PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None

Select a Filter Below

Journals
Authors
more »
Year of Publication
Document Types
1.  A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation 
BMC Plant Biology  2011;11:13.
Background
Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species.
Results
We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis.
Conclusions
We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis.
doi:10.1186/1471-2229-11-13
PMCID: PMC3030533  PMID: 21232107
2.  Five QTL hotspots for yield in short rotation coppice bioenergy poplar: The Poplar Biomass Loci 
BMC Plant Biology  2009;9:23.
Background
Concern over land use for non-food bioenergy crops requires breeding programmes that focus on producing biomass on the minimum amount of land that is economically-viable. To achieve this, the maximum potential yield per hectare is a key target for improvement. For long lived tree species, such as poplar, this requires an understanding of the traits that contribute to biomass production and their genetic control. An important aspect of this for long lived plants is an understanding of genetic interactions at different developmental stages, i.e. how genes or genetic regions impact on yield over time.
Results
QTL mapping identified regions of genetic control for biomass yield. We mapped consistent QTL across multiple coppice cycles and identified five robust QTL hotspots on linkage groups III, IV, X, XIV and XIX, calling these 'Poplar Biomass Loci' (PBL 1–5). In total 20% of the variation in final harvest biomass yield was explained by mapped QTL. We also investigated the genetic correlations between yield related traits to identify 'early diagnostic' indicators of yield showing that early biomass was a reasonable predictor of coppice yield and that leaf size, cell number and stem and sylleptic branch number were also valuable traits.
Conclusion
These findings provide insight into the genetic control of biomass production and correlation to 'early diagnostic' traits determining yield in poplar SRC for bioenergy. QTL hotspots serve as useful targets for directed breeding for improved biomass productivity that may also be relevant across additional poplar hybrids.
doi:10.1186/1471-2229-9-23
PMCID: PMC2657785  PMID: 19245718
3.  A cross-species transcriptomics approach to identify genes involved in leaf development 
BMC Genomics  2008;9:589.
Background
We have made use of publicly available gene expression data to identify transcription factors and transcriptional modules (regulons) associated with leaf development in Populus. Different tissue types were compared to identify genes informative in the discrimination of leaf and non-leaf tissues. Transcriptional modules within this set of genes were identified in a much wider set of microarray data collected from leaves in a number of developmental, biotic, abiotic and transgenic experiments.
Results
Transcription factors that were over represented in leaf EST libraries and that were useful for discriminating leaves from other tissues were identified, revealing that the C2C2-YABBY, CCAAT-HAP3 and 5, MYB, and ZF-HD families are particularly important in leaves. The expression of transcriptional modules and transcription factors was examined across a number of experiments to select those that were particularly active during the early stages of leaf development. Two transcription factors were found to collocate to previously published Quantitative Trait Loci (QTL) for leaf length. We also found that miRNA family 396 may be important in the control of leaf development, with three members of the family collocating with clusters of leaf development QTL.
Conclusion
This work provides a set of candidate genes involved in the control and processes of leaf development. This resource can be used for a wide variety of purposes such as informing the selection of candidate genes for association mapping or for the selection of targets for reverse genetics studies to further understanding of the genetic control of leaf size and shape.
doi:10.1186/1471-2164-9-589
PMCID: PMC2621207  PMID: 19061504

Results 1-3 (3)