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Disease progression and solid tumor survival: a transcriptome decoherence model.

ABSTRACT: Networks of genes are typically generated from expression changes observed between control and test conditions. Nevertheless, within a single control state many genes show expression variance across biological replicates. These transcripts, typically termed unstable, are usually excluded from analyses because their behavior cannot be reconciled with biological constraints. Grouped as pairs of covariant genes they can however show a consistent response to the progression of a disease. We present a model of coherence arising from sets of covariant genes that was developed in-vitro then tested against a range of solid tumors. DGPMs, Decoherence Gene Pair Models, showed changes in network topology reflective of the metastatic transition. Across a range of solid tumor studies the model generalizes to reveal a richly connected topology of networks in healthy tissues that becomes sparser as the disease progresses reaching a minimum size in the advanced tumors with minim survivability.

SUBMITTER: Platts AE

PROVIDER: S-EPMC2818311 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Disease progression and solid tumor survival: a transcriptome decoherence model.

Platts Adrian E AE Lalancette Claudia C Emery Benjamin R BR Carrell Douglas T DT Krawetz Stephen A SA

Molecular and cellular probes 20091014 1

Networks of genes are typically generated from expression changes observed between control and test conditions. Nevertheless, within a single control state many genes show expression variance across biological replicates. These transcripts, typically termed unstable, are usually excluded from analyses because their behavior cannot be reconciled with biological constraints. Grouped as pairs of covariant genes they can however show a consistent response to the progression of a disease. We present ...[more]

PMID: 19835949

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Project description:BackgroundNeuronal ceroid lipofuscinoses, (NCLs or Batten disease) are a group of inherited, early onset, fatal neurodegenerative diseases associated with mutations in 13 genes. All forms of the disease are characterized by lysosomal accumulation of fluorescent storage material, as well as profound neurodegeneration, but the relationship of the various genes' function to a single biological process is not obvious. In this study, we used a well-characterized mouse model of classical late infantile NCL (cLINCL) in which the tripeptidyl peptidase 1 (Tpp1) gene is disrupted by gene targeting, resulting in loss of detectable TPP1 activity and leading to progressive neurological phenotypes including ataxia, increased motor deficiency, and early death.MethodsIn order to identify genes and pathways that may contribute to progression of the neurodegenerative process, we analyzed forebrain/midbrain and cerebellar transcriptional differences at 1, 2, 3 and 4 months of age in control and TPP1-deficient mice by global RNA-sequencing.ResultsProgressive neurodegenerative inflammatory responses involving microglia, astrocytes and endothelial cells were observed, accompanied by activation of leukocyte extravasation signals and upregulation of nitric oxide production and reactive oxygen species. Several astrocytic (i.e., Gfap, C4b, Osmr, Serpina3n) and microglial (i.e., Ctss, Itgb2, Itgax, Lyz2) genes were identified as strong markers for assessing disease progression as they showed increased levels of expression in vivo over time. Furthermore, transient increased expression of choroid plexus genes was observed at 2 months in the lateral and fourth ventricle, highlighting an early role for the choroid plexus and cerebrospinal fluid in the disease pathology. Based on these gene expression changes, we concluded that neuroinflammation starts, for the most part, after 2 months in the Tpp1-/- brain and that activation of microglia and astrocytes occur more rapidly in cerebellum than in the rest of the brain; confirming increased severity of inflammation in this region.ConclusionsThese findings have led to a better understanding of cLINCL pathological onset and progression, which may aid in development of future therapeutic treatments for this disease.

Dataset Information

Disease progression and solid tumor survival: a transcriptome decoherence model.

Publications

Disease progression and solid tumor survival: a transcriptome decoherence model.

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