Interrogatory cell-based assays and uses thereof

Patent metadata

Field	Value
Publication number	US-12437835-B2
Application number	US-201816056830-A
Country	US
Kind code	B2
Filing date	Aug 7, 2018
Priority date	Apr 2, 2012
Publication date	Oct 7, 2025
Grant date	Oct 7, 2025

Abstract

Methods for identifying a modulator of angiogenesis and methods for modulating angiogenesis in a mammalian subject are described herein. In some embodiments, the methods include obtaining data sets from a model for angiogenesis and generating a causal relationship network model based on the obtained data. In some embodiments, the methods include identifying, from the causal relationship network model, a causal relationship unique in angiogenesis, where a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of angiogenesis.

First claim

The invention claimed is:

1. A method for identifying a modulator of angiogenesis, said method comprising:
(1) obtaining a first data set from a model for angiogenesis that uses cells associated with angiogenesis to represent a characteristic aspect of angiogenesis, wherein the first data set represents one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis;
(2) obtaining a second data set from the model for angiogenesis, wherein the second data set represents one or more functional activities or cellular responses of the cells associated with angiogenesis;
(3) generating a first causal relationship network model among the one or more of genomic data, lipidomic data, proteomic data, metabolic data, transcriptomic data, and single nucleotide polymorphism (SNP) data characterizing the cells associated with angiogenesis, and the one or more functional activities or cellular responses of the cells associated with angiogenesis based on the first data set and the second data set using a programmed computing system including a plurality of processors, wherein generating the first causal relationship network comprises:
(i) creating a list of network fragments, each network fragment including a plurality of variables connected by one or more relationships, and determining a probabilistic score associated with each network fragment based on the first data set and/or the second data set, wherein the variables correspond to the one or more of genomic data, lipidomic data, proteomic data, metabolomic data, transcriptomic data, and single nucleotide polymorphism (SNP) data and the one or more functional activities or cellular responses of the cells associated with angiogenesis;
(ii) creating an ensemble of trial networks, each trial network including a different subset of the list of network fragments; and
(iii) globally optimizing the ensemble of trial networks by evolving the trial networks in parallel using the plurality of processors;
wherein relationships in the first causal relationship network model and causality in the first causal relationship network model are determined based on the first data set and the second data set and not based on previously identified or known biological relationships between variables;

(4) generating a differential causal relationship network from the first causal relationship network model and a second causal relationship network model based on control cell data using a computing device by steps including:
(i) for each relationship between two nodes in a selected one of the first causal relationship network model and the second causal relationship network model, determining if the other causal relationship network model includes a relationship between the same two nodes, and, where the other causal relationship network model includes a relationship between the same two nodes, determining if the relationship between the same two nodes in the other causal relationship network model has at least one significantly different parameter than that of the relationship in the selected causal relationship network model; and
(ii) forming the differential causal relationship network by including the relationships in the selected causal relationship network model that are absent from the other causal relationship network model and including the relationships in the selected causal relationship network model that have at least one significantly different parameter in the other causal relationship network model; and

(5) identifying, from the differential causal relationship network, a causal relationship unique in angiogenesis, wherein a gene, lipid, protein, metabolite, transcript, or SNP associated with the unique causal relationship is identified as a modulator of angiogenesis.

2. The method of claim 1
wherein the first data set represents lipidomic data; and
wherein a lipid associated with the unique causal relationship is identified as a modulator of angiogenesis.

3. The method of claim 1 , wherein the second data set representing one or more functional activities or cellular responses of the cells associated with angiogenesis comprises global enzymatic activity and/or an effect of the global enzymatic activity on enzyme metabolites or substrates in the cells associated with angiogenesis.

4. The method of claim 3 ,
wherein an enzyme associated with the unique causal relationship is identified as a modulator of angiogenesis.

5. The method of claim 3 , wherein the global enzymatic activity comprises global kinase activity, and an effect of the global enzymatic activity on the enzyme metabolites or substrates in the cells associated with angiogenesis comprises an effect on the phosphoproteome of the cell.

6. The method of claim 3 , wherein the global enzymatic activity comprises global protease activity.

7. The method of claim 1 , wherein the modulator stimulates or promotes angiogenesis.

8. The method of claim 1 , wherein the modulator inhibits angiogenesis.

9. The method of claim 1 , wherein the model for angiogenesis that uses cells associated with angiogenesis is selected from the group consisting of an in vitro cell culture angiogenesis model, a rat aorta microvessel model, a newborn mouse retina model, chick chorioallantoic membrane (CAM) model, a corneal angiogenic growth factor pocket model, a subcutaneous sponge angiogenic growth factor implantation model, an angiogenic growth factor implantation model, and a tumor implantation model.

10. The method of claim 9 , wherein the in vitro cell culture angiogenesis model is selected from the group consisting of a tube formation assay, a migration assay, a Boyden chamber assay, and a scratch assay.

11. The method of claim 9 , wherein the cells associated with angiogenesis in the in vitro cell culture angiogenesis model are human endothelial vessel cells (HUVEC).

12. The method of claim 9 , wherein an angiogenic growth factor in the corneal angiogenic growth factor pocket model, the subcutaneous sponge angiogenic growth factor implantation model, or the angiogenic growth factor implantation model is selected from the group consisting of FGF-2 and VEGF.

13. The method of claim 9 , wherein the cells in the model of angiogenesis are subject to an environmental perturbation, and control cells from which the control cell data is obtained are identical cells not subject to the environmental perturbation.

14. The method of claim 13 , wherein the environmental perturbation comprises one or more of a contact with an agent, a change in culture condition, an introduced genetic modification or mutation, a vehicle that causes a genetic modification or mutation, and induction of ischemia.

15. The method of claim 14 , wherein the agent is a pro-angiogenic agent or an anti-angiogenic agent.

16. The method of claim 15 , wherein the pro-angiogenic agent is selected from the group consisting of FGF-2 and VEGF.

17. The method of claim 15 , wherein the anti-angiogenic agent is selected from the group consisting of VEGF inhibitors, integrin antagonists, angiostatin, endostatin, tumstatin, Avastin, sorafenib, sunitinib, pazopanib, and everolimus, soluble VEGF-receptor, angiopoietin 2, thrombospondin1, thrombospondin 2, vasostatin, calreticulin, prothrombin (kringle domain-2), antithrombin III fragment, vascular endothelial growth inhibitor (VEGI), Secreted Protein Acidic and Rich in Cysteine (SPARC) and a SPARC peptide corresponding to the follistatin domain of the protein (FS-E), and coenzym

Assignees

• Berg Llc
• Bpgbio Inc

Inventors

• Vishnudas Vivek K
• Sarangarajan Rangaprasad
• Narain Niven Rajin
• Du Min
• Walshe Tony

Classifications

• G01N33/68

  involving proteins, peptides or amino acids {(involving lipoproteins G01N33/92)} · CPC title

• G01N33/5008

  for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics · CPC title

• C12Q1/025

  for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics (antimicrobial activity C12Q1/18) · CPC title

• G16B5/00 Primary

  ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks · CPC title

• C12Q1/68

  involving nucleic acids · CPC title