Methods for identification of novel protein drug targets and biomarkers utilizing functional networks
View Patent ↗The process of System Reconstruction is used to integrate sequence data, clinical data, experimental data, and literature into functional models of disease pathways. System Reconstruction models serve as informational skeletons for integrating various types of high-throughput data. The present invention provides the first metabolic reconstruction study of a eukaryotic organism based solely on expressed sequence tag (EST) data. System Reconstruction also provides a method for the identification of novel therapeutic targets and biomarkers using network analysis. The initial seed networks are built from the lists of novel targets for diseases with the high-throughput experimental data being superimposed on the seed networks to identify specific targets.
1. A method for identifying a human drug or gene therapy target, said method comprising:
a) collecting metabolic data for a non-disease state and a disease state;
b) linking the data into at least two metabolic pathways using a relational database;
c) ranking the metabolic pathways based on their relevance to human metabolism, wherein the ranking of the metabolic pathways comprises assigning each pathway to one of the following categories, from the most relevant to the least relevant to human metabolism:
(i) a multi-step pathway wherein all of the reactions are catalyzed by known human enzymes and/or enzymes that have open reading frame (ORF) candidates in the human genome;
(ii) a multi-step pathway wherein only the first and last reactions are catalyzed by known human enzymes and/or enzymes that have ORF candidates in the human genome;
(iii) a multi-step pathway wherein only an intermediate reaction is catalyzed by an identified human enzyme or an enzyme that has an ORF candidate in the human genome; and
(iv) a multi-step pathway wherein none of the reactions are catalyzed by identified human enzymes or enzymes that have ORF candidates in the human genome;
d) generating structured annotations of the ranked metabolic pathways;
e) identifying interconnections between the ranked and annotated metabolic pathways;
f) reconstructing human metabolism by integrating information obtained in steps a) to e); and
g) identifying a human drug or gene therapy target by comparing differences between the non-disease and disease states using the reconstruction of step (f).
2. The method according to claim 1 , wherein the metabolic data comprise a list of genes associated with the onset of the disease state.
3. The method according to claim 1 , wherein the metabolic data comprise a list of genes that are over- or under-expressed in the disease state relative to the non-disease state.
4. The method according to claim 1 , wherein the metabolic data are selected from the group consisting of enzymes, proteins, and metabolites.
5. The method of claim 1 , wherein the generation of structured annotations of the ranked metabolic pathways comprises:
a) comparing the ranked metabolic pathways to published information;
b) confirming, modifying or rejecting the ranked metabolic pathways based on their differences from the published information; and
c) describing the ranked pathways as a hierarchy of biochemical units.
6. The method of claim 5 , wherein the biochemical units comprise a ranked metabolic pathway, metabolic steps that constitute the pathway, chemical compounds, reactions and/or enzymatic functions.
7. The method of claim 6 , wherein the enzymatic functions comprise genes and proteins.
8. The method of claim 6 , wherein each of the biochemical units is linked to an annotation table.
9. The method of claim 8 , wherein the annotation table comprises a field selected from the group consisting of organ localization, tissue localization, intracellular localization, intracellular compartmentalization, subcellular localization in another organism, a relationship to a disease, and a reference to an information source.