Biotechnology for BiofuelsSystems BiologyPeer-reviewed journal article
Network Reconstruction And Systems Analysis Of Plant Cell Wall Deconstruction By Neurospora crassa
Areejit SamaliD, James P. Craig, Samuel T. CoradettiiD, J. Philipp BenziD, James A. EddyiD, Nathan D. PriceiD, N. Louise GlassiD
Abstract
Plant biomass degradation by fungal derived enzymes is rapidly expanding in economic importance as a clean and efficient source for biofuels. The ability to rationally engineer filamentous fungi would facilitate biotechnological applications for degradation of plant cell wall polysaccharides. However, incomplete knowledge of biomolecular networks responsible for plant cell wall deconstruction impedes experimental efforts in this direction. To expand this knowledge base, a detailed network of reactions important for deconstruction of plant cell wall polysaccharides into simple sugars was constructed for the filamentous fungus Neurospora crassa. To reconstruct this network, information was integrated from five heterogeneous data types: functional genomics, transcriptomics, proteomics, genetics, and biochemical characterizations. The combined information was encapsulated into a feature matrix and the evidence weighed to assign annotation confidence scores for each gene within the network. Comparative analyses of RNA-seq and ChIP-seq data shed light on the regulation of the plant cell wall degradation network (PCWDN), leading to a novel hypothesis for degradation of the hemicellulose mannan. The transcription factor CLR-2 was subsequently experimentally shown to play a key role in the mannan degradation pathway of Neurospora crassa. Our network serves as a scaffold for integration of diverse experimental data, leading to elucidation of regulatory design principles for plant cell wall deconstruction by filamentous fungi, and guiding efforts to rationally engineer industrially relevant hyper-production strains.
- organism: fungi (other)
- assay: bulk RNA-seq
- assay: ChIP-seq
- assay: proteomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 46
- FWCI
- 2.91
- Field percentile
- top 8%
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