Systems, compositions, and methods for treatment of chronic obstructive pulmonary disease

Abstract

The present invention relates to the systems and methods of identifying compounds to treat or prevent chronic obstructive pulmonary disorders (COPD). The present invention also provides compositions to treat or prevent COPD comprising modulators of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, AP0A1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGATE 1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, S100P, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TEN, TWIST2, VIM, and WT1, and methods of use thereof.

Description

[0001] Attorney Docket No.: 206017-0291-00WO

[0002] Systems, Compositions, and Methods for Treatment of Chronic Obstructive Pulmonary

[0003] Disease

[0004] CROSS-REFERENCE TO RELATED APPLICATIONS

[0005] This application claims priority to U.S. Provisional Application No. 63 / 702,292, filed October 2, 2024, and U.S. Provisional Application No. 63 / 843,633, filed July 14, 2025, the contents of each of which are incorporated by reference herein in their entireties.

[0006] BACKGROUND OF THE INVENTION

[0007] Previous drug repurposing studies for chronic obstructive pulmonary disease (COPD) have been mostly based on the plasma, or a single set of data from lung. To better understand the molecular changes underlying the pathogenesis of COPD, it is imperative to incorporate data from emerging omics technologies on COPD tissues. Several studies have identified COPD molecular signatures from the genome, transcriptome, proteome, and other omics data; however, minimal overlap was observed among them. Meanwhile, few of these independent omics studies evaluated the drug development feasibility of the identified signature genes.

[0008] As such, there is a pressing need for a systematic integration strategy to identify candidate target genes with both biological relevance and drug development feasibility for COPD. The present invention meets this long felt, but unmet, need.

[0009] SUMMARY OF THE INVENTION

[0010] In some embodiments, the present invention provides a composition for preventing or treating chronic obstructive pulmonary disease (COPD) in a subject in need thereof. In some embodiments, the composition comprises a modulator for one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, Attorney Docket No.: 206017-0291-00WO

[0011] GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1 , IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MG AM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SI OOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1.

[0012] In some embodiments, the composition comprises a modulator of one or more selected from the group consisting of: ADRB2, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CHRM3, CHRNA1, CSK, CTSD, CXCR1, FGA, IGF1R, IL6, ITGAV, MAPK3, MGAM, MMP1, MMP13, MMP9, NR3C1, PPIB, SLC2A5, and VIM. In some embodiments, the modulator is one or more selected from the group consisting of: Spermine; Amphetamine; Betaxolol; Bethanidine; Isoetarine; Cabergoline; Metoprolol; Amitriptyline; Olanzapine; Atenolol; Norepinephrine; Mirtazapine; Timolol; Phenylpropanolamine; Dipivefrin; Sotalol; Carteolol; Nortriptyline; Propranolol; Labetalol; Bisoprolol; Epinephrine; Trimipramine; Orciprenaline; Dobutamine; Pseudoephedrine; Alprenolol; Ritodrine; Terbutaline; Bitolterol; Phenoxybenzamine; Salmeterol; Pindolol; Formoterol; Salbutamol; Ergoloid mesylate; Isoprenaline; Arbutamine; Carvedilol; Desipramine; Propafenone; Acebutolol; Nadolol; Levobunolol; Metipranolol; Arformoterol; Fenoterol; Pirbuterol; Bevantolol; Penbutolol; Ephedra; Mephentermine; Procaterol; Clenbuterol; Bambuterol; Oxprenolol; Putrescine; Spermidine; Celiprolol; Nebivolol; Indacaterol; NCX 950; Asenapine; Droxidopa; Bufuralol; Protokylol; (2S)-l-(9H-Carbazol-4-yloxy)-3-(isopropylamino)propan-2-ol; Bopindolol; Bupranolol; Befunolol; Olodaterol; Vilanterol; Arotinolol; Doxofylline; Racepinephrine; Dihydroergocornine; DL-Methylephedrine; Etafedrine; Tulobuterol; Levosalbutamol; Dihydroergocri stine; Digoxin; Acetyldigitoxin; Hydroflumethiazide; Etacrynic acid; Trichlormethiazide; Deslanoside; Ouabain; Diazoxide; Bretylium; Ciclopirox; Bepridil; Potassium cation; Aluminium; Magnesium cation; Digitoxin; Almitrine; Istaroxime; Rubidium Rb-82; Potassium gluconate; Magnesium gluconate; Magnesium acetate; Potassium acetate; Potassium sulfate; Magnesium levulinate; Magnesium lactate; Aluminium phosphate; Aluminum acetate; 4'-FLUORO-l,l'- BIPHENYL-4-CARBOXYLIC ACID; Isosorbide; Gossypol; Ramipril; Captopril; Zinc; Attomey Docket No.: 206017-0291-00WO

[0013] Enalaprilat; Zinc acetate; Zinc chloride; Sorafenib; XL281 ; RAF-265; N-{3-[(5-chloro- lH-pyrrolo[2,3-b]pyridin-3-yl)carbonyl]-2,4-difluorophenyl}propane-l -sulfonamide; N- {2,4-difluoro-3-[(5-pyridin-3-yl-lH-pyrrolo[2,3-b]pyridin-3- yl)carbonyl]phenyl} ethanesulfonamide; (!E)-5-(l-piperidin-4-yl-3-pyridin-4-yl-lH- pyrazol-4-yl)-2,3-dihydro-lH-inden-l-one oxime; Vemurafenib; Regorafenib;

[0014] Dabrafenib; Encorafenib; Fostamatinib; Olcegepant; Telcagepant; Erenumab; CAT-213;

[0015] Cevimeline; Tramadol; Succinylcholine; Ziprasidone; Disopyramide; Amitriptyline;

[0016] Ipratropium; Olanzapine; Metixene; Terfenadine; Clozapine; Trihexyphenidyl;

[0017] Oxyphencyclimine; Procyclidine; Loxapine; Carbamoylcholine; Promazine;

[0018] Hyoscyamine; Cyproheptadine; Meperidine; Imipramine; Methscopolamine bromide; Chlorpromazine; Darifenacin; Tridihexethyl; Anisotropine methylbromide; Nortriptyline; Amoxapine; Lamotrigine; Cinnarizine; Atropine; Thiopental; Nicardipine; Paroxetine; Homatropine Methylbromide; Trimipramine; Diphemanil Methyl sulfate; Scopolamine; Benzquinamide; Propiomazine; Cryptenamine; Tropicamide; Brompheniramine;

[0019] Maprotiline; Glycopyrronium; Bethanechol; Tolterodine; Oxybutynin; Promethazine; Pilocarpine; Doxepin; Desipramine; Ketamine; Quetiapine; Mivacurium; Diphenidol; Aripiprazole; Chlorprothixene; Pancuronium; Pipecuronium; Methotrimeprazine; Tiotropium; Solifenacin; Isopropamide; Acetylcholine; Arecoline; Mepenzolate; ALKS 27; Pizotifen; Fesoterodine; Methacholine; Hexocyclium; Aclidinium; Umeclidinium;

[0020] Trimebutine; Dosulepin; Etoperidone; Imidafenacin; Butylscopolamine; Homatropine; Thonzylamine; Revefenacin; Propiverine; Aripiprazole lauroxil; Galantamine; Agmatine; Dasatinib; Staurosporine; TG-100801; Fostamatinib; Insulin Human; Insulin Pork; S- Methylcysteine; lh-Benoximidazole-2-Carboxylic Acid; N-Aminoethylmorpholine; 5- AMINO-6-CYCLOHEXYL-4-HYDROXY-2-ISOBUTYL-HEXANOIC ACID;

[0021] C YCLOHEXYLMETHYL-2, 3 -DIHYDROXY-5 -METH YL-HEX YL AMIDE;

[0022] Ketoprofen; Alteplase; Reteplase; Anistreplase; Tenecteplase; Sucralfate; Zinc;

[0023] Alfimeprase; Ancrod; EP-2104R; Lanoteplase; Thrombin; Prothrombin; Human Thrombin; Thrombin Alfa; Anti-inhibitor coagulant complex; Zinc acetate; Zinc chloride; Sucralfate; Thrombin; Human Thrombin; Thrombin Alfa; Insulin Human; Insulin Lispro; Insulin Glargine; Insulin Pork; Mecasermin; Phosphoaminophosphonic Acid- Adenylate Ester; ATL1101; XL228; rhIGFBP-3; Linsitinib; Teprotumumab; (4Z)-6-bromo-4-({[4- Attomey Docket No.: 206017-0291-00WO

[0024] (pyrrolidin-l -ylmethyl)phenyl]amino}methylidene)isoquinoline-l,3(2H,4H)-dione; 3-[5- (lH-IMIDAZOL-l-YL)-7-METHYL-lH-BENZIMIDAZOL-2-YL]-4-[(PYRIDIN-2- YLMETHYL)AMIN0]PYRIDIN-2( I H)-0NE; Somatrem; Cixutumumab; Brigatinib; Ginseng; YSIL6; VX-702; Atiprimod; CRx-139; Andrographolide; Tapinarof;

[0025] Siltuximab; Polaprezinc; Foreskin fibroblast (neonatal); Foreskin keratinocyte (neonatal); Binimetinib; Dilmapimod; Antithymocyte immunoglobulin (rabbit); Sulindac; Arsenic trioxide; Purvalanol; 5-iodotubercidin; Seliciclib; Cholecystokinin; Ulixertinib; Acarbose; Miglitol; Voglibose; Marimastat; N-HYDROXY-2(R)-[[(4- METHOXYPHENYL)SULFONYL](3-PICOLYL)AMINO]-3- METHYLBUTANAMIDE HYDROCHLORIDE; N-[3-(N'- HYDROXYCARBOXAMIDO)-2-(2-METHYLPROPYL)-PROPANOYL]-O- TYROSINE-N-METHYLAMIDE; METHYLAMINO-PHENYLALANYL-LEUCYL- HYDROXAMIC ACID; [[l-[N-HYDR0XY-ACETAMIDYL]-3-METHYL-BUTYL]- CARBONYL-LEUCINYLJ-ALANINE ETHYL ESTER; N-HYDROXY-2-[4-(4- PHEN0XY-BENZENESULF0NYL)-TETRAHYDR0-PYRAN-4-YL]-ACETAMIDE; Marimastat; 3 -Methylpyridine; 2-{4-[4-(4-Chloro-Phenoxy)-Benzenesulfonyl]- Tetrahydro-Pyran-4-Yl}-N-Hydroxy -Acetamide; WAY-151693; Hydroxyaminovaline; 1- Methyloxy-4-Sulfone-Benzene; PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-(3- METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-(4- FLU0R0-3-METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-[(PYRIDIN-3-YLMETHYL)- AMIDE]; TERT-BUTYL 4-({[4-(BUT-2-YN-l- YLAMINO)PHENYL] SULFONYL }METHYL)-4- [(HYDROXYAMINO)CARBONYL]PIPERIDINE- 1 -CARBOXYLATE; 4- { [ 1 - METHYL-2,4-DIOXO-6-(3 -PHENYLPROP- 1 - YN- 1 - YL)- 1,4- DIHYDROQUINAZOLIN-3(2H)-YL]METHYL (BENZOIC ACID; 5-(2- ETHOXYETHYL)-5-[4-(4-FLUOROPHENOXY)PHENOXY]PYRIMIDINE- 2,4,6(1H,3H,5H)-TRIONE; CTS-1027; BENZYL 6-BENZYL-5,7-DIOXO-6,7- DIHYDRO-5H-[l,3]THIAZOLO[3,2-C]PYRIMIDINE-2-CARBOXYLATE;

[0026] Glutathione; Marimastat; Minocycline; Captopril; Glucosamine; Zinc; 2-Amino-N,3,3- Trimethylbutanamide; 2-{[Formyl(Hydroxy)Amino]Methyl}-4-Methylpentanoic Acid; AE-941; PG-530742; 5-(4-PHENOXYPHENYL)-5-(4-PYRIMIDIN-2-YLPIPERAZIN- Attorney Docket No.: 206017-0291-00WO

[0027] 1 -YL)PYRIMIDINE-2,4,6(2H,3H)-TRIONE; (2R)-2-AMINO-3,3,3-TRIFLUORO-N- HYDROXY-2- { [(4-PHEN0XYPHENYL)SULF0NYL]METHYL }PROPAN AMIDE; (3R)-4,4-DIFLUORO-3-[(4-METHOXYPHENYL)SULFONYL]BUTANOIC ACID; Zinc acetate; Zinc chloride; Flunisolide; Diflorasone; Alclometasone; Medrysone; Amcinonide; FluoromethoIone; Megestrol acetate; Beclomethasone dipropionate; Progesterone; Spironolactone; Betamethasone; Desoximetasone; Fluticasone propionate; Fluocinolone acetonide; Ulobetasol; Triamcinolone; Prednisone; Flumethasone; Fludrocortisone; Hydrocortisone; Mometasone; Hydrocortamate; Mifepristone;

[0028] Clocortolone; Flurandrenolide; Prednisolone; Loteprednol etabonate; Rimexolone; Methylprednisolone; Clobetasol propionate; Fluocinonide; Prednicarbate;

[0029] Fluoxymesterone; Budesonide; Dexamethasone; Desonide; Cortisone acetate; Paramethasone; Ciclesonide; Hexane- 1,6-Diol; Aldosterone; ORG-34517; Difluprednate; Ulipristal; Fluticasone furoate; Tixocortol; Difluocortolone; Gestrinone; Onapristone; Cortivazol; Clobetasone; Mometasone furoate; Hydrocortisone aceponate;

[0030] Hydrocortisone acetate; Hydrocortisone butyrate; Hydrocortisone cypionate; Hydrocortisone phosphate; Hydrocortisone probutate; Hydrocortisone valerate; Segesterone acetate; Proline; 1,4-Dithiothreitol; D-glucose; Invert sugar;

[0031] Fludeoxyglucose F-18; Artenimol; and Phenethyl Isothiocyanate

[0032] In some embodiments, the modulator is an inhibitor of one or more selected from the group consisting of AP0A1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1. In some embodiments, the inhibitor is selected from the group consisting of S-Methylcysteine, Insulin pork, N-Aminoethylmorpholine, Plerixafor, Framycetin, AMD-070, Mogamulizumab, Ibalizumab, Zinc, Thrombin, Prothrombin, Zinc chloride, Tenecteplase, Alteplase, Sucralfate, Reteplase, Anistreplase, Alfimeprase, Ancrod, EP-2104R, Lanoteplase, Human Thrombin, Thrombin alfa, Antiinhibitor coagulant complex, Zinc acetate, Hyaluronic acid, Natalizumab, Nafamostat, Ginseng, Andrographolide, Dilmapimod, Siltuximab, Polaprezinc, YSIL6, VX-702, Atiprimod, CRx-139, Tapinarof, Foreskin fibroblast, Foreskin keratinocyte, Binimetinib, Marimastat, 3 -Methylpyridine, Endostatin, WAY-151693, Hydroxy aminovaline, 4- methoxybenzenesulfinate, and CTS-1027. In some embodiments, the modulator is an Attorney Docket No.: 206017-0291-00WO activator of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1.

[0033] In some embodiments, the modulator is selected from the group consisting of a chemical compound, a protein, a peptide, a peptidomimetic, an antibody, a ribozyme, a small molecule chemical compound, a nucleic acid, a vector, an antisense molecule, an siRNA, an shRNA, and a guide RNA.

[0034] In some embodiments, the present invention provides a composition for preventing or treating COPD in a subject in need thereof comprising an inhibitor of Histone 3.3 acetylation. In some embodiments, the Histone 3.3 is hyperacetylated.

[0035] In some embodiments, the inhibitor is selected from the group consisting of a chemical compound, a protein, a peptide, a peptidomimetic, an antibody, a ribozyme, a small molecule chemical compound, a nucleic acid, a vector, an antisense molecule, an siRNA, an shRNA, and a guide RNA.

[0036] In some embodiments, the inhibitor is a polyamine. In some embodiments, the polyamine is one or more selected from the group consisting of spermine, spermidine, putrescine, and andrographolide.

[0037] In some embodiments, the present invention provides a method of preventing or treating COPD in a subject in need thereof comprising administering the composition of the invention.

[0038] In some embodiments, the present invention provides a system and a method to identify targets for a disease. In some embodiments, the disease is chronic obstructive pulmonary disorder (COPD). In some embodiments, the system and method comprise the steps of a) obtaining at least one omic dataset for the disease; b) obtaining at least one set of genes comprising upregulated genes, downregulated genes, or a combination thereof from the at least one omic dataset; c) generating an interactome comprising the at least one set of genes and a set of candidate genes, wherein the candidate genes comprise every gene in the interactome; d) calculating at least one distance between a gene from the at least one set of genes and the candidate genes; and e) identifying the genes from the set of associated genes as targets for the disease when the distance first distance is low, the second distance is low, or a combination thereof Attorney Docket No.: 206017-0291-00WO

[0039] In some embodiments, the system and method comprises obtaining at least two omic dataset, at least two set of genes, or a combination thereof.

[0040] In some embodiments, the step of obtaining a second set of genes from the at least one additional omics dataset comprises: a) identifying genes comprising loci associated with the disease from the genomic dataset; b) identifying upregulated proteins, downregulated proteins, or a combination thereof from the proteomic dataset and further matching the upregulated proteins, downregulated proteins, or a combination thereof with corresponding genes; c) identifying metabolites associated with the disease from the metabolomics dataset, identifying the proteins that regulate the metabolites, and matching the proteins with corresponding genes; d) performing a differential expression gene analysis; or e) a combination thereof.

[0041] In some embodiments, the system and method further comprises generating a set of genes from the interactome.

[0042] In some embodiments, the calculating the distance comprises: a) providing a score of 0 if the candidate gene is a gene from a gene from the at least one set of genes; b) providing a score of 1 if the candidate gene is one gene away from the gene from the at least one set of genes; c) providing a score of 2 if the candidate gene is two gene away from the gene from the at least one set of genes; or d) providing a score of 3 or more if the candidate gene is 3 or more gene away from the gene from the at least one set of genes.

[0043] In some embodiments, the system and method further comprises summing the distance. In some embodiments, the system and method further comprises the step of performing an in silico validation. In some embodiments, the system and method further comprises the step of identifying a modulator of the target.

[0044] In some embodiments, the system and method further comprising treating a subject in need thereof with the modulator.

[0045] In some embodiments, the disease is chronic obstructive pulmonary disease (COPD).

[0046] BRIEF DESCRIPTION OF THE DRAWINGS Attorney Docket No.: 206017-0291-00WO

[0047] The following detailed description of preferred embodiments of the invention will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there are shown in the drawings embodiments which are presently preferred. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities of the embodiments shown in the drawings.

[0048] Figure 1, comprising Figure 1A and Figure IB, depicts a representative analysis of COPD transcriptomic data. Figure 1A depicts a diagram illustrating genes consistently up-regulated or down-regulated across COPD stages. Each circle represents the number of DEGs identified in individual GOLD levels. Figure IB depicts a graph quantifying the numbers of DEGs identified across different stages of COPD. The flows between each GOLD column represent the status change of genes in the down-regulated, up-regulated and not differentially expressed category. DEG: Differentially expressed genes.

[0049] Figure 2, comprising Figure 2A and Figure 2B, depicts a representative analysis of GOLD4 patients. Figure 2A depicts a graph comparing DEGs between healthy subjects and GOLD 4 COPD patients. Dotted lines represent adjusted p-value < 0.05, |log2 fold change] >= 1. Figure 2B depicts a bar graph ranking the top 20 over- represented biological processes pathways in GOLD 4 patients. The adjusted p-value from the GO enrichment analysis is indicated. X-axis represents the count of the measured genes in the pathway.

[0050] Figure 3 depicts a diagram illustrating the number of signature genes at each omics level.

[0051] Figure 4, comprising Figure 4A through Figure 4C, depicts representative a representative interactome distance network. Figure 4A depicts an illustration of distances between genes in the network. The grey circle is the candidate gene, and the white circles are the additional genes in the network. Black lines represent physical or functional interactions between the genes. Blue lines were added to denote candidate genes' distances from the network's genes. Figure 4B depicts an illustrating of shortest distances of a candidate gene from omics signature genes. The shortest distances of a candidate gene (gray circle) to signatures genes from the transcriptomics (red circles) is Attorney Docket No.: 206017-0291-00WO genomics (green circles), and proteomics signature genes and proteins linked to COPD- associated metabolomics (yellow circles) are 1, 1 and 2 respectively. Figure 4C depicts ninety-two prioritized candidate genes with close proximity to all omics levels. Numbers represent the shortest distance of the candidate gene from the omics level on each row.

[0052] Figure 5, comprising Figure 5A and Figure 5B, depicts the drug repurposing and in silico validation pipeline. Figure 5A depicts a diagram of the composition of druggable targets in COPD. Figure 5B depicts COPD signature genes with high connectivity scores through in silico perturbation evaluation. Dashed lines represent high connectivity score of 90% or - 90%. A549 is a cell line of human adenocarcinoma alveolar basal epithelial cells. HCC515 is a cell line of human non-small cell lung adenocarcinoma.

[0053] Figure 6 depicts differentially expressed genes identified by discovery and validation sets. In the validation dataset, 528 genes were identified as DEGs in the GOLD IV patients by the same analysis approach in the discovery dataset. There are 34 overlapping DEGs between the discovery and validation sets. It was statistically evaluated whether the ratio of 34 / 139 overlapping genes would appear due to random chance by comparing with the ratio of 528 validation DEGs in the 12,864 background genome. The over-representation test reported a p-value < 10'15based on the Fisher exact test on the contingency table, indicating that the DEGs identified in the discovery set was also associated with the DEGs in the validation set.

[0054] Figure 7 depicts a gene set enrichment analysis plot for up-regulated and down-regulated DEGs in the discovery set. Rank: after differential gene expression analysis, genes in the validation set are ranked in decreasing order by -loglO(p-values) * sign of the fold change. Vertical line: gene set hits - DEGs in the discovery set are found in the validation set. Line: Enrichment score profile - when there is a hit, the enrichment score will increase and the line will climb up. Otherwise, the enrichment score will decrease and the line will go down. Dotted line: max and min enrichment score.

[0055] Figure 8, comprising Figure 8A through Figure 8C, depicts representative data demonstrating polyamines effect in COPD in vitro cell models. Figure 8A depicts a representative high content image analysis. Cells treated with CSC 50 pg / mL induce 50% toxicity in 72h incubation at 37°C, which is rescued by spermine, spermidine, and Attorney Docket No.: 206017-0291-00WO putrescine at 10 pM. Figure 8B depicts representative single-cell identification using Harmony 4.8 software for quantification. Figure 8C depicts a heat map per well of triplicate using 16 fields quantified per well.

[0056] Figure 9 depicts representative data demonstrating spermidine induces in vitro AcH3.3 inhibition. A549 Cells treated with CSs 50 ug / mL for 72h at 37 °C induce Histone h3.3 hyperacetylation, which is evident when treated with histone deacetylase inhibitor (HDACi). Spermine prevents the hyperacetylation of histones in the reporter and alveolar epithelial cells tested in a dose-response manner.

[0057] Figure 10 depicts a table of sources for omics data. * Include both COPD and healthy control subjects.

[0058] Figure 11 depicts a table summarizing the variant-to-gene mapping evidence for the 135 suspected genes associated with COPD. *For more detailed information on the specific SNPs and genes, please refer to the Supplementary Table 13 in the original paper from Shrine et al.

[0059] Figure 12 depicts a table of the proteomics studies on lung tissues from COPD patients.

[0060] Figure 13 depicts a table of metabolites input for MAGI. ^Compounds not in MAGI database were not searched.

[0061] Figure 14 depicts a table of signature genes identified on more than one omics level. In genomics data, multiple variants can be mapped to the same gene. The p value of the most significant variant implicating the gene is reported in the table based on its association with the lung function measured by FEV1 / FVC. In the proteomics data, nominal p-values are provided due to the lack of raw data.

[0062] Figure 15 depicts a table of significantly enriched pathways for the 92 COPD signature genes. 196 significant pathways with adjusted p-value < 0.01 based on GO enrichment analysis.

[0063] Figure 16 depicts a table of the of drugs and drugs targeting the tier 1 druggable genes.

[0064] DETAILED DESCRIPTION Attorney Docket No.: 206017-0291-00WO

[0065] The present invention relates to systems and methods to identify gene targets to treat diseases and disorders, such as chronic obstructive pulmonary disorder (COPD). The present invention is based, in part, on the development of a pipeline to integrate at least one omic dataset to identify druggable gene targets. For example, the data provided herein demonstrates the identification of gene targets in COPD. Therefore, in some embodiments, the present invention relates to methods of identifying gene targets for COPD.

[0066] The present invention is also based, in part, on genes that can be targeted to treat COPD.

[0067] The present invention is also based, in part, on the discovery that polyamines prevent the hyperacetylation of Histone 3.3 thereby treating COPD.

[0068] Definitions

[0069] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0070] As used herein, each of the following terms has the meaning associated with it in this section.

[0071] The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.

[0072] “About” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, or ±0.1% from the specified value, as such variations are appropriate to perform the disclosed methods.

[0073] The term “abnormal” when used in the context of organisms, tissues, cells or components thereof, refers to those organisms, tissues, cells or components thereof that differ in at least one observable or detectable characteristic (e.g., age, treatment, time of day, etc.) from those organisms, tissues, cells or components thereof that display the “normal” (expected) respective characteristic. Characteristics which are normal or expected for one cell or tissue type, might be abnormal for a different cell or tissue type. Attorney Docket No.: 206017-0291-00WO

[0074] The term “analog” as used herein generally refers to compounds that are generally structurally similar to the compound of which they are an analog, or “parent” compound. Generally, analogs will retain some characteristics of the parent compound, e.g., a biological or pharmacological activity. An analog may lack other, less desirable characteristics, e.g., antigenicity, proteolytic instability, toxicity, and the like. An analog includes compounds in which a particular biological activity of the parent is reduced, while at least one distinct biological activities of the parent are unaffected in the “analog.”

[0075] The term “clinical factors” as used herein, refers to any data that a medical practitioner may consider in determining a diagnosis or prognosis of disease. Such factors include, but are not limited to, the patient's medical history, a physical examination of the patient, complete blood count, analysis of the activity of enzymes, examination of cells, cytogenetics, and immunophenotyping of blood cells. As used herein, an “immunoassay” refers to any binding assay that uses an antibody capable of binding specifically to a target molecule to detect and quantify the target molecule.

[0076] The term “compound,” as used herein, unless otherwise indicated, refers to any specific chemical compound disclosed herein. In one embodiment, the term also refers to stereoisomers and / or optical isomers (including racemic mixtures) or enantiomerically enriched mixtures of disclosed compounds.

[0077] The term “tautomers” are constitutional isomers of organic compounds that readily interconvert by a chemical process (tautomerization).

[0078] The term “isomers” or “stereoisomers” refer to compounds, which have identical chemical constitution, but differ with regard to the arrangement of the atoms or groups in space.

[0079] A “disease” is a state of health of an animal wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal’s health continues to deteriorate.

[0080] In contrast, a “disorder” in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal’s state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal’s state of health. Attorney Docket No.: 206017-0291-00WO

[0081] A disease or disorder is “alleviated” if the severity of a sign or symptom of the disease or disorder, the frequency with which such a sign or symptom is experienced by a patient, or both, is reduced.

[0082] As used herein, the terms “therapy” or “therapeutic regimen” refer to those activities taken to prevent, treat or alter a disease or disorder, e.g., a course of treatment intended to reduce or eliminate at least one sign or symptom of a disease or disorder using pharmacological, surgical, dietary and / or other techniques. A therapeutic regimen may include a prescribed dosage of one or more compounds or surgery. Therapies will most often be beneficial and reduce or eliminate at least one sign or symptom of the disorder or disease state, but in some instances the effect of a therapy will have nondesirable or side-effects. The effect of therapy will also be impacted by the physiological state of the subject, e g., age, gender, genetics, weight, other disease conditions, etc.

[0083] An “effective amount” as used herein, means an amount which provides a therapeutic, prophylactic, or other desired benefit.

[0084] The term “activate,” as used herein, means to enhance or increase an activity or function by at least about ten percent relative to a control value. In one embodiment, the activity is enhanced or increased by 50% compared to a control value. In one embodiment, the activity is enhanced or increased by 75%. In one embodiment, the activity is enhanced or increased by 95%.

[0085] The term “inhibit,” as used herein, means to suppress or block an activity or function by at least about ten percent relative to a control value. In one embodiment, the activity is suppressed or blocked by 50% compared to a control value. In one embodiment, the activity is suppressed or blocked by 75%. In one embodiment, the activity is suppressed or blocked by 95%.

[0086] As used herein, the term “pharmaceutically acceptable” refers to a material, such as a carrier or diluent, which does not abrogate the biological activity or properties of the compound, and is relatively non-toxic, i.e., the material may be administered to an individual without causing an undesirable biological effect or interacting in a deleterious manner with any of the components of the composition in which it is contained. Attorney Docket No.: 206017-0291-00WO

[0087] As used herein, the term “pharmaceutical composition” refers to a mixture of at least one compound useful within the invention with a pharmaceutically acceptable carrier. The pharmaceutical composition facilitates administration of the compound to a patient or subject. Multiple techniques of administering a compound exist in the art including, but not limited to, intravenous, oral, aerosol, parenteral, ophthalmic, pulmonary, and topical administration.

[0088] As used herein, the term “pharmaceutically acceptable carrier” means a pharmaceutically acceptable material, composition, or carrier, such as a liquid or solid filler, stabilizer, dispersing agent, suspending agent, diluent, excipient, thickening agent, solvent or encapsulating material, involved in carrying or transporting a compound useful within the invention within or to the patient such that it may perform its intended function. Typically, such constructs are carried or transported from one organ, or portion of the body, to another organ, or portion of the body. Each carrier must be “acceptable” in the sense of being compatible with the other ingredients of the formulation, including the compound useful within the invention, and not injurious to the patient. Some examples of materials that may serve as pharmaceutically acceptable carriers include: sugars, such as lactose, glucose and sucrose; starches, such as corn starch and potato starch; cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; powdered tragacanth; malt; gelatin; talc; excipients, such as cocoa butter and suppository waxes; oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil and soybean oil; glycols, such as propylene glycol; polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; esters, such as ethyl oleate and ethyl laurate; agar; buffering agents, such as magnesium hydroxide and aluminum hydroxide; surface active agents; alginic acid; pyrogen-free water; isotonic saline; Ringer’s solution; ethyl alcohol; phosphate buffer solutions; and other non-toxic compatible substances employed in pharmaceutical formulations. As used herein, “pharmaceutically acceptable carrier” also includes any and all coatings, antibacterial and antifungal agents, and absorption delaying agents, and the like that are compatible with the activity of the compound useful within the invention and are physiologically acceptable to the patient. Supplementary active compounds may also be incorporated into the compositions. The “pharmaceutically acceptable carrier” may further include a Attorney Docket No.: 206017-0291-00WO pharmaceutically acceptable salt of the compound useful within the invention. Other additional ingredients that may be included in the pharmaceutical compositions used in the practice of the invention are known in the art and described, for example in Remington's Pharmaceutical Sciences (Genaro, Ed., Mack Publishing Co., 1985, Easton, PA), which is incorporated herein by reference.

[0089] As used herein, the term “potency” refers to the dose needed to produce half the maximal response (ED50).

[0090] As used herein, the term “efficacy” refers to the maximal effect (Emax) achieved within an assay.

[0091] “Sample” or “biological sample” as used herein means a biological material from a subject, including but is not limited to organ, tissue, exosome, blood, plasma, saliva, urine and other body fluid. A sample can be any source of material obtained from a subject.

[0092] The terms “subject,” “patient,” “individual,” and the like are used interchangeably herein, and refer to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein. In certain non-limiting embodiments, the patient, subject or individual is a human.

[0093] The term “therapeutically effective amount” refers to the amount of the subject compound or composition that will elicit the biological, physiologic, clinical or medical response of a cell, tissue, organ, system, or subject that is being sought by the researcher, veterinarian, medical doctor or other clinician. The term “therapeutically effective amount” includes that amount of a compound or composition that, when administered, is sufficient to prevent development of, or treat to some extent, one or more of the signs or symptoms of the disorder or disease being treated. The therapeutically effective amount will vary depending on the compound or composition, the disease and its severity and the age, weight, etc., of the subject to be treated.

[0094] To “treat” a disease or disorder as the term is used herein, means to reduce the frequency or severity of at least one sign or symptom of a disease or disorder experienced by a subject. The terms "treatment", "treating", "treat" and the like are used herein to generally refer to obtaining a desired pharmacologic and / or physiologic effect. The effect can be prophylactic in terms of completely or partially preventing a disease or Attorney Docket No.: 206017-0291-00WO symptom(s) thereof and / or may be therapeutic in terms of a partial or complete stabilization or cure for a disease and / or adverse effect attributable to the disease. The term “treatment" encompasses any treatment of a disease in a mammal, particularly a human, and includes: (a) preventing the disease and / or symptom(s) from occurring in a subject who may be predisposed to the disease or symptom but has not yet been diagnosed as having it; (b) inhibiting the disease and / or symptom(s), e.g., slowing or arresting their development; or (c) relieving the disease symptom(s), i.e., causing regression of the disease and / or symptom(s). Those in need of treatment include those already inflicted as well as those in which prevention is.

[0095] “Sample” or “biological sample” as used herein means a biological material isolated from a subject. The biological sample may contain any biological material suitable for detecting a mRNA, polypeptide or other marker of a physiologic or pathologic process in a subject, and may comprise fluidRanges: throughout this disclosure, various embodiments of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0096] “Omics” refers to fields of study or collections of data that characterize multiple biological molecules, sometimes relative to each other, in the context of cells or organisms. Examples include genomics (evaluation / data pertaining to the genome of a cell or organism); proteomics (proteins); metabolomics (metabolites); transcriptome (RNA molecules); and other specialized classes of information, such as information about subsets or subtypes of any of the foregoing). Omics data may contain information about hundreds or thousands of species within a class of molecule. “Multi-omics” data refers to more than one type of omics data. Attorney Docket No.: 206017-0291-00WO

[0097] Methods for Identifying Drug Candidates for Repurposing

[0098] In various embodiments, the present invention includes methods and systems for identifying targets for a disease. In one embodiment, the target is a gene, a protein, a metabolite, a lipid, or a combination thereof. In one embodiment, the target is a gene.

[0099] In one embodiment, the method and system comprise obtaining at least one set of genes from a transcriptomic dataset for the disease. Methods of generating or obtaining a transcriptomic dataset are well known in the art and include, but are not limited to, gene expression (mRNA) profiling, RNA sequencing (RNA-Seq), DNA microarrays, and single-cell RNA sequencing (scRNA-Seq), spatial transcriptomics (ST), quantitative PCR (qPCR), and ribosome profiling (Ribo-Seq).

[0100] In one embodiment, the method and system comprise obtaining at least one set of genes from the transcriptomic dataset. In one embodiment, the set of genes comprises differentially expressed genes. In one embodiment, the differentially expressed genes comprise upregulated genes, downregulated genes, or a combination thereof. Methods of obtaining the set of genes (e.g. differentially expressed genes) are well known in the art and include, but are not limited to, differential expression analysis by limma approach. The dataset may be pre-processed before being subjected to the analysis. Such pre-processing steps include, but are not limited to, background correction and normalization.

[0101] In one embodiment, the method and system comprise obtaining at least one set of genes from a genomic dataset for the disease. Genomic dataset comprises loci that are associated with a control sample and a test sample. The test sample may be a diseased sample or a manipulated sample such as a sample with a genetic modification or a treatment. Methods of generating or obtaining a genomic dataset are well known in the art and include, but are not limited to DNA sequencing, genome assembly, DNA microarrays, next-generation sequencing and polymerase chain reactions. Additionally, methods of identifying loci associated with a sample are known in the art and include, but is not limited to, mapping the loci to a genome. Attorney Docket No.: 206017-0291-00WO

[0102] In one embodiment, the method and system comprise obtaining at least one set of genes from a proteomic dataset for the disease. Methods of generating or obtaining a proteomic dataset are well known in the art and include but are not limited to protein microarray, two-dimensional polyacrylamide gel electrophoreses (2D-PAGE), and mass spectrometry-based assays. Proteins whose abundance change in a sample relative to a control sample are of particular interest. For example, proteins that change in abundance in a patient with COPD relative to a patient without COPD. These proteins may then be matched with the genes that encode them.

[0103] In one embodiment, the method and system comprise obtaining at least one set of genes from a metabolomic dataset for the disease. Methods of generating or obtaining a metabolomic dataset are well known in the art and include but are not limited to mass spectrometry -based assays. Proteins (and genes) that regulate the metabolic processes that generate these metabolites may then be identified.

[0104] Other omics datasets are contemplated in the present invention including, but not limited to, epigenomic datasets and lipidomics datasets.

[0105] In one embodiment, the method and system comprise obtaining a second, third, fourth, fifth, sixth, seventh, eight, ninth, tenth, and more than 10 set of genes. The second, third, fourth, fifth, sixth, seventh, eight, ninth, tenth, and more than 10 set of genes may be obtained from a genomic dataset, a transcriptomic dataset, a proteomic dataset, a metabolomic dataset, or a combination thereof.

[0106] In one embodiment, the method and system comprise obtaining at least one set of genes from a genomic dataset for the disease, at least one set of genes from a transcriptomic dataset for the disease, at least one set of genes from a proteomic dataset for the disease, at least one set of genes from a metabolomic dataset for the disease, or a combination thereof.

[0107] In one embodiment, the method and system comprise generating an interactome from the genes from 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more dataset. The interactome comprises protein-protein interactions which include the proteins encoded by the genes identified in the 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more dataset and proteins that have a structural or functional relationship with said proteins (i.e. associated proteins). It Attorney Docket No.: 206017-0291-00WO will be understood by those skilled in the art that proteins and genes may be used interchangeably when described or referred to in the context of an interactome.

[0108] In one embodiment, the dataset is one or more selected from the group consisting of a genomic dataset, a transcriptomic dataset, a proteomic dataset, a metabolomic dataset, or a combination thereof. In one embodiment, the interactome is generated from a one or more dataset selected from the group consisting of a genomic dataset, a transcriptomic dataset, a proteomic dataset, a metabolomic dataset, or a combination thereof. In one embodiment, the method and system comprise generating an interactome from the genes from a genomic dataset. In one embodiment, the method and system comprise generating an interactome from the genes from a transcriptomic dataset. In one embodiment, the method and system comprise generating an interactome from the genes from a proteomic dataset. In one embodiment, the method and system comprise generating an interactome from the genes from a metabolomic dataset. In one embodiment, the method and system comprise generating one interactome for each dataset. In one embodiment, the method and system comprise generating one interactome for two or more datasets. In one embodiment, the method and system comprise generating two or more interactome for each dataset.

[0109] The interactome may also be used to expand genes in the dataset. Methods of identifying genes to target are known to those of ordinary in the art. Said methods are contemplated herein.

[0110] In one embodiment, the method and system comprise providing a distance score based on the interactome and as illustrated by Figure 4A. Signature genes are genes identified in the dataset described above. Candidate genes are all the genes in the interactome. Candidate genes have a functional or structural relationship with the signature genes. In one embodiment, a distance score is provided between signature genes and candidate genes. In one embodiment, a distance score is provided between signature genes from a genomic dataset and candidate genes. In one embodiment, a distance score is provided between signature genes from a transcriptomic dataset and candidate genes. In one embodiment, a distance score is provided between signature genes from a proteomic dataset and candidate genes. In one embodiment, a distance score is provided between signature genes from a metabolomic dataset and candidate genes. Attorney Docket No.: 206017-0291-00WO

[0111] A score of 0 is provided if the candidate gene is a signature gene obtained from the dataset. A score of 1 is provided if the candidate gene is 1 gene away from a signature gene obtained from the dataset. A score of 2 is provided if the candidate gene is 2 genes away from a signature gene obtained from the dataset. A score of 3 is provided if the candidate gene is 3 genes away from a signature gene obtained from the dataset. A score of 4 is provided if the candidate gene is 4 genes away from a signature gene obtained from the dataset. A score of 5 is provided if the candidate gene is 5 genes away from a signature gene obtained from the dataset. A score of n is provided if the candidate gene is n genes away from a signature gene obtained from the dataset, wherein n is 1, 2,

[0112] 3, 4, 5, 6, 7, 8, 9, 10, etc.

[0113] In one embodiment, the distance scores are added together. Genes are labeled as targets for the disease if the sum of the scores is low, if the genes appear in one or more dataset, or a combination thereof. A score is low if it is lower than 5, lower than

[0114] 4, lower than 3, lower than 2, lower than 1, or a combination thereof.

[0115] In one embodiment, the method and system comprise a validation step. Targets identified by the method and system described herein may be validated by any method known in the art, including knockout, knockdown, overexpression, etc. These methods may be performed in vitro, in vivo, in silico, or a combination thereof.

[0116] Knowledge of the genes and targets identified from the method and system of the invention may be leveraged to treat or prevent the disease. In some embodiments, the disease is chronic obstructive pulmonary disorder.

[0117] Methods and compositions of modulating the targets (i.e. genes) are known in the art and are further described below. Additionally, drug databases may be utilized to obtain compounds that modulate the targets. Such drug databases include the DrugBank database, ChEMBL, PubChem, Therapeutic Target Database, or any other drug database repository.

[0118] Compositions

[0119] In various embodiments, the present invention includes compositions for changing the level or activity of one or more selected from the group consisting of ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, Attorney Docket No.: 206017-0291-00WO

[0120] ATP1 Al , BCL2L1, BDKRB1, BRAF, CALCRL, CCL1 1, CDH1 , CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1. Therefore, in some embodiments, the composition comprises a modulator of the expression or activity of one or more selected from the group consisting of ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1.

[0121] In some embodiments, the composition comprises a modulator of one or more selected from the group consisting of: ADRB2, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CHRM3, CHRNA1, CSK, CTSD, CXCR1, FGA, IGF1R, IL6, ITGAV, MAPK3, MGAM, MMP1, MMP13, MMP9, NR3C1, PPIB, SLC2A5, and VIM. In some embodiments, the modulator of one or more selected from the group consisting of: ADRB2, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CHRM3, CHRNA1, CSK, CTSD, CXCR1, FGA, IGF1R, IL6, ITGAV, MAPK3, MGAM, MMP1, MMP13, MMP9, NR3C1, PPIB, SLC2A5, and VIM is one or more selected from Figure 16. In some embodiments, the modulator is one or more selected Attorney Docket No.: 206017-0291-00WO from the group consisting of: Spermine; Amphetamine; Betaxolol; Bethanidine; Isoetarine; Cabergoline; Metoprolol; Amitriptyline; Olanzapine; Atenolol;

[0122] Norepinephrine; Mirtazapine; Timolol; Phenylpropanolamine; Dipivefrin; Sotalol;

[0123] Carteolol; Nortriptyline; Propranolol; Labetalol; Bisoprolol; Epinephrine; Trimipramine; Orciprenaline; Dobutamine; Pseudoephedrine; Alprenolol; Ritodrine; Terbutaline;

[0124] Bitolterol; Phenoxybenzamine; Salmeterol; Pindolol; Formoterol; Salbutamol; Ergoloid mesylate; Isoprenaline; Arbutamine; Carvedilol; Desipramine; Propafenone; Acebutolol; Nadolol; Levobunolol; Metipranolol; Arformoterol; Fenoterol; Pirbuterol; Bevantolol; Penbutolol; Ephedra; Mephentermine; Procaterol; Clenbuterol; Bambuterol; Oxprenolol; Putrescine; Spermidine; Celiprolol; Nebivolol; Indacaterol; NCX 950; Asenapine;

[0125] Droxidopa; Bufuralol; Protokylol; (2S)-l-(9H-Carbazol-4-yloxy)-3- (isopropylamino)propan-2-ol; Bopindolol; Bupranolol; Befunolol; Olodaterol; Vilanterol; Arotinolol; Doxofylline; Racepinephrine; Dihydroergocornine; DL-Methylephedrine;

[0126] Etafedrine; Tulobuterol; Levosalbutamol; Dihydroergocri stine; Digoxin; Acetyldigitoxin; Hydroflumethiazide; Etacrynic acid; Trichlormethiazide; Deslanoside; Ouabain;

[0127] Diazoxide; Bretylium; Ciclopirox; Bepridil; Potassium cation; Aluminium; Magnesium cation; Digitoxin; Almitrine; Istaroxime; Rubidium Rb-82; Potassium gluconate;

[0128] Magnesium gluconate; Magnesium acetate; Potassium acetate; Potassium sulfate;

[0129] Magnesium levulinate; Magnesium lactate; Aluminium phosphate; Aluminum acetate; 4'- FLUORO-l,r-BIPHENYL-4-CARBOXYLIC ACID; Isosorbide; Gossypol; Ramipril;

[0130] Captopril; Zinc; Enalaprilat; Zinc acetate; Zinc chloride; Sorafenib; XL281; RAF -265; N- {3-[(5-chloro-lH-pyrrolo[2,3-b]pyridin-3-yl)carbonyl]-2,4-difluorophenyl}propane-l- sulfonamide; N-{2,4-difluoro-3-[(5-pyridin-3-yl-lH-pyrrolo[2,3-b]pyridin-3- yl)carbonyl]phenyl} ethanesulfonamide; (lE)-5-(l-piperidin-4-yl-3-pyridin-4-yl-lH- pyrazol-4-yl)-2,3-dihydro-lH-inden-l-one oxime; Vemurafenib; Regorafenib;

[0131] Dabrafenib; Encorafenib; Fostamatinib; Olcegepant; Telcagepant; Erenumab; CAT-213; Cevimeline; Tramadol; Succinylcholine; Ziprasidone; Disopyramide; Amitriptyline;

[0132] Ipratropium; Olanzapine; Metixene; Terfenadine; Clozapine; Trihexyphenidyl; Oxyphencyclimine; Procyclidine; Loxapine; Carbamoylcholine; Promazine; Hyoscyamine; Cyproheptadine; Meperidine; Imipramine; Methscopolamine bromide; Chlorpromazine; Darifenacin; Tridihexethyl; Anisotropine methylbromide; Nortriptyline; Attorney Docket No.: 206017-0291-00WO

[0133] Amoxapine; Lamotrigine; Cinnarizine; Atropine; Thiopental; Nicardipine; Paroxetine; Homatropine Methylbromide; Trimipramine; Diphemanil Methyl sulfate; Scopolamine; Benzquinamide; Propiomazine; Cryptenamine; Tropicamide; Brompheniramine;

[0134] Maprotiline; Glycopyrronium; Bethanechol; Tolterodine; Oxybutynin; Promethazine; Pilocarpine; Doxepin; Desipramine; Ketamine; Quetiapine; Mivacurium; Diphenidol; Aripiprazole; Chlorprothixene; Pancuronium; Pipecuronium; Methotrimeprazine;

[0135] Tiotropium; Solifenacin; Isopropamide; Acetylcholine; Arecoline; Mepenzolate; ALKS 27; Pizotifen; Fesoterodine; Methacholine; Hexocyclium; Aclidinium; Umeclidinium; Trimebutine; Dosulepin; Etoperidone; Imidafenacin; Butylscopolamine; Homatropine; Thonzylamine; Revefenacin; Propiverine; Aripiprazole lauroxil; Galantamine; Agmatine; Dasatinib; Staurosporine; TG-100801; Fostamatinib; Insulin Human; Insulin Pork; S- Methylcysteine; lh-Benoximidazole-2-Carboxylic Acid; N-Aminoethylmorpholine; 5- AMINO-6-CYCLOHEXYL-4-HYDROXY-2-ISOBUTYL-HEXANOIC ACID;

[0136] C YCLOHEXYLMETH YL-2, 3 -DIHYDROXY-5 -METHYL-HEX YL AMIDE; Ketoprofen; Alteplase; Reteplase; Anistreplase; Tenecteplase; Sucralfate; Zinc;

[0137] Alfimeprase; Ancrod; EP-2104R; Lanoteplase; Thrombin; Prothrombin; Human Thrombin; Thrombin Alfa; Anti-inhibitor coagulant complex; Zinc acetate; Zinc chloride; Sucralfate; Thrombin; Human Thrombin; Thrombin Alfa; Insulin Human; Insulin Lispro; Insulin Glargine; Insulin Pork; Mecasermin; Phosphoaminophosphonic Acid- Adenylate Ester; ATL1101; XL228; rhIGFBP-3; Linsitinib; Teprotumumab; (4Z)-6-bromo-4-({[4- (pyrrolidin-l-ylmethyl)phenyl]amino}methylidene)isoquinoline-l,3(2H,4H)-dione; 3-[5- ( 1 H-IMID AZOL- 1 - YL)-7-METHYL- 1 H-BENZIMID AZOL-2-YL] -4- [(P YRIDIN-2- YLMETHYL)AMINO]PYRIDIN-2(1H)-ONE; Somatrem; Cixutumumab; Brigatinib;

[0138] Ginseng; YSIL6; VX-702; Atiprimod; CRx-139; Andrographolide; Tapinarof;

[0139] Siltuximab; Polaprezinc; Foreskin fibroblast (neonatal); Foreskin keratinocyte (neonatal); Binimetinib; Dilmapimod; Antithymocyte immunoglobulin (rabbit); Sulindac; Arsenic trioxide; Purvalanol; 5-iodotubercidin; Seliciclib; Cholecystokinin; Ulixertinib; Acarbose;

[0140] Miglitol; Vbglibose; Marimastat; N-HYDROXY-2(R)-[[(4- METHOXYPHENYL)SULFONYL](3-PICOLYL)AMINO]-3- METHYLBUTANAMIDE HYDROCHLORIDE; N-[3-(N'- HYDROXYCARBOXAMIDO)-2-(2-METHYLPROPYL)-PROPANOYL]-O- Attorney Docket No.: 206017-0291-00WO

[0141] TYROSINE-N-METHYLAMIDE; METHYLAMTNO-PHENYLALANYL-LEUCYL- HYDROXAMIC ACID; [[l-[N-HYDROXY-ACETAMIDYL]-3-METHYL-BUTYL]- CARBONYL-LEUCINYL]-ALANINE ETHYL ESTER; N-HYDROXY-2-[4-(4- PHENOXY-BENZENESULFONYL)-TETRAHYDRO-PYRAN-4-YL]-ACETAMIDE; Marimastat; 3 -Methylpyridine; 2-{4-[4-(4-Chloro-Phenoxy)-Benzenesulfonyl]- Tetrahydro-Pyran-4-Yl}-N-Hydroxy -Acetamide; WAY-151693; Hydroxyaminovaline; 1- Methyloxy-4-Sulfone-Benzene; PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-(3- METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-(4- FLUORO-3-METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-[(PYRIDIN-3-YLMETHYL)- AMIDE]; TERT-BUTYL 4-( { [4-(BUT-2-YN- 1 - YL AM I NO PHEN YL] SULFONYL }METHYL)-4- [(HYDROXYAMINO)C ARB ONYL]PIPERIDINE- 1 -CARBOXYLATE; 4- { [ 1 - METHYL-2,4-DIOXO-6-(3 -PHENYLPROP- 1 - YN- 1 - YL)- 1,4- DIHYDROQUINAZOLIN-3(2H)-YL]METHYL}BENZOIC ACID; 5-(2- ETHOXYETHYL)-5-[4-(4-FLUOROPHENOXY)PHENOXY]PYRIMIDINE- 2,4,6(1H,3H,5H)-TRIONE; CTS-1027; BENZYL 6-BENZYL-5,7-DIOXO-6,7- DIHYDRO-5H-[l,3]THIAZOLO[3,2-C]PYRIMIDINE-2-CARBOXYLATE;

[0142] Glutathione; Marimastat; Minocycline; Captopril; Glucosamine; Zinc; 2-Amino-N,3,3- Trimethylbutanamide; 2-{[Formyl(Hydroxy)Amino]Methyl}-4-Methylpentanoic Acid; AE-941; PG-530742; 5-(4-PHENOXYPHENYL)-5-(4-PYRIMIDIN-2-YLPIPERAZIN- 1-YL)PYRIMIDINE-2,4,6(2H,3H)-TRIONE; (2R)-2-AMINO-3,3,3-TRIFLUORO-N- HYDROXY-2- { [(4-PHENOXYPHEN YL)SULFON YL]METHYL JPROPAN AMIDE; (3R)-4,4-DIFLUORO-3-[(4-METHOXYPHENYL)SULFONYL]BUTANOIC ACID; Zinc acetate; Zinc chloride; Flunisolide; Diflorasone; Alclometasone; Medrysone; Amcinonide; FluoromethoIone; Megestrol acetate; Beclomethasone dipropionate; Progesterone; Spironolactone; Betamethasone; Desoximetasone; Fluticasone propionate; Fluocinolone acetonide; Ulobetasol; Triamcinolone; Prednisone; Flumethasone; Fludrocortisone; Hydrocortisone; Mometasone; Hydrocortamate; Mifepristone; Clocortolone; Flurandrenolide; Prednisolone; Loteprednol etabonate; Rimexolone; Methylprednisolone; Clobetasol propionate; Fluocinonide; Prednicarbate;

[0143] Fluoxymesterone; Budesonide; Dexamethasone; Desonide; Cortisone acetate; Attorney Docket No.: 206017-0291-00WO

[0144] Paramethasone; Ciclesonide; Hexane-1,6-Diol; Aldosterone; ORG-34517; Difluprednate; Ulipristal; Fluticasone furoate; Tixocortol; Difluocortolone; Gestrinone; Onapristone; Cortivazol; Clobetasone; Mometasone furoate; Hydrocortisone aceponate;

[0145] Hydrocortisone acetate; Hydrocortisone butyrate; Hydrocortisone cypionate; Hydrocortisone phosphate; Hydrocortisone probutate; Hydrocortisone valerate; Segesterone acetate; Proline; 1,4-Dithiothreitol; D-glucose; Invert sugar; Fludeoxyglucose F-18; Artenimol; and Phenethyl Isothiocyanate.

[0146] In some embodiments, the modulator is an inhibitor. In some embodiments, the inhibitor decreases the expression or activity of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MG AM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1. In some embodiments, the inhibitor decreases the expression or activity of one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1. In some embodiments, the compositions of the invention decrease the level of one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1; the amount of mRNA encoding for one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1; the activity of one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1; or a combination thereof.

[0147] In some embodiments, the modulator modulates acetylation of Histone 3.3. In some embodiments, the modulator is an inhibitor that decreases acetylation of Attorney Docket No.: 206017-0291-00WO

[0148] Histone 3.3. In some embodiments, the inhibitor is a polyamine. In some embodiments, the polyamine is one or more selected from the group consisting of spermine, spermidine, putrescine, and andrographolide.

[0149] In some embodiments, the modulator is an activator. In some embodiments, the activator increases the expression or activity of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1. In some embodiments, the activator increases the expression or activity of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1. In some embodiments, the compositions of the invention increase the level of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1; the amount of mRNA encoding for one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1; the activity of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1; or a combination thereof.

[0150] In some embodiments, the invention provides compositions for the treatment of COPD associated with dysregulation of one or more selected from the group consisting of ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, Attorney Docket No.: 206017-0291-00WO

[0151] H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1.

[0152] Modulation of a gene, or gene product, can be assessed using a wide variety of methods, including those disclosed herein, as well as methods known in the art or to be developed in the future. That is, the routineer would appreciate, based upon the disclosure provided herein, that increasing or decreasing the level or activity of a gene, or gene product, can be readily assessed using methods that assess the level of a nucleic acid encoding a gene product (e.g., mRNA), the level of polypeptide gene product present in a biological sample, the activity of polypeptide gene product present in a biological sample, or combinations thereof.

[0153] The modulator compositions and methods of the invention that increase or decrease the level or activity of a gene, or gene product, include, but should not be construed as being limited to, a chemical compound, a protein, a peptide, a peptidomemetic, an antibody, a ribozyme, a small molecule chemical compound, a nucleic acid, a vector, an antisense nucleic acid molecule (e.g., siRNA, miRNA, etc.), or combinations thereof. Exemplary modulators can include positive allosteric modulators (PAMs), negative allosteric modulators (NAM), antagonists, potentiators, or agonists. One of skill in the art would readily appreciate, based on the disclosure provided herein, that a modulator composition encompasses a chemical compound that increases or decreases the level or activity of a gene, or gene product. Additionally, a modulator composition encompasses a chemically modified compound, and derivatives, as is well known to one of skill in the chemical arts.

[0154] In some embodiments, the inhibitor is one or more selected from the group consisting of S-Methylcysteine, Insulin pork, N-Aminoethylmorpholine, Plerixafor, Framycetin, AMD-070, Mogamulizumab, Ibalizumab, Zinc, Thrombin, Prothrombin, Zinc chloride, Tenecteplase, Alteplase, Sucralfate, Reteplase, Anistreplase, Alfimeprase, Ancrod, EP-2104R, Lanoteplase, Human Thrombin, Thrombin alfa, Anti -inhibitor Attorney Docket No.: 206017-0291-00WO coagulant complex, Zinc acetate, Hyaluronic acid, Natalizumab, Nafamostat, Ginseng, Andrographolide, Dilmapimod, Siltuximab, Polaprezinc, YSIL6, VX-702, Atiprimod, CRx-139, Tapinarof, Foreskin fibroblast, Foreskin keratinocyte, Binimetinib, Marimastat, 3 -Methylpyridine, Endostatin, WAY-151693, Hydroxyaminovaline, 4- methoxybenzenesulfinate, and CTS-1027. In some embodiments, the inhibitor of one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1 is one or more selected from the group consisting of S-Methylcysteine, Insulin pork, N-Aminoethylmorpholine, Plerixafor, Framycetin, AMD-070, Mogamulizumab, Ibalizumab, Zinc, Thrombin, Prothrombin, Zinc chloride, Tenecteplase, Alteplase, Sucralfate, Reteplase, Anistreplase, Alfimeprase, Ancrod, EP-2104R, Lanoteplase, Human Thrombin, Thrombin alfa, Anti-inhibitor coagulant complex, Zinc acetate, Hyaluronic acid, Natalizumab, Nafamostat, Ginseng, Andrographolide, Dilmapimod, Siltuximab, Polaprezinc, YSIL6, VX-702, Atiprimod, CRx-139, Tapinarof, Foreskin fibroblast, Foreskin keratinocyte, Binimetinib, Marimastat, 3 -Methylpyridine, Endostatin, WAY-151693, Hydroxyaminovaline, 4- methoxybenzenesulfinate, and CTS-1027. In some embodiments, the inhibitor is a polyamine. In some embodiments, the polyamine is one or more selected from the group consisting of spermine, spermidine, putrescine, and andrographolide

[0155] In some embodiments, the modulator composition of the present invention is an agonist, which increases the expression, activity, or biological function of a gene or gene product. For example, in certain embodiments, the modulator of the present invention is an agonist of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1.

[0156] In some embodiments, the modulator composition of the present invention is an antagonist, which decreases the expression, activity, or biological function of a gene or gene product. For example, in certain embodiments, the modulator of the present invention is an antagonist of one or more selected from the group consisting of APOA1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1. In certain embodiments, the modulator of the present invention is an antagonist of Histone

[0157] 3.3 acetylation. Attorney Docket No.: 206017-0291-00WO

[0158] Further, one of skill in the art would, when equipped with this disclosure and the methods exemplified herein, appreciate that modulators include such modulators as discovered in the future, as can be identified by well-known criteria in the art of pharmacology, such as the physiological results of modulation of the genes, and gene products, as described in detail herein and / or as known in the art. Therefore, the present invention is not limited in any way to any particular modulator composition as exemplified or disclosed herein; rather, the invention encompasses those modulator compositions that would be understood by the routineer to be useful as are known in the art and as are discovered in the future.

[0159] Further methods of identifying and producing modulator compositions are well known to those of ordinary skill in the art. Alternatively, a modulator can be synthesized chemically. Further, the routineer would appreciate, based upon the teachings provided herein, that a modulator composition can be obtained from a recombinant organism. Compositions and methods for chemically synthesizing modulators and for obtaining them from natural sources are well known in the art and are described in the art.

[0160] One of skill in the art will appreciate that a modulator can be administered as a small molecule chemical, a polypeptide, a peptide, an antibody, a nucleic acid construct encoding a protein, an antisense nucleic acid, a nucleic acid construct encoding an antisense nucleic acid, or combinations thereof. Numerous vectors and other compositions and methods are well known for administering a protein or a nucleic acid construct encoding a protein to cells or tissues. Therefore, the invention includes a peptide or a nucleic acid encoding a peptide that is an activator of a gene, or gene product. For example, the invention includes a peptide or a nucleic acid encoding a peptide that comprises one or more proteins, peptides, or combination thereof of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, Attorney Docket No.: 206017-0291-00WO

[0161] ITGA1 , ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MG AM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1. (Sambrook et al., 2001, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, New York; Ausubel et al., 1997, Current Protocols in Molecular Biology, John Wiley & Sons, New York). In another example, the invention includes an agonist or positive allosteric molecule. In another example, the invention includes a combination of an agonist or positive allosteric molecule and a peptide or a nucleic acid encoding a peptide that comprises one or more proteins, peptides, or a combination thereof of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B 1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1. In another example, the invention includes a combination of shRNA and a peptide or a nucleic acid encoding a peptide that comprises one or more proteins, peptides or a combination thereof of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, Attorney Docket No.: 206017-0291-00WO

[0162] MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, and WT1.

[0163] The invention includes an antisense nucleic acid that is an inhibitor of a gene. For example, the invention includes an antisense nucleic acid that targets one or more proteins, peptides or a combination thereof of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation. In another example, the invention includes an antagonist or negative allosteric molecule. In another example, the invention includes a combination of an antagonist or negative allosteric molecule and a peptide or a nucleic acid targeting one or more proteins, peptides or a combination thereof of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, Attorney Docket No.: 206017-0291-00WO

[0164] TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation. In another example, the invention includes a combination of shRNA and a peptide or a nucleic acid targeting one or more proteins, peptides or a combination thereof of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation.

[0165] In some embodiments, the modulators of the invention results in an increase or decrease in expression of at least one protein, including transcription, translation, or both of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation. In some embodiments, the modulators of the invention results in an increase or decrease in at least one activity of a protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, Attorney Docket No.: 206017-0291-00WO

[0166] CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation. Thus, increasing or decreasing the level or activity of at least one protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation includes, but is not limited to, increasing or decreasing the amount of at the least one protein, increasing or decreasing transcription, translation, or both, of a nucleic acid encoding the at least one protein; and it also includes increasing or decreasing any activity of a polypeptide as well.

[0167] One of skill in the art will realize that diminishing or elevating the amount or activity of a molecule that itself diminishes or elevates the amount or activity of at least one protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, Attorney Docket No.: 206017-0291-00WO

[0168] CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, IT GAI, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MG AM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation can serve to increase or decrease the amount or activity of the protein. Any inhibitor or activator of a negative regulator of a protein of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation is encompassed in the invention. Any inhibitor of activator of a positive regulator of a protein of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL 12 Al, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, Attorney Docket No.: 206017-0291-00WO

[0169] THBS1, TIMP1, TLN1 , TPM1 , TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation is encompassed in the invention. As a non-limiting example, antisense is described as a form of inhibiting a regulator of proteasome or a proteasome subunit in order to increase the amount or activity of proteasome or a proteasome subunit. Antisense oligonucleotides are DNA or RNA molecules that are complementary to some portion of a mRNA molecule. When present in a cell, antisense oligonucleotides hybridize to an existing mRNA molecule and inhibit translation into a gene product. Inhibiting the expression of a gene using an antisense oligonucleotide is well known in the art (Marcus- Sekura, 1988, Anal. Biochem. 172:289), as are methods of expressing an antisense oligonucleotide in a cell (Inoue, U.S. Pat. No. 5,190,931). The methods of the invention include the use of antisense oligonucleotide to diminish or elevate the amount of a molecule that causes a decrease or increase in the amount or activity of a protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B 1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation, thereby increasing or decreasing the amount or activity of the protein. Antisense nucleic acid molecules targeting the genes encoding the proteins of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, Attorney Docket No.: 206017-0291-00WO

[0170] LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation are encompassed in this invention. Contemplated in the present invention are antisense oligonucleotides that are synthesized and provided to the cell by way of methods well known to those of ordinary skill in the art. As an example, an antisense oligonucleotide can be synthesized to be between about 10 and about 100, more preferably between about 15 and about 50 nucleotides long. The synthesis of nucleic acid molecules is well known in the art, as is the synthesis of modified antisense oligonucleotides to improve biological activity in comparison to unmodified antisense oligonucleotides (Tullis, 1991, U.S. Pat. No. 5,023,243).

[0171] Similarly, the expression of a gene may be inhibited by the hybridization of an antisense molecule to a promoter or other regulatory element of a gene, thereby affecting the transcription of the gene. Methods for the identification of a promoter or other regulatory element that interacts with a gene of interest are well known in the art, and include such methods as the yeast two hybrid system (Bartel and Fields, eds., In: The Yeast Two Hybrid System, Oxford University Press, Cary, N.C.). (contrast variations - tags, promoters, includible systems)

[0172] Alternatively, inhibition of a gene expressing a protein that diminishes or elevates the level or activity of a protein of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL 12 Al, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, Attorney Docket No.: 206017-0291-00WO

[0173] TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation can be accomplished through the use of an siRNA, shRNA, antisense oligonucleotide or ribozyme. Given the nucleotide sequence of the molecule, one of ordinary skill in the art could synthesize an antisense oligonucleotide or ribozyme without undue experimentation, provided with the disclosure and references incorporated herein.

[0174] Nucleic Acids

[0175] In one aspect, the invention includes a nucleic acid encoding a peptide that is an activator of a gene, or gene product. In some embodiments, the composition of the invention comprises one or more isolated nucleic acids. For example, in some embodiments, the one or more isolated nucleic acids encodes protein of one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation.

[0176] In another aspect, the invention includes antisense nucleic acid molecules targeting a gene. In some embodiments, the composition of the invention comprises one or more isolated antisense nucleic acid molecules. For example, in some embodiments, the one or more isolated antisense nucleic acid molecules targeting one or more protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, Attorney Docket No.: 206017-0291-00WO

[0177] CTTN, CXCR1, CXCR4, CXCR5, FBN1 , FGA, FGG, FHL2, FTH1, GNB1 , GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation.

[0178] In some embodiments, the invention encompasses expression vectors and methods for the introduction of exogenous DNA into cells with concomitant expression of the exogenous DNA in the cells such as those described, for example, in Sambrook et al. (2012, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, New York), and in Ausubel et al. (1997, Current Protocols in Molecular Biology, John Wiley & Sons, New York).

[0179] The desired nucleic acid encoding the one or more proteins and the antisense nucleic acid molecules targeting the one or more proteins can be cloned into a number of types of vectors. However, the present invention should not be construed to be limited to any particular vector. Instead, the present invention should be construed to encompass a wide plethora of vectors which are readily available and / or well-known in the art. For example, a desired polynucleotide of the invention can be cloned into a vector including, but not limited to a plasmid, a phagemid, a phage derivative, an animal virus, and a cosmid. Vectors of particular interest include expression vectors, replication vectors, and transgene vectors.

[0180] In specific embodiments, the expression vector is selected from the group consisting of a viral vector, a bacterial vector and a mammalian cell vector. Numerous expression vector systems exist that comprise at least a part or all of the compositions discussed above. Prokaryote- and / or eukaryote-vector based systems can be employed for use with the present invention to produce polynucleotides, or their cognate polypeptides. Many such systems are commercially and widely available.

[0181] Further, the expression vector may be provided to a cell in the form of a viral vector. Viral vector technology is well known in the art and is described, for example, in Sambrook et al. (2012), and in Ausubel et al. (1997), and in other virology Attorney Docket No.: 206017-0291-00WO and molecular biology manuals. Viruses, which are useful as vectors include, but are not limited to, retroviruses, adenoviruses, adeno-associated viruses, herpes viruses, and lentiviruses. In general, a suitable vector contains an origin of replication functional in at least one organism, a promoter sequence, convenient restriction endonuclease sites, and one or more selectable markers. (See, e.g., WO 01 / 96584; WO 01 / 29058; and U.S. Pat. No. 6,326,193.)

[0182] A number of viral based systems have been developed for gene transfer into mammalian cells. For example, retroviruses provide a convenient platform for gene delivery systems. A selected gene can be inserted into a vector and packaged in retroviral particles using techniques known in the art. The recombinant virus can then be isolated and delivered to cells of the subject either in vivo or ex vivo. A number of retroviral systems are known in the art. In some embodiments, adenovirus vectors are used. A number of adenovirus vectors are known in the art. In some embodiments, lentivirus vectors are used.

[0183] For example, vectors derived from retroviruses such as the lentivirus are suitable tools to achieve long-term gene transfer since they allow long-term, stable integration of a transgene and its propagation in daughter cells. Lentiviral vectors have the added advantage over vectors derived from onco-retroviruses such as murine leukemia viruses in that they can transduce non-proliferating cells, such as hepatocytes. They also have the added advantage of low immunogenicity. In a preferred embodiment, the composition includes a vector derived from an adeno-associated virus (AAV). Adeno- associated viral (AAV) vectors have become powerful gene delivery tools for the treatment of various disorders. AAV vectors possess a number of features that render them ideally suited for gene therapy, including a lack of pathogenicity, minimal immunogenicity, and the ability to transduce postmitotic cells in a stable and efficient manner. Expression of a particular gene contained within an AAV vector can be specifically targeted to one or more types of cells by choosing the appropriate combination of AAV serotype, promoter, and delivery method.

[0184] In some embodiments, the encoding sequence is contained within an AAV vector. More than 30 naturally occurring serotypes of AAV are available. Many natural variants in the AAV capsid exist, allowing identification and use of an AAV with Attorney Docket No.: 206017-0291-00WO properties specifically suited for skeletal muscle. AAV viruses may be engineered using conventional molecular biology techniques, making it possible to optimize these particles for cell specific delivery of nucleic acid sequences, for minimizing immunogenicity, for tuning stability and particle lifetime, for efficient degradation, for accurate delivery to the nucleus, etc.

[0185] Thus, expression of the one or more proteins can be achieved by delivering a recombinantly engineered AAV or artificial AAV that contains one or more encoding sequences. The use of AAVs is a common mode of exogenous delivery of DNA as it is relatively non-toxic, provides efficient gene transfer, and can be easily optimized for specific purposes. Exemplary AAV serotypes include, but is not limited to AAV1, AAV2, AAV3, AAV4, AAV5, AAV6, AAV7, AAV8, AAV9, and AAV PHP.eB.

[0186] Desirable AAV fragments for assembly into vectors include the cap proteins, including the vpl, vp2, vp3 and hypervariable regions, the rep proteins, including rep 78, rep 68, rep 52, and rep 40, and the sequences encoding these proteins. These fragments may be readily utilized in a variety of vector systems and host cells. Such fragments may be used alone, in combination with other AAV serotype sequences or fragments, or in combination with elements from other AAV or non-AAV viral sequences. As used herein, artificial AAV serotypes include, without limitation, AAV with a non-naturally occurring capsid protein. Such an artificial capsid may be generated by any suitable technique, using a selected AAV sequence (e g., a fragment of a vpl capsid protein) in combination with heterologous sequences which may be obtained from a different selected AAV serotype, non-contiguous portions of the same AAV serotype, from a non-AAV viral source, or from a non-viral source. An artificial AAV serotype may be, without limitation, a chimeric AAV capsid, a recombinant AAV capsid, or a "humanized" AAV capsid. Thus, exemplary AAVs, or artificial AAVs, suitable for expression of the one or more proteins, include AAV2 / 8 (see U.S. Pat. No. 7,282,199), AAV2 / 5 (available from the National Institutes of Health), AAV2 / 9 (International Patent Publication No. W02005 / 033321), AAV2 / 6 (U.S. Pat. No. 6,156,303), and AAVrh8 (International Patent Publication No. W02003 / 042397), among others.

[0187] For expression of the desired polynucleotide, at least one module in each promoter functions to position the start site for RNA synthesis. The best known example Attorney Docket No.: 206017-0291-00WO of this is the TATA box, but in some promoters lacking a TATA box, such as the promoter for the mammalian terminal deoxynucleotidyl transferase gene and the promoter for the SV40 genes, a discrete element overlying the start site itself helps to fix the place of initiation.

[0188] Additional promoter elements, i.e., enhancers, regulate the frequency of transcriptional initiation. Typically, these are located in the region 30-110 bp upstream of the start site, although a number of promoters have recently been shown to contain functional elements downstream of the start site as well. The spacing between promoter elements frequently is flexible, so that promoter function is preserved when elements are inverted or moved relative to one another. In the thymidine kinase (tk) promoter, the spacing between promoter elements can be increased to 50 bp before activity begins to decline. Depending on the promoter the individual elements can function either cooperatively or independently to activate transcription.

[0189] A promoter may be one naturally associated with a gene or polynucleotide sequence, as may be obtained by isolating the 5’ non-coding sequences located upstream of the coding segment and / or exon. Such a promoter can be referred to as “endogenous.” Similarly, an enhancer may be one naturally associated with a polynucleotide sequence, located either downstream or upstream of that sequence. Alternatively, certain advantages will be gained by positioning the coding polynucleotide segment under the control of a recombinant or heterologous promoter, which refers to a promoter that is not normally associated with a polynucleotide sequence in its natural environment. A recombinant or heterologous enhancer refers also to an enhancer not normally associated with a polynucleotide sequence in its natural environment. Such promoters or enhancers may include promoters or enhancers of other genes, and promoters or enhancers isolated from any other prokaryotic, viral, or eukaryotic cell, and promoters or enhancers not “naturally occurring,” i.e., containing different elements of different transcriptional regulatory regions, and / or mutations that alter expression. In addition to producing nucleic acid sequences of promoters and enhancers synthetically, sequences may be produced using recombinant cloning and / or nucleic acid amplification technology, including PCR™, in connection with the compositions disclosed herein (U.S. Patent 4,683,202, U.S. Patent 5,928,906). Furthermore, it is contemplated the control sequences that direct transcription Attorney Docket No.: 206017-0291-00WO and / or expression of sequences within non-nuclear organelles such as mitochondria, chloroplasts, and the like, can be employed as well.

[0190] Naturally, it will be important to employ a promoter and / or enhancer that effectively directs the expression of the DNA segment in the cell type, organelle, and organism chosen for expression. Those of skill in the art of molecular biology generally know how to use promoters, enhancers, and cell type combinations for protein expression, for example, see Sambrook et al. (2012). The promoters employed may be constitutive, tissue-specific, inducible, and / or useful under the appropriate conditions to direct high level expression of the introduced DNA segment, such as is advantageous in the large-scale production of recombinant proteins and / or peptides. The promoter may be heterologous or endogenous. Exemplary expression systems include, but are not limited to, tamoxifen-inducible Nestin-CreERT2 and floxed-stop channelrhodopsin (‘Nest- ChR’), tamoxifen-inducible ASCL-CreERT2 and floxed-stop channelrhodopsin (‘ASCL- ChR’).

[0191] In order to assess the expression of the desired polynucleotide, the expression vector to be introduced into a cell can also contain either a selectable marker gene or a reporter gene or both to facilitate identification and selection of expressing cells from the population of cells sought to be transfected or infected through viral vectors. In other embodiments, the selectable marker may be carried on a separate piece of DNA and used in a co-transfection procedure. Both selectable markers and reporter genes may be flanked with appropriate regulatory sequences to enable expression in the host cells. Useful selectable markers are known in the art and include, for example, antibioticresistance genes, such as neo and the like.

[0192] Reporter genes are used for identifying potentially transfected cells and for evaluating the functionality of regulatory sequences. Reporter genes that encode for easily assayable proteins are well known in the art. In general, a reporter gene is a gene that is not present in or expressed by the recipient organism or tissue and that encodes a protein whose expression is manifested by some easily detectable property, e.g., enzymatic activity. Expression of the reporter gene is assayed at a suitable time after the DNA has been introduced into the recipient cells. Attorney Docket No.: 206017-0291-00WO

[0193] Suitable reporter genes may include genes encoding luciferase, betagalactosidase, chloramphenicol acetyl transferase, secreted alkaline phosphatase, diphtheria toxin subunit A, the green fluorescent protein gene or the SNAP-tag (see, e.g., Ui-Tei et al., 2000 FEBS Lett. 479:79-82). Suitable expression systems are well known and may be prepared using well known techniques or obtained commercially. Internal deletion constructs may be generated using unique internal restriction sites or by partial digestion of non-unique restriction sites. Constructs may then be transfected into cells that display high levels of siRNA polynucleotide and / or polypeptide expression. In general, the construct with the minimal 5' flanking region showing the highest level of expression of reporter gene is identified as the promoter. Such promoter regions may be linked to a reporter gene and used to evaluate agents for the ability to modulate promoter- driven transcription.

[0194] In the context of an expression vector, the vector can be readily introduced into a host cell, e.g., mammalian, bacterial, yeast or insect cell by any method in the art. For example, the expression vector can be transferred into a host cell by physical, chemical or biological means.

[0195] Physical methods for introducing a polynucleotide into a host cell include calcium phosphate precipitation, lipofection, particle bombardment, microinjection, electroporation, and the like. Methods for producing cells comprising vectors and / or exogenous nucleic acids are well-known in the art. See, for example, Sambrook et al. (2012, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory, New York), and in Ausubel et al. (1997, Current Protocols in Molecular Biology, John Wiley & Sons, New York).

[0196] Biological methods for introducing a polynucleotide of interest into a host cell include the use of DNA and RNA vectors. Viral vectors, and especially retroviral vectors, have become the most widely used method for inserting genes into mammalian, e.g., human cells. Other viral vectors can be derived from lentivirus, poxviruses, herpes simplex virus I, adenoviruses and adeno-associated viruses, and the like. See, for example, U.S. Pat. Nos. 5,350,674 and 5,585,362.

[0197] Chemical means for introducing a polynucleotide into a host cell include colloidal dispersion systems, such as macromolecule complexes, nanocapsules, Attorney Docket No.: 206017-0291-00WO microspheres, beads, and lipid-based systems including oil-in-water emulsions, micelles, mixed micelles, and liposomes. A preferred colloidal system for use as a delivery vehicle in vitro and in vivo is a liposome (i.e., an artificial membrane vesicle). The preparation and use of such systems is well known in the art.

[0198] Regardless of the method used to introduce exogenous nucleic acids into a host cell, in order to confirm the presence of the recombinant DNA sequence in the host cell, a variety of assays may be performed. Such assays include, for example, “molecular biological” assays well known to those of skill in the art, such as Southern and Northern blotting, RT-PCR and PCR; “biochemical” assays, such as detecting the presence or absence of a particular peptide, e.g., by immunological means (ELISAs and Western blots) or by assays described herein to identify agents falling within the scope of the invention.

[0199] Any DNA vector or delivery vehicle can be utilized to transfer the desired polynucleotide to a cell in vitro or in vivo. In the case where a non-viral delivery system is utilized, a preferred delivery vehicle is a liposome. The above-mentioned delivery systems and protocols therefore can be found in Gene Targeting Protocols, 2ed., pp 1-35 (2002) and Gene Transfer and Expression Protocols, Vol. 7, Murray ed., pp 81-89 (1991).

[0200] “Liposome” is a generic term encompassing a variety of single and multilamellar lipid vehicles formed by the generation of enclosed lipid bilayers or aggregates. Liposomes may be characterized as having vesicular structures with a phospholipid bilayer membrane and an inner aqueous medium. Multilamellar liposomes have multiple lipid layers separated by aqueous medium. They form spontaneously when phospholipids are suspended in an excess of aqueous solution. The lipid components undergo self rearrangement before the formation of closed structures and entrap water and dissolved solutes between the lipid bilayers. However, the present invention also encompasses compositions that have different structures in solution than the normal vesicular structure. For example, the lipids may assume a micellar structure or merely exist as nonuniform aggregates of lipid molecules. Also contemplated are lipofectamine nucleic acid complexes.

[0201] In some embodiments, the composition of the invention comprises in vitro transcribed (IVT) RNA encoding one or more components of the one or more proteins. In Attorney Docket No.: 206017-0291-00WO some embodiments, an IVT RNA can be introduced to a cell as a form of transient transfection. The RNA is produced by in vitro transcription using a plasmid DNA template generated synthetically. DNA of interest from any source can be directly converted by PCR into a template for in vitro mRNA synthesis using appropriate primers and RNA polymerase. The source of the DNA can be, for example, genomic DNA, plasmid DNA, phage DNA, cDNA, synthetic DNA sequence or any other appropriate source of DNA. The desired template for in vitro transcription is the one or more proteins or protein fragment.

[0202] In some embodiments, the DNA to be used for PCR contains an open reading frame. The DNA can be from a naturally occurring DNA sequence from the genome of an organism. In some embodiments, the DNA is a full length gene of interest of a portion of a gene. The gene can include some or all of the 5' and / or 3' untranslated regions (UTRs). The gene can include exons and introns. In some embodiments, the DNA to be used for PCR is a human gene. In another embodiment, the DNA to be used for PCR is a human gene including the 5' and 3' UTRs. The DNA can alternatively be an artificial DNA sequence that is not normally expressed in a naturally occurring organism. An exemplary artificial DNA sequence is one that contains portions of genes that are ligated together to form an open reading frame that encodes a fusion protein. The portions of DNA that are ligated together can be from a single organism or from more than one organism.

[0203] In some embodiments, the composition of the present invention comprises a modified nucleic acid encoding one or more proteins described herein. In some embodiments, the composition of the present invention comprises a modified antisense nucleic acid molecule targeting one or more proteins described herein. For example, in some embodiments, the composition comprises a nucleoside- modified RNA. In some embodiments, the composition comprises a nucleoside-modified mRNA. Nucleoside-modified mRNA have particular advantages over non-modified mRNA, including for example, increased stability, low immunogenicity, and enhanced translation. Nucleoside-modified mRNA useful in the present invention is further described in U.S. Patent No. 8,278,036, which is incorporated by reference herein in its entirety. Attorney Docket No.: 206017-0291-00WO

[0204] Peptides

[0205] In some embodiments, the invention includes a peptide that is a modulator of a gene. In some embodiments, a peptide of the composition comprises an amino acid sequence of one or more protein of one or more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL 10 Al, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, IT GAI, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation.

[0206] In some embodiments, a peptide of the composition comprises an amino acid sequence of an inhibitor of more selected from the group consisting of: ACAN, ACTN1, ADAMTS4, ADRB2, AGER, ANO6, AP3B1, APOA1, ARRB1, ATP1A1, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PROK2, RUNX2, RXFP1, S100A12, SIOOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, TSPAN14, TTN, TWIST2, VIM, WT1, and Histone 3.3 acetylation.

[0207] In some embodiments, the composition of the invention comprises a peptide, a fragment of a peptide, a homolog, a variant, a derivative or a salt of a peptide Attorney Docket No.: 206017-0291-00WO described herein. For example, in certain embodiments, the composition comprises a peptide comprising the one or more proteins, or a fragment, a homolog, a variant, a derivative, or a salt thereof. In some embodiments, the composition comprises a peptide comprising an amino acid of an inhibitor of the one or more proteins.

[0208] In certain embodiments, the peptide comprises a targeting domain, which targets the peptide to a desired location. For example, in certain embodiments, the targeting domain binds to a targeted cell or protein. In some embodiments, the targeting domain comprises a transmembrane domain, which is required for embedding the peptide into membranes or lipid bilayers. In some embodiments, these targeting domains deliver the therapeutic peptide to a desired location.

[0209] In certain embodiments, the targeting domain comprises a peptide, nucleic acid, small molecule, or the like, which has the ability to bind to the targeted cell, protein, or protein aggregate. In some embodiments, the targeting domain comprises an antibody or antibody fragment which binds to a targeted cell, protein, or protein aggregate.

[0210] The peptide may alternatively be made by recombinant means or by cleavage from a longer polypeptide. The composition of a peptide may be confirmed by amino acid analysis or sequencing.

[0211] The variants of the peptides according to the present invention may be (i) one in which one or more of the amino acid residues are substituted with a conserved or non-conserved amino acid residue (preferably a conserved amino acid residue) and such substituted amino acid residue may or may not be one encoded by the genetic code, (ii) one in which there are one or more modified amino acid residues, e.g., residues that are modified by the attachment of substituent groups, (iii) one in which the peptide is an alternative splice variant of the peptide of the present invention, (iv) fragments of the peptides and / or (v) one in which the peptide is fused with another peptide, such as a leader or secretory sequence or a sequence which is employed for purification (for example, His-tag) or for detection (for example, Sv5 epitope tag). The fragments include peptides generated via proteolytic cleavage (including multi-site proteolysis) of an original sequence. Variants may be post-translationally, or chemically modified. Such variants are deemed to be within the scope of those skilled in the art from the teaching herein. Attorney Docket No.: 206017-0291-00WO

[0212] The peptides of the invention can be post-translationally modified. For example, post-translational modifications that fall within the scope of the present invention include signal peptide cleavage, glycosylation, acetylation, isoprenylation, proteolysis, myristoylation, protein folding and proteolytic processing, etc. Some modifications or processing events require introduction of additional biological machinery. For example, processing events, such as signal peptide cleavage and core glycosylation, are examined by adding canine microsomal membranes or Xenopus egg extracts (U.S. Pat. No. 6,103,489) to a standard translation reaction.

[0213] The peptides of the invention may include unnatural amino acids formed by post-translational modification or by introducing unnatural amino acids during translation. A variety of approaches are available for introducing unnatural amino acids during protein translation. By way of example, special tRNAs, such as tRNAs which have suppressor properties, suppressor tRNAs, have been used in the process of site- directed non-native amino acid replacement (SNAAR). In SNAAR, a unique codon is required on the mRNA and the suppressor tRNA, acting to target a non-native amino acid to a unique site during the protein synthesis (described in W090 / 05785). However, the suppressor tRNA must not be recognizable by the aminoacyl tRNA synthetases present in the protein translation system. In certain cases, a non-native amino acid can be formed after the tRNA molecule is aminoacylated using chemical reactions which specifically modify the native amino acid and do not significantly alter the functional activity of the aminoacylated tRNA. These reactions are referred to as post-aminoacylation modifications. For example, the epsilon-amino group of the lysine linked to its cognate tRNA (tRNALYS), could be modified with an amine specific photoaffinity label.

[0214] The peptides of the invention may be conjugated with other molecules, such as proteins, to prepare fusion proteins. This may be accomplished, for example, by the synthesis of N-terminal or C-terminal fusion proteins provided that the resulting fusion protein retains the functionality of the peptide of the invention.

[0215] The peptides of the invention may be converted into pharmaceutical salts by reacting with inorganic acids such as hydrochloric acid, sulfuric acid, hydrobromic acid, phosphoric acid, etc., or organic acids such as formic acid, acetic acid, propionic acid, glycolic acid, lactic acid, pyruvic acid, oxalic acid, succinic acid, malic acid, tartaric Attorney Docket No.: 206017-0291-00WO acid, citric acid, benzoic acid, salicylic acid, benezenesulfonic acid, and toluenesulfonic acids.

[0216] Peptides of the invention may also have modifications. Modifications (which do not normally alter primary sequence) include in vivo, or in vitro chemical derivatization of polypeptides, e.g., acetylation, or carboxylation. Also included are modifications of glycosylation, e.g., those made by modifying the glycosylation patterns of a polypeptide during its synthesis and processing or in further processing steps; e.g., by exposing the polypeptide to enzymes which affect glycosylation, e.g., mammalian glycosylating or deglycosylating enzymes. Also embraced are sequences which have phosphorylated amino acid residues, e.g., phosphotyrosine, phosphoserine, or phosphothreonine.

[0217] Also included are peptides which have been modified using ordinary molecular biological techniques so as to improve their resistance to proteolytic degradation or to optimize solubility properties or to render them more suitable as a therapeutic agent. Such variants include those containing residues other than naturally- occurring L-amino acids, e.g., D-amino acids or non-naturally-occurring synthetic amino acids. The peptides of the invention may further be conjugated to non-amino acid moieties that are useful in their therapeutic application. In particular, moieties that improve the stability, biological half-life, water solubility, and / or immunologic characteristics of the peptide are useful. A non-limiting example of such a moiety is polyethylene glycol (PEG).

[0218] Covalent attachment of biologically active compounds to water-soluble polymers is one method for alteration and control of biodistribution, pharmacokinetics, and often, toxicity for these compounds (Duncan et al., 1984, Adv. Polym. Sci. 57:53- 101). Many water-soluble polymers have been used to achieve these effects, such as poly(sialic acid), dextran, poly(N-(2-hydroxypropyl)methacrylamide) (PHPMA), poly(N- vinylpyrrolidone) (PVP), poly(vinyl alcohol) (PVA), poly(ethylene glycol-co-propylene glycol), poly(N-acryloyl morpholine (PAcM), and poly(ethylene glycol) (PEG) (Powell, 1980, Polyethylene glycol. In R. L. Davidson (Ed.) Handbook of Water Soluble Gums and Resins. McGraw-Hill, New York, chapter 18). PEG possess an ideal set of properties: very low toxicity (Pang, 1993, 1. Am. Coll. Toxicol. 12: 429-456) excellent solubility in Attorney Docket No.: 206017-0291-00WO aqueous solution (Powell, supra), low immunogenicity and antigenicity (Dreborg et al., 1990, Crit. Rev. Then Drug Carrier Syst. 6: 315-365). PEG-conjugated or “PEGylated” protein therapeutics, containing single or multiple chains of polyethylene glycol on the protein, have been described in the scientific literature (Clark et al., 1996, J. Biol. Chem. 271: 21969-21977; Hershfteld, 1997, Biochemistry and immunology of poly(ethylene glycol)-modified adenosine deaminase (PEG-ADA). In J. M. Harris and S. Zalipsky (Eds) Poly(ethylene glycol): Chemistry and Biological Applications. American Chemical Society, Washington, D.C., p 145-154; Olson et al., 1997, Preparation and characterization of polyethylene glycol)ylated human growth hormone antagonist. In J. M. Harris and S. Zalipsky (Eds) Poly(ethylene glycol): Chemistry and Biological Applications. American Chemical Society, Washington, D.C., p 170-181).

[0219] Pharmaceutical Compositions

[0220] The present invention provides, in part, a composition to treat or prevent COPD. In some embodiments, the composition comprises a modulator. In some embodiments, at least one modulator is administered to a subject having a disease or disorder wherein the modulator thereof targets a gene identified by the method of the present invention. In some embodiments, the composition comprises an inhibitor of Histone acetylation. In some embodiments, the composition comprises a polyamine. In some embodiments, the polyamine is one or more selected from the group consisting of spermine, spermidine, putrescine, and andrographolide.

[0221] In some embodiments, the present invention provides pharmaceutical compositions comprising at least one agent for use in the methods of the invention. The relative amounts of the agent(s), any pharmaceutically acceptable carrier, and any additional ingredients in a pharmaceutical composition of the invention will vary, depending upon the identity, size, and condition of the subject treated and further depending upon the route by which the composition is to be administered.

[0222] The formulations of the pharmaceutical compositions described herein may be prepared by any method known or hereafter developed in the art of pharmacology. In general, such preparatory methods include the step of bringing the active ingredient into association with a carrier or at least one other accessory ingredient. Attorney Docket No.: 206017-0291-00WO

[0223] Said compositions may comprise additional medicinal agents, pharmaceutical agents, carriers, buffers, adjuvants, dispersing agents, diluents, and the like depending on the intended use and application.

[0224] Examples of suitable pharmaceutical carriers, excipients and / or diluents are well known in the art and include, but are not limited to, a gum, a starch (e g. corn starch, pregelatinized starch), a sugar (e.g., lactose, mannitol, sucrose, dextrose), a cellulosic material (e.g. microcrystalline cellulose), an acrylate (e.g. polymethylacrylate), calcium carbonate, magnesium oxide, talc, or mixtures thereof.

[0225] Pharmaceutically acceptable carriers for liquid formulations are aqueous or non-aqueous solutions, suspensions, emulsions or oils, Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, and injectable organic esters such as ethyl oleate. Examples of oils are those of animal, vegetable, or synthetic origin, for example, peanut oil, soybean oil, olive oil, sunflower oil, turmeric oil, fish-liver oil, another marine oil, or a lipid from milk or eggs.

[0226] Aqueous carriers include water, alcoholic / aqueous solutions, emulsions or suspensions, including saline and buffered media such as phosphate buffered saline solutions, water, emulsions, such as oil / water emulsions, various types of wetting agents, sterile solutions etc. Compositions comprising such carriers can be formulated by well- known conventional methods. Suitable carriers may comprise any material which, when combined with the biologically active compound of the invention, retains the biological activity. Preparations for parenteral administration may include sterile aqueous or nonaqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic / aqueous solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles may include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles may include fluid and nutrient replenishers, electrolyte replenishers (such as those based on Ringer's dextrose), and the like. Preservatives and other additives may also be present including, for example, antimicrobials, antioxidants, chelating agents, and inert gases and the like, in addition, the pharmaceutical composition of the present invention might comprise Attorney Docket No.: 206017-0291-00WO proteinaceous carriers, e.g., serum albumin or immunoglobulin, in some embodiments of human origin.

[0227] The composition may contain at least one added materials such as carriers and / or excipients. As used herein, “carriers” and “excipients” generally refer to substantially inert, non-toxic materials that do not deleteriously interact with other components of the composition. These materials may be used to increase the amount of solids in particulate pharmaceutical compositions, such as to form a powder of drug particles. Examples of suitable carriers include water, silicone, gelatin, waxes, and the like.

[0228] Examples of normally employed “excipients,” include pharmaceutical grades of mannitol, sorbitol, inositol, dextrose, sucrose, lactose, trehalose, dextran, starch, cellulose, sodium or calcium phosphates, calcium sulfate, citric acid, tartaric acid, glycine, high molecular weight polyethylene glycols (PEG), and the like and combinations thereof. In one embodiment, the excipient may also include a charged lipid and / or detergent in the pharmaceutical compositions. Suitable charged lipids include, without limitation, phosphatidylcholines (lecithin), and the like. Detergents will typically be a nonionic, anionic, cationic or amphoteric surfactant. Examples of suitable surfactants include, for example, Tergitol® and Triton® surfactants (Union Carbide Chemicals and Plastics, Danbury, Conn.), polyoxyethylenesorbitans, for example, TWEEN surfactants (Atlas Chemical Industries, Wilmington, Del ), polyoxyethylene ethers, for example, Brij®, pharmaceutically acceptable fatty acid esters, for example, lauryl sulfate and salts thereof (SDS), and the like. Such materials may be used as stabilizers and / or antioxidants. Additionally, they may be used to reduce local irritation at the site of administration.

[0229] In some embodiment, the composition is formulated in a lyophilized form. In certain embodiments, the lyophilized formulation of the composition allows for maintaining structure and achieving remarkably superior long-term stability conditions which might occur during storage or transportation of the composition. Attorney Docket No.: 206017-0291-00WO

[0230] Formulations

[0231] The therapeutic agent or combination thereof described herein can be administered at therapeutically effective doses. Therapeutically effective doses can be determined by one of ordinary skill in the art based on the type of therapeutic agent or combination thereof administered. Dosage, routes of administration, and administration schedules described in the art can be used. Representative doses are available in the Merck Manual Professional Edition (see the internet at merckmanuals(dot)com / professional).

[0232] Further, doses of a therapeutic agent, or combination thereof, administered to animals can be converted to equivalent doses for humans based on the body surface area (BSA) (represented in mg / m2) normalization method (see, e.g., Reagan-Shaw, S. et al., “Dose translation from animal to human studies revisited,” FASEB J. 22, 659-661 (2007); and “Guidance for Industry — Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers,” U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), July 2005, Pharmacology and Toxicology; which are incorporated by reference herein).

[0233] In some embodiments, the therapeutic agent or combination thereof described herein are administered in therapeutically effective amounts for periods of time effective to treat COPD. The effective amount of the therapeutic agent or combination thereof described herein can be determined by one of ordinary skill in the art and includes dosage amounts for a mammal of from about 0.5 to about 200 mg / kg, about 0.5 to about 150 mg / kg, about 0.5 to 100 mg / kg, about 0.5 to about 75 mg / kg, about 0.5 to about 50 mg / kg, about 0.01 to about 50 mg / kg, about 0.05 to about 25 mg / kg, about 0.1 to about 25 mg / kg, about 0.5 to about 25 mg / kg, about 1 to about 20 mg / kg, about 1 to about 10 mg / kg, about 20 mg / kg of body weight, about 10 mg / kg, about 5 mg / kg, about 2.5 mg / kg, about 1.0 mg / kg, or about 0.5 mg / kg of body weight of the therapeutic agent or combination thereof described herein, or any range derivable therein. In some embodiments, the dosage amounts of the therapeutic agent or combination thereof described herein are from about 0.01 mg / kg to about 10 mg / kg of body weight. In some embodiments, the dosage amount of the inhibitors is from about 0.01 mg / kg to about 5 Attorney Docket No.: 206017-0291-00WO mg / kg, or from about 0.01 mg / kg to about 2.5 mg / kg of body weight. The compositions described herein can be administered in a single dose or in the form of individual divided doses, such as from 1 to 4 times per day, or once every 2 days, 3 days, 4 days, 5 days, 6 days, weekly, or monthly. The compositions described herein can also be administered for various treatment cycles, such as 2, 3, 4, 5, 6, 7, 8, 9, 10 treatment cycles. The treatment cycles can be different lengths of time depending on the disease to be treated, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 week treatment cycles. In addition, the effective amount of at least one therapeutic agent can be determined during pre-clinical trials and clinical trials by methods known to physicians and clinicians.

[0234] Alternatively, administration of a specific amount of may be given which is not based upon the weight of the patient such as an amount in the range of 1 pg-100 pg, 1 mg-100 mg, or 1 gm-100 gm. In one embodiment, site specific administration of an effective amount of at least one therapeutic agent is performed through endoscopic ultrasound (EUS) guided delivery. However, the method of delivery is not limited to the above methods, and includes the delivery of at least one or more treatment element to a target site with the aid of a device for example including but not limited to guiding catheter, catheter, endoscope, trocar, introducer, endoscope working channel, endoscope with ultrasound probe, sheath introducer, sleeve, stepper, port, or the like as is known in the art.

[0235] In some embodiments, at least one therapeutic agent of the present invention is formulated according to known methods to prepare pharmaceutically useful compositions, whereby these materials, or their functional derivatives, are combined in admixture with a pharmaceutically acceptable carrier vehicle. Suitable vehicles and their formulation, inclusive of other human proteins, e.g., human serum albumin, are described, for example, in Remington's Pharmaceutical Sciences (16th ed., Osol, A. ed., Mack Easton Pa. (1980)). In order to form a pharmaceutically acceptable composition suitable for effective administration, such compositions will contain an effective amount of the above-described compounds together with a suitable amount of carrier vehicle. Additional pharmaceutical methods may be employed to control the duration of action. Controlled release preparations may be achieved through the use of polymers to complex or absorb the compounds. Another possible method to control the duration of action by Attorney Docket No.: 206017-0291-00WO controlled release preparations is to incorporate the compounds of the present invention into particles of a polymeric material such as polyesters, polyamino acids, hydrogels, poly(lacticacid) or ethylene vinylacetate copolymers. Alternatively, instead of incorporating these agents into polymeric particles, it is possible to entrap these materials in microcapsules prepared, for example, by interfacial polymerization, for example, hydroxymethylcellulose or gelatin-microcapsules and poly(methylmethacylate)- microcapsules, respectively, or in colloidal drug delivery systems, for example, liposomes, albumin microspheres, microemulsions, nanoparticles, and nanocapsules or in macroemulsions. Exemplary microparticles and nanoparticles that can be used to encapsulate a therapeutic agent of the invention include, but are not limited to, microparticles and nanoparticles as described in US Patent Publication No.: US 2017 / 0273909A1, the contents of which are incorporated herein in their entirety.

[0236] Therefore, in one embodiment, the therapeutic agent of the invention is encapsulated in a crystalline or semi-crystalline matrix which confers enhanced stability to the agent.

[0237] The microparticles of this invention may have a size range from about 1 to 250 microns diameter, 10 to 200 microns diameter, 10 to 130 microns diameter, or about 10 to 90 microns diameter. The amount of therapeutic agent present in the formulation depends on the desired daily release dosage and thus on the biodegradation rate of the encapsulating matrix. The exact amount of therapeutic agent may be ascertained by bioavailability trials.

[0238] The treatment may be given in a single dose schedule, or as a multiple dose schedule in which a primary course of treatment may be with 1-100 separate doses, followed by other doses given at subsequent time intervals required to maintain and or reinforce the response, for example, at 1-4 months for a second dose, and if needed, a subsequent dose(s) after several months. Examples of suitable treatment schedules include: (i) 0, 1 month and 6 months, (ii) 0, 7 days and 1 month, (iii) 0 and 1 month, (iv) 0 and 6 months, or other schedules sufficient to elicit the desired responses expected to reduce disease symptoms, or reduce severity of disease.

[0239] EXPERIMENTAL EXAMPLES Attorney Docket No.: 206017-0291-00WO

[0240] The invention is further described in detail by reference to the following experimental examples. These examples are provided for purposes of illustration only and are not intended to be limiting unless otherwise specified. Thus, the invention should in no way be construed as being limited to the following examples, but rather, should be construed to encompass any and all variations which become evident as a result of the teaching provided herein.

[0241] Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the present invention and practice the claimed methods. The following working examples therefore, specifically point out certain embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.

[0242] Example 1; Multi-Omics Analysis Identified Drug Repurposing for Chronic Obstructive Pulmonary Disease

[0243] Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified potential therapeutic targets systematically by integrating multiple omics datasets. The present example describes the development of a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety -two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some Attorney Docket No.: 206017-0291-00WO genes identified in the pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, promising therapeutic targets for COPD were identified. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.

[0244] Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death worldwide and sixth in the United States (Vos T, et al., The Lancet, 2020, 396: 1204-1222; Ahmad F B, et al., JAMA, 2021, 325: 1829-1830). It is characterized by increased breathlessness due to obstructed airflow caused by abnormalities of the airways and / or alveoli (Agusti A, et al., Am J Respir Crit Care Med, 2023, 207:819-837). These phenotypes are associated with long-term dysregulated inflammatory response in the airways and the lungs (Barnes P J, Clinics in Chest Medicine, 2014, 35:71-86).

[0245] Drug discovery and development are increasingly costly, requiring approximately $2.6 billion and 6-15 years for a new FDA-approved medication (DiMasi J A, et al., J Health Econ, 2016, 47:20-33; Prasad V, et al., JAMA Intern Med, 2017, 177: 1569). Repurposing approved therapeutic compounds for new indications can significantly reduce costs and time. Examples include thalidomide's transformation from a birth defect-causing drug to an FDA-approved multiple myeloma treatment in 2006 (Paravar T, et al., Int Rev Immunol, 2008, 27:111-135), and Pfizer's repurposing of sildenafil from a cardiovascular drug to Viagra for erectile dysfunction (Ghofrani H A, Osterloh I H, et al., Nat Rev Drug Discov, 2006, 5:689-702).

[0246] Multiple strategies have been utilized to identify drug repurposing opportunities in COPD. One approach is to generate testable drug repurposing hypotheses based on previous knowledge and expert insights. The bisphosphonate alendronate, a drug indicated for the treatment of osteoporosis, has been reported to induce apoptosis in macrophages (Monkkonen J, et al., Phosphorus Sulphur, 1999, 146:321-324). Based on this premise, alendronate inhalation was studied in a mouse model of emphysema to determine its therapeutic utility in COPD. One mouse study demonstrated that alendronate induces apoptosis in alveolar macrophages and inhibits airspace enlargement Attorney Docket No.: 206017-0291-00WO

[0247] (Ueno M, et al., Nature Communications, 2015, 6:7332). In addition, high throughput screening of compound libraries can identify potential lead compounds for targets already linked to COPD. WNT / p-catenin signaling, for example, is known to decrease in patients with COPD. Several compounds that activate WNT / p-catenin signaling in vitro and induce lung repair in a mouse model of emphysema have been identified by high- throughput drug screening (Costa R, et al., Br J Pharmacol, 2021, 178:4026-4041). Last, genomics-based strategies have also identified novel therapeutic targets for COPD. In a recent genome-wide association study (GWAS) with 588,452 subjects, 1,020 signals were identified to be associated with lung function, implicating 559 putative genes. 55 of these genes, including ITGA2, have been targeted by drugs in approved or ongoing clinical trials (Shrine N, et al., Nat Genet, 2023, 55:410-422). These example studies highlight that drug repurposing hypotheses may be helpful in the quest for novel COPD therapies.

[0248] The strategies mentioned above were based on a single source of omics data. A multiple omics integration strategy to help identify novel therapeutic targets for COPD was proposed. The present example is the first to integrate genome, transcriptome, proteome, and metabolome data from COPD patients’ lungs and bronchoalveolar lavage fluid (BALF). In addition, a distance-based computational model was developed to prioritize potential candidate genes by incorporating interactome and drug-target data. This systems biology approach identifies COPD-associated genes through holistic molecular profiling while evaluating the feasibility of these genes as potential therapeutic targets for COPD.

[0249] The results are described herein.

[0250] Differentially Expressed Genes across COPD Stages DEG analyses were performed on the transcriptome data from healthy control subjects and compared against data from ex-smoker patients with mild (GOLD 1), moderate (GOLD 2), severe (GOLD 3), and very severe (GOLD 4) COPD to explore the dysregulated genes across different stages of COPD. The dataset GSE47460 from the Gene Expression Omnibus database was used as the discovery set of 203 COPD patients. Attorney Docket No.: 206017-0291-00WO

[0251] Patient demographic information is recorded in Table 1, which includes 23 GOLD 1, 94 GOLD 2, 32 GOLD 3, and 54 GOLD 4 patients.

[0252] A comparison of the transcriptomic data between the control and early- stage COPD patients identified five dysregulated DEGs in the GOLD 1 group and nine DEGs (three up-regulated and six down-regulated genes) in the GOLD 2 group. In contrast, 83 DEGs (67 up-regulated and 16 down-regulated genes) were identified between the control and GOLD 3 group, and 168 DEGs (125 up-regulated and 43 down- regulated genes) were identified between the control and GOLD 4 group (adjusted P value < 0.05 and fold change > 2 or < 0.5, Table 6).

[0253] Table 1. Basic Demographics of the Subjects in the Discovery Set Grouped by

[0254] GOLD Criteria

[0255] Healthy GOLD 1 GOLD 2 GOLD 3 GOLD 4 P Significant Intergroup

[0256] (0) (1) (2) (3) (4) Value Differences*

[0257] Totals 65 23 94 32 54 NA NA

[0258] (0) > (4)

[0259] Age, yr, mean ± 65.6 ± 70.9 ± 67.8 ± 65.5 ± 57.2 ± (1) > (4)

[0260] SD 10.4 8.4 8.9 8.2 8.4 (2) > (4)

[0261] (3) > (4)

[0262] Sex, M / F 36 / 29 17 / 6 57 / 37 19 / 13 24 / 30 0.15 No intergroup difference

[0263] Smoking status,

[0264] Current / Ever / Ne 2 / 63 / 0 0 / 21 / 2 11 / 79 / 4 3 / 28 / 1 0 / 53 / 1 0.0107 No intergroup difference ver

[0265] *A11 significant intergroup differences listed achieved a Bonferroni corrected P value of P < 0.005

[0266] The overlap between the DEGs identified across the different stages of COPD is shown in Figure 1 A. Several genes were consistently up-regulated or down- regulated across COPD stages. These include MMP1, FGG, SPP1, SYT13, HAPLN1, and GRM8, each demonstrating altered expression starting at early COPD stages of GOLD 1 and 2. Some genes were only dysregulated in the later stages of COPD (Figure IB). Attorney Docket No.: 206017-0291-00WO

[0267] Differentially Expressed Genes in GOLD 4 COPP

[0268] Due to the heterogeneity of COPD, subsequent analyses was focused on the most severe COPD subjects, namely, the GOLD 4 patients. In the discovery set, transcriptomic analysis identified 168 DEGs between GOLD 4 patients and healthy control subjects, including 125 up-regulated and 43 down-regulated genes (adjusted P value < 0.05, |log2 fold change] > 1) (Figure 2A). A GO enrichment analysis on DEGs revealed 161 statistically significant over-represented biological processes, highlighting immune response events in GOLD 4 patients compared with healthy control patients (adjusted P value < 0.05). These pathways included leukocyte chemotaxis, granulocyte chemotaxis, and extracellular matrix organization (Figure 2B). Compared with a similar GOLD 4 validation dataset (GSE76925), the 168 DEGs were supported by both gene and pathway-level validation approaches.

[0269] Signature Genes from Genomics, Proteomics, and Metabolomics Analysis Genomics, proteomics, and metabolomics data from COPD patients were included in the pipeline to complement the transcriptomic signatures (Figure 10). In a recent multi-ancestry genome-wide association meta-analysis, 1,020 independent signals were found to be associated with lung functions, implicating 135 genes supported by at least two variant-to-gene mapping evidences (Figure 11). Five COPD lung proteomics studies were analyzed, and 113 proteins were identified as differentially expressed in COPD patients compared with healthy control subjects (Figure 12). The metabolomics data from BALF was used to identify additional lung proteins potentially associated with COPD through the MAGI algorithm. One BALF metabolomics study identifying 25 metabolites associated with COPD was also included in the pipeline (Figure 13). The MAGI algorithm searched its biochemical reaction database and identified 33 unique proteins highly associated with the 25 metabolites (confidence score > 4). The 13 genes corresponding to the 33 proteins were included in the pipeline for integration. 8 genes were identified by more than one omics levels. AGER, TPM1, THBS1, DMTR2, TTN, HPGD, CA3, and MZB1 were identified as signature genes on more than one omics level (Figure 3 and Figure 14). Attorney Docket No.: 206017-0291-00WO

[0270] In summary, 429 signature genes were identified from the re-analysis of multiple omics data, including 135 from the genome, 168 from the transcriptome, 113 from the proteome, and 13 from the metabolome (Figure 3).

[0271] COPP Signature Genes by Interactome Distance Network

[0272] To increase the overlap between the signature genes identified on each omics level, the selection of candidate genes was expanded by including genes directly interacting with these signature genes using the STRING database. 11,759,455 proteinprotein interactions of physical and functional associations were retrieved from the STRING database for 19,344 genes in the database. High confidence (STRING score > 0.7) protein-protein interactions of physical and functional associations were retrieved to minimize the likelihood of computational artifact and random association. 841,069 (7.15%) of the association were with a score greater than 0.7. These interactions were included in the analysis. Subsequently, a distance-based network algorithm was created to rank the candidate genes based on their closeness to signature genes identified at all omics levels. Priority was given to candidate genes that were signature genes from one omics level, and also interact with signature genes / proteins identified from other omics levels.

[0273] The distance network evaluated such relationship by calculating interaction distance between the candidate genes and signature genes from transcriptomics, genetic and protein level (Equation 1). For each gene, the sum of its shortest distance to three sets of signature genes was calculated to represent the overall proximity of a candidate gene to the signature genes identified at different omics levels (Figure 4A and Figure 4B). A total of 6,917 direct neighbors to the signature genes were identified. Among this expanded list of genes, 92 were prioritized as the final set of COPD candidate signature genes. The 92 genes are selected based on two types of interaction distance (Figure 4C). Distance of 0 indicates the specific gene is identified as the signature gene on the corresponding omics level. Distance of 1 indicates the specific gene directly interacts with signature genes from the other omics levels. Thus, the 92 genes represent genes in close proximity to the signature genes identified at different omics levels - all of them were identified on one omics level and were also direct Attorney Docket No.: 206017-0291-00WO neighbors to signature genes on other omics levels. They are composed of genes involved in extracellular matrix structural constituent (ACAN, COL12A1, COL6A3, LAMA2, COL10A1, FBN1, FGG, LAMA4, FGA, COL4A2), collagen binding (COL14A1, COL6A1, ITGA1, MMP13, MMP9, PPIB, SMAD3, SPARCL1), protease binding (ADAMTS4, ITGAV, TF, THBS1, TIMP1, TTN), actin-binding proteins (ACTN1, MYH10, MYH9, P4HB, TLN1, TPM1, TPM2, TPM4), and genes with other functions (Figure 4D and Figure 15).

[0274] ACAN is one of the prioritized potential drug targets identified by the pipeline. This gene was not only genetically associated with COPD but was also directly linked to COPD signature genes identified on transcriptomics and proteomics levels.

[0275] Drug Repurposing and in silico Validation

[0276] 70 of the 92 COPD signature genes were considered druggable targets based on the definition from the druggable genome study (Figure 5) (Finan C, et al., Science Translational Medicine, 2017, 9:eaagl 166). These druggable genes are targets of existing drugs, genes with protein structures or sequences similar to these targets, or genes in well-studied drug target families, such as the kinase family of enzymes. Among these 70 druggable genes, 32 demonstrated Tier 1 druggability, indicating they were targets of approved drugs or drug candidates in clinical trials. Therefore, they could be considered prioritized targets for potential COPD treatment. By extracting drug-gene target pair information from the DrugBank database, 393 drugs or biomedical entities targeting the 32 druggable genes in Tier 1 (Figure 16) were identified, including spermine, andrographolide, and several drugs currently prescribed for the management of COPD symptoms. Interestingly, of the 32 drugs reported in drugbank for the treatment of COPD, 21 were identified by the pipeline.

[0277] In addition, an in silico evaluation of the potential effect of the genetic modifications of the 92 candidate genes with the CMap database as an alternative validation step was performed. This database provides transcriptomic profiles of human cell lines that underwent knockout or over-expression experiments for 3,848 unique genes, covering 41 of the 92 COPD signature candidate genes. Through the CMap Attorney Docket No.: 206017-0291-00WO analysis, knockout or over-expression of six COPD candidate genes were found to result in transcriptomic profiles positively or negatively associated with COPD (Figure 5B). The over-expression of SPP1, APOA1 genes and the knockout of the CTSD, TIMP1, RXFP1 gene were positively connected with COPD transcriptomics (connectivity scores > 90%), indicating a transcriptomic pattern similar to COPD. Conversely, the overexpression of SMAD3 genes was observed to be negatively connected with the COPD transcriptomics profile (connectivity scores < -90%), indicating a reversed transcriptomic profile compared with COPD. Therefore, without being bound by a particular theory, it is hypothesized that the inhibitors or antagonists targeting the SMAD3 gene may be further evaluated as COPD therapies as they might lead to a reversed transcriptomic change pattern compared with COPD.

[0278] In summary, an in silico validation analysis was used to corroborate the involvement of SPP1, APOA1, CTSD, TIMP1, RXFP1 and SMAD3 as causal or upstream genes that may drive the cell transcriptomics to a status similar to or contrasting with COPD.

[0279] The present example is the first study that evaluates both the disease association and the target development feasibility of COPD candidate genes through the systematic integration of four types of omics data. There have been other COPD omics studies, but they primarily relied on one or two types of omics data and aimed to explain the pathogenesis or subtypes of COPD (Tang Y, et al., J Breath Res, 2021, 15:044001; Gillenwater L A, et al., PLOS ONE, 2021, 16:e0255337; Liu Z, et al., Mol BioSyst, 2015, 12:191-204; Li C X, et al., European Respiratory Journal, 2018, 51 : 1930-2017). Moreover, the present example is particular in selecting omics data from lung tissue and BALF samples to ensure a more direct representation of COPD-involved organs, as opposed to blood or plasma in previous studies.

[0280] DEGs were first identified across different stages of COPD. The number of DEGs increased as COPD progressed to later stages. Considering disease heterogeneity and the lower detection power of signature genes in the early stages, the current pipeline focused on the DEGs in GOLD 4 patients. The transcriptomic analysis revealed 43 down -regulated and 125 up-regulated genes in GOLD 4 COPD patients. The Attorney Docket No.: 206017-0291-00WO higher count of up-regulated genes aligns with prior findings that smoking leads to chromosome opening and increased active transcription (Kopa P N, et al., Toxicology Mechanisms and Methods, 2018, 28:397-409). The disrupted immune response and altered extracellular composition are the hallmarks of COPD (Bhat T A, et al., Am Thorac Soc, 2015, 12:S169-S175; Annoni R, et al., European Respiratory Journal, 2012, 40: 1362-1373). Related pathways were observed to be over-represented in the functional analysis of these DEGs. The 168 DEGs were validated by both gene set and pathway approaches in comparison with a similar COPD dataset. Although the DEGs exhibited limited overlap in the discovery and validation sets, this does not negate the validity of most identified DEGs, illustrated in a microarray reproducibility study (Zhang M, et al., Bioinformatics, 2008, 24:2057-2063).

[0281] Genomics, proteomics, and metabolomics data were also included in the pipeline. Genomics data can provide signatures from the genome-wide level over a vast population. Proteomics and metabolomics data, although with a lower throughput, can provide signatures related to the downstream functional molecules closely related to the disease status. However, minimum overlap between the signatures from these omics levels was observed. This was also observed in another study of genomics and transcriptomics integration in COPD (Morrow J D, et al., Scientific Reports, 2017, 7:44232). For example, matrix metalloproteinase 13 was identified as a protein associated with COPD in proteomics data but not in transcriptomic data. Previous studies indicated that protein abundance does not always correlate well with mRNA expression (Wang J, et al., Methods in Molecular Biology, 2016, 1375: 123-136). Therefore, incorporating various omics data is essential for a comprehensive understanding of the biological processes across multiple molecular levels.

[0282] Due to the limited number of overlapping genes identified between different omics technologies, they were integrated with interactome data to expand the candidate gene pool and enable prioritization using distance matrices. The distance-based network model prioritized 92 genes as the final set of COPD candidate genes due to their overall proximity to all omics levels. The distance model was compared with a heat diffusion network model and observed minimal overlap due to their distinct underlying assumptions. The heat diffusion model prioritizes genes with fewer neighbors (Carlin D Attorney Docket No.: 206017-0291-00WO

[0283] E, et al., PLOS Computational Biology, 2017, 13:el005598), which could introduce bias due to incomplete interactome data. Conversely, the present model prioritizes genes connected with many neighbors. Although well-studied genes could be favored in such cases, the supporting protein-protein interactions have a high confidence score to ensure a robust connection to candidate genes.

[0284] The target development feasibility was further evaluated regarding their drug repurposing potential and in silico target perturbation evaluation. Interestingly, 70 of 92 candidate genes were considered druggable, while only 22% of the genes in the human genome are considered druggable (Finan C, et al., Science Translational Medicine, 2017, 9:eaagl l66). Since COPD is a complex disease involving multiple pathways and genes, it is not surprising to find COPD-related genes targeted by drugs that treat other conditions. Fifteen candidate genes were Tier 1 druggable genes, which are the targets of approved drugs or drug candidates in clinical trials. 393 drugs or biomedical entities targeting these 32 genes were identified. One of the examples is spermine, a potent regulator of inflammatory responses (Eisenberg T, et al., Nat Cell Biol, 2009, 11 :1305-1314). In the recent studies, spermine demonstrated a protective role in lung and myogenesis in COPD (Balnis J, et al., Am J Respir Cell Mol Biol, 2022, 66:623-637; Wawrzyniak M, et al., Pharmacol Res Perspect, 2021, 9:e00837). Another example is andrographolide, a natural anti-inflammatory agent. It has been reported to show antioxi dative benefits against cigarette smoke-induced lung injury in a mouse in vivo model (Guan S, et al., Br J Pharmacol, 2013, 168: 1707-1718). Interestingly, the pipeline also identified 21 current drugs used for the treatment of COPD, including Tiotropium, Formoterol, Salmeterol, and others. These examples support the theory that a drug repurposing strategy may offer new treatment opportunities for a complex disease like COPD. Other drugs have not been explored for their role in COPD, but several of them are used for treating immune and nervous system diseases or cardiovascular disorders, suggesting common pathways are impacted in COPD as well (van Gestel A J, et al., J Thorac Dis, 2010, 2:215-222).

[0285] Conventionally, only a handful of candidate genes will be empirically chosen for validation by an in vitro or in vivo experiment. In contrast, an in silico evaluation was performed and six COPD candidate genes were identified that may lead to Attorney Docket No.: 206017-0291-00WO transcriptomic profiles positively or negatively associated with COPD. These genes are likely the upstream genes contributing to COPD patients' dysregulated gene expression profiles. SPP1, the gene encoding secreted phosphoprotein 1 or osteopontin, is one of the in silico validated genes. The over-expression of SPP1 leads to a transcriptomic profile similar to COPD. Moreover, it was identified as one of the few genes dysregulated across COPD stages. SPP1 was also suggested as a potential biomarker for COPD exacerbation and was associated with an increased risk of lung cancer in COPD patients (Lee S J, et al., Clin Respir J, 2014, 8:305-311; Miao T, et al., FEBS Open Bio, 2021, 11 : 1237- 1249). In the phases I and II clinical trials for rheumatoid arthritis, the drug ASK8007, a monoclonal antibody targeting SPP1, was investigated and presented no safety concern (Boumans M J H, et al., Ann Rheum Dis, 2012, 71 : 180-185).

[0286] SMAD3 is another in silico validated genes identified by the pipeline as a potential drug repurposing target. In the pipeline, SMAD3 showed a genetic association with COPD and directly interacted with signature genes identified from transcriptomic and protein level. In the cMap analysis, the over-expression of SMAD3 genes was observed to be negatively connected with the COPD transcriptomics profile. Previously, Smad3 signaling pathway has been shown to be involved emphysema (Gauldie J, et al., Proc Am Thorac Soc, 2006, 3:696-702). One study has found that microRNA 145 targets SMAD3 and negatively regulates proinflammatory cytokine release in COPD (O’Leary L, et al., FEBS Lett, 2016, 590:1324-1334). Another study showed that ligustilide, a novel Smad3 covalent inhibitor, successfully suppressed airway remodeling in COPD mice model (Lu Y J, et al., Phytother Res, 2023, 37:717-730). The above findings indicate the high potential of SPP1 and SMAD3 as drug repurposing targets for COPD and the success of the pipeline in discovering such a target.

[0287] Certain limitations exist within this study. First, some susceptible genes reported by GWAS are based on the location of the variants to the nearby genes. If raw data is available, the eQTL analysis may offer more robust evidence for mapping genetic variants to causal genes (Nica A C, et al., PLoS Genetics, 2010, 6:el000895). Second, protein detection coverage is limited in the published proteomics studies, with a throughput of up to thousands of proteins. This was compensated for by identifying additional proteins linked to COPD-associated metabolites. However, the metabolomics Attorney Docket No.: 206017-0291-00WO data itself is subject to low detection coverage, as well as the more significant challenge of assigning the metabolite structure to the metabolomics feature (Vuckovic D, et al., Chemical Communications, 2018, 54:6728-6749). Third, the cell type specificity was not taken into consideration in the current pipeline. This can be a future direction to investigate when there are more publicly available omics data generated by single cell RNA-Seq or data from specific cell types in lung. In addition, the cell lines selected for the CMap evaluation consist of two lung adenocarcinoma cell lines. Although the two cell lines most representative of the lung tissue from the database have been chosen, future in vitro experiments with non-cancer cell lines will be more relevant to COPD. Lastly, inter-individual variability was not adjusted in the pipeline due to the lack of omics measurement from paired samples. There is one 2023 paper from Zhang et al that measured paired samples for multi -omics integration (Zhang Y H, et al., Am J Respir Cell Mol Biol, 2023, 68:651-663), but such data is currently still very limited. Therefore, at this stage, there was first a focus on integrating the summary level omics data to generate new insights on the COPD population, especially with the increased availability of the individual omics level data across studies.

[0288] This study represents the first systematic approach integrating four types of omics data to explore drug repurposing targets for COPD. Seventy genes were identified by the pipeline as candidate drug repurposing targets based on their overall proximity to COPD signature genes and their druggable properties. The hypothesisgenerating pipeline is supported by rich information from omics data, not limited by prior knowledge; it evaluates both disease relevance and development feasibility of targets and can be readily applied to other diseases. Future directions will include expanding the pipeline to the earlier stage of COPD, capturing network relationships across omics levels with advanced computation models, and in vitro and in vivo validation of the prioritized candidate genes with lung cell lines and tissues.

[0289] The materials and methods are described herein.

[0290] Study Population and Omics Data Attorney Docket No.: 206017-0291-00WO

[0291] Gene expression data were obtained from lung samples of 268 COPD GOLD 1-4 patients and control subjects (Tan J, et al., Am J Respir Crit Care Med, 2016, 194: 1392-1402). Gene expression data from a similar COPD cohort (n = 150) were used as the validation set for COPD transcriptomic signatures (Morrow J D, et al., Scientific Reports, 2017, 7:44232). Genomic data were curated from a recent GWAS meta-analysis of lung functions in 588,452 subjects from 49 cohorts (Shrine N, et al., Nat Genet, 2023, 55:410-422). Proteomic data of lung tissue samples (n = 155) of COPD patients and controls were obtained from five previous studies (Zhang Y H, et al., Am J Respir Cell Mol Biol, 2023, 68:651-663; Ohlmeier S, et al., Am J Physiol Lung Cell Mol Physiol, 2016, 310:Ll 155-L1165; Barrero C A, et al., Am J Respir Crit Care Med, 2013, 188:673-683; Lee E J, et al., Chest, 2009, 135:344-352; Ohlmeier S, et al., J Proteome Res, 2008, 7:5125-5132). Metabolomic data from BALF samples of 115 COPD patients and healthy control subjects were also included (Halper-Stromberg E, et al., Metabolites, 2019, 9: 157). Comprehensive information about each omics dataset used for this analysis is available in the online supplementary data.

[0292] Transcriptomic Analysis

[0293] Differential expression gene (DEG) analyses were performed using the limma package in R (Ritchie M E, et al., Nucleic Acids Research, 2015, 43:e47). Healthy controls were compared with subjects in each COPD stage as defined by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) criteria. Functional enrichment analysis of the DEGs was conducted through the gene ontology (GO) enrichment analysis method with the clusterProfiler package in R (Yu G, et al., Bioinformatics, 2015, 31 :608- 609; Yu G, et al., OMICS, 2012, 16:284-287). The DEGs identified in the discovery set were confirmed in the validation set using hypergeometric and gene set enrichment tests (Sprent P, et al., International Encyclopedia of Statistical Science, 2011, 524-525; Subramanian A, et al., Proc Natl Acad Sci USA, 2005, 102: 15545-15550).

[0294] Metabolomics, Proteomics, and Genomics signatures

[0295] Genomic, proteomic, and metabolomic signatures in COPD patients’ lungs and BALF were identified through a literature search. The metabolome and genome Attorney Docket No.: 206017-0291-00WO integration (MAGI) software was used to identify proteins and reactions linked to the metabolites associated with COPD (Erbilgin O, et al., ACS Chemical Biology, 2019, 14:704-714). Proteins work as enzymes that catalyze the biological reactions to produce metabolites. The assumption is that if a dysregulated metabolite is associated with a certain phenotype or disease, the protein involved in the reaction to generate the specific metabolite are also dysregulated. MAGI searches its biochemical reaction database with sources from MetaCyc and RHEA to identify the proteins involved in the reactions for the metabolites of interest. Protein signatures were mapped to gene names for pipeline integration. Genetic loci and implicated genes were obtained from the original GWAS paper, where SNPs were mapped using a variety approaches including genetic region annotation, linkage disequilibrium, eQTL, variant functional consequences, ancestry analysis or a combination of these approaches. The common genes between gene signatures from each omics level were identified using a Venn diagram.

[0296] Qmics Integration

[0297] The interactome data in the STRING database vl 1 (Szklarczyk D, et al., Nucleic Acids Research, 2019, 47:D607-D613) was included to build the systems biology pipeline for integrative analysis with the previously identified signatures. STRING database provide the physical and functional protein-protein interaction data based on several types of sources, including experimental data, computational prediction, and public literature text mining. Interactions with high confidence level interactions (interaction score >=0.7) were filtered for to be included in the analysis to minimize the likelihood of computational artifact and random association. A distance-based network model was developed to integrate omics and rank candidate genes on their proximity to signature genes on all omics levels (Equation 1). The model was implemented in R software (v4.2.2). See supplementary data for detailed R running environment and code availability. Attorney Docket No.: 206017-0291-00WO

[0298] Equation 1 : Sum distance as overall proximity to all omics signature genes, where i is the candidate gene, and j is the gene to calculate the distance from the network.

[0299] Drug Repurposing and in silico Validation

[0300] The final steps in the pipeline were druggability analysis and in silico validation. The druggability of a candidate gene was assessed based on the definition from the previous druggable genome study (Finan C, et al., Sci Transl Med, 2017, 9:eaagl 166). These genes were categorized into three tiers based on their use in approved therapeutics or ongoing clinical trials, protein similarity to the drug targets of approved therapeutics, and protein classes (e.g., well-studied drug target families). Drugs targeting the druggable genes were retrieved and filtered from the DrugBank database (version 5.1.4). The Connectivity Map (CMap) database was used for in silico validation to evaluate the effect of knockout or overexpressing of candidate genes in human cell lines (Subramanian A, et al., Cell, 2017, 171 : 1437).

[0301] Example 2; Multi-Omic Analysis and Drug Repurposing

[0302] The materials and methods are described herein.

[0303] Transcriptomic data

[0304] COPD lung transcriptomics datasets were obtained for the identification of initial COPD signature genes from publicly available sources. Transcriptomics data generated by microarray and RN A- Sequencing experiments were sought. The search was performed on the Gene Expression Omnibus (GEO) database (www(dot)ncbi.nlm.nih(dot)gov / geo / ) (Barrett T, et al., Nucleic Acids Res, 2012, 41(D1):D991-D995).

[0305] Specifically, the GEO database was queried with the keyword “COPD” and “Homo sapiens” and only GEO Series datasets generated on lung tissues were screened for. Datasets without COPD status and healthy control samples were excluded. Finally, the dataset with the greatest number of human samples across COPD GOLD Attorney Docket No.: 206017-0291-00WO stages was used as the discovery dataset for the identification of initial signature genes in COPD. COPD subjects from GOLD 0 group or without GOLD stage level, demographic or smoking status were excluded from the analysis. One additional dataset with subjects of similar COPD stages (based on the COPD GOLD classification) was included as the validation dataset.

[0306] All datasets were downloaded within R by the GEOquery package (Davis S, et al., Bioinformatics, 2007, 23(14): 1846-1847). The subjects with missing gender, age, or smoking status were excluded. The comparison of gender and smoking status between groups was performed using Fisher’s exact test. The difference in the age between groups was analyzed with one way ANOVA test followed by pairwise t test.

[0307] Differential expression analysis

[0308] For the differential expression analysis, the limma package was used (Ritchie M E, et al., Nucleic Acids Res, 2015, 43(7):e47). It fits the gene expression to a linear model and used empirical Bayes to borrow information between genes in a dynamic way. Age, gender of the subjects and sample batch were used in the linear model as covariates. Then t-statistics was applied to identify the up-regulated and down- regulated probes in GOLD IV patients vs. healthy controls. In order to adjust for the random appearance of significant results during multiple statistics analysis, adjusted p- values were obtained by applying Benjamini and Hochberg multiple comparison adjustment to the raw p-values (Benjamini Y, et al., J R Stat Soc Ser B Methodol, 1995, 57(1 ):289— 300). The probe IDs were mapped to gene symbols with the Agilent chip annotation package “hgug4112a.db” in R (Carlson M, et al., hgug4112a.db, 2016). Finally, COPD signature genes were selected based on both adjusted p-values and fold change of the gene expression levels. The differential gene analysis results were visualized as a volcano plot.

[0309] Functional enrichment

[0310] Over-representation analysis was performed on the DEGs from differential expression analysis. Enriched biological processes in COPD were identified with the pathway annotations in the gene ontology database (http: / / geneontology(dot)org). This Attorney Docket No.: 206017-0291-00WO analysis was carried out by DOSE package in R and visualized with clusterProfiler package (Yu G, et al., Bioinformatics, 2015, 31(4): 608-609; Yu G, et al., OMICS J Integr Biol, 2012, 16(5):284-287).

[0311] Gene set enrichment analysis

[0312] Gene set enrichment analysis (GSEA) was performed to evaluate whether the discovery set DEGs were enriched in the validation COPD cohort. GSEA algorithm compares gene sets to a ranked gene list and calculates enrichment score and p-values by permutation test (Subramanian A, et al., Proc Natl Acad Sci U S A, 2005, 102(43): 15545-15550). First, a ranked gene list was constructed by sorting genes in the validation set by the signed p-values from differential expression analysis (equation 1) in decreasing order. Next, differentially expressed genes (DEGs) in the discovery set were split into two gene sets of up-regulated and down-regulated DEGs. A negative control gene set of genes up-regulated in Alzheimer’s disease was obtained from GSEA Molecular Signatures Database (Blalock E M, et al., Proc Natl Acad Sci U S A, 2004, 101(7):2173— 2178; Liberzon A, et al., Bioinformatics, 2011, 27(12): 1739-1740). The selection of the background genome may affect the performance of GSEA by sample source bias (Simillion C, et al., BMC Bioinformatics, 2017, 18(1): 151). Therefore, the genes that were detected in both discovery and validation sets were selected as background genome. The ranked gene list and gene sets were updated to only include genes from the background genome. Finally, the GSEA analysis and visualization were carried out by the fgsea package in R (Korotkevich G, et al., bioRxiv, 2019).

[0313] Ranking metrics = siiqn( / oig2f°ld_change) x — Zo^10(p_value) Equation 2. Determination of ranking metrics

[0314] Over-representation test of the differentially expressed genes The over-representation test was performed to assess whether the differentially expressed genes (DEGs) identified in the discovery set were associated with the DEGs identified in the validation set. In principle, the ratio of the overlapping DEGs is compared to the random chance that such overlap may happen to another gene set in Attorney Docket No.: 206017-0291-00WO the genome (DEGs in the validation set in this case) with hypergeometric distribution by fisher exact test (Sprent P, et al., Int Encycl Stat Sci, 2011, 524-525). In order to properly run the over-representation test, a uniform background genome was first defined to address the inconsistent numbers of probe genes in the microarray platforms in the discovery and validation set. The set of genes screened by both microarray platforms was used as the background genome. Next, DEGs in GOLD IV compared to healthy controls in the validation set with the same age-and-gender-adjusted linear model as in the discovery set were identified. Then, both discovery and validation DEGs were updated by only including the ones in the previously defined background genome. Finally, the Fisher exact test was performed on the 2 x 2 contingency table with the number of overlapping DEGs, the number of remaining non-overlapping DEGs in the discovery set, the number of DEGs in the validation datasets, the number of genes not identified as validation DEGs in the genome (Table 2).

[0315] Table 2. Contingency table for over-representation test

[0316] DEGs in validation set. G: Common genes screened by both discovery and validation set

[0317] GWAS data

[0318] GWAS related to COPD was identified through PubMed search with the keyword “COPD AND (‘genome-wide association study’)”. The abstracts and material sections were reviewed to filter out unrelated publications. In addition, publications mentioned in the COPD GWAS meta-analysis were reviewed to complement the GWAS literature that may be missed during the PubMed search. Information on the subject Attorney Docket No.: 206017-0291-00WO disease status, genetic loci associated with COPD, and susceptible genes were obtained from GWAS literature.

[0319] Proteomics data

[0320] A list of differentially expressed proteins in lung tissues of COPD patients was obtained from COPD proteomics literature. The literature search was done by searching the keyword “(COPD lung) AND (proteome OR proteomics)” on the PubMed website (pubmed.ncbi.nlm.nih.gov). Then the abstracts and the material sections of the papers in the search results were checked for the information on the disease status of subjects, the tissue source, and the experimental approaches on sample analysis. Finally, a list of differentially expressed proteins was compiled based on the results of the papers containing proteomics analysis on lung tissues from COPD patients. The protein names were matched to corresponding gene symbols by searching on the UniProt website (www(dot)uniprot(dot)com) for the purpose of multi-omics integration.

[0321] Metabolomics data

[0322] Metabolomics study related to COPD was identified through PubMed search with the keyword “(COPD lung) AND (metabolomics OR metabolome)”. Information on the subject disease status, sample source, and experimental approaches were obtained from the abstracts and the material sections of the papers in the search results. Metabolomics studies based on bronchoalveolar lavage fluid from COPD patients were included as the source for the identification of metabolites associated with COPD. Metabolomics class information was obtained from the Metabolomics Workbench (www(dot)metab ol omi csworkb ench(dot)org) .

[0323] Integration of metabolomics data with MAGI

[0324] The MAGI (metabolome and genome integration) software was used to identify proteins and reactions linked to the metabolites associated with COPD (Erbilgin O, et al., ACS Chem Biol, 2019, 14(4):704 -714). The metabolites compound names were mapped to the IUPAC International Chemical Identifier (InChi) keys as MAGI inputs (Heller S R, et al., J Cheminformatics, 2015, 7(1):23). MAGI score of greater than 4 was Attorney Docket No.: 206017-0291-00WO used as criteria to filter proteins with a high confidence level of the metabolites and genes association. Protein names were mapped to gene symbols for the uniform integration with other omics levels.

[0325] Protein-protein interaction data

[0326] The physical and functional protein-protein interaction data was obtained from the STRING database vl 1 (Szklarczyk D, et al., Nucleic Acids Res, 2019, 47(Dl):D607-D613). The interactions were based on several types of sources, including experimental data, computational prediction, and public literature text mining. Interactions with high confidence level interactions (interaction score >=0.7) were filtered for to be included in the analysis.

[0327] Calculation of distances between candidate genes and omics signature genes

[0328] The distances between genes were calculated from the protein-protein interaction network. Figure 4 is an illustration of the distances between a candidate gene and two other genes in the network, as well as the distance of the candidate gene to itself.

[0329] First, three distances matrices were created from the paired protein interaction table retrieved from STRING database: 1) distance between COPD genomics signature genes and all candidate genes in the genome, 2) distance between COPD transcriptomics signature genes and all candidate genes in the genome, and 3) distance from all candidate genes in the genome to COPD signature genes identified from proteomics study or proteins linked to COPD metabolomics. The distance matrices were created with the igraph package and visualized with the pheatmap package in R.

[0330] Next, the shortest distances of an individual candidate gene to three sets of omics signature genes were calculated as dTranscriPtomicsjJGWAS,ancj ^Protein,as spownjnpieexample in Figure 4. For each candidate gene, it had multiple distance values representing distances to all genes in the signature gene set. The shortest distance was used to represent the proximity of the candidate gene to the individual omics signature gene set. Attorney Docket No.: 206017-0291-00WO

[0331] Finally, dTranscnPtomics3JGWAS,an(j ^Proteinweresummed up to a sum distance to represent the overall proximity of a candidate gene to all omics signature genes (Equation 3). A smaller sum distance indicates a closer association to signature genes from all omics levels.

[0332] Equation 3. Determination of sum distance as overall proximity to all omics signature genes

[0333] *i is the candidate gene and j is the gene to calculate distance from in the network

[0334] Heat diffusion network analysis with Cytoscape

[0335] Cytoscape 3.7.0 (Shannon P, et al., Genome Res, 2003, 13(11 ):2498— 2504), a network analysis software, was used for the implementation of the heat diffusion network analysis. First, the whole human interactome was retrieved from the STRING database through the EnrichmentMap app 3.1.0 in Cytoscape. Only interactions with greater than 0.7 STRING interaction scores were loaded into the software. Next, the signature genes identified from genomics, transcriptomics, proteomics, metabolomics levels were imported into Cytoscape as gene node annotation table. Then, heat diffusion network analysis was carried out with the diffusion plugin 1.6.0 in Cytoscape (Carlin D E, et al., PLOS Comput Biol, 2017, 13(10):el005598). The nodes representing signature genes from all omics levels were selected as diffusion network input, and the analysis was run with the default settings.

[0336] The retrieval of drug-gene target information

[0337] The drugs targeting the druggable genes were found on the DrugBank database (version 5.1.4, released 2019-07-02). This database is a well-organized resource that holds both comprehensive drug information and drug-target data (Wishart D S, et al., Nucleic Acids Res, 2018, 46(Dl):D1074-D1082). Specifically, drug-gene pairs were obtained by parsing the xml database files with the XML package in R. Attorney Docket No.: 206017-0291-00WO

[0338] The automated literature search for COPP studies with repurposing drugs The literature search was carried out to find supportive information on drug repurposing opportunities. The PubMed search was performed on batch with an R script and NCBI API. For each drug targeting the candidate genes, the number of literatures found in PubMed search by keyword “COPD + drug” was obtained. This number was used for the preliminarily screening of drug relevance to COPD. A manual literature search was carried out for the follow-up with the drugs that have been involved in COPD studies.

[0339] Connectivity map analysis

[0340] The COPD transcriptomic profiles obtained from the previous differential gene expression analysis were analyzed on the Connectivity Map (CMap) database (Subramanian A, et al., Cell, 2017, 171(6): 1437). First, COPD differentially expressed genes were organized as up-regulated and down-regulated gene lists. Next, the two lists were uploaded to CMap query website (https: / / clue(dot)io / query) as COPD disease signature. The LI 000 gene expression Touchstone dataset, which provides the transcriptomic profiles of genetically modified cell lines, was chosen as the reference in the analysis. From the initial analysis result on disease connectivity, the results from the genetically modified cell lines undergone knockout or overexpression on the 52 candidate genes were filtered out. Specifically, the genes with the connectivity scores greater than 90% or smaller than -90% in the lung cancer cell line A549 and HCC515 were examined. The other seven cell lines in the reference Touchstone dataset were not included in the analysis as the cell lines are not derived from the lung tissue.

[0341] R for data analysis

[0342] All the data analysis, including data cleaning, pre-processing, integration, statistical test, and visualization, was implemented with R version 3.5.1 (2018-07-02). The coding was saved as R scripts for reproducibility (https: / / github(dot)com / fwbioinfo / COPD_repurpose). The code consists four steps: Attorney Docket No.: 206017-0291-00WO signature identification, signature validation, omics integration, and cMAP analysis. The detailed R version information and the running environment are listed below.

[0343] The results are described herein.

[0344] Validation of GOLD IV DEGs

[0345] In order to validate the 168 differentially expressed genes (DEGs) identified in the discovery dataset, this gene list was compared to the DEGs identified in a validation dataset with similar COPD cohort (GSE76925, Table 3). Although different microarray platforms were used in the two datasets, 12,864 common genes were screened by both platforms in the discovery and validation dataset. In the discovery dataset, 139 out of the 168 DEGs belong to the commonly screened genes. In the validation dataset, 528 genes were identified as DEGs in the GOLD IV patients by the same analysis approach in the discovery dataset. There are 34 overlapping DEGs between the discovery and validation sets (Figure 6). Moreover, the fold change directions of most the overlapping DEGs are consistent (Table 5). Next, it was statistically evaluated whether the ratio of 34 / 139 overlapping genes would appear due to random chance by comparing with the ratio of 528 validation DEGs in the 12,864 background genome. The overrepresentation test reported a p-value < IO'13based on the Fisher exact test on the contingency table in (Figure 6), indicating that the DEGs identified in the discovery set was also associated with the DEGs in the validation set.

[0346] Table 3. Sample demographics of discovery and validation transcriptomics sets Attorney Docket No.: 206017-0291-00WO

[0347] In addition, the DEGs was further evaluated with a second approach of gene set enrichment analysis. Both the up-regulated and down-regulated DEGs in the discovery set were found to be significantly enriched in the validation COPD cohort when they were compared to the genes of the validation set ranked by differential expression analysis p-values (Table 4). As shown in the enrichment plot, genes in the validation set were ranked by the signed log-transformed p-values. Hence, genes most significantly up-regulated will be at the top of the ranked list, and genes most significantly down -regulated will be at the bottom of the ranked list (Figure 7). GSEA found that up-regulated genes in the discovery set showed up more frequently at the top of the ranked validation gene list. Similarly, the down-regulated genes in the discovery set located more frequently at the bottom of the ranked validation gene list. This suggested an enrichment of both the up-regulated and down-regulated genes in the validation set. On the other hand, the negative control of genes up-regulated in Alzheimer’s disease was not shown to be enriched in the validation set.

[0348] Table 4. Gene set enrichment analysis result table for DEGs in the discovery set

[0349] Besides the gene level comparison, the similarity of the DEGs between the discovery and the validation dataset on the pathway level was also evaluated. Gene Ontology enrichment analysis on the 528 DEGs from the validation dataset revealed 33 Attorney Docket No.: 206017-0291-00WO same over-represented biological processes in GOLD IV patients, including leukocyte activation (G0:0045321), cellular response to cytokine stimulus (G0:0071345) and positive regulation of cell-cell adhesion (GO: 0022409).

[0350] Overall, compared with a similar COPD transcriptomics dataset, the DEGs were well supported by two different validation approaches. The over-representation test and GSEA are widely used in the bioinformatics fields as approaches for pathway enrichment analysis. Here, these two approaches were employed for the validation of DEGs identified in the discovery set, as one can readily extend the use of the two methods when the DEGs were treated as a pathway or a gene set related to COPD. The same approach has been used by another published study to compare their DEGs to other transcriptomic datasets (Campbell J D, et al., Genome Med, 2012, 4(12):67). However, there are still some limitations to such validation approaches. The percentage of DEGs detected by both discovery and validation set was not high. A similar inconsistency was also observed in another COPD transcriptomic study. Several factors may contribute to this. The first reason was the biological difference among subjects. Even though the analysis was limited to GOLD IV patients to control the disease heterogeneity, the patients may still represent different subgroups of the disease in terms of other pathophysiological status related to COPD. The second reason is the sample size. Despite that this is already the largest COPD transcriptomics dataset with the necessary disease clinical information for re-analysis, thousands of samples may be needed to obtain a more consistent list of DEGs due to the heterogeneous nature of the disease and small variations on the expression level changes. The third reason is the different platforms. The discovery set was based on the Agilent platform and the validation set was based on the Illumina platform, and cross-platform variation was shown to exist for the expression pattern of some genes (Chen J J, et al., BMC Bioinformatics, 2007, 8(1):412). Nevertheless, it has been demonstrated in a microarray reproducibility study that most of the DEGs identified in a microarray study were still likely true positives even the percent of overlapping genes with other studies appeared to be low (Zhang M, et al., Bioinformatics, 2008, 24(18):2057-2063). Therefore, the DEGs in the discovery set were moved forward with for further analysis in the pipeline. Attorney Docket No.: 206017-0291-00WO

[0351] Table 5. DEGs between GOLD IV vs healthy control in discovery and validation sets

[0352] Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO

[0353] Table 6. Differentially expressed genes (DEG) in GOLD I to IV patients vs healthy controls Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO

[0354] Table 7. Sources for omics data Attorney Docket No.: 206017-0291-00WO

[0355] *Include both COPD and healthy control subjects.

[0356] Table 8. Genetic loci and suspected genes associated with COPD Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO

[0357] NPNT rs34712979

[0358] HHIP rsl38641402 GYPA AS1

[0359] ITGA1 rsl551943

[0360] SPATA9 rsl53916 RH0BTB3

[0361] C5orf56 rs7713065

[0362] CYFIP2 rsl0515750 ADAM 19

[0363] ADAM 19 rsl990950

[0364] LST1 rs28986170 MICB MICA

[0365] NCR3 rs2857595 AIF1

[0366] MICB

[0367] AGER rs2070600

[0368] KCNQ5 rsl41651520

[0369] ARMC2 rs2768551

[0370] LOC153910 rs7753012 GPR126

[0371] GPR126 rsl48274477

[0372] C1GALT1 rsl0246303

[0373] ZKSCAN1 rs72615157

[0374] PTCHI rsl6909859

[0375] ASTN2 rs803923

[0376] CDC123 rs7090277

[0377] SVIL rs3847402 KIAA1462

[0378] LRP1 rslll72113

[0379] FGD6 rsll3745635

[0380] CCDC38 rsl2820313 Attorney Docket No.: 206017-0291-00WO

[0381] LINC01467 rsl698268 LINC00911

[0382] MGA rs72724130

[0383] THSD4 rsl0851839

[0384] THSD4 rsl2591467

[0385] SH3GL3 rs66650179

[0386] TEKT5 rsl2149828

[0387] MMP15 rsl2447804

[0388] CFDP1 rs3743609 TMEM170A BCAR1

[0389] EFCAB5 rs62070270 CRYBAI SSH2

[0390] SLC6A4

[0391] CISD3 rsll658500

[0392] LTBP4 rsll3473882

[0393] KCNE2 rs2834440

[0394] AP1S2 rs7050036

[0395] SPAG17 rs200154334 TBX15

[0396] EFEMP1 rsl430193

[0397] SUCLG2 rsl490265

[0398] RSRC1 rsl595029

[0399] LOC340113 rs91731 TARS

[0400] ARL15 rs2441026

[0401] BMP6 rs6924424

[0402] LHX3 rsl0858246

[0403] QS0X2

[0404] DNLZ rsl0870202 INPP5E CARD9

[0405] MYPN rs7095607 Attorney Docket No.: 206017-0291-00WO

[0406] HSD17B12 rs4237643

[0407] PRDM11 rs2863171

[0408] CCDC91 rs2348418 FU35252

[0409] TBX3 rs35506 MED13L

[0410] WWOX rsl079572

[0411] KCNJ2 rs6501431

[0412] CASC20 rs6140050 BMP2

[0413] ENSA rs6681426 MCLl GOLPH3L

[0414] TNS1 rs2571445

[0415] MECOM rsl344555

[0416] TET2 rs34480284

[0417] GSTCD rsl0516526 INTS12 NPNT

[0418] HTR4 rs7715901

[0419] ABLIM3 rs3839234

[0420] GRPEL2

[0421] ZKSCAN3 rs34864796 LINCO1O12

[0422] OR2B2

[0423] HLA-DQB1 rsll4544105

[0424] HLA-DQA2

[0425] APOM

[0426] RNF5

[0427] HLA-DQB1 rsll4229351 HLA-DQA2 (rs9275068)

[0428] LOC389602 rsl2698403 LOC285889

[0429] GLIS3 rs7872188

[0430] ClOorfll rs2637254 Attorney Docket No.: 206017-0291-00WO

[0431] AHNAK rs2509961 R0M1 EML3 MTA2 GANAB

[0432] Cllorf83

[0433] ME3 rsl45729347 PRSS23

[0434] CDON rs567508 RPUSD4

[0435] MSRB3 rsl494502

[0436] RBM19 12:114743533

[0437] TBX3 rsl0850377

[0438] TRIP11 rs7155279 ATXN3

[0439] RIN3 rsll7068593

[0440] ARHGAP27 rs35524223

[0441] ARL17A

[0442] ARL17B

[0443] CRHR1

[0444] FM NL1

[0445] KANSL1

[0446] LRRC37A

[0447] LRRC37A2

[0448] LRRC37A4

[0449] MAPT

[0450] NSF

[0451] NUDT1

[0452] PLEKHM1

[0453] SPPL2C

[0454] WNT3

[0455] TSEN54 rs7218675 CASKIN2 Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO

[0456] Table 9. Proteomics studies on lung tissues from COPD patients Attorney Docket No.: 206017-0291-00WO

[0457] Table 10. Metabolites input for MAGI Attorney Docket No.: 206017-0291-00WO

[0458] *Compounds not in MAGI database were not searched

[0459] Table 11. Number of COPD studies involved with drugs targeting the tier druggable genes

[0460] No. of COPD Papers on All Drugs

[0461] S-Methylcysteine:l, Insulin pork:0, N- Aminoethylmorpholine: 0 Plerixafor:4, Framycetin:O, AMD- 070:0, Mogamulizumab:0, lbalizumab:0 Zinc:108, Thrombin:52, Prothrombin:39, Zinc chloride:3, Attorney Docket No.: 206017-0291-00WO Attorney Docket No.: 206017-0291-00WO

[0462] R version and running environment

[0463] > sessionlnfo()

[0464] R version 3.5.1 (2018-07-02)

[0465] Platform: x86_64-w64-mingw32 / x64 (64-bit)

[0466] Running under: Windows >= 8 x64 (build 9200)

[0467] Matrix products: default locale:

[0468] [1] LC_COLLATE=English_United States.1252

[0469] [2] LC_CTYPE=English_United States.1252

[0470] [3] LC_MONETARY=English_United States.1252

[0471] [4] LC_NUMERIC=C

[0472] [5] LC_TIME=English_United States.1252 attached base packages:

[0473] [1] parallel stats4 grid stats graphics grDevices

[0474] [7] utils datasets methods base other attached packages:

[0001] clusterProfiler_3.10. 1 ggalluvial_0.11.3 [3] limma_3.36.5 annotate_ 1.58.0

[0475] [5] XML_3.98-1.19 hgug4112a.db_3.2.3

[0476] [7] org.Hs.eg.db_3.6.0 AnnotationDbi l .44.0

[0477] [9] IRanges_2.14.12 S4Vectors_0.18.3 Attorney Docket No.: 206017-0291-00WO

[0478]

[0011] Biobase 2.40.0 BiocGenerics 0.26.0

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[0013] tidyr_1.0.2 magrittr_1.5

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[0015] ggplot2_3.3.0 plyr_1.8.4

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[0017] VennDiagram l.6.20 futile.logger_1.4.3

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[0019] data.table_1.11.8 loaded via a namespace (and not attached):

[0483] [1] bitops_1.0-6 enrichplot_1.2.0 bit64_0.9-7

[0484] [4] progress_1.2.0 RColorBrewer_l.l-2 httr_1.4.1

[0485] [7] UpSetR_1.4.0 tools_3.5.1 R6 2.3.0

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[0010] DBI 1.0.0 colorspace_1.4-0 withr_2.1.2

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[0013] pretty units_ 1.0.2 tidy sclcct_ 1.0.0 gridExtra_2.3

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[0016] bit_l.l-14 compiler_3.5.1 formatR_1.5

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[0019] xml2 1.3.2 triebeard 0.3.0 labeling 0.3

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[0022] scales l.0.0 ggridges_0.5.1 stringr_ 1.4.0

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[0025] digest_0.6.18 DOSE 3.8.2 pkgconfig_2.0.2

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[0028] rlang_0.4.5 rstudioapi O.11 RSQLite_2.1.1

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[0031] gridGraphics 0.4-1 farver 1.1.0 jsonlite_1.6

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[0034] BiocParallel_1.14.2 GOSemSim_2.8.0 dplyr_0.8.3

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[0037] RCurl_1.95-4.12 ggplotify_0.0.4 GO.db_3.7.0

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[0040] Matrix l.2-14 Rcpp_1.0.2 munsell_0.5.0

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[0043] viridis_0.5.1 lifecycle_0.2.0 stringi_1.2.4

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[0046] yaml_2.2.0 ggraph_2.0.0 MASS_7.3-50

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[0049] qvalue_2.14.1 blob l.1.1 ggrepel_0.8.0

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[0052] DO.db_2.9 crayon_1.3.4 latticc_0.20-35

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[0055] graphlayouts_0.5.0 cowplot_0.9.4 splines_3.5.1

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[0058] hms_0.5.3 pillar_1.4.4 fgsea_1.8.0

[0503]

[0061] igraph l.2.4.2 reshape2_1.4.3 futile, options l .0.1

[0504]

[0064] fastmatch_ 1.1-0 glue_1.3.0 lambda. r_ 1.2.3

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[0067] urltools_1.7.3 vctrs_0.2.4 tweenr l.0.1

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[0070] gtable_0.2.0 purrr_0.3.4 polyclip_1.10-0

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[0073] assertthat_0.2.0 ggforce_0.3.1 europepmc_0.3

[0508]

[0076] xtable_1.8-3 tidy graph l.1.2 viridisLite_0.3.0 Attorney Docket No.: 206017-0291-00WO

[0509]

[0079] tibble 3.0.1 rvcheck 0.1.3 memoise 1.1.0

[0510]

[0082] ellipsis_0.3.0

[0511] Example 3: Polyamines as a possible treatment for chronic obstructive pulmonary disease

[0512] Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple omics datasets. Histone 3.3 and its hyperacetylated form (AcH3.3) play a crucial role in the development and progression of COPD, suggesting that AcH3.3 could be a therapeutic and biomarker target for COPD. Polyamines have been described to have a positive effect in chronic lung diseases. A systems biology approach was developed that can identify the signature genes underlying the physiopathology of COPD and target them for drug repurposing.

[0513] A bioinformatics pipeline was developed that integrates analysis from genomics, transcriptomics, proteomics, metabolomics, interactomics, and drug repurposing analysis to identify target signature genes for COPD drug repurposing (Figure 4). In Silico gene profile confirmation analysis for these druggable signature genes was also performed (Figure 4). Confirmation analysis was performed on in vitro cell models of COPD, which were established by the inventors. A histone H3.3 C-term CRISPR-tagged reporter cell line and human lung alveolar epithelial cells was used, exposed to cigarette smoke condensate (CSC), and treated with increasing concentrations of polyamines, including spermine, spermidine, and putrescine (Figure 8). The CSC effects and toxicity were evaluated by measuring AcH3.3 hyperacetylation and cell viability, using high-content imaging analysis in a confocal Operetta CLS (PerkinElmer).

[0514] 393 drugs or biomedical entities targeting the 32 druggable genes in Tier 1 were identified, including spermine, andrographolide, and several drugs currently prescribed for the management of COPD symptoms. Interestingly, of the 32 drugs reported in DrugBank for the treatment of COPD, 21 were identified by the pipeline. The studies identified ninety-two genes as COPD signature genes based on their overall proximity to signature genes on all omics levels. Among them, seventy signature genes were determined to be targeted by several drug compounds and have the potential for Attorney Docket No.: 206017-0291-00WO drug repurposing. Polyamines were identified as a compound that could be a possible drug treatment for COPD. The in vitro effect of poly amines was characterized. It was identified that spermine and spermidine at lOmM inhibit the CSC toxicity in the reporter cell line and also prevent the hyperacetylation of histones in the reporter and alveolar epithelial cells tested (Figure 9). Spermine, a potent regulator of inflammatory responses, has demonstrated a protective role in the lung and myogenesis in COPD.

[0515] It was identified that polyamines inhibit the CSC toxicity in a histone H3.3 C-term CRISPR-tagged reporter cell line. The present example highlights that polyamines, in particular spermine and spermidine, could be used for the treatment of COPD. This has been identified through bioinformatic system analysis of drug repurposing and confirmed in vitro in cell model assays of COPD. Further studies will require additional in vitro validation, ADME characterization, and confirmation in animal models of COPD. This work highlights the pharmacological potential of polyamines for further development in the treatment of COPD.

[0516] Example 4; Identified Drug Repurposing Targets for Chronic Obstructive Pulmonary Disease Using a Systems Biology Approach

[0517] Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. COPD is characterized by increasing breathlessness and an accelerated lung function decline. So far, the existing pharmacological treatment for COPD focuses on symptom relief, but none of the available drugs have shown to modify the progressive decline in lung function. Drug repurposing is the application of existing approved therapeutic compounds for new disease indications, which may reduce the cost and time in new drug development. A systems biology pipeline was developed using multi-omics integration to identify putative drug repurposing targets for COPD.

[0518] A computational methodology was implemented to identify putative drug repurposing targets for COPD by integrating transcriptomic, genomic, protein-protein interaction data and drug-target information. Gene expression profiles of 16,272 genes from microarray data of 308 lung samples of COPD patients and healthy subjects obtained from GEO database (GEO47460) was analyzed. Patients were stratified with the most severe COPD (GOLD IV) and 153 COPD signature genes were identified by Attorney Docket No.: 206017-0291-00WO differential gene expression analysis. These genes were further integrated and prioritized based on protein-protein interaction data, genetic association with COPD, and drug-gene target information. Cluster R was used for data analysis and integration.

[0519] A systematic approach was used combining multiple -omics data to find drug repurposing targets for COPD. As a result, 153 COPD signature genes were identified through lung transcriptomics data from 308 COPD patients and healthy subjects. Gene Ontology enrichment analysis revealed 33 biological processes, including leukocyte activation, cellular response to cytokine stimulus, and positive regulation of cell-cell adhesion. These genes were further integrated and prioritized based on proteinprotein interaction data, using protein interactomics data from the STRING database. Additional genetic associations with COPD and drug-gene target information were included in our analysis. 87 genes were found that could be considered “druggable” targets for COPD based on the Drugbank database and druggable genome dataset. Furthermore, 29 out of the 87 are targets for approved drugs or as clinical phase drug candidates.

[0520] 87 putative drug repurposing targets were identified that could prevent COPD progression. These molecules have a high translation for future COPD pharmacotherapy. The molecules identified in this project fulfill the characteristics as targets for drug repurposing for COPD treatment. Additional in vitro and in vivo experiments are done to explore the 29 drug repurposing opportunities identified for COPD.

[0521] Example 5; Developing Integrative multi-omics analysis pipeline to reveal novel drug repurposing targets for COPD

[0522] Chronic obstructive pulmonary disease (COPD) is characterized by an accelerated decline in lung function and is the third leading cause of death worldwide. So far, none of the existing pharmacological treatment for COPD has been shown to modify the long-term decline in lung function successfully. Drug repurposing is the application of existing approved therapeutic drugs for new disease indications. Drug repurposing will offer the advantage of reducing the cost and time compare to new drug development. The Attorney Docket No.: 206017-0291-00WO example herein identifies putative drug repurposing targets for COPD through bioinformatics approaches.

[0523] The gene expression profiles of lung tissues of COPD patients and healthy subjects were obtained from the public Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified by the limma package in R. Functional enrichment was performed based on the Gene Ontology database. Pathway analysis was performed with Ingenuity® Pathway Analysis Software (IP A®) and Cytoscape 3.7. Protein-protein interactions for the differentially expressed genes were extracted from the STRING database. R 3.5.1 was utilized to perform all of the statistical analyses. Repurposing drug candidates for each of the genes in our list were analyzed using the DrugBank database and the Druggable Genome dataset.

[0524] Microarray data of 308 lung samples of COPD patients and healthy subjects (GEO47460) were obtained from the GEO database. Due to the heterogeneity of COPD, analysis was focused on the most severe COPD patients (GOLD4). The expression levels for 16,272 genes were compared between very severe COPD patients and healthy controls. 141 differentially expressed genes were identified between healthy subjects and GOLD4 COPD patients (adjusted p-value < 0.05, and absolute value of log2 fold change > 1). Gene Ontology enrichment analysis revealed 46 statistically significant overrepresented biological processes in GOLD4 patients. The gene list was expanded to a total of 153 genes by integrating protein-protein interaction data for the 141 differentially expressed genes. Additionally, MAPT, CXCR4, and TMEM26 genes in the list have been previously reported to be genetically associated with COPD. Out of the 153 signature genes for COPD, 87 genes can be considered as “druggable” targets, based on the DrugBank database and Druggable Genome dataset. MAPT and CXCR4, specifically, show both drug repurposing potential as well as genetic association with COPD. There are several drugs targeting MAPT, including the proton-pump inhibitor Lansoprazole. One study found that Lansoprazole reduced the frequency of exacerbation in COPD patients. One of the drugs that target CXCR4 is Plerixafor. It is a bone marrow stimulant drug for lymphoma or multiple myeloma. Recently, it completed Phase 1 trials for COPD treatment. Attorney Docket No.: 206017-0291-00WO

[0525] Here, a computational methodology was implemented to identify drug repurposing targets for COPD by integrating transcriptomic, genomic, protein-protein interaction data, and drug-target information. It is contemplated that in silico target screening is improved by integrating metabolomics and proteomics data, meta-analysis on additional lung transcriptomics data, and in vitro validation of potential targets.

[0526] The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety. While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by others skilled in the art without departing from the true spirit and scope of the invention. The appended claims are intended to be construed to include all such embodiments and equivalent variations.

[0527] I l l

Claims

Attorney Docket No.: 206017-0291-00WOCLAIMSWhat is claimed is:

1. A composition for preventing or treating chronic obstructive pulmonary disease in a subject in need thereof comprising a modulator for one or more selected from the group consisting of: AC AN, ACTN1, ADAMTS4, ADRB2, AGER, AN06, AP3B1, AP0A1, ARRB1, ATP 1 Al, BCL2L1, BDKRB1, BRAF, CALCRL, CCL11, CDH1, CEACAM8, CHRM3, CHRNA1, CLU, COL10A1, COL12A1, COL14A1, COL4A2, COL6A1, COL6A3, COLGALT1, CRH, CSK, CTSD, CTTN, CXCR1, CXCR4, CXCR5, FBN1, FGA, FGG, FHL2, FTH1, GNB1, GPIHBP1, GPR84, GPR97, GRM8, HAPLN1, H2BFS, HBB, HSP90B1, ICAM1, IGF1R, IL6, ITGA1, ITGAV, LAMA2, LAMA4, LRP1, LTBP1, MAPK3, MGAM, MMP1, MMP13, MMP3, MMP9, MYBPH, MYH10, MYH9, NMU, NR3C1, P4HB, PLCB3, PLAG2G7, POU2AF1, PPIB, PR0K2, RUNX2, RXFP1, S100A12, SI OOP, SAA1, SDC2, SLC2A5, SMAD3, SPARCL1, SPP1, SYT13, TBX1, TF, TGFB2, THBS1, TIMP1, TLN1, TPM1, TPM2, TPM4, T SPAN 14, TTN, TWIST2, VIM, and WT1.

2. The composition of claim 1, wherein the modulator is selected from the group consisting of a chemical compound, a protein, a peptide, a peptidomimetic, an antibody, a ribozyme, a small molecule chemical compound, a nucleic acid, a vector, an antisense molecule, an siRNA, an shRNA, and a guide RNA.

3. The composition of claim 1, wherein the composition comprises a modulator of one or more selected from the group consisting of: ADRB2, ATP1A1, BCL2L1, BDKRB 1, BRAF, CALCRL, CCL11, CHRM3, CHRNA1, CSK, CTSD, CXCR1, FGA, IGF1R, IL6, ITGAV, MAPK3, MGAM, MMP1, MMP13, MMP9, NR3C1, PPIB, SLC2A5, and VIM4. The composition of claim 3, wherein the modulator is an inhibitor of one or more selected from the group consisting of AP0A1, CTSD, CXCR4, FGA, FGG, ICAM1, IL6, MMP1, MMP13, MMP3, and SPP1.Attorney Docket No.: 206017-0291-00WO5. The composition of claim 1, wherein the modulator is an activator of one or more selected from the group consisting of CTSD, GRM8, HAPLN1, RXFP1, SYT13, and TIMP1.

6. A method of preventing or treating chronic obstructive pulmonary disease in a subject in need thereof comprising administering the composition of claim 1.

7. A composition for preventing or treating chronic obstructive pulmonary disease in a subject in need thereof comprising an inhibitor of Histone 3.3 acetylation.

8. The composition of claim 7, wherein the Histone 3.3 is hyperacetylated.

9. The composition of claim 7, wherein the inhibitor is selected from the group consisting of a chemical compound, a protein, a peptide, a peptidomimetic, an antibody, a ribozyme, a small molecule chemical compound, a nucleic acid, a vector, an antisense molecule, an siRNA, an shRNA, and a guide RNA.

10. The composition of claim 7, wherein the inhibitor is a polyamine.

11. The composition of claim 10, wherein the polyamine is one or more selected from the group consisting of spermine, spermidine, putrescine, and andrographolide.

12. A method of preventing or treating chronic obstructive pulmonary disease in a subject in need thereof comprising administering the composition of claim 7.

13. A method of identifying targets for a disease comprising the steps of: a) Obtaining at least one omic dataset for the disease;Attorney Docket No.: 206017-0291-00WO b) Obtaining at least one set of genes comprising upregulated genes, downregulated genes, or a combination thereof from the at least one omic dataset; c) Generating an interactome comprising the at least one set of genes and a set of candidate genes, wherein the candidate genes comprise every gene in the interactome; d) Calculating at least one distance between a gene from the at least one set of genes and the candidate genes; and e) Identifying the genes from the set of associated genes as targets for the disease when the distance first distance is low, the second distance is low, or a combination thereof.

14. The method of claim 13, comprising at least two omic dataset, at least two set of genes, or a combination thereof.

15. The method of claim 13, wherein the step of obtaining a second set of genes from the at least one additional omics dataset comprises: a) Identifying genes comprising loci associated with the disease from the genomic dataset; b) Identifying upregulated proteins, downregulated proteins, or a combination thereof from the proteomic dataset and further matching the upregulated proteins, downregulated proteins, or a combination thereof with corresponding genes; c) Identifying metabolites associated with the disease from the metabolomics dataset, identifying the proteins that regulate the metabolites, and matching the proteins with corresponding genes; d) Performing a differential expression gene analysis; or e) A combination thereof.

16. The method of claim 13, wherein the calculating the distance comprises:Attorney Docket No.: 206017-0291-00WO a) Providing a score of 0 if the candidate gene is a gene from a gene from the at least one set of genes; b) Providing a score of 1 if the candidate gene is one gene away from the gene from the at least one set of genes; c) Providing a score of 2 if the candidate gene is two gene away from the gene from the at least one set of genes; or d) Providing a score of 3 or more if the candidate gene is 3 or more gene away from the gene from the at least one set of genes.

17. The method of claim 16, further comprising summing the distance.

18. The method of claim 13, further comprising the step of performing an in silico validation.

19. The method of claim 13, further comprising the step of identifying a modulator of the target.

19. The method of claim 19, further comprising treating a subject in need thereof with the modulator.

20. The method of claim 13, wherein the disease is chronic obstructive pulmonary disease (COPD).

21. The method of claim 13, further comprising generating a set of genes from the interactome.

23. The composition of claim 3, wherein the modulator is one or more selected from the group consisting of: Spermine; Amphetamine; Betaxolol; Bethanidine; Isoetarine; Cabergoline; Metoprolol; Amitriptyline; Olanzapine; Atenolol; Norepinephrine; Mirtazapine; Timolol; Phenylpropanolamine; Dipivefrin; Sotalol; Carteolol; Nortriptyline; Propranolol; Labetalol; Bisoprolol; Epinephrine; Trimipramine;Attorney Docket No.: 206017-0291-00WOOrciprenaline; Dobutamine; Pseudoephedrine; Alprenolol; Ritodrine; Terbutaline;Bitolterol; Phenoxybenzamine; Salmeterol; Pindolol; Formoterol; Salbutamol; Ergoloid mesylate; Isoprenaline; Arbutamine; Carvedilol; Desipramine; Propafenone; Acebutolol; Nadolol; Levobunolol; Metipranolol; Arformoterol; Fenoterol; Pirbuterol; Bevantolol; Penbutolol; Ephedra; Mephentermine; Procaterol; Clenbuterol; Bambuterol; Oxprenolol; Putrescine; Spermidine; Celiprolol; Nebivolol; Indacaterol; NCX 950; Asenapine;Droxidopa; Bufuralol; Protokylol; (2S)-l-(9H-Carbazol-4-yloxy)-3- (isopropylamino)propan-2-ol; Bopindolol; Bupranolol; Befunolol; Olodaterol; Vilanterol; Arotinolol; Doxofylline; Racepinephrine; Dihydroergocornine; DL-Methylephedrine;Etafedrine; Tulobuterol; Levosalbutamol; Dihydroergocri stine; Digoxin; Acetyldigitoxin; Hydroflumethiazide; Etacrynic acid; Trichlormethiazide; Deslanoside; Ouabain;Diazoxide; Bretylium; Ciclopirox; Bepridil; Potassium cation; Aluminium; Magnesium cation; Digitoxin; Almitrine; Istaroxime; Rubidium Rb-82; Potassium gluconate;Magnesium gluconate; Magnesium acetate; Potassium acetate; Potassium sulfate;Magnesium levulinate; Magnesium lactate; Aluminium phosphate; Aluminum acetate; 4'- FLUORO-1 J'-BIPHENYL-4-CARBOXYLIC ACID; Isosorbide; Gossypol; Ramipril;Captopril; Zinc; Enalaprilat; Zinc acetate; Zinc chloride; Sorafenib; XL281; RAF -265; N- {3-[(5-chloro-lH-pyrrolo[2,3-b]pyridin-3-yl)carbonyl]-2,4-difluorophenyl}propane-l- sulfonamide; N-{2,4-difluoro-3-[(5-pyridin-3-yl-lH-pyrrolo[2,3-b]pyridin-3- yl)carbonyl]phenyl}ethanesulfonamide; (lE)-5-(l-piperidin-4-yl-3-pyridin-4-yl-lH- pyrazol-4-yl)-2, 3 -dihydro- IH-inden-l -one oxime; Vemurafenib; Regorafenib;Dabrafenib; Encorafenib; Fostamatinib; Olcegepant; Telcagepant; Erenumab; CAT-213;Cevimeline; Tramadol; Succinylcholine; Ziprasidone; Disopyramide; Amitriptyline; Ipratropium; Olanzapine; Metixene; Terfenadine; Clozapine; Trihexyphenidyl;Oxyphencyclimine; Procyclidine; Loxapine; Carbamoylcholine; Promazine;Hyoscyamine; Cyproheptadine; Meperidine; Imipramine; Methscopolamine bromide; Chlorpromazine; Darifenacin; Tridihexethyl; Anisotropine methylbromide; Nortriptyline; Amoxapine; Lamotrigine; Cinnarizine; Atropine; Thiopental; Nicardipine; Paroxetine; Homatropine Methylbromide; Trimipramine; Diphemanil Methyl sulfate; Scopolamine;Benzquinamide; Propiomazine; Cryptenamine; Tropicamide; Brompheniramine; Maprotiline; Glycopyrronium; Bethanechol; Tolterodine; Oxybutynin; Promethazine;Attorney Docket No.: 206017-0291-00WOPilocarpine; Doxepin; Desipramine; Ketamine; Quetiapine; Mivacurium; Diphenidol; Aripiprazole; Chlorprothixene; Pancuronium; Pipecuronium; Methotrimeprazine; Tiotropium; Solifenacin; Isopropamide; Acetylcholine; Arecoline; Mepenzolate; ALKS 27; Pizotifen; Fesoterodine; Methacholine; Hexocyclium; Aclidinium; Umeclidinium; Trimebutine; Dosulepin; Etoperidone; Imidafenacin; Butylscopolamine; Homatropine; Thonzylamine; Revefenacin; Propiverine; Aripiprazole lauroxil; Galantamine; Agmatine; Dasatinib; Staurosporine; TG-100801; Fostamatinib; Insulin Human; Insulin Pork; S- Methylcysteine; lh-Benoximidazole-2-Carboxylic Acid; N-Aminoethylmorpholine; 5- AMINO-6-CYCLOHEXYL-4-HYDROXY-2-ISOBUTYL-HEXANOIC ACID;C YCLOHEXYLMETHYL-2, 3 -DIHYDROXY-5 -METHYL-HEXYL AMIDE; Ketoprofen; Alteplase; Reteplase; Anistreplase; Tenecteplase; Sucralfate; Zinc; Alfimeprase; Ancrod; EP-2104R; Lanoteplase; Thrombin; Prothrombin; Human Thrombin; Thrombin Alfa; Anti-inhibitor coagulant complex; Zinc acetate; Zinc chloride; Sucralfate; Thrombin; Human Thrombin; Thrombin Alfa; Insulin Human; Insulin Lispro; Insulin Glargine; Insulin Pork; Mecasermin; Phosphoaminophosphonic Acid-Adenylate Ester; ATL 1101; XL228; rhIGFBP-3; Linsitinib; Teprotumumab; (4Z)-6-bromo-4-({[4- (pyrrolidin-l-ylmethyl)phenyl]amino}methylidene)isoquinoline-l,3(2H,4H)-dione; 3-[5- (lH-IMIDAZOL-l-YL)-7-METHYL-lH-BENZIMIDAZOL-2-YL]-4-[(PYRIDIN-2- YLMETHYL)AV1INO]PYRIDIN-2( I H)-ONE; Somatrem; Cixutumumab; Brigatinib;Ginseng; YSIL6; VX-702; Atiprimod; CRx-139; Andrographolide; Tapinarof;Siltuximab; Polaprezinc; Foreskin fibroblast (neonatal); Foreskin keratinocyte (neonatal); Binimetinib; Dilmapimod; Antithymocyte immunoglobulin (rabbit); Sulindac; Arsenic trioxide; Purvalanol; 5-iodotubercidin; Seliciclib; Cholecystokinin; Ulixertinib; Acarbose; Miglitol; Voglibose; Marimastat; N-HYDROXY-2(R)-[[(4- METHOXYPHENYL)SULFONYL](3-PICOLYL)AMINO]-3-METHYLBUTAN AMIDE HYDROCHLORIDE; N-[3-(N'-HYDROXYCARBOXAMIDO)-2-(2-METHYLPROPYL)-PROPANOYE]-O- TYROSINE-N-METHYL AMIDE; METHYLAMINO-PHENYLALANYL-LEUCYL- HYDROXAMIC ACID; [[l-[N-HYDROXY-ACETAMIDYL]-3-METHYL-BUTYL]- CARBONYL-LEUCINYL]-ALANINE ETHYL ESTER; N-HYDROXY-2-[4-(4- PHENOXY-BENZENESULFONYL)-TETRAHYDRO-PYRAN-4-YL]-ACETAMIDE;Attorney Docket No.: 206017-0291-00WOMarimastat; 3-Methylpyridine; 2-{4-[4-(4-Chloro-Phenoxy)-Benzenesulfonyl]- Tetrahydro-Pyran-4-Yl}-N-Hydroxy -Acetamide; WAY-151693; Hydroxyaminovaline; 1- Methyloxy-4-Sulfone-Benzene; PYRIMIDINE-4,6-DICARBOXYLIC ACID BIS-(3- METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DIC ARBOXYLIC ACID BIS-(4- FLUORO-3-METHYL-BENZYLAMIDE); PYRIMIDINE-4,6-DIC ARBOXYLIC ACID BIS-[(PYRIDIN-3-YLMETHYL)- AMIDE]; TERT-BUTYL 4-( { [4-(BUT-2-YN- 1 - YL AMINO)PHENYL] SULFONYL }METHYL)-4- [(HYDROXYAMINO)C ARB ONYL]PIPERIDINE- 1 -CARBOXYLATE; 4- { [ 1 - METHYL-2,4-DIOXO-6-(3 -PHENYLPROP- 1 - YN- 1 - YL)- 1,4- DIHYDROQUINAZOLIN-3(2H)-YL]METHYL}BENZOIC ACID; 5-(2- ETHOXYETHYL)-5-[4-(4-FLUOROPHENOXY)PHENOXY]PYRIMIDINE- 2,4,6(1H,3H,5H)-TRIONE; CTS-1027; BENZYL 6-BENZYL-5,7-DIOXO-6,7- DIHYDRO-5H-[l,3]THIAZOLO[3,2-C]PYRIMIDINE-2-CARBOXYLATE;Glutathione; Marimastat; Minocycline; Captopril; Glucosamine; Zinc; 2-Amino-N,3,3- Trimethylbutanamide; 2-{[Formyl(Hydroxy)Amino]Methyl}-4-Methylpentanoic Acid; AE-941; PG-530742; 5-(4-PHENOXYPHENYL)-5-(4-PYRIMIDIN-2-YLPIPERAZIN- 1-YL)PYRIMIDINE-2,4,6(2H,3H)-TRIONE; (2R)-2-AMINO-3,3,3-TRIFLUORO-N- HYDROXY-2- { [(4-PHENOXYPHENYL)SULFONYL]METHYL J PROPAN AMIDE; (3R)-4,4-DIFLUORO-3-[(4-METHOXYPHENYL)SULFONYL]BUTANOIC ACID; Zinc acetate; Zinc chloride; Flunisolide; Diflorasone; Alclometasone; Medrysone; Amcinonide; FluoromethoIone; Megestrol acetate; Beclomethasone dipropionate; Progesterone; Spironolactone; Betamethasone; Desoximetasone; Fluticasone propionate; Fluocinolone acetonide; Ulobetasol; Triamcinolone; Prednisone; Flumethasone; Fludrocortisone; Hydrocortisone; Mometasone; Hydrocortamate; Mifepristone; Clocortolone; Flurandrenolide; Prednisolone; Loteprednol etabonate; Rimexolone; Methylprednisolone; Clobetasol propionate; Fluocinonide; Prednicarbate;Fluoxymesterone; Budesonide; Dexamethasone; Desonide; Cortisone acetate; Paramethasone; Ciclesonide; Hexane- 1,6-Diol; Aldosterone; ORG-34517; Difluprednate; Ulipristal; Fluticasone furoate; Tixocortol; Difluocortolone; Gestrinone; Onapristone; Cortivazol; Clobetasone; Mometasone furoate; Hydrocortisone aceponate; Hydrocortisone acetate; Hydrocortisone butyrate; Hydrocortisone cypionate;Attorney Docket No.: 206017-0291-00WOHydrocortisone phosphate; Hydrocortisone probutate; Hydrocortisone valerate; Segesterone acetate; Proline; 1,4-Dithiothreitol; D-glucose; Invert sugar; Fludeoxyglucose F-18; Artenimol; and Phenethyl Isothiocyanate.

24. The composition of claim 4, wherein the inhibitor is one or more selected from the group consisting of S -Methyl cysteine, Insulin pork, N- Aminoethylmorpholine, Plerixafor, Framycetin, AMD-070, Mogamulizumab, Ibalizumab, Zinc, Thrombin, Prothrombin, Zinc chloride, Tenecteplase, Alteplase, Sucralfate, Reteplase, Anistreplase, Alfimeprase, Ancrod, EP-2104R, Lanoteplase, Human Thrombin, Thrombin alfa, Anti-inhibitor coagulant complex, Zinc acetate, Hyaluronic acid, Natalizumab, Nafamostat, Ginseng, Andrographolide, Dilmapimod, Siltuximab, Polaprezinc, YSIL6, VX-702, Atiprimod, CRx-139, Tapinarof, Foreskin fibroblast, Foreskin keratinocyte, Binimetinib, Marimastat, 3 -Methylpyridine, Endostatin, WAY-151693, Hydroxyaminovaline, 4-methoxybenzenesulfinate, and CTS-1027.

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