts of C that might further be sequestered (http://dbkgroup.org/carbonsequestration/rootsystem.html) are actually very great. Breeding crops with desirable below-ground C sequestration traits, and exploiting attendant agronomic practices optimised for individual species in their relevant environments, are therefore important goals. These bring additional benefits related to improvements in soil structure and in the usage of other nutrients and water.

Improving metabolic flux predictions using absolute gene expression data

Abstract: Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se.

Extracting semantically enriched events from biomedical literature.

Abstract: Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

The metabolome of human placental tissue: investigation of first trimester tissue and changes related to preeclampsia in late pregnancy

Abstract: Unique biochemical and physical challenges to both mother and fetus are observed during human pregnancy, and the placenta plays an important role in protecting the fetus and supporting its development. Consequently, many pregnancy complications are associated with altered placental biochemistry and structure. Here we have further developed a combination of analytical tools for determining the tissue metabolome of placental tissue by applying a methanol/water/chloroform extraction method followed by analysis of the polar fraction (methanol/water) using GC–ToF–MS and of the non-polar fraction (chloroform) using UPLC–LTQ–Orbitrap–MS. This combination maximises the number of different metabolites detected and is the first holistic investigation of placental tissue applying UPLC–MS. Placental tissue differs between early and late first trimester pregnancies in that the developing placenta is exposed to significantly different oxygen tensions and undergoes a change from histiotrophic to haemotrophic nutrition. Application of these metabolomic methods detected 156 unique and chemically identified metabolites that showed statistically significant differences (P < 0.05). These included changes in di- and triglycerides, phospholipids, sphingolipids, fatty acids and fatty acid carnitines. This is the first metabolomics study to identify these changes that potentially show the initiation or switch to fatty acid beta-oxidation for mitochondrial ATP production. A separate study showed a small number of changes that were related to the position of sampling of the placental tissue and to the type of delivery from pregnancy. This result indicates that variations associated with sampling position and delivery type are small compared to between-subject variation. However, the authors recommend robust experimental design which may include sampling from the same position of the placenta and from the same delivery type. When comparing tissue from term-uncomplicated pregnancies with those exhibiting preeclampsia at term, 86 unique and chemically identified metabolites showed statistically significant differences (P < 0.05). Potential changes in metabolism operating in the mitochondria, in vitamin D metabolism and in oxidative and nitrative stress were observed. These proof-of-principle studies demonstrate the sensitivity of placental tissue metabolomics to define changes related to alterations in environment and perfusion and related to diseases of pregnancy including preeclampsia. Data are available on request.

The genetic control of growth rate: a systems biology study in yeast

Abstract: Control of growth rate is mediated by tight regulation mechanisms in all free-living organisms since long-term survival depends on adaptation to diverse environmental conditions. The yeast, Saccharomyces cerevisiae, when growing under nutrient-limited conditions, controls its growth rate via both nutrient-specific and nutrient-independent gene sets. At slow growth rates, at least, it has been found that the expression of the genes that exert significant control over growth rate (high flux control or HFC genes) is not necessarily regulated by growth rate itself. It has not been determined whether the set of HFC genes is the same at all growth rates or whether it is the same in conditions of nutrient limitation or excess. HFC genes were identified in competition experiments in which a population of hemizygous diploid yeast deletants were grown at, or close to, the maximum specific growth rate in either nutrient-limiting or nutrient-sufficient conditions. A hemizygous mutant is one in which one of any pair of homologous genes is deleted in a diploid, These HFC genes divided into two classes: a haploinsufficient (HI) set, where the hemizygous mutants grow slower than the wild type, and a haploproficient (HP) set, which comprises hemizygotes that grow faster than the wild type. The HI set was found to be enriched for genes involved in the processes of gene expression, while the HP set was enriched for genes concerned with the cell cycle and genome integrity. A subset of growth-regulated genes have HFC characteristics when grown in conditions where there are few, or no, external constraints on the rate of growth that cells may attain. This subset is enriched for genes that participate in the processes of gene expression, itself (i.e. transcription and translation). The fact that haploproficiency is exhibited by mutants grown at the previously determined maximum rate implies that the control of growth rate in this simple eukaryote represents a trade-off between the selective advantages of rapid growth and the need to maintain the integrity of the genome.

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

Abstract: A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes.

Genome‐wide analysis of longevity in nutrient‐deprived Saccharomyces cerevisiae reveals importance of recycling in maintaining cell viability

Abstract: Although typically cosseted in the laboratory with constant temperatures and plentiful nutrients, microbes are frequently exposed to much more stressful conditions in their natural environments where survival and competitive fitness depend upon both growth rate when conditions are favourable and on persistence in a viable and recoverable state when they are not In order to determine the role of genetic heterogeneity in environmental fitness we present a novel approach that combines the power of fluorescence-activated cell sorting with barcode microarray analysis and apply this to determining the importance of every gene in the Saccharomyces cerevisiae genome in a high-throughput, genome-wide fitness screen We have grown > 6000 heterozygous mutants together and exposed them to a starvation stress before using fluorescence-activated cell sorting to identify and isolate those individual cells that have not survived the stress applied Barcode array analysis of the sorted and total populations reveals the importance of cellular recycling mechanisms (autophagy, pexophagy and ribosome breakdown) in maintaining cell viability during starvation and provides compelling evidence for an important role for fatty acid degradation in maintaining viability In addition, we have developed a semi-batch fermentor system that is a more realistic model of environmental fitness than either batch or chemostat culture Barcode array analysis revealed that arginine biosynthesis was important for fitness in semi-batch culture and modelling of this regime showed that rapid emergence from lag phase led to greatly increased fitness One hundred and twenty-five strains with deletions in unclassified proteins were identified as being over-represented in the sorted fraction, while 27 unclassified proteins caused a haploinsufficient phenotype in semi-batch culture These methods thus provide a screen to identifying other genes and pathways that have a role in maintaining cell viability

Exploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computing.

Abstract: Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information).

Short- and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment.

Abstract: Quantitative data on the dynamic changes in the transcriptome and the metabolome of yeast in response to an impulse-like perturbation in nutrient availability was integrated with the metabolic pathway information in order to elucidate the long-term dynamic re-organization of the cells. This study revealed that, in addition to the dynamic re-organization of the de novo biosynthetic pathways, salvage pathways were also re-organized in a time-dependent manner upon catabolite repression. The transcriptional and the metabolic responses observed for nitrogen catabolite repression were not as severe as those observed for carbon catabolite repression. Selective up- or down regulation of a single member of a paralogous gene pair during the response to the relaxation from nutritional limitation was identified indicating a differentiation of functions among paralogs. Our study highlighted the role of inosine accumulation and recycling in energy homeostasis and indicated possible bottlenecks in the process.