Dataset Information

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

ABSTRACT: As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregating Eucalyptus hybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.

SUBMITTER: Mizrachi E

PROVIDER: S-EPMC5293113 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

Mizrachi Eshchar E Verbeke Lieven L Christie Nanette N Fierro Ana C AC Mansfield Shawn D SD Davis Mark F MF Gjersing Erica E Tuskan Gerald A GA Van Montagu Marc M Van de Peer Yves Y Marchal Kathleen K Myburg Alexander A AA

Proceedings of the National Academy of Sciences of the United States of America 20170117 5

As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trai ...[more]

PMID: 28096391

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Project description:Lignocellulosic biomass conversion inhibitors, furfural and HMF, inhibit microbial growth and interfere with subsequent fermentation of ethanol, posing significant challenges for a sustainable cellulosic ethanol conversion industry. Numerous yeast genes were found to be associated with the inhibitor tolerance. However, limited knowledge is available about mechanisms of the tolerance and the detoxification of the biomass conversion inhibitors. Using a robust standard for absolute mRNA quantification assay and a recently developed tolerant ethanologenic yeast Saccharomyces cerevisiae NRRL Y-50049, we investigate pathway-based transcription profiles relevant to the yeast tolerance and the inhibitor detoxification. Under the synergistic inhibitory challenges by furfural and HMF, Y-50049 was able to withstand the inhibitor stress, in situ detoxify furfural and HMF, and produce ethanol, while its parental control Y-12632 failed to function till 65 h after incubation. The tolerant strain Y-50049 displayed enriched genetic background with significantly higher abundant of transcripts for at least 16 genes than a non-tolerant parental strain Y-12632. The enhanced expression of ZWF1 appeared to drive glucose metabolism in favor of pentose phosphate pathway over glycolysis at earlier steps of glucose metabolisms. Cofactor NAD(P)H generation steps were likely accelerated by enzymes encoded by ZWF1, GND1, GND2, TDH1, and ALD4. NAD(P)H-dependent aldehyde reductions including conversion of furfural and HMF, in return, provided sufficient NAD(P)(+) for NAD(P)H regeneration in the yeast detoxification pathways. Enriched genetic background and a well maintained redox balance through reprogrammed expression responses of Y-50049 were accountable for the acquired tolerance and detoxification of furfural to furan methanol and HMF to furan dimethanol. We present significant gene interactions and regulatory networks involved in NAD(P)H regenerations and functional aldehyde reductions under the inhibitor stress.

Project description:Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate?<?0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Dataset Information

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

Publications

Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing.

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