Compounds to induce an adaptive state in acute hypoxic cells, methods to identify compounds, and methods to treat hypoxic diseases

Abstract

Methods to identify compounds that accelerate a transition of a cell from an acute hypoxic state to a hypoxic adaptive state include contacting a test compound with the cell in the acute hypoxic state, and detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state and measuring the length of time of the transition; wherein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the compound that accelerates the transition of the cell from an acute hypoxic state to a hypoxic adaptive state. The compounds identified by the methods can be used to treat acute hypoxia and acute ischemia.

Description

COMPOUNDS TO INDUCE AN ADAPTIVE STATE IN ACUTE HYPOXIC CELLS, METHODS TO IDENTIFY COMPOUNDS, AND METHODS TO TREAT HYPOXIC DISEASESCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to US Application No. 63 / 559,109 filed February 28, 2024, the disclosure of which is incorporated by reference herein in its entirety.STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

[0002] This invention was made with government support under HR0011-19-2-0018 awarded by the Defense Advanced Research Projects Agency. The government has certain rights in the invention.BACKGROUND

[0003] Oxygen is indispensable for multicellular organisms, serving crucial roles in aerobic respiration, metabolism, and development. (1,2). However, hypoxia (low oxygen availability) underlies numerous disease states, including ischemic diseases, respiratory diseases, anemia, and non-alcoholic fatty liver disease. (3,4). Despite its pivotal role in disease pathology, pharmacological options to alleviate hypoxic stress are limited.

[0004] Cells are know n to have endogenous mechanisms of hypoxia adaptation, which can be activated to protect against hypoxia-related stress. (5,6). For example, pre-exposure of tissues to hypoxia can protect against subsequent stress from ischemia (lack of oxygen and nutrients). (7- 10). How ever, the process of hypoxia adaptation takes time, during w hich cellular damage may have already occurred. (11-13). Accelerating hypoxia adaptation (i.e., fast-tracking acutely stressed cells tow ard an adapted state) could serve as a therapeutic strategy to alleviate hypoxic stress.

[0005] The cellular response to hypoxia involves diverse and incompletely understood biological pathways. (4,5). The most well-studied of these pathways involve hypoxia-inducible factors (HIFs), transcription factors that are regulated in an oxygen-dependent manner by the prolyl hydroxylase domain enzymes (PHDs). (14, 15). However, the human genome encodes over 200 oxygen-dependent enzymes, each with the potential to regulate various cellular responses to hypoxia. (16). Accumulating evidence highlights the importance of non-HIF mechanisms (e.g., pathways involving KDM5A, KDM6A, and ADO) in sensing and responding to hypoxia. (17-21).

[0006] Innate cellular adaptations to hypoxia remain an unexplored avenue for small molecule therapeutic intervention. Hypoxia adaptation is a multifaceted response and presents challenges that are not tractable by conventional screening strategies. Current reductionistic drug discovery approaches are ideally suited for diseases with known single molecular targets and when small molecule engagement can be assessed from one-dimensional readouts. For example, life / death (L / D) screens are well-suited to identify cytotoxic compounds (as in anti-cancer drug screens) but are ill-suited to test compounds that rescue cells from an acute stressor, e.g., hypoxia. Critically, preventing cell death is not equivalent to inducing adaptation or rescuing cell health, outcomes that cannot be distinguished in L / D screens. Thus, there is a need in the art for compounds and for methods to screen for compounds that can induce cellular adaptations to hypoxia. The disclosure is directed to this, as well as other, important ends.BRIEF SUMMARY

[0007] Provided herein is a method of identifying a compound that accelerates a transition of a cell from an acute hypoxic state to a hypoxic adaptive state comprising: (i) contacting a test compound with the cell in the acute hypoxic state, and (ii) detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state and measuring the length of time of the transition; wherein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the compound that accelerates the transition.

[0008] Provided herein is a method of treating acute hypoxia or acute ischemia in a patient in need thereof comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound (alternatively referred to as a phenopushing compound or AH-to-CH phenopushing compound). In embodiments, the method comprises administering an effective amount of a mTOR inhibitor, a BET inhibitor, or a combination thereof.

[0009] These and other embodiments are described herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIGS. 1A-1F. Characterization of cellular states in response to hypoxia in HepG2 cells. FIG. 1A: Schematic overview of our study to identify and validate compounds that fast- track hypoxia adaptation. FIG. IB: Phenotypic profiling of cellular responses to hypoxia by metabolism-focused biomarkers at different timepoints (6h, Id, 2d and 6d). FIG. 1C: Prediction accuracy for cellular states by kNN. FIG. ID: Nuclear HIF1 a levels in normoxia, hypoxia Id, and hypoxia 6d. Left: mean nuclear HIFla per cell measured by immunofluorescence. Right: representative images of HIFla immunofluorescence. One-way ANOVA followed by Tukey'spost hoc test: ****: P<0.0001. Error bars: ±S.D. FIG. IE: Normalized ATP production rate from oxidative phosphorylation (OXPHOS) and glycolysis in normoxia, hypoxia Id. and hypoxia 6d. Two-way ANOVA followed by Tukey’s post hoc test: OXPHOS, P<0.0001 for N vs Hypoxia Id, N vs Hypoxia 6d, P<0.01 for Hypoxia Id vs Hypoxia 6d; glycolysis, ns for N vs Hypoxia Id, P<0.01 for N vs Hy poxia 6d, P<0.0001 for Hy poxia Id vs Hypoxia 6d. Error bars: ±S.D. FIG. IF: Normalized cell survival under ischemic stress with different pre-treatment conditions. One-way ANOVA followed by Tukey’s post hoc test: ns: P>0.05, ****: P<0.0001. Error bars: ±S.D.

[0011] FIGS. 2A-2D. Identification of compound hits that phenopush cells from acute to chronic hypoxia states. FIG. 2A: Overview of AH-to-CH phenopushing screening and hitcalling framework. N: Normoxia. AH: Acute hypoxia (Id). CH: Chronic hypoxia (6d). D: Distance from the phenotypic profile of a perturbation (a given compound at a given dose) to the centroid of the phenoty pic profiles of CH DMSO controls. 0: Angle of deviation from the phenotypic profile of a perturbation relative to the centroid of the phenotypic profiles of CH DMSO controls. FIG. 2B: Summary of geometric hit calls in the primary screen. Scatterplot and density7map for distance (x-axis) and direction (y-axis) of bioactive compounds. Red solid line and dotted line show cutoffs for distance and angle, respectively. Vehicle controls in N, AH, and CH are shown for reference. Bioactive non-hit compounds (black dots), hits (red dots), and four hit examples (large red dots) are shown. FIG. 2C: Screening funnel for AH-to-CH phenopushing hits. FIG. 2D: Representative images for DMSO under N, AH, or CH compared to hit examples (highlighted in b) in AH (Temsirolimus, Deforolimus, KU-0063794, WYE-354).

[0012] FIGS. 3A-3H. mT0R / PI3K and BETs are targets for AH-to-CH phenopushing. FIG. 3A: Enriched targets (by gene name) from overrepresentation analysis of hit compounds. Bars: hit (red) or non-hit (gray) compound counts per target; Blue squares: adjusted p-values for overrepresentation analysis. FIG. 3B-3D: Hit rates of screened compounds annotated as targeting (FIG. 3B) mTOR, (FIG. 3C) P13Ks and (FIG. 3D) BETs in different potency ranges (represented by IC50). For PI3Ks and BETs, the median IC50 across isoforms was used. FIGS. 3E-3G: (FIG. 3E) Cellular pS6 (at S235 / S236), (FIG. 3F) cellular pAKT (at S473) and (FIG. 3G) Nuclear pPol II (at S2) in normoxia (N), acute hypoxia (AH, Id) and chronic hypoxia (CH, 6d). Quantification of immunofluorescence intensity (well-level average of per-cell mean intensity) and representative images are shown for (FIG. 3E) cellular pS6 (at S235 / S236), (FIG. 3F) cellular pAKT (at S473), or (FIG. 3G) nuclear pPol II (at S2). One-way ANOVA followed by Tukey’s post hoc test: *: PO.05, **: P<0.01, ***: PO.OOI, ****: P<0.0001. Error bars: ±S.D. FIG. 3H: Visualization of the effects of confirmed hits in AH on cellular pS6 (atS235 / S236) (x-axis), cellular pAKT (at S473) (y-axis) and nuclear pPol II (at S2) (z-axis) based on immunofluorescence intensity (normalized to N DMSO controls).

[0013] FIGS. 4A-4D. AH-to-CH phenopushing hits rescue cells from ischemia-like stress. FIG. 4A: Overview of the ischemia rescue assay. FIG. 4B: Scatter plot of hit compound effects on HepG2 survival in ischemia-like stress. Cell survival: normalized to DMSO-treated cells, averaged across all tested well replicates and doses for each drug, and ranked based on their survival rate. Dashed lines: survival of DMSO-treated hypoxia naive cells (bottom) or DMSO- treated cells pre-exposed to hypoxia for 6 days (top). FIG. 4C: Normalized cell survival of each tested dose for the top 25 hits shown in (b). FIG. 4D: Ischemia rescue effect comparison between the most effective tested dose of phenopushing (right) and non-phenopushing, bioactive (left) compounds. Kernel density estimation used to smooth histograms. Dashed lines: mean survival of DMSO-treated cells or (red) 3x DMSO survival.

[0014] FIGS. 5A-5D. AH-to-CH phenopushing hits rescue iPSC-CM’s from acute hypoxic stress. FIG. 5A: Overview of the cardiomyocyte rescue assay. FIG. 5B: Survey of phenopushing hit effects on iPSC-CM beating in hypoxia. Beating patterns of iPSC-CM over the time frame (~20sec) are visualized using principal component analysis. Dot: drug-dose replicate. Colors: (white) 0 beats; (Cluster 1 : light grey) 1 beat per time frame; (Cluster 2: dark grey) > 1 beats per time frame; and (black) DMSO normoxia. Representative beating traces for each cluster are shown. FIG. 5C: Hit compounds in Cluster 2 rescue beating behaviors as well as sarcomere structures of iPSC-CM's in hypoxia. Scatterplot of beating similarity compared to normoxia DMSO (x-axis) and percentage of intact sarcomere structure (y-axis) for all compounds in Cluster 2 shown in (b). Dots: average over all replicates for each dose dose. FIG. 5D: Representative immunofluorescence images of sarcomere structure (sarcomeric a-actinin) and corresponding beating traces for selected hits shown in FIG. 5C.

[0015] FIGS. 6A-6B. Selection of HepG2 for phenotypic profiling of cellular response to hypoxia. To assess similarity between 934 human cell lines (CCLE and 53 human tissues), the top 200 most highly expressed genes were identified for each cell line and each tissue, and the number of genes shared in the “top 200 gene list’ ’ for pairwise cell line-tissue comparisons was determined. FIG. 6A: For each tissue, the top two best-matching cell lines were identified. All 53 tissues are shown in the bar graph. FIG. 6B: For each cell line, the top two best-matching tissues were identified. The top 53 cell lines (ranked by the number of genes shared with its topmatching tissue) are shown in the bar graph.

[0016] FIGS. 7A-7G. Inhibiting mT0R / PI3K or BETs to induce AH-to-CH phenopushing.FIG. 7A: Quantification of mean cellular pAKT (at S473) in normoxia (N), acute hypoxia (AH, Id) and chronic hypoxia (CH, 6d) measured by immunofluorescence intensity. One-way ANOVA followed by Tukey’s post hoc test: *: P<0.05, **: P<0.01. Error bars: ±S.D. FIG. 7B: Counts of phenopushing hits vs. non-hits in screened compounds that are selective for mTOR, PI3Ks, mT0R / PI3K (dual-selective), and BETs. A compound was considered “selective” for chosen target(s) if only these targets had annotated IC50 < IpM. FIGS. 7C-7D: Annotated target activity profiles for phenopushing hits selective for (FIG. 7C) mTOR and / or PI3K and (FIG. 7D) BETs, based on the IC50 (see Methods) for each annotated target. FIGS. 7E-7G: PCA visualization of dose-dependent phenopushing curves of representative (FIG. 7E) mTOR- selective hits, (FIG. 7F) PI3K-selective and PI3K / mT0R-dual selective hits, and (FIG. 7G) BETs-selective hits. Size of dot reflects concentration (by decreasing size: 10, 2. 0.4. 0.08 pM). For ARV-825, the top 2 doses are not shown due to toxicity.

[0017] FIGS. 8A-8J. Examining other hypoxic states and compound treatment conditions by the phenopushing platform. FIGS. 8A-8B: Phenotypic profiling of cellular responses to hypoxia at earlier timepoints (2h, 4h, and 6h) (FIG. 8A) and accuracy of distinguishing cellular states by Knn (FIG. 8B). FIG. 8C: Phenoty pic profiling of cellular responses to hypoxia at later timepoints (lOd and 14d). FIG. 8D: Normalized cell survival under ischemic stress with different pre-treatment conditions (Id. 6d. 10d and 14d). One-way ANOVA followed by Tukey’s post hoc test: ns: P>0.05, ****: P<0.0001. Error bars: ±S.D. FIG. 8E: Venn diagram of top-ranking phenopushing compounds across destination states (H6d, HlOd or H14d). FIGS. 8F-8G: Venn diagram (FIG. 8F) and list of top enriched targets (by gene name) (FIG. 8G) from overrepresentation analysis of top-ranking phenopushing compounds in FIG. 8E. Color bar: adjusted p-values for overrepresentation analysis. FIG. 8H: Comparison of molecular responses at the chronic hypoxic state (6d) under different oxygen levels (5% and 1%) vs. the normoxic state (21% oxygen), based on immunofluorescence intensity (well-level average of per-cell mean intensity). Top left: nuclear HIFla. Top right: cellular pS6 (at S235 / S236).Bottom left: cellular pAKT (at S473). Bottom right: nuclear pPol II (at S2). One-way ANOVA followed by Tukey’s post hoc test: *: P<0.05 ****: P<0.0001. Error bars: ±S.D. FIG. 81: Phenotypic profiling of 6d hypoxia response at different oxygen levels (5% and 1%). FIG. 8J: Hit-calling comparison of selected compounds with different compound treatment conditions.DETAILED DESCRIPTION

[0018] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by a person of ordinary skill in the art. See, e.g., Singleton etal., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al, Molecular Cloning. A Laboratory Manual, Cold Springs Harbor Press (Cold Springs Harbor, NY 1989). Any methods, devices and materials similar or equivalent to those described herein can be used. Definitions are provided to facilitate understanding of certain terms used frequently herein and are not meant to limit the scope of the disclosure.

[0019] “Nor noxia” refers to normal oxygen levels in cells and living organisms.

[0020] "Hypoxia" refers to low levels of oxygen in cells (i.e.. levels of oxygen that are lower than normal levels of oxygen).

[0021] “Acute hypoxia” refers to a sudden and significant decrease in oxygen levels in cells, whereby the normal functions of the cell are disrupted due to insufficient oxygen supply.

[0022] “Chronic hypoxia” refers to a condition where a cell experiences a prolonged lack of oxygen, causing significant disruptions to its normal functions due to the inability to produce sufficient energy through the standard metabolic pathways that rely on oxygen.

[0023] “Hypoxic adaptive state” or “hypoxic adapted cells” refers to cells that have adapted to chronic low oxygen levels, i.e., series of cellular changes that occur in response to long-term low oxygen levels. Cells adapt to chronic hypoxia by modulating protein synthesis, metabolism, and nutrient uptake. Aspects of adaptive chronic hypoxia include a decrease in HIF-1 a protein expression, a shift of the primary energy' source from oxidative phosphorylation to glycolysis, thereby producing less ATP; mitochondrial changes that include reduced oxidative phosphorylation activity and increased production of reactive oxygen species.

[0024] “Cell hypoxic stress marker” refers to an objective, quantifiable characteristic of cellular stress that can be used to distinguish hypoxic adapted cells from acutely stressed cells. Exemplary cellular hypoxic stress markers include oxidative phosphorylation markers. glycolysis markers, ATP production markers, and the like. In embodiments, a cellular hypoxic stress marker is an imaging marker that can be used to distinguish hypoxic adapted cells from acutely stressed cells. In embodiments, a cell hypoxic stress markers is a phenotypic feature of a cell.

[0025] “Imaging marker” refers cellular changes that can be quantified and expressed as image features with commercial tools like Revvity’s high-content image analysis software Harmony or the open-source tool CellProfiler. Imaging markers include lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology, nucleolus markers, endoplasmic reticulum / Golgi markers, actin cytoskeleton, plasma membrane markers, and the like. Forimaging markers, morphological features for each marker are quantified / calculated from high- content images. In embodiments, lipid droplets are analyzed, for example, using the dye BODIPY™ 493 / 503 (4,4-difluoro-l,3,5,7,8-pentamethyl-4-bora-3a,4a-diaza-s-indacene) where an increase in number and intensity of punctate structures and / or a decrease in diffuse staining across the cell body are observed in hypoxic adapted cells compared to acutely stressed cells. In embodiments, lipid peroxidation is analyzed, for example, using the dye Cl 1-BODIPY (Image- iT® Lipid Peroxidation Sensor) where an increase in number and intensity of punctate structures and / or an increase in diffuse staining across the cell body are observed in hypoxic adapted cells compared to acutely stressed cells. In embodiments, mitochondrial structure is analyzed, for example, using MitoTracker dyes, where an increase in perinuclear staining and / or a decreased staining throughout the cell body is observed in hypoxic adapted cells compared to acutely stressed cells. In embodiments, nuclear morphology is analyzed, for example, using the dye Hoechst 33342, where a decrease in staining is observed in hypoxic adapted cells compared to acutely stressed cells. The changes for lipid droplets, lipid peroxidation, mitochondrial structure, and nuclear morphology can be quantified and expressed as image features with commercial tools like Revvity’s high-content image analysis software Harmony or the open-source tool CellProfiler.

[0026] “Oxidative phosphorylation marker” refers to an objective, quantifiable characteristic of oxidative phosphorylation, which is a metabolic process that generates adenosine triphosphate (ATP) in mitochondria. Oxidative phosphorylation markers include, but are not limited to, oxy gen consumption rate and mitochondrial membrane potential. Oxygen consumption rate (OCR) is measured using a Seahorse XF Analyzer where a decrease of OCR compared to acutely stressed cells is observed for adapted cells. Measurement of mitochondrial membrane potential can be measured with tetramethyl rhodamine ethyl ester (TMRE) where a decreased fluorescence intensity is observed in adapted cells compared to acutely stressed cells.

[0027] “Glycolysis marker” refers to an objective, quantifiable characteristic of glycolysis, which refers to a metabolic pathway that converts glucose into pyruvate. Glycolysis markers include, but are not limited to, extracellular acidification rate and glucose uptake. Extracellular Acidification Rate (ECAR) is measured using a Seahorse XF Analyzer where an increase of ECAR compared to acutely stressed cells is observed for adapted cells. Glucose uptake can be measured with 2-deoxy-2-[(7-nitro-2,l ,3-benzoxadiazol-4-yl) amino]-D-glucose (2-NBDG) where intracellular accumulation and increased fluorescence intensity' is observed in adapted cells compared to acutely stressed cells.

[0028] “ATP production marker” refers to an objective, quantifiable characteristic of ATP production, which refers to the process by which cells generate energy in the form of adenosine triphosphate. ATP production markers include, but are not limited to, bioluminescent ATP and ATP production rate. Bioluminescent ATP assay uses the firefly luciferase enzymatic reaction, where decreased luciferase activity' is observed in adapted compared to acutely stressed cell. ATP production rate can be quantified from measurements of OCR and ECAR using a Seahorse XF Analyzer where a decreased ATP production rate is observed in adapted compared to acutely stressed cells.

[0029] An “adapted” state for a given stressor can be identified by pre-exposing cells to mild stress for a prolonged period, which then protects the cells from a subsequent severe stress that is otherwise lethal for non-adapted cells. Thus, for example, in situations in which hypoxia is the stressor, cells can be treated to low oxygen, e.g., 1% O2, for a prolonged period, e.g., 6 days.One of skill understands that timeframes of adaptation and stress severity depend on the stressor to be evaluated and can vary with the cell model employed. Such conditions can be empirically determined.

[0030] The methods of the disclosure typically employ imaging analysis, e.g., high-content imaging, to provide a phenotypic profile characteristic of an adaptive cellular state in which cells tolerate exposure to the stressor such that cell death is prevented or reduced. Such a phenotypic profile is compared to a control phenoty pic profile obtained from normal cells and / or phenotypic profiles of cells acutely exposed to the stressor, e.g., cells exposed for a shorter period of time or more extreme stressor conditions. Phenotypic profiles thereby generated can then be used to identify agents that induce an adaptive cellular state, e.g., without exposure to the stressor, or that accelerate induction into an adaptive cellular state upon exposure to the stressor.

[0031] In embodiments, the stressor is hypoxia. Thus, for example, high-content image analysis can be employed to provide a phenotypic profile specific to cells exposed to chronic hypoxia for a time sufficient to lead to an adaptive state compared to a phenoty pic profile that occur in cells exposed to acute hypoxia and / or normoxic conditions.

[0032] Profiling of cells in an adaptive state

[0033] The methods of the disclosure can be applied to any cells or population of cells, including cell lines, primary cells, organoid cultures, and the like. Such cells may be from any tissue, including, but not limited to, liver, kidney, pancreas, heart, skeletal muscle, embryonic, hematological, or neuronal tissue. In embodiments, cell hypoxic stress markers (e.g., adaptive phenoty pical profiles) in response to a stressor can be determined for cells including, withoutlimitation, hepatocytes, myocytes, including cardiomyocy tes, neurons, astrocytes, glial cells, enterocytes, epithelial cells, pancreatic cells, or endothelial cells. In embodiments, the cells are mammalian cells. In embodiments, the cells are human cells. In embodiments, cells are derived from inducible pluripotent stem cells (iPSCs). In embodiments, cells are cultured using 2- dimensional culture techniques. In alternative, cells are cultured using 3-dimensional culture techniques, e.g., using various scaffolds to allow growth in all directions. In embodiments, a population of cells evaluated in accordance with the methods described herein comprises multiple cell types. For example. In embodiments, cell hypoxic stress markers are assessed for organoids subj ected to a stressor.

[0034] Image analysis

[0035] Profiles for chronic adaptation, i.e., an adaptive state, are ty pically generated by measuring cell hypoxic stress markers (e.g., cellular phenotypic features). In embodiments, the cell hypoxic stress markers are assessed by image analysis. In embodiments, such cell hypoxic stress markers are lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology , nucleolus markers, endoplasmic reticulum / Golgi complex, actin cytoskeleton, plasma membrane, or other structural hallmarks of cell morphology . In embodiments, cell hypoxic stress markers are characterized using cell “painting’ techniques (e.g.. Nature Protocols 1 1 : 1757-1774, 2016) in which specific cellular compartments are simultaneously stained with different fluorescent probes. One or more cell hypoxic stress markers are used to provide a phenoty pical profile. Typically, at least two. three, four, fifty, one hundred, two hundred, or more cell hypoxic stress markers are imaged and processed.

[0036] Various different types of imaging can be employed for image analysis, including, e.g., brightfield microscopy, fluorescence microscopy, or other types. A skilled artisan will appreciate that other methods that capture a large number of cellular features such as transcriptomics. proteomics, phosphor-proteomics, metabolomics, lipidomics or ATAC seq can also be used to provide additional cellular features that supplement image analysis. In embodiments, image analysis employs high-content imaging. In embodiments, cellular changes can be quantified and expressed as image features with commercial tools like Revvity’s high-content image analysis software Harmony or the open-source tool CellProfiler

[0037] High-content imaging generally refers to imaging of cells with automated quantitative analysis of the acquired images such that many images are captured to provide an expanded field of analyzed cells compared to standard imaging. In embodiments, multiple cell hypoxic stress markers are assessed using different fluorescent labels to distinguish the individual markers.Such cell hypoxic stress markers can be labeled directly with a fluorescent label, e.g., a fluorescent dye that binds to a particular molecule; or indirectly labeled, e.g.. using an antibody labeled with a fluorescent moiety, or a secondary reagent labeled with a detectable label that binds to the antibody, either directly or indirectly. One of skill understands that alternative detectable labels can be employed for labeling cell hypoxic stress markers. Such labels include luminescent labels, labels having enzymatic activity, and the like.

[0038] Determining a profile for a chronic adaptive state

[0039] An adaptive state to a stressor can be determined relative to normal control cells and / or cell that are acutely exposed to the stressor for a shorter period of time that is insufficient to induce an adaptive state. Any number of morphological features of control populations of cells, e.g., normal and acutely stressed cell populations, and a population of cells chronically adapted to the stressor, e.g., hypoxia, can be labeled with distinguishable detectable labels, e.g., fluorescent labels, and subject to high-content imaging. The images are then analyzed to determine morphological hallmarks of the adaptive stage relative to normal and / or acutely stressed populations.

[0040] Adaptive-state cells and control populations of cells (acutely exposed and / or normal cells) are each typically distributed into multiple individual compartments, e.g., wells and multiple fields of view are imaged in each compartment. For example, imaging can be performed using any high-content microscopic platform, e.g., the Operetta CLS confocal spinning-disk high-content analysis system (Perkin Elmer). Image data from each field of view can then be extracted, e g., cell segmentation and single-cell feature extraction can be performed using the Harmony™ software (v4.9, Perkin Elmer) or equivalent software for the image system that is employed. Multiple features (e.g., over 500) are then calculated for each cell, including intensity, morphology, and texture features. Phenotypic profiles are calculated using any appropriate statistical methodology (e.g., using the Kolmogorov-Smirnov (KS) statistic as described by Kang, et al., Nat. Biotechnol. 34, 70-77, 2016).

[0041] The determination of a profile for a chronic adaptive state is further detailed below using hypoxia as an example of the stressor. One of skill understands that this procedure can be modified for application to assess a chronic adaptive profile for alternative stressors, such as nutrient deprivation, reactive oxygen species, or stressor such as pH, temperature, gravity or radiation, or others.

[0042] Phenotypic profiling of cells chronically adapted to hypoxia

[0043] Phenotypic profiling of cells exposed to hypoxia can be performed using any cellsource of interest. For example, In embodiments the cells are from a cell line. Alternatively, the cells may be primary cells obtained from a tissue of interest or cells derived from iPSCs. Accordingly, a profile can be determined for cells including, but not limited to, hepatocytes, myocytes, including cardiomyocytes, neurons, astrocytes, glial cells, enterocytes, epithelial cells, pancreatic cells, or endothelial cells, adapted to chronic hypoxia. Hypoxic conditions typically used in the art for mammalian cells are typically 1% oxygen, but may vary, so long as the oxygen level induces an adaptive state upon prolonged exposure.

[0044] One of skill understands that different cells have different sensitivities to oxygen deprivation. Thus, for any given cell type, in order to determine a distinguishable adaptive cell state that occurs over time, cells can be exposed to low oxygen for varying durations to determine the time of exposure that provides an adaptive cell state distinguishable from normoxic and acute hypoxic states. For example, a time course of from hours to days can be employed to determine length of exposure that provides an adaptive state. An adaptive state relative to an acute hypoxic and / or normoxic state can be defined by any number of cell hypoxic stress markers, as described herein. For example, under hypoxic conditions, hypoxia-inducible factor 1-a (HIF-1α) expression is induced; and metabolic adaptations take place in which metabolism moves away from oxidative metabolism to glycolytic metabolism with accompany changes in expression of enzymes involved in these metabolic pathways, reduced ATP production, enhanced glucose consumption, and production of glycolytic biproducts. Thus, parameters that can be evaluated to assess cell state includes RNA and protein expression, ATP production, production of glycolysis metabolic products, as well as structural changes that occur to mitochondria and other structural components of cells. Further, cells subjected to various durations of low oxygen can be also assessed for tolerance to nutrient deprivation, e.g., tolerance to reduced serum and / or growth factor concentrations employed in media, and hypoxia to further evaluate an adaptive state.

[0045] In embodiments, cells can be exposed from 6 hours to 6 days to identify expression and / or structural hallmarks of short-term exposure to low oxygen, e.g., 1% oxygen, and expression and / or structural hallmarks of longer term exposure to low oxygen conditions. In embodiments, cells that are highly sensitive to oxygen levels, such as muscle cells, e.g., cardiomyocytes, can be treated for shorter period of times, such as 2 hours to 24 or 48 hours. Once these states are determined cell hypoxic stress markers that characterize an acute hypoxia cell state and a chronic hypoxia cell state can be determined. One of skill understands that a normoxic cell state is also typically determined, e.g., as a control.

[0046] Profiles characteristic of chronic and acute hypoxic states can then be determined using image analysis, in this example, high-content imaging, and feature extraction and analysis to calculate cell hypoxic stress markers, e.g., using the Kolmogorov-Smirnov (KS) statistic as described by Kang, et al., Nat. Biotechnol. 34, 70-77, 2016.

[0047] Screening of agents to identify compounds that induce a chronically adapted state.

[0048] Adaptive profiles can be used as a basis to screen agents to identify those that induce a chronically adapted state, i.e., that “push” cells into an adaptive state (e.g., hypoxic adaptive state) without long-term exposure to the stressor. In embodiments, libraries are screened to identify' compounds that accelerate transition to the adaptive state. For example, compounds can be evaluated using cell populations that are acutely exposed to a stressor to identify those compounds that can induce a chronically adaptive phenotypic profile in a shorter time frame compared to the time frame required for transition from a normoxic state to a chronically- adapted state in the absence of the compound. The following description is for screening for compounds that accelerate adaptation to a chronic hypoxic state. The skilled artisan will appreciate that this screening assay can be applied to other stressors, such as nutrient deprivation and other environmental stressors.

[0049] A library of compounds can be tested on normoxic and / or acutely hypoxic cells to induce a chronic hypoxic state. In embodiments, the screening method comprise evaluating effects of compounds using multiple concentrations of each compounds. In embodiments, multiple compounds can be assessed together, i.e., incubated with cells in the same compartment and subsequently further analyzed if a bioactive compartment is identified.

[0050] In embodiments, cells are distributed to compartments, initially grown under normoxic conditions to establish the cultures and then subjected to hypoxia, e.g., 1% oxygen, for a duration that has been determined to provide an acute hypoxic cell state. The hypoxic cells are then treated with compounds and additionally cultured under hypoxic conditions, but for a length of time that is shorter than that required to establish a chronic hypoxic cell state. For example, cells treated with compounds may be cultured for 18-24 hours under hypoxic conditions compared to 2-6 days and then evaluated for distance from and direction towards the phenotypic profile for chronic hypoxia adaptation. In embodiments, cells subjected to only normoxic conditions are used in the screen to identify agents that induce a chronically adapted hypoxic state.

[0051] One of skill understands that a phenotypic profile for chronic hypoxia need not be independently determined in each screening assay, but can be determined once, e.g., for a givencell line, and used as the reference for subsequent compound screening. In embodiments, e.g., use of primary cells or organoids for screening, the phenotypic profile for the chronic hypoxic state may need to be determined for each compound screening that employs a new primary cell or organoid culture compared to a previous screen performed with a different primary cell or organoid culture even if it is obtained from the same tissue or source.

[0052] To quantify the impact of each compound on the hypoxia response, cell hypoxic stress markers relative to the hypoxia response cell hypoxic stress markers are characterized. Bioactive compounds can be identified that induce profiles significantly distinct from the acutely stressed state. For these compounds, the distance from, and direction towards the chronically adapted hypoxia state is then determined to identify compounds that “phenopush” cells (i. e. , that induce transition) from the acutely stressed to chronically adapted state.

[0053] “Bioactivity” of an agent that is screened, as used herein, refers to a detectable alteration in a cell hypoxic stress marker. This can be calculated again using numerous different approaches. In embodiments, detecting cell hypoxic stress markers is performed as described, e.g., by Kang et al, 2016, supra. The following is an example of detecting cell hypoxic stress markers. A group of negative control compartments is selected, and its centroid is calculated. For bioactivity calculations of compound perturbations under normoxia or acute hypoxia, solvent-control-treated compartments at the corresponding hypoxia treatment timepoint are employed as the negative control population. Next, Mahalanobis distance of the phenotypic profile of all compartments (both and test) towards the control centroid are calculated. The Mahalanobis distance of test compartments are compared with that of control compartments by t-test, and the test compartments with a significantly larger Mahalanobis distance (p valued O-6) are identified as bioactive compartments. One of skill understands that alternative methodology may be employed. The ability to induce transition to the chronically adaptive state is then calculated.

[0054] Phenopushing calculation.

[0055] Phenopushing is quantified based on distance, and direction towards chronic hypoxia solvent control as the “destination” cell state in phenotypic space. In this illustrative calculation, all calculations are performed using the compartment-level KS-phenotypic profiles. In the following statistical analyses, the term “well” is used interchangeably with “compartment” and “DMSO” is interchangeable with “solvent”, i.e., used to dissolve test compounds. For distancebased phenopushing, the Mahalanobis distance (MDwell_CH) of each well can be calculated as the distance from its phenotypic profile to the centroid of destination cell state(CHcentroid). Fordirection-based phenopushing, acute hypoxia DMSO compartments can be defined as the initial cell state. The chronic hypoxia reference pheno-shift vector can be calculated as: VCH _AH= CHcentroid- AHcentroid. For each well, the cosine similarity between its pheno-shift vector from AHcentroid(Vwell_AH) can be calculated and converted to an angle ( θwell_CH): θwell_CH:::cos- 1( VCH AH • Vwell_AH / || CHcentroid- AHcentroid|| ||Vwell_AH||). For each tested compound at each concentration, MDwell_CHand θwell_CHcan be calculated as an average over the two well replicates. Among all tested bioactive concentrations for the compound, the smallest (i.e., most significant) MDwell_CHand θwell_CHvalues, respectively, are used to define the distance-based and direction-based phenopushing ability of the compound.

[0056] Hit calling for phenopushing screening

[0057] Feature selection for batch bias removal. PCA embeddings based on combining all cell hypoxic stress markers from all batches can be calculated. Among the top 5 cell hypoxic stress markers, the cell hypoxic stress marker dimension that accounted for the most variance can be identified. Feature loading scores for cell hypoxic stress marker-2 can be further checked, for example to identity' ty pes with the strongest batch effects from removal for subsequent analysis. Remaining features are employed for bioactivity calculation and phenopushing hit-calling.

[0058] Hit-calling for large screen. Hit-calling was performed as follows:

[0059] (1) Bioactivity of screening compartments can be calculated using the acute hypoxia solvent-control-treated compartments from the same batch as the negative control.

[0060] (2) For phenopushing calculations, the chronic hypoxia solvent-control-treated treated compartments across all batches can be combined to define the destination cell state; similarly, the acute hypoxia solvent control compartments across all batches can be combined to define the initial cell state. The MDwell_CHand 0well_CHfor all screening and solvent control compartments at all three hypoxia treatment timepoints can be calculated.

[0061] (3) For hit-calling by distance-based phenopushing, the MDwell_CHof each screening plate is first normalized to acute hypoxia solvent-control-treated wells within the same screening plate by Z-score. ZScorewell(MDwellCH - MDSolven _CH) / σSolven _CHwhere MDSolven _HCand σSolven _HCrepresent the mean value and the standard deviation of MDwell_CHof acute hypoxia Solvent-control-treated wells on the same screening plate. For a tested compound, the smallest ZScorewellamong all bioactive (bioactivity p-value <10-6) wells across all tested doses can be selected to represent the distance-based pheno-pushing ability of the compound (ZScorecmpd). In this example, compounds with ZScorecmpd< -3 (ZScorewell< 0 indicate a closer distance towards the chronic hypoxia centroid) can be identified as distance-based pheno-pushing hitcompounds. However, alternative Z Score cut offs can be employed to refine the number of hits.

[0062] (4) For hit-calling by direction-based phenopushing, (θwell_CHthe chronic hypoxia DMSOwells across all batches can first be combined and ranked them from smallest to largest (θwell_CH). For each compound, the smallest θwell_CHamong all bioactive (bioactivity p value <10-6) wells across all tested doses can be selected to represent the direction-based phenopushing ability of the compound (θcmpd _CH).In this example, compounds whose θcmpd _CHfall in the top 10 percentile of θch_CHpopulation (θcmpd _CH<= P90 ( θch_CH) are identified as direction-based phenopushing hit compounds. One of skill understands that alternative percentiles can be used in defining direction-based phenopushing compounds, for example to refine the number of hits.

[0063] One of skill understands that the analyses to identify hits presented herein is applicable to any instance in which a compound is tested for its ability to induce a transition induced by a stressor to a chronic adaptive state. Further, one of skill understands that alternative statistical methodologies can be employed. For example, other methods, such as Euclidian distance or Manhattan distance can be employed to establish phenotypic movement towards or away from a cellular state. Once an active compartment is identified, bioactive agents can be further tested, e.g., at multiple concentrations to validate the findings. In embodiments, alternative cell models for a stressor are used to validate the findings. For example, cardiomyocytes may be employed to further assess “phenopushing” of selected compounds to an adaptive state. In embodiments, compounds may be tested in vivo for the ability to induce an adaptive state in an animal to stressor conditions.

[0064] Methods of Identifying Compounds

[0065] Methods of identifying a hypoxic adaptive state-inducing compound (i. e. , a compound that accelerates a transition of a cell from an acute hypoxic state to a hypoxic adaptive state) are described throughout.

[0066] In embodiments, the method of identifying a compound that accelerates a transition of a cell from an acute hypoxic state to a hypoxic adaptive state comprises (i) contacting a test compound with the cell in the acute hypoxic state, and (ii) detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state and measuring the length of time of the transition; wherein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the compound that accelerates the transition. In embodiments, detecting the transition from the cell in the acute hypoxic state to the cell in the hypoxic adaptive state comprises detecting a cell hypoxic stress marker in the cell in the hypoxic adaptive state. In embodiments, the method further comprisesbefore step (i): culturing a cell in a normoxic state under acute hypoxic conditions to transition the cell in the normoxic state to the cell in the acute hypoxic state. In embodiments, the method further comprises after step (i): further culturing the test compound with the cell in the acute hypoxic state under acute hypoxic conditions.

[0067] In embodiments, the method of identifying a compound that accelerates a transition of cells from an acute hypoxic state to a hypoxic adaptive state comprises: (a) culturing cells in a normoxic state under acute hypoxic conditions to induce an acute hypoxic state in the cells, thereby producing cells in an acute hypoxic state; (b) adding a test compound to the cells in the acute hypoxic state; (c) optionally further continuing to culture the test compound and the cells in the acute hypoxic state under acute hypoxic conditions; (d) detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state by detecting a cell hypoxic stress marker in the cell in the hypoxic adaptive state; and (e) measuring the length of time of the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state; wherein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the test compound that accelerates the transition.

[0068] In embodiments, the length of time of the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state is at least 10% shorter in the presence of the test compound relative to the absence of the test compound. The phrase “at least 10% shorter” can alternatively be referred to as “at least 10% faster,” i.e., the test compound accelerates the transition of the cell from an acute hypoxic state to a hypoxic adaptive state 10% faster than in the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 20% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 30% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 40% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 50% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 60% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 70% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 80% shorter in the presence of the test compound relative to the absence of the test compound. Inembodiments, the length of time of the transition of the cell is at least 90% shorter in the presence of the test compound relative to the absence of the test compound. In embodiments, the length of time of the transition of the cell is at least 100% shorter in the presence of the test compound relative to the absence of the test compound.

[0069] In embodiments, the length of time of the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state is statistically significantly shorter in the presence of the test compound relative to the absence of the test compound. As described herein, the phrase “statistically significantly shorter” can alternatively be referred to as “statistically significantly faster,” i.e., the test compound accelerates the transition of the cell from an acute hypoxic state to a hypoxic adaptive state statistically significantly faster than in the absence of the test compound.

[0070] In embodiments, the cell hypoxic stress marker is an oxidative phosphorylation marker, a glycolysis marker, an ATP production marker, HIF-1α protein expression, an imagining marker, or a combination of two or more thereof. In embodiments, the cell hypoxic stress marker is an oxidative phosphorylation marker. In embodiments, the oxidative phosphorylation marker is oxygen consumption rate or mitochondrial membrane potential. In embodiments, the cell hypoxic stress marker is a glycolysis marker. In embodiments, the glycolysis marker is extracellular acidification rate or glucose uptake. In embodiments, the cell hypoxic stress marker is an ATP production marker. In embodiments, the ATP production marker is ATP amount or ATP production rate. In embodiments, the cell hypoxic stress marker is HIF-1α protein expression. In embodiments, the cell hypoxic stress marker is a decrease in HIF-1α protein expression. In embodiments, the cell hypoxic stress marker is a decrease in HIF- 1α protein expression in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0071] In embodiments, the cell hypoxic stress marker is a imaging marker selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology', nucleolus markers, endoplasmic reticulum / Golgi complex, actin cytoskeleton, plasma membrane, or a combination of two or more thereof. In embodiments, the cell hypoxic stress marker is a imaging marker selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology, or a combination of two or more thereof.

[0072] In embodiments, the imaging marker is lipid droplets. In embodiments, the lipid droplets exhibit an increase in number and intensity of punctuate structures, a decrease in diffusestaining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state. In embodiments, the imaging marker is lipid droplets; and the method comprises detecting an increase in number and intensify of punctuate structures, a decrease in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0073] In embodiments, the imaging marker is lipid peroxidation. In embodiments, the lipid peroxidation exhibits an increase in number and intensify of punctate structures, an increase in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state. In embodiments, the imaging marker is lipid peroxidation; and the method comprises detecting an increase in number and intensify of punctate structures, an increase in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state

[0074] In embodiments, the imaging marker is mitochondrial structure. In embodiments, the mitochondrial structure exhibits an increase in perinuclear staining, a decreased staining throughout the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state. In embodiments, the imaging marker is mitochondrial structure; and the method comprises detecting an increase in perinuclear staining, a decreased staining throughout the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0075] In embodiments, the imaging marker is nuclear morphology. In embodiments, the nuclear morphology exhibits a decrease in staining in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state. In embodiments, the imaging marker is nuclear morphology'; and the method comprises detecting a decrease in staining in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0076] Methods of Treatment

[0077] Provided herein is a method of treating acute hypoxia in a patient in need thereof comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute hypoxia. In embodiments, the hypoxic adaptive state-inducing compound is a mTOR inhibitor or a BET inhibitor.

[0078] Provided herein is a method of treating acute hypoxia in a patient in need thereof comprising administering to the patient an effective amount of a mTOR inhibitor, thereby treating acute hypoxia.

[0079] Provided herein is a method of treating acute hypoxia in a patient in need thereofcomprising administering to the patient an effective amount of a BET inhibitor, thereby treating acute hypoxia.

[0080] Provided herein is a method of treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in a patient in need thereof comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound. In embodiments, the hypoxic adaptive state-inducing compound is a mTOR inhibitor or a BET inhibitor. In embodiments, the method is for treating acute respiratory’ distress syndrome. In embodiments, the method is for treating anemia. In embodiments, the method is for treating asthma. In embodiments, the method is for treating pulmonary embolism. In embodiments, the method is for treating pneumothorax. In embodiments, the method is for treating pulmonary edema. In embodiments, the method is for treating pulmonary' fibrosis. In embodiments, the method is for treating bronchitis. In embodiments, the method is for treating chronic obstructive pulmonary disease. In embodiments, the method is for treating emphysema. In embodiments, the method is for treating pneumonia. In embodiments, the method is for treating congestive heart failure. In embodiments, the method is for treating sleep apnea. In embodiments, the method is for treating interstitial lung disease. In embodiments, the method is for treating cyanide poisoning. In embodiments, the acute respiratory distress syndrome, anemia, asthma, pulmonary' embolism, pneumothorax, pulmonaiy edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused by hypoxia. In embodiments, the acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary' disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused by acute hypoxia.

[0081] Provided herein is a method of treating acute respiratory distress syndrome, anemia, asthma, pulmonary' embolism, pneumothorax, pulmonary' edema, pulmonary' fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in a patient in need thereof comprising administering to the patient an effective amount of a mTOR inhibitor. In embodiments, the method is for treating acute respiratory distress syndrome. In embodiments, the method is for treating anemia. In embodiments, the method is for treating asthma. In embodiments, the method is for treating pulmonary embolism. In embodiments, the method isfor treating pneumothorax. In embodiments, the method is for treating pulmonary' edema. In embodiments, the method is for treating pulmonary fibrosis. In embodiments, the method is for treating bronchitis. In embodiments, the method is for treating chronic obstructive pulmonary disease. In embodiments, the method is for treating emphysema. In embodiments, the method is for treating pneumonia. In embodiments, the method is for treating congestive heart failure. In embodiments, the method is for treating sleep apnea. In embodiments, the method is for treating interstitial lung disease. In embodiments, the method is for treating cyanide poisoning. In embodiments, the acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary' edema, pulmonary' fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused by hypoxia. In embodiments, the acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary’ edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused byacute hypoxia.

[0082] Provided herein is a method of treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in a patient in need thereof comprising administering to the patient an effective amount of a BET inhibitor. In embodiments, the method is for treating acute respiratory' distress syndrome. In embodiments, the method is for treating anemia. In embodiments, the method is for treating asthma. In embodiments, the method is for treating pulmonary embolism. In embodiments, the method is for treating pneumothorax. In embodiments, the method is for treating pulmonary' edema. In embodiments, the method is for treating pulmonary' fibrosis. In embodiments, the method is for treating bronchitis. In embodiments, the method is for treating chronic obstructive pulmonary disease. In embodiments, the method is for treating emphysema. In embodiments, the method is for treating pneumonia. In embodiments, the method is for treating congestive heart failure. In embodiments, the method is for treating sleep apnea. In embodiments, the method is for treating interstitial lung disease. In embodiments, the method is for treating cyanide poisoning. In embodiments, the acute respiratory’ distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused by hypoxia. In embodiments, the acute respiratory'distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning are caused by acute hypoxia.

[0083] Provided herein is a method of treating acute ischemia in a patient in need thereof comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute ischemia. In embodiments, the hypoxic adaptive stateinducing compound is a mTOR inhibitor or a BET inhibitor.

[0084] Provided herein is a method of treating acute ischemia in a patient in need thereof comprising administering to the patient an effective amount of a mTOR inhibitor, thereby treating acute ischemia.

[0085] Provided herein is a method of treating acute ischemia in a patient in need thereof comprising administering to the patient an effective amount of a BET inhibitor, thereby treating acute ischemia.

[0086] Provided herein is a method of treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof comprising administering to the patient an effective amount of hypoxic adaptive stateinducing compound, thereby treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof. In embodiments, the hypoxic adaptive state-inducing compound is a mTOR inhibitor or a BET inhibitor. In embodiments, the method is for treating stroke. In embodiments, the method is for treating acute limb ischemia. In embodiments, the method is for treating myocardial ischemia. In embodiments, the method is for treating myocardial infarction. In embodiments, the method is for treating intestinal ischemia. In embodiments, the method is for treating mesenteric ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by hypoxia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute hypoxia.

[0087] Provided herein is a method of treating stroke, acute limb ischemia, my ocardialischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof comprising administering to the patient an effective amount of a mTOR inhibitor. In embodiments, the method is for treating stroke. In embodiments, the method is for treating acute limb ischemia. In embodiments, the method is for treating myocardial ischemia. In embodiments, the method is for treating myocardial infarction. In embodiments, the method is for treating intestinal ischemia. In embodiments, the method is for treating mesenteric ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by hypoxia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute hypoxia.

[0088] Provided herein is a method of treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof comprising administering to the patient an effective amount of a BET inhibitor. In embodiments, the method is for treating stroke. In embodiments, the method is for treating acute limb ischemia. In embodiments, the method is for treating myocardial ischemia. In embodiments, the method is for treating myocardial infarction. In embodiments, the method is for treating intestinal ischemia. In embodiments, the method is for treating mesenteric ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute ischemia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by hypoxia. In embodiments, the stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia are caused by acute hypoxia.

[0089] Provided herein is a method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state ex vivo or in vivo comprising contacting the cell with a hypoxic adaptive state-inducing compound, thereby transitioning the cell in the acute hypoxic state to the cell in the hypoxic adaptive state. In embodiments, the hypoxic adaptive state-inducing compound is a mTOR inhibitor or a BET inhibitor. In embodiments, the method of transitioninga cell in an acute hypoxic state to a cell in a hypoxic adaptive state is ex vivo. In embodiments, the method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state is in vivo.

[0090] Provided herein is a method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state ex vivo or in vivo comprising contacting the cell with a mTOR inhibitor. In embodiments, the method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state is ex vivo. In embodiments, the method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state is in vivo.

[0091] Provided herein is a method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state ex vivo or in vivo comprising contacting the cell with a BET inhibitor. In embodiments, the method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state is ex vivo. In embodiments, the method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state is in vivo.

[0092] The term “mTOR inhibitor” refers to compounds which down-regulate, i.e. reduce, block or even suppress, the activation of the mTOR signaling pathway, by competing, advantageously selectively, with the substrates at the level of mTORCI and / or mT0RC2 or by modifying the active site of these enzy mes which can thus no longer catalyze a given substrate. The term “mTOR inhibitor” includes PI3K / mT0R dual inhibitors and other mTOR dual inhibitors. mTOR inhibitors are known in the art and described herein.

[0093] The methods described herein, including embodiments thereof, comprise administering a mTOR inhibitor. In embodiments, the mTOR inhibitor is sirolimus, everolimus, temsirolimus, ridaforolimus, Torin-1, Torin-2, PP242, rapamycin, AZD8055, niclosamide, omipalisib, KU- 0063794. RMC-5552, WYE-687, ETP -46464, alpelisib, apitolisib, bimiralisib. buparlisib, copanlisib, dactolisib, duvelisib, gedatolisib, idelalisib, umbralisib, voxtalisib, PI-103, VS-5584, PKI-402, BGT226, SF1126, PKI-587, PF-04691502, or a pharmaceutically acceptable salt of any one of the foregoing. In embodiments, the mTOR inhibitor sirolimus. In embodiments, the mTOR inhibitor is everolimus. In embodiments, the mTOR inhibitor is temsirolimus. In embodiments, the mTOR inhibitor is ridaforolimus. In embodiments, the mTOR inhibitor is Torin-1. In embodiments, the mTOR inhibitor is Torin-2. In embodiments, the mTOR inhibitor is PP242. In embodiments, the mTOR inhibitor is rapamy cin. In embodiments, the mTOR inhibitor is AZD8055. In embodiments, the mTOR inhibitor is niclosamide. In embodiments, the mTOR inhibitor is omipalisib. In embodiments, the mTOR inhibitor is KU-0063794. In embodiments, the mTOR inhibitor is RMC-5552. In embodiments, the mTOR inhibitor is WYE-687. In embodiments, the mTOR inhibitor is ETP -46464. In embodiments, the mTOR inhibitor is alpelisib. In embodiments, the mTOR inhibitor is apitolisib. In embodiments, the mTOR inhibitor is bimiralisib. In embodiments, the mTOR inhibitor is buparlisib. In embodiments, the mTOR inhibitor is copanlisib. In embodiments, the mTOR inhibitor is dactolisib. In embodiments, the mTOR inhibitor is duvelisib. In embodiments, the mTOR inhibitor is gedatolisib. In embodiments, the mTOR inhibitor is idelalisib. In embodiments, the mTOR inhibitor is umbralisib. In embodiments, the mTOR inhibitor is voxtalisib. In embodiments, the mTOR inhibitor is PI-103. In embodiments, the mTOR inhibitor is VS-5584. In embodiments, the mTOR inhibitor is PKI-402. In embodiments, the mTOR inhibitor is BGT226. In embodiments, the mTOR inhibitor is SF1126. In embodiments, the mTOR inhibitor is PKI-587. In embodiments, the mTOR inhibitor is PF-04691502. In embodiments, the mTOR inhibitor is a pharmaceutically acceptable salt of any one of the foregoing.

[0094] The term “BET inhibitor” refers to refers to compounds which down-regulate, i.e. reduce, block or even suppress, the bromodomain domains of BET proteins (e.g., BRD2. BRD3. BRD3, BRDT) and / or alter cellular epigenetic, and transcriptional programs. BET inhibitors are known in the art and described herein.

[0095] The methods described herein, including embodiments thereof, comprise administering a BET inhibitor. In embodiments, the BET inhibitor ismivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib, molibresib, GSK2820151, INCB054329, birabresib, PLX51107, R06870810, ZEN003694, or a pharmaceutically acceptable salt of any one of the foregoing. In embodiments, the BET inhibitor is mivebresib. In embodiments, the BET inhibitor is BAY 1238097. In embodiments, the BET inhibitor is amredobresib. In embodiments, the BET inhibitor is BMS-986158. In embodiments, the BET inhibitor is pelabresib. In embodiments, the BET inhibitor is FT-1101. In embodiments, the BET inhibitor is alobresib. In embodiments, the BET inhibitor is molibresib. In embodiments, the BET inhibitor is GSK2820151. In embodiments, the BET inhibitor is 1NCB054329. In embodiments, the BET inhibitor is birabresib. In embodiments, the BET inhibitor is PLX51107. In embodiments, the BET inhibitor is R06870810. In embodiments, the BET inhibitor is ZEN003694. In embodiments, the BET inhibitor is a pharmaceutically acceptable salt of any one of the foregoing.

[0096] The methods described herein, including embodiments thereof, comprise administering a hypoxic adaptive state-inducing compound (HASIC). In embodiments, the HASIC is sirolimus, omipalisib, RMC-5552. alpelisib. apitolisib, copanlisib, duvelisib, idelalisib,umbralisib, PI-103, BGT226, SF1126, PKI-587, mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib. molibresib, GSK2820151. INCB054329, birabresib, PLX51107, R06870810, ZEN003694, (-)-MK 801, (+)-JQl, 7 -methoxy tacnne, 9- aminoacridine, ABT-737, AG-1024, Akt inhibitor VIII, AMG-458, AMG-548, amitriptyline, amonafide, aprepitant, AR-A014418, arctigenin, tivantinib, arteether, artesunate, Aurora A Inhibitor I, axitinib, AZ20. azacitidine, azaguanine-8, azathioprine, AZD2014, AZD2858, AZD8055. bedaquiline, belotecan. benzalkonium chloride, benzethonium chloride, dactolisib, bimiralisib, brigatinib, bromocriptine, broxyquinoline, buparlisib, BYL719, camptothecin, carmofur, CC-115, CC-223, CGP-37157, CHIR-124, chlorquinaldol, clotrimazole, cloxiquine, CO-101244, COTI-2, CP-99994, crizotinib, defactinib, ridaforolimus, dichlorophene, digoxigenin, digoxin, dihydroartemisinin, eltrombopag. entinostat, epetraborole, erastin, eravacy cline, erlotinib, ethacridine, ethidium, ETP-46464, everolimus, 5-fluoracil, fosaprepitant, G1T38, GDC-0084, GDC-0349, GDC-0980, gedatolisib, Go 6983, GSK J4, harmine, hexachlorophene, HTH-01-015, hydroxy camptothecine, I-BET151, 1-CBP-112, ICG-001, ICI- 199441, IMD 0354, indacaterol, sapanisertib, itraconazole, KU-0063794. LDN193189, LDN- 212854, letrozole, liothyronine, LM-22A4, lonafamib, LRRK2-IN-1, LY3023414, M344, malotilate, mechlorethamine, 6-mercaptopurine, miconazole, mirin, MK-2206, ML-297, ML- 348, MRK-560, my cophenolate, mycophenolic acid, NAV-26, NBI-27914, nelarabine, neratinib, nexturastat A, NH125, niclosamide, nifuratel. nitazoxanide, nitrofurantoin, nitroxoline, NNC-05-2090, NU6027, obatoclax. octenidine. oligomycin A. OSI-027. OSI-420. OTX015, oxaliplatin, pentamidine, PF-04691502, PF-05212384, PF-06463922, PF-477736, PHA-767491, PHTPP, pifithrin-cyclic, PIK-93, pirfenidone, piroctone, PKI-402, PLX-4720, PNU-74654, PP-121, PP242, PPT, prexasertib, PRT062607, PSB-1115, PTC -209, rapamycin, resiquimod, resminostat. RG7112. RGFP966, Ro-90-7501, RX-3117, SB415286, SB-328437, SB505124, scriptaid, securinine, serabelisib, silodosin, SN-38, STF-118804, T0901317, tannic acid, TC-G-1004, TCS-2210, telotristat etiprate, temsirolimus, tenovin-1, TG100713, thioguanine, tizoxanide, topotecan, torcetrapib, Torin-1, Torin-2, trichostatin A, triclocarban, tubastatin A, tucidinostat, tyrphostin 9, valnemulin, velneperit, vemurafenib, vilazodone. voxtalisib, VS-5584, WAY-100635, wiskostatin, WYE-125132, WYE-354, WYE-687, XL388, yoda-1, ZSTK474, or a pharmaceutically acceptable salt of any one of the foregoing. In embodiments, the HASIC is sirolimus. In embodiments, the HASIC is omipalisib. In embodiments, the HASIC is RMC-5552. In embodiments, the HASIC is alpelisib. In embodiments, the HASIC is apitolisib. In embodiments, the HASIC is copanlisib. In embodiments, the HASIC is duvelisib. In embodiments, the HASIC is idelalisib. Inembodiments, the HASIC is umbralisib. In embodiments, the HASIC is PI-103. In embodiments, the HASIC is BGT226. In embodiments, the HASIC is SF1126. In embodiments, the HASIC is P KI-587. In embodiments, the HASIC is mivebresib. In embodiments, the HASIC is BAY 1238097. In embodiments, the HASIC is amredobresib. In embodiments, the HASIC is BMS-986158. In embodiments, the HASIC is pelabresib. In embodiments, the HASIC is FT- 1101. In embodiments, the HASIC is alobresib. In embodiments, the HASIC is molibresib. In embodiments, the HASIC is GSK2820151. In embodiments, the HASIC is INCB054329. In embodiments, the HASIC is birabresib. In embodiments, the HASIC is PLX51107. In embodiments, the HASIC is R06870810. In embodiments, the HASIC is ZEN003694. In embodiments, the HASIC is (-)-MK 801. In embodiments, the HASIC is (+)-JQl. In embodiments, the HASIC is 7-methoxytacrine. In embodiments, the HASIC is 9-aminoacridine. In embodiments, the HASIC is ABT-737. In embodiments, the HASIC is AG-1024. In embodiments, the HASIC is Akt inhibitor VIII. In embodiments, the HASIC is AMG-458. In embodiments, the HASIC is AMG-548. In embodiments, the HASIC is amitriptyline. In embodiments, the HASIC is amonafide. In embodiments, the HASIC is aprepitant. In embodiments, the HASIC is AR-A014418. In embodiments, the HASIC is arctigenin. In embodiments, the HASIC is tivantinib. In embodiments, the HASIC is arteether. In embodiments, the HASIC is artesunate. In embodiments, the HASIC is Aurora A Inhibitor I. In embodiments, the HASIC is axitinib. In embodiments, the HASIC is AZ20. In embodiments, the HASIC is azacitidine. In embodiments, the HASIC is azaguanine-8. In embodiments, the HASIC is azathioprine. In embodiments, the HASIC is AZD2014. In embodiments, the HASIC is AZD2858. In embodiments, the HASIC is AZD8055. In embodiments, the HASIC is bedaquiline. In embodiments, the HASIC is belotecan. In embodiments, the HASIC is benzalkonium chloride. In embodiments, the HASIC is benzethonium chloride. In embodiments, the HASIC is dactolisib. In embodiments, the HASIC is bimiralisib. In embodiments, the HASIC is brigatinib. In embodiments, the HASIC is bromocriptine. In embodiments, the HASIC is broxyquinoline. In embodiments, the HASIC is buparlisib. In embodiments, the HASIC is BYL719. In embodiments, the HASIC is camptothecin. In embodiments, the HASIC is carmofur. In embodiments, the HASIC is CC-115. In embodiments, the HASIC is CC-223. In embodiments, the HASIC is CGP-37157. In embodiments, the HASIC is CHIR-124. In embodiments, the HASIC is chlorquinaldol. In embodiments, the HASIC is clotrimazole. In embodiments, the HASIC is cloxiquine. In embodiments, the HASIC is CO-101244. In embodiments, the HASIC is COTI-2. In embodiments, the HASIC is CP-99994. In embodiments, the HASIC is crizotinib. In embodiments, the HASIC is defactinib. Inembodiments, the HASIC is ridaforolimus. In embodiments, the HASIC is dichlorophene. In embodiments, the HASIC is digoxigenin. In embodiments, the HASIC is digoxin. In embodiments, the HASIC is dihydroartemisinin. In embodiments, the HASIC is eltrombopag. In embodiments, the HASIC is entinostat. In embodiments, the HASIC is epetraborole. In embodiments, the HASIC is erastin. In embodiments, the HASIC is eravacy cline. In embodiments, the HASIC is erlotinib. In embodiments, the HASIC is ethacridine. In embodiments, the HASIC is ethidium. In embodiments, the HASIC is ETP-46464. In embodiments, the HASIC is everolimus. In embodiments, the HASIC is 5-fluoracil. In embodiments, the HASIC is fosaprepitant. In embodiments, the HASIC is G1T38. In embodiments, the HASIC is GDC-0084. In embodiments, the HASIC is GDC-0349. In embodiments, the HASIC is GDC-0980. In embodiments, the HASIC is gedatolisib. In embodiments, the HASIC is Go 6983. In embodiments, the HASIC is GSK J4. In embodiments, the HASIC is harmine. In embodiments, the HASIC is hexachlorophene. In embodiments, the HASIC is HTH-01-015. In embodiments, the HASIC is hydroxy camptothecine. In embodiments, the HASIC is I-BET151. In embodiments, the HASIC is I-CBP-112. In embodiments, the HASIC is ICG-001. In embodiments, the HASIC is ICI- 199441. In embodiments, the HASIC is IMD 0354. In embodiments, the HASIC is indacaterol. In embodiments, the HASIC is sapanisertib. In embodiments, the HASIC is itraconazole. In embodiments, the HASIC is KU-0063794. In embodiments, the HASIC is LDN193189. In embodiments, the HASIC is LDN-212854. In embodiments, the HASIC is letrozole. In embodiments, the HASIC is liothyronine. In embodiments, the HASIC is LM-22A4. In embodiments, the HASIC is lonafamib. In embodiments, the HASIC is LRRK2-IN-1. In embodiments, the HASIC is LY3023414. In embodiments, the HASIC is M344. In embodiments, the HASIC is malotilate. In embodiments, the HASIC is mechlorethamine. In embodiments, the HASIC is 6-mercaptopurine. In embodiments, the HASIC is miconazole. In embodiments, the HASIC is mirin. In embodiments, the HASIC is MK-2206. In embodiments, the HASIC is ML-297. In embodiments, the HASIC is ML-348. In embodiments, the HASIC is MRK-560. In embodiments, the HASIC is my cophenol ate. In embodiments, the HASIC is mycophenolic acid. In embodiments, the HASIC is NAV-26. In embodiments, the HASIC is NBI-27914. In embodiments, the HASIC is nelarabine. In embodiments, the HASIC is neratinib. In embodiments, the HASIC is nexturastat A. In embodiments, the HASIC is NH125. In embodiments, the HASIC is niclosamide. In embodiments, the HASIC is nifuratel. In embodiments, the HASIC is nitazoxanide. In embodiments, the HASIC is nitrofurantoin. In embodiments, the HASIC is nitroxoline. In embodiments, the HASIC is NNC-05-2090. Inembodiments, the HASIC is NU6027. In embodiments, the HASIC is obatoclax. In embodiments, the HASIC is octenidine. In embodiments, the HASIC is oligomycin A. In embodiments, the HASIC is OSI-027. In embodiments, the HASIC is OSI-420. In embodiments, the HASIC is OTX015. In embodiments, the HASIC is oxaliplatin. In embodiments, the HASIC is pentamidine. In embodiments, the HASIC is PF-04691502. In embodiments, the HASIC is PF-05212384. In embodiments, the HASIC is PF-06463922. In embodiments, the HASIC is PF- 477736. In embodiments, the HASIC is PHA-767491. In embodiments, the HASIC is PHTPP. In embodiments, the HASIC is pifithrin-cyclic. In embodiments, the HASIC is PIK-93. In embodiments, the HASIC is pirfenidone. In embodiments, the HASIC is piroctone. In embodiments, the HASIC is PKI-402. In embodiments, the HASIC is PLX-4720. In embodiments, the HASIC is PNU-74654. In embodiments, the HASIC is PP-121. In embodiments, the HASIC is PP242. In embodiments, the HASIC is PPT. In embodiments, the HASIC is prexasertib. In embodiments, the HASIC is PRT062607. In embodiments, the HASIC is PSB-1115. In embodiments, the HASIC is PTC-209. In embodiments, the HASIC is rapamycin. In embodiments, the HASIC is resiquimod. In embodiments, the HASIC is resminostat. In embodiments, the HASIC is RG7112. In embodiments, the HASIC is RGFP966. In embodiments, the HASIC is Ro-90-7501. In embodiments, the HASIC is RX-3117. In embodiments, the HASIC is SB415286. In embodiments, the HASIC is SB-328437. In embodiments, the HASIC is SB505124. In embodiments, the HASIC is scriptaid. In embodiments, the HASIC is securinine. In embodiments, the HASIC is serabelisib. In embodiments, the HASIC is silodosin. In embodiments, the HASIC is SN-38. In embodiments, the HASIC is STF-118804. In embodiments, the HASIC is T0901317. In embodiments, the HASIC is tannic acid. In embodiments, the HASIC is TC-G-1004. In embodiments, the HASIC is TCS-2210. In embodiments, the HASIC is telotristat etiprate. In embodiments, the HASIC is temsirolimus. In embodiments, the HASIC is tenovin-1. In embodiments, the HASIC is TGI 00713. In embodiments, the HASIC is thioguanine. In embodiments, the HASIC is tizoxanide. In embodiments, the HASIC is topotecan. In embodiments, the HASIC is torcetrapib. In embodiments, the HASIC is Torin-1. In embodiments, the HASIC is Torin-2. In embodiments, the HASIC is trichostatin A. In embodiments, the HASIC is triclocarban. In embodiments, the HASIC is tubastatin A. In embodiments, the HASIC is tucidinostat. In embodiments, the HASIC is tyrphostin 9. In embodiments, the HASIC is valnemulin. In embodiments, the HASIC is velneperit. In embodiments, the HASIC is vemurafenib. In embodiments, the HASIC is vilazodone. In embodiments, the HASIC is voxtalisib. In embodiments, the HASIC is VS-5584. In embodiments, the HASIC is WAY-100635. Inembodiments, the HASIC is wiskostatin. In embodiments, the HASIC is WYE-125132. In embodiments, the HASIC is WYE-354. In embodiments, the HASIC is WYE-687. In embodiments, the HASIC is XL388. In embodiments, the HASIC is yoda-1. In embodiments, the HASIC is ZSTK474. In embodiments, the HASIC is a pharmaceutically acceptable salt of any one of the foregoing.

[0097] The terms "patient" is used in accordance with its plain and ordinary meaning and refers to a living organism suffering from or prone to a disease that can be treated by administration of a HASIC (such as mTOR inhibitor or BET inhibitor) described herein. Nonlimiting examples include humans, other mammals, dogs, cats, monkeys, and other nonmammalian animals. In embodiments, a patient is human.

[0098] The terms “treating” or “treatment” are used in accordance with their plain and ordinary meaning and broadly includes any approach for obtaining beneficial or desired results in a patient's condition, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of the extent of a disease, stabilizing (i.e., not worsening) the state of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and remission, whether partial or total and whether detectable or undetectable. Treatment may inhibit the disease’s spread; relieve the disease’s symptoms, fully or partially remove the disease’s underlying cause, shorten a disease’s duration, or do a combination of these things. Treatment methods include administering to a patient a therapeutically effective amount of a therapeutic agent (HASIC. such as mTOR inhibitor or BET inhibitor). The term “treating” does not including preventing.

[0099] An “effective amount” is an amount sufficient to accomplish a stated purpose (e.g. achieve the effect for which it is administered, treat a disease). An example of an “effective amount” is an amount sufficient to contribute to the treatment or reduction of a symptom or symptoms of a disease, which could also be referred to as a “therapeutically effective amount.” A “reduction” of a symptom or symptoms (and grammatical equivalents of this phrase) means decreasing of the severity or frequency of the symptom(s), or elimination of the symptom(s). The exact amounts will depend on the purpose of the treatment, and will be ascertainable by one skilled in the art using known techniques. In embodiments, “therapeutically effective amount” refers to the amount of the therapeutic agent sufficient to treat or ameliorate a disease described herein. For any therapeutic agent described herein, the therapeutically effective amount can be initially determined from cell culture assays. Target concentrations will be those concentrationsof active compound(s) that are capable of achieving the methods described herein, as measured using the methods described herein or known in the art. As is well known in the art, therapeutically effective amounts for use in humans can also be determined from animal models. For example, a dose for humans can be formulated to achieve a concentration that has been found to be effective in animals. The dosage in humans can be adjusted by monitoring compounds effectiveness and adjusting the dosage upwards or downwards, as described above. Adjusting the dose to achieve maximal efficacy in humans based on the methods described above and other methods is well within the capabilities of the ordinarily skilled artisan. Dosages may be varied depending upon the requirements of the patient and the therapeutic agent being employed. The dose administered to a patient should be sufficient to effect a beneficial therapeutic response in the patient over time. The size of the dose also will be determined by the existence, nature, and extent of any adverse side-effects. Determination of the proper dosage for a particular situation is within the skill of the practitioner. Treatment can be initiated with smaller dosages which are less than the optimum dose of the compound. Thereafter, the dosage can be increased by small increments until the optimum effect under circumstances is reached. Dosage amounts and intervals can be adjusted individually to provide levels of the administered compound effective for the particular clinical indication being treated. This will provide a therapeutic regimen that is commensurate with the severity of the patient's disease state. A “therapeutically effective amount” can also be found on the label or Prescribing Information for commercially available therapeutic agents.

[0100] The term “administering” means oral administration, administration as a suppository , topical contact, intravenous, parenteral, intraperitoneal, intramuscular, intralesional, intrathecal, intranasal or subcutaneous administration, or the implantation of a slow-release device, e.g.. a mini-osmotic pump, to a patient. Administration is by' any route, including parenteral and transmucosal (e.g., buccal, sublingual, palatal, gingival, nasal, vaginal, rectal, or transdermal). Parenteral administration includes, e.g., intravenous, intramuscular, intra-arteriole, intradermal, subcutaneous, intraperitoneal, intraventricular, and intracranial. Other modes of delivery include, but are not limited to, the use of liposomal formulations, intravenous infusion, transdermal patches, etc. In embodiments, the administering does not include administration of any active agent other than the recited active agent.

[0101] The term “pharmaceutically acceptable salt” is meant to include salts of active compounds (e.g., HASIC, such as mTOR inhibitor or BET inhibitor) that are prepared with relatively nontoxic acids or bases, depending on the particular substituents found on the compounds described herein. When compounds disclosed herein contain relatively acidicfunctionalities, base addition salts can be obtained by contacting the neutral form of such compounds with a sufficient amount of the desired base, either neat or in a suitable inert solvent. Examples of pharmaceutically acceptable base addition salts include sodium, potassium, calcium, ammonium, organic amino, or magnesium salt, or a similar salt. When compounds disclosed herein contain relatively basic functionalities, acid addition salts can be obtained by contacting the neutral form of such compounds with a sufficient amount of the desired acid, either neat or in a suitable inert solvent. Examples of pharmaceutically acceptable acid addition salts include those derived from inorganic acids tike hydrochloric, hydrobromic, nitric, carbonic, monohydrogencarbonic, phosphoric, monohydrogenphosphoric, dihydrogenphosphoric, sulfuric, monohydrogensulfuric, hydriodic, or phosphorous acids and the like, as well as the salts derived from relatively nontoxic organic acids like acetic, propionic, isobutyric, maleic, malonic, benzoic, succinic, suberic, fumaric, lactic, mandelic, phthalic, benzenesulfonic, p-tolylsulfonic, citric, tartaric, oxalic, methanesulfonic, and the like. Also included are salts of amino acids such as arginate and the like, and salts of organic acids like glucuronic or galactunoric acids and the like (see, for example, Berge et al., “ Pharmaceutical Salts”. Journal of Pharmaceutical Science, 66: 1-19 (1977)).

[0102] “Contacting” is used in accordance with its plain ordinary meaning and refers to the process of allowing at least two distinct species (e.g. chemical compounds including biomolecules or cells) to become sufficiently proximal to react, interact or physically touch. It should be appreciated, however, that the resulting reaction product can be produced directly from a reaction between the added reagents or from an intermediate from one or more of the added reagents which can be produced in the reaction mixture. The term “contacting” may include allowing two species to react, interact, or physically touch, wherein the two species may be a compound as described herein and a cell.

[0103] Embodiments A1 -A23

[0104] Embodiment A1. A method of identifying a compound that accelerates a transition of a cell from an acute hypoxic state to a hypoxic adaptive state, the method comprising: (i) contacting a test compound with the cell in the acute hypoxic state, and (ii) detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state and measuring the length of time of the transition; w herein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the compound that accelerates the transition.

[0105] Embodiment A2. The method of Embodiment A1, wherein detecting the transitionfrom the cell in the acute hypoxic state to the cell in the hypoxic adaptive state comprises detecting a cell hypoxic stress marker in the cell in the hypoxic adaptive state.

[0106] Embodiment A3. The method of Embodiment A2, wherein the cell hypoxic stress marker is an oxidative phosphory lation marker, a glycolysis marker, an ATP production marker, HIF- la protein expression, an imaging marker, or a combination of two or more thereof.

[0107] Embodiment A4. The method of Embodiment A3, wherein the cell hypoxic stress marker is an oxidative phosphory lation marker.

[0108] Embodiment A5. The method of Embodiment A4, wherein step (ii) comprises detecting oxygen consumption rate or mitochondrial membrane potential.

[0109] Embodiment A6. The method of Embodiment A3, wherein the cell hypoxic stress marker is a glycolysis marker.

[0110] Embodiment A7. The method of Embodiment A6, wherein step (ii) comprises detecting extracellular acidification rate or glucose uptake.

[0111] Embodiment A8. The method of Embodiment A3, wherein the cell hypoxic stress marker is an ATP production marker.

[0112] Embodiment A9. The method of Embodiment A8, wherein step (ii) comprises detecting ATP amount or ATP production rate.

[0113] Embodiment A10. The method of Embodiment A3, wherein the cell hypoxic stress marker is HIF- la protein expression; and wherein step (ii) comprises detecting a decrease in HIF- la protein expression in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0114] Embodiment A1l. The method of Embodiment A3, wherein the cell hypoxic stress marker is a imaging marker selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology, nucleolus markers, endoplasmic reticulum / Golgi complex, actin cytoskeleton, plasma membrane, or a combination of two or more thereof.

[0115] Embodiment A12. The method of Embodiment A11, wherein the imaging marker is lipid droplets; and wherein step (ii) comprises detecting an increase in number and intensity of punctuate structures, a decrease in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0116] Embodiment A13. The method of Embodiment A1l, wherein the imaging marker is lipid peroxidation; and wherein step (ii) comprises detecting an increase in number and intensityof punctate structures, an increase in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0117] Embodiment A14. The method of Embodiment A11, wherein the imaging marker is mitochondrial structure; and wherein step (ii) comprises detecting an increase in perinuclear staining, a decreased staining throughout the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0118] Embodiment A15. The method of Embodiment A11, wherein the imaging marker is nuclear morphology; and wherein step (ii) comprises detecting a decrease in staining in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

[0119] Embodiment A16. The method of any one of Embodiments A1-A15, further comprising before step (i): culturing a cell in a normoxic state under acute hypoxic conditions to transition the cell in the normoxic state to the cell in the acute hypoxic state.

[0120] Embodiment A17. The method of any one of Embodiments A1-A16, further comprising after step (i): further culturing the test compound with the cell in the acute hypoxic state under acute hypoxic conditions.

[0121] Embodiment A18. The method of any one of Embodiments A1 to A17, wherein the test compound is a mTOR inhibitor.

[0122] Embodiment A19. The method of Embodiment A18, wherein the mTOR inhibitor is sirolimus, everolimus, temsirolimus, ridaforolimus, Torin-1, Torin-2, PP242, rapamycin.AZD8055, niclosamide, omipalisib, KU-0063794, RMC-5552, WYE-687, ETP-46464, alpelisib, apitolisib, bimiralisib, buparlisib, copanlisib, dactolisib, duvelisib, gedatolisib, idelalisib, umbrahsib, voxtalisib, PI-103, VS-5584, PKI-402, BGT226, SF1126, PKI-587, PF-04691502, or a pharmaceutically acceptable salt of any one of the foregoing.

[0123] Embodiment A20. The method of any one of Embodiments A1 to A17, wherein the test compound is a BET inhibitor.

[0124] Embodiment A21. The method of Embodiment A20, wherein the BET inhibitor is mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1 101, alobresib, molibresib, GSK2820151, INCB054329, birabresib, PLX51107, R06870810, ZEN003694, or a pharmaceutically acceptable salt of any one of the foregoing.

[0125] Embodiment A22. The method of any one of Embodiments A1 to A17, wherein the test compound is sirolimus, omipalisib, RMC-5552, alpelisib, apitolisib, copanlisib, duvelisib, idelalisib, umbralisib, PI-103, BGT226, SF1126, PKI-587, mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib, molibresib. GSK2820151,INCB054329, birabresib, PLX51107, R06870810, ZEN003694, (-)-MK 801, (+)-JQl, 7- methoxytacrine, 9-aminoacridine, ABT-737, AG-1024, Akt inhibitor VIII, AMG-458, AMG- 548, amitriptyline, amonafide, aprepitant, AR-A014418, arcti genin, tivantinib, arteether, artesunate, Aurora A Inhibitor I, axitinib, AZ20, azacitidine, azaguanine-8, azathioprine, AZD2014, AZD2858, AZD8055, bedaquiline, belotecan, benzalkonium chloride, benzethonium chloride, dactolisib, bimiralisib, brigatinib, bromocriptine, broxyquinoline, buparlisib, BYL719, camptothecin, carmofur. CC-115, CC-223, CGP-37157, CH1R-124, chlorquinaldol, clotrimazole, cloxiquine, CO-101244, COTI-2, CP-99994, crizotinib, defactinib, ridaforolimus, dichlorophene, digoxigenin, digoxin, dihydroartemisinin, eltrombopag, entinostat, epetraborole, erastin, eravacycline, erlotinib, ethacridine, ethidium, ETP -46464, everolimus, 5-fluoracil, fosaprepitant, G1T38, GDC-0084. GDC-0349, GDC-0980, gedatolisib. Go 6983, GSK J4. harmine, hexachlorophene, HTH-01-015, hydroxy camptothecine, I-BET151, 1-CBP-112, ICG- 001, ICI-199441, IMD 0354, indacaterol, sapanisertib, itraconazole, KU-0063794, LDN193189, LDN-212854, letrozole, liothyronine, LM-22A4, lonafamib, LRRK2-IN-1, LY3023414, M344, malotilate, mechlorethamine, 6-mercaptopurine, miconazole, mirin, MK-2206. ML-297, ML- 348, MRK-560, my cophenolate, mycophenolic acid, NAV-26, NBI-27914, nelarabine, neratinib, nexturastat A, NH125, niclosamide, nifuratel, nitazoxanide, nitrofurantoin, nitroxoline, NNC-05-2090, NU6027, obatoclax, octenidine, oligomycin A, OSI-027, OSI-420, OTX015, oxaliplatin, pentamidine, PF-04691502. PF-05212384, PF-06463922, PF-477736, PHA-767491, PHTPP. pifithrin-cyclic, PIK-93, pirfenidone, piroctone. PKI-402. PLX-4720. PNU-74654, PP-121, PP242, PPT, prexasertib, PRT062607, PSB-1115, PTC -209, rapamycin, resiquimod, resminostat, RG7112, RGFP966, Ro-90-7501, RX-3117, SB415286, SB-328437, SB505124, scriptaid. securinine, serabelisib, silodosin, SN-38, STF-118804, T0901317, tannic acid, TC-G-1004, TCS-2210, telotristat etiprate, temsirolimus, tenovin-1. TG100713, thioguanine, tizoxanide, topotecan, torcetrapib, Torin-1, Torin-2, trichostatin A, triclocarban, tubastatin A, tucidinostat, tyrphostin 9, valnemulin, velneperit, vemurafenib, vilazodone, voxtalisib, VS-5584, WAY-100635, wiskostatin, WYE-125132, WYE-354, WYE-687, XL388, yoda-1. or ZSTK474.

[0126] Embodiment A23. The method of any one of Embodiments A1 to A17, wherein the test compound is a compound shown in FIG. 4C, FIG. 5C, FIG. 5D, FIG. 6B, FIG. 7C. FIG. 7D, FIG. 7E, FIG. 7F, FIG. 7G, or FIG. 8J.

[0127] Embodiments Ml -M21

[0128] Embodiment Ml. A method of treating acute hypoxia in a patient in need thereof, themethod comprising administering to the patient an effective amount of hypoxic adaptive stateinducing compound, thereby treating acute hypoxia.

[0129] Embodiment M2. A method of treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in need thereof in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in need thereof.

[0130] Embodiment M3. A method of treating acute hypoxia in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive stateinducing compound, thereby treating acute hypoxia.

[0131] Embodiment M4. A method of treating acute ischemia in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive stateinducing compound, thereby treating acute hypoxia.

[0132] Embodiment M5. A method of treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof.

[0133] Embodiment M6. A method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state ex vivo or in vivo, the method comprising contacting the cell with a hypoxic adaptive state-inducing compound, thereby transitioning the cell in the acute hypoxic state to the cell in the hypoxic adaptive state.

[0134] Embodiment M7. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a mTOR inhibitor.

[0135] Embodiment M8. The method of Embodiment M7, wherein the mTOR inhibitor is sirolimus, everolimus, temsirolimus, ridaforolimus, Torin-1, Torin-2, PP242. rapamycin.AZD8055, niclosamide, omipalisib. KU-0063794, RMC-5552, WYE-687, ETP-46464, alpehsib, apitolisib, bimiralisib, buparlisib, copanlisib, dactolisib, duvelisib, gedatolisib, idelalisib.umbralisib, voxtalisib, PI-103, VS-5584, PKI-402, BGT226, SF1126, PKI-587, PF-04691502, or a pharmaceutically acceptable salt of any one of the foregoing.

[0136] Embodiment M9. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a BET inhibitor.

[0137] Embodiment MI0. The method of Embodiment M9, wherein the BET inhibitor is mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib, molibresib, GSK2820151, INCB054329, birabresib, PLX51107, R06870810, ZEN003694, or a pharmaceutically acceptable salt of any one of the foregoing.

[0138] Embodiment Mil. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is sirolimus, omipalisib, RMC-5552, alpelisib, apitolisib, copanlisib, duvelisib, idelalisib. umbralisib, PI-103, BGT226, SF1126, PKI-587, mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib, molibresib, GSK2820151, INCB054329, birabresib, PLX51107, R06870810, ZEN003694, (-)- MK 801, (+)-JQl, 7 -methoxy tacrine, 9-aminoacridine, ABT-737, AG-1024, Akt inhibitor VIII, AMG-458, AMG-548, amitriptyline, amonafide, aprepitant, AR-A014418, arctigenin, tivantinib, arteether, artesunate, Aurora A Inhibitor I, axitimb, AZ20, azacitidine, azaguanine-8, azathioprine, AZD2014, AZD2858, AZD8055, bedaquiline, belotecan, benzalkonium chloride, benzethonium chloride, dactolisib, bimiralisib, brigatinib, bromocriptine, broxyquinoline, buparlisib, BYL719, camptothecin, carmofur, CC-115, CC-223, CGP-37157, CHIR-124, chlorquinaldol. clotrimazole, cloxiquine, CO- 101244. COTI-2, CP-99994, crizotinib, defactinib. ridaforolimus, dichlorophene, digoxigenin, digoxin, dihydroartemisinin, eltrombopag, entinostat, epetraborole, erastin, eravacycline, erlotinib, ethacridine, ethidium, ETP-46464, everolimus, 5- fluoracil, fosaprepitant, G1T38, GDC-0084, GDC-0349, GDC-0980, gedatolisib. Go 6983, GSK J4, harmine, hexachlorophene. HTH-01-015, hydroxy camptothecine, I-BET151. 1-CBP-112, ICG-001, ICI-199441, IMD 0354, indacaterol, sapanisertib, itraconazole, KU-0063794, LDN193189, LDN-212854, letrozole, liothyronine, LM-22A4, lonafamib, LRRK2-IN-1, LY3023414, M344, malotilate, mechlorethamine, 6 -mercaptopurine, miconazole, mirin, MK- 2206, ML-297. ML-348. MRK-560. my cophenolate, mycophenolic acid. NAV-26, NBI-27914, nelarabine. neratinib, nexturastat A. NH125, niclosamide, nifuratel. nitazoxanide, nitrofurantoin, nitroxoline, NNC-05-2090, NU6027, obatoclax, octenidine, oligomycin A, OSI-027, OSI-420, OTX015, oxaliplatin, pentamidine, PF-04691502, PF-05212384, PF-06463922, PF-477736, PHA-767491, PHTPP, pifithrin-cyclic, PIK-93, pirfenidone, piroctone, PKI-402, PLX-4720. PNU-74654, PP-121, PP242, PPT, prexasertib, PRT062607, PSB-1115, PTC -209, rapamycin.resiquimod, resminostat, RG7112, RGFP966, Ro-90-7501, RX-3117. SB415286, SB-328437, SB505124. scriptaid. securinine. serabelisib, silodosin, SN-38, STF-118804, T0901317, tannic acid, TC-G-1004, TCS-2210, telotristat etiprate, temsirolimus, tenovin-1, TG100713, thioguanine, tizoxanide, topotecan, torcetrapib, Torin-1, Torin-2, trichostatin A, triclocarban, tubastatin A, tucidinostat, tyrphostin 9, valnemulin, velneperit, vemurafenib, vilazodone, voxtalisib, VS-5584, WAY-100635, wiskostatin, WYE-125132, WYE-354, WYE-687, XL388, yoda-1. ZSTK474. or a pharmaceutically acceptable salt of any one of the foregoing.

[0139] Embodiment M12. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 4C.

[0140] Embodiment M13. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 5C.

[0141] Embodiment M14. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 5D.

[0142] Embodiment M15. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 6B.

[0143] Embodiment M16. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 7C.

[0144] Embodiment Ml 7. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 7D.

[0145] Embodiment Ml 8. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 7E.

[0146] Embodiment M19. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 7F.

[0147] Embodiment M20. The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable saltthereof shown in FIG. 7G.

[0148] Embodiment M21 . The method of any one of Embodiments Ml to M6, wherein the hypoxic adaptive state-inducing compound is a compound or a pharmaceutically acceptable salt thereof shown in FIG. 8J.

[0149] Embodiment P1 -P23

[0150] Embodiment P1 A method of identifying a compound that induces a hypoxic adaptive state in a population of cells from a mammalian cell line or mammalian cell source of interest, the method comprising: (a) distributing cells of the population to individual compartments to provide a subpopulation of the cells in each compartment for compound treatment; (b) culturing the subpopulations of cells in the individual compartment under acute hypoxic conditions for a predetermined amount of time that is sufficient to induce a cellular phenotypic profile of an acute hypoxic state in the cell line or cell source of interest; (c) adding one or more test compounds to each individual compartment; (d) further culturing the subpopulations of cells under acute hypoxic conditions for a predetermined length of time shorter than a predetermined amount of time that induces the phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest; (e) detecting at least one morphological feature of cells from each compartment, wherein the morphological feature is a phenotypic characteristic of a phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest; (f) performing image analysis to obtain a phenotypic profile for the at least one morphological feature for each of the treated subpopulation of cells; and (g) identifying a compound that induces a shift of the phenotype of the at least one morphological feature towards the phenotypic profile of the hypoxic adaptive state in the subpopulation of cells treated with the compound, thereby identify ing a compound that induces a hypoxic adaptive state.

[0151] Embodiment P2. The method of Embodiment P1, wherein the population of cells is human.

[0152] Embodiment P3. The method of Embodiment P1 or P2. wherein the population of cells is from a cell line.

[0153] Embodiment P4. The method of Embodiment P3, wherein the cell line is HepG2.

[0154] Embodiment P5. The method of Embodiment P1 or P2, wherein the population of cells is obtained from inducible pluripotent stem cells.

[0155] Embodiment P6. The method of Embodiment P5, wherein the population of cells obtained from inducible pluripotent stem cells are cardiomyocytes.

[0156] Embodiment P7. The method of Embodiment P 1 or P2, wherein the subpopulation ofcells in each individual compartment is an organoid generated from the mammalian cell line or cell source of interest.

[0157] Embodiment P8. The method of any one of Embodiments P1 to P7, wherein the at least one morphological feature is selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial polarization, and nuclear morphology.

[0158] Embodiment P9, The method of Embodiment P8, wherein the morphological feature is detected by addition of an agent that binds, directly or indirectly, to a component of the morphological feature, wherein the agent is labeled with a detectable label.

[0159] Embodiment P10. The method of any one of Embodiments P1 to P9, wherein the label is a fluorescent or chromogenic label.

[0160] Embodiment P11. A method of identifying a compound that induces a hypoxic adaptive state in a population of cells from a mammalian cell line or mammalian cell source of interest, the method comprising: (a) distributing cells of the population to individual compartments to provide a subpopulation of the cells in each compartment for compound treatment; (b) culturing the subpopulations of cells in the individual compartment under acute hypoxic conditions for a predetermined amount of time that is sufficient to induce a cellular phenotypic profile of an acute hypoxic state in the cell line or cell source of interest; (c) adding one or more test compounds to each individual compartment; (d) further culturing the subpopulations of cells under acute hypoxic conditions for a predetermined length of time shorter than a predetermined amount of time that induces a phenotypic profile of a hypoxic adaptive state in the cell line or cell source of interest; (e) detecting at least two different morphological features of cells from each compartment, wherein the morphological features are phenotypic characteristics of a phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest; (f) performing image analysis to obtain a phenotypic profile for the at least two different morphological features for each of the treated subpopulation of cells; and (g) identify ing a compound that induces a shift of the phenotypes of the at least two morphological features towards the phenotypic profile of the hypoxic adaptive state in the subpopulation of cells treated with the compound, thereby identifying a compound that induces a hypoxic adaptive state.

[0161] Embodiment P12. The method of Embodiment P11. wherein the population of cells is human.

[0162] Embodiment P13. The method of Embodiment P1l or P12, wherein the population of cells is from a cell line.

[0163] Embodiment P14. The method of Embodiment P13, wherein the cell line is HepG2.

[0164] Embodiment P15. The method of Embodiment P11 or P12, wherein the population of cells is obtained from inducible pluripotent stem cells.

[0165] Embodiment P16. The method of Embodiment P15. wherein the populations of cells obtained from inducible pluripotent stem cells are cardiomyocytes.

[0166] Embodiment P17. The method of Embodiment P11 or P12, wherein the subpopulation of cells in each individual compartment is an organoid generated from the mammalian cell line or cell source of interest.

[0167] Embodiment P18. The method of any one of Embodiments P1 to P17, wherein the at least two morphological features are selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial polarization, and nuclear morphology.

[0168] Embodiment P 19. The method of Embodiment P 18. wherein detecting the at least two morphological features comprises adding to each of the compartments a first agent labeled w ith a first detectable label that directly or indirectly binds to a component of one of the at least two morphological features to identify and a second agent labeled w ith a second detectable label that directly or indirectly binds to a component of the second of the at least tw o morphological features, wherein the first and second detectable labels generate signals that are distinguishable from one another.

[0169] Embodiment P20. The method of Embodiment P18, comprising detecting a third morphological feature selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial polarization, and nuclear morphology.

[0170] Embodiment P21. The method of Embodiment P20, w herein the third morphological feature is detected using a third reagent that directly or indirectly binds to component of the third morphological feature, w h erein the third reagent is labeled with a label distinguishable from the first and the second labels.

[0171] Embodiment P22. The method of Embodiment P18. wherein a fourth morphological feature selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial polarization, and nuclear morphology are detected.

[0172] Embodiment P23. The method of Embodiment P22. wherein the fourth morphological feature is detected using a fourth reagent that directly or indirectly binds to component of the fourth morphological feature, wherein the fourth reagent is labeled with a label distinguishable from the first, second, and third labels.EXAMPLES

[0173] Presented herein is a phenotypic profiling platform to discover compounds and targets that can fast-track cellular adaptation to hypoxic stress (FIG. 1A). We first defined a highdimensional space to track the progression of hypoxia response, which captures distinct phenotypes that reflect acutely stressed and chronically adapted states. Using this highdimensional space, we screened an annotated compound library to identify compounds that shift cells toward the chronic hypoxia phenotype. Analysis of compound hits highlighted inhibition of mTOR / PI3K or BETs as strategies to achieve this shift. Moreover, we demonstrated that this shift confers functional protection against hypoxia-related stress in orthogonal cellular assays. Overall, our platform provides a framework for drug discovery aimed at alleviating cellular stress by fast-tracking adaptation.

[0174] Results.

[0175] Phenotypic characterization of cellular hypoxia response trajectories.

[0176] We sought to establish a high-content image-based cellular screening platform to model and capture the temporal dynamics of cellular response and adaptation to hypoxia. First, we searched for a cell model that would be both amenable to screening and have similarity to human tissue. In comparing 1034 cancer cell lines with 53 healthy human tissues, we found that the liver cancer cell line, HepG2, and liver tissue exhibited the highest transcriptional similarity (FIGS. 6A-6B). (22,23).Given the liver’s crucial role in hypoxia adaptation, HepG2 cells were chosen as the cell model for our study. (24,25). Second, we searched for experimental conditions to capture acute- and chronic-like hypoxic states. Guided by prior in vitro studies, we treated HepG2 cells with hypoxia (1% O2) of various durations, ranging from six hours to six days (FIG. IB). (26-29). Third, we selected multiplexed image-based biomarkers to capture and distinguish these phenotypic states. Cells were labeled with biomarkers to capture expected hypoxia-induced changes to mitochondria (MitoTracker) and lipids (BODIPY 493 / 503 for neutral lipids and Cl 1-BODIPY 581 / 591 for unsaturated lipids), as well as with a biomarker for segmenting cell images (Hoechst) (FIG. IB). Finally, we summarized HepG2 cellular responses to hypoxia as high-dimensional phenotypic profiles (Methods). (30). These profiles revealed a temporal -response trajectory to hypoxia (FIG. IB). Multiple timepoints along the hypoxia trajectory could be distinguished phenotypically from each other and from normoxia with high accuracy (k-nearest neighbor with prediction accuracy >0.98; Methods) (FIG. 1C).

[0177] Along the hypoxia trajectory , we examined whether the Id (day 1) and 6d (day 6) timepoints could represent the acute and adapted hypoxic states (respectively). We observedsignificant accumulation of nuclear HIFla at Id and a lesser degree of accumulation by 6d hypoxia (FIG. ID), consistent with known activation and subsequent negative feedback in the HIF-PHD-VHL circuit. (31-34). Further, we observed a progressive shift from aerobic metabolism to glycolysis, accompanied by corresponding decreases in total ATP production (FIG. IE). (5). Finally, a key feature of hypoxia adaptation is tolerance to ischemia-like stress (hypoxia with deprivation of glucose and FBS). (8). Importantly, we found that cells pretreated with Id of hypoxia were intolerant to ischemia-like stress. By contrast, cells pretreated with 6d hypoxia showed tolerance to ischemia-like stress, suggesting that six days (but not one day) was sufficient to establish an adapted state (FIG. IF). We note that transcriptional profiles also captured distinct cellular hypoxic states, mirroring the image-based profiles. Altogether, we identified a cellular system and high-dimensional phenotypic space in which normoxia (N), Id acute hypoxia (AH), and 6d chronic adapted hypoxia (CH) states could be modeled and distinguished.

[0178] Identification of compounds that phenopush cells from the acute towards the chronic hypoxia state.

[0179] We next screened for perturbations that fast-track cellular adaptation to hypoxia, as measured by their ability to “phenopush” cells from AH towards CH. For our screening library-, we curated 6,01 1 well -characterized compounds to facilitate target identification. Together, this library covered 1,928 protein targets, representing about 50% of all druggable human proteins, and 98% (325) of all KEGG pathways (FIG. 2A). (35-36).

[0180] We investigated whether these compounds could alter cellular response to acute hypoxic stress. Cells were pretreated with compounds for 24h and then exposed to acute hypoxia for 24h for a total of 48h compound treatment. At the end of the 48h, cells were labeled, fixed, and imaged. Drugs were screened at two concentrations and across multiple batches. To ensure that we captured consistent hypoxia and drug responses, we included three control plates in each batch: N control and CH control (the vehicle, DMSO) plates and a plate with reference compounds. See Methods.

[0181] We summarized cellular responses as phenotypic profile vectors, calculated by comparing drug with DMSO treatment in the AH condition (Methods). We additionally computed N and CH phenotypic profiles through comparison with the AH condition (Methods). To establish phenotypic regions associated with the oxygen conditions, replicate DMSO wells were used to define N. AH, and CH “point clouds.” We then searched for “phenopushing hit” compounds whose profiles (at either tested concentration) are positioned outside of AH andtowards the CH cloud (FIG. 2A). As a measurement of “phenopushing,” we quantified: (1) bioactivity as measured by a phenotypic difference from the AH cloud, and (2) “phenopushing” as measured by the phenotypic shift of a perturbation in distance (D) and direction (6) relative to the CH cloud (Methods, FIG. 2A). Bioactive compounds that met either the distance cutoff (Z Score < -3) and / or direction cutoff (top 90% percentile of shift angles) were identified as phenopushing hits (Methods, FIG. 2B).

[0182] In total, we identified 198 AH-to-CH phenopushing hit compounds (“Primary hits”, FIG. 2C) A retest of these compounds over a wide range of doses confirmed that 92 (46%) passed the original hit-calling criteria (“Confirmed hits” FIG. 2C). Reassuringly, these confirmed hit compounds aligned with the chronic hypoxia state, as reflected by both the highdimensional phenotypic profiles and images (FIG. 2D). Together, our strategy provided a list of high-confidence compounds that phenotypically push cells in an acute hypoxia state towards an adapted, chronic hypoxia state.

[0183] Identification of mTOR and BETs as potential targets for phenopushing towards CH.

[0184] We wondered whether the AH-to-CH phenopushing hits would reveal molecular targets related to hypoxia adaptation. Using the Chembl bioactivity database, we constructed a target activity profile for each screened compound (compound-to-target activity map, Methods). (37). We identified mTOR, PI3Ks, and BET family members (BRD2 / 3 / 4) as top targets overrepresented among hits compared to non-hits (Methods; FIG. 3A). In concordance with the overrepresentation analysis, compounds with higher potency for mTOR and BETs (and P13Ks to some extent) were more likely to phenopush cells towards the adapted state compared to those with lower potency (FIGS. 3B-3D). Reassuringly, the potent phenopushing doses of these compounds were similar to potent doses reported in published studies for on-target cellular effects. (38-41).

[0185] We next investigated whether downregulation of mTOR, PI3K, and / or BET activity is seen in the endogenous CH state, since pharmacologic inhibition of mTOR, PI3Ks, and BETs phenotypically mimicked the chronic state at an early timepoint. We monitored phosphorylation of S6 (S235 / S236) and AKT (S473) as markers of mTORC l and mT0RC2 activity, respectively; phosphorylation of AKT (T308) as a marker of PI3K activity; and phosphorylation of RNA Pol II (S2) as a marker of BETs activity. (42-45). We observed a progressive decrease in marker intensity for mTOR and BETs, but not PI3K, from normoxia to AH and then to CH (FIGS. 3E-3G, 7A). Thus, downregulation of the activity of mTOR and BETs, but not of PI3K, is characteristic of the CH state. Consistent with the relevance of mTOR and BETs to the CHstate, high hit rates in the screen were observed for mTOR-selective compounds (86%, 12 / 14), PI3K / mT0R dual-selective compounds (50%. 4 / 8), and BETs-selective compounds (75%. 6 / 8), but not PI3K.-S elective compounds (14%, 3 / 22) (FIGS. 7B-7G). Since PI3K and mTOR pathways are tightly coupled, and many mTOR-targeting compounds also target PI3Ks, we grouped compounds that target mTOR and / or PI3K into one hit compound category . (44,46).

[0186] We wondered whether downregulation of mTOR and / or BETs activity is a general feature of the AH-to-CH phenopushing hits. We assessed how all hits, at their phenopushing doses, affect mTOR and BET activities. First, we confirmed that in AH, relative to DMSO, all 36 mTOR / PI3K-targeting hits (regardless of selectivity) downregulated both pS6 (S235 / S236) and pAkt (S473). reflecting inhibition of both mTORCl and mTORC2 (FIG. 3H). Moreover, 73% (41 / 56) of other hits (including BET inhibitors) induced decreases in both pS6 (S235 / S236) and pAkt (S473) (FIG. 3H). Similarly, all 5 BETs-targeting hits and 91% (79 / 87) of other hits (including mTOR / PI3K inhibitors) induced a decrease in pPol-II level (FIG. 3H). In fact, 79% (73 / 92) of all confirmed hits inhibited both mTOR and BETs pathways compared with DMSO- treated AH cells. Together, we observed that the activities of mTOR / PI3K / BETs monotonically decrease from N, to AH, to CH. The majority of hit compounds, applied at AH, accelerate this observed decrease towards lower CH levels.

[0187] Hit compounds rescue HepG2 survival in a cellular model of ischemia.

[0188] The hit compounds fast-track the development of cellular phenotypic features seen in the CH cellular state. However, it is unclear whether this translates to fast-tracking functional protection of the cells. We next investigated whether phenopushing compounds ‘’fast-track” functional protection against ischemia-like stress, motivated by the observation that HepG2 cells in the hypoxia-adapted CH state, but not the acutely stressed AH state, are tolerant to the combination of oxygen and nutrient (glucose / FBS) deprivation (FIGS. IF, 4A).

[0189] To determine if AH-to-CH phenopushing hit compounds increase tolerance to ischemia-like conditions, we compared survival of drug-treated to control (DMSO-treated) cells. We ranked hit compounds based on their average survival across the tested drug doses (FIG. 4B; ischemia exposure was calibrated so that about 10% of control cells survive by the end of treatment). Our analysis showed that about 83% (75 / 90) of the tested phenopushing hits significantly outperformed the controls for survival in at least one of the tested doses, with the best compounds reaching survival close to that of the hypoxia-adapted CH state. Compounds associated with overrepresented targets (mTOR and / or PI3K. or BETs) had the strongest survival benefit (FIG. 4B). The majority of the top 25 compounds increased survival comparedto DMSO across all doses, and nearly all were associated with overrepresented targets (FIG. 4C).

[0190] We wondered whether our phenopushing approach identifies compounds enriched for protecting cells in ischemia-like stress. We measured cell survival in ischemia-like conditions for 102 compounds that were bioactive (i.e.. pushed cells away from AH) but non-phenopushing (i. e. , did not push towards CH). Reanalysis of cell survival for all compounds at the most protective tested dose showed that the AH-to-CH phenopushing hits were more likely to improve cell survival. For example, 41% of our phenopushing hits (FIG. 4D, red) vs. 3% of non-phenopushing bioactive compounds (FIG. 4D, blue) increased survival more than 3-fold compared to DMSO (FIG. 4D, red dashed line). Together, these results demonstrate that the AH-to-CH phenopushing hits also fast-track the development of ischemia-like tolerance, a hallmark of hypoxia adapted cells (FIG. IF).

[0191] Phenopushing compounds protect mature iPSC-derived cardiomyocytes against hypoxic stress.

[0192] We next examined whether phenopushing compounds provide functional rescue in other cellular contexts beyond the originally screened HepG2 cell line. While HepG2 cells are well-suited to studying adaptation to hypoxia, matured iPSC-derived cardiomyocytes (iPSC- CM’s) are well-suited to studying susceptibility to hypoxic stress. (47-49). Matured iPSC-CM’s are highly sensitive to hypoxia and stop spontaneous beating within 48-72h of exposure to 1% Cty Further, matured iPSC-CM’s provide a functional cell-health phenotype, namely spontaneous regular beating patterns (FIG. 5 A).

[0193] We tested a subset of our phenopushing hits, focusing on mT0R / PI3K and BET inhibitors, for their ability to rescue cardiomyocyte contractility in acute hypoxia. As a proxy for cardiomyocyte beating, we monitored the Ca2+transients by live cell imaging the GFP fluorescence intensity of the calcium sensor GCaMP6, which was constitutively expressed in our iPSC-CM’s. Principle components analysis (PC A) of features extracted from the resulting beating time series data revealed two clusters in which cardiomyocytes retained an ability to beat, with cluster #2 most similar to matured iPSC-CM’s in normoxia (FIG. 5B). To complement cardiomyocyte beating, we further investigated sarcomeric structure of all hits in cluster 2 (FIGS. 5C-5D). As in the HepG2 ischemia assay, compounds associated with mTOR / PI3K and BETs were the most effective at rescuing iPSC-CM’s from hypoxic stress, with cardiomyocyte beating often deteriorating before observable loss of sarcomere structure (FIG. 5C). These findings demonstrate that phenopushing in high-dimensional space cantranslate into functional protection across cell models.

[0194] AH-to-CH Phenopushing Hit Compounds

[0195] The AH-to-CH phenopushing hit compounds identified by the studies described herein included the compounds shown in FIGS. 4C, 5C. 5D, 6B, 7C-7G, and 8J. In embodiments, the AH-to-CH phenopushing hit compounds identified by the studies described herein included sirolimus, omipalisib, RMC-5552, alpelisib, apitolisib, copanlisib, duvelisib, idelalisib, umbralisib, PI-103, BGT226, SF1126, PKI-587, mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib. molibresib, GSK2820151. INCB054329, birabresib, PLX51107, R06870810, ZEN003694, (-)-MK 801, (+)-JQl, 7 -methoxy tacrine, 9- aminoacridine, ABT-737, AG-1024, Akt inhibitor VIII, AMG-458, AMG-548, amitriptyline, amonafide, aprepitant, AR-A014418, arctigenin, tivantinib, arteether, artesunate, Aurora A Inhibitor I, axitinib, AZ20. azacitidine, azaguanine-8, azathioprine, AZD2014, AZD2858, AZD8055, bedaquiline, belotecan, benzalkonium chloride, benzethonium chloride, dactolisib, bimiralisib, brigatinib, bromocriptine, broxyquinoline, buparlisib, BYL719, camptothecin, carmofur, CC-115, CC-223, CGP-37157, CHIR-124, chlorquinaldol, clotrimazole, cloxiquine, CO-101244, COTI-2, CP-99994, crizotinib, defactinib, ridaforolimus, dichlorophene, digoxigenin, digoxin, dihydroartemisinin, eltrombopag. entinostat, epetraborole, erastin. eravacy cline, erlotinib, ethacridine, ethidium, ETP-46464, everolimus, 5-fluoracil, fosaprepitant, G1T38, GDC-0084, GDC-0349, GDC-0980, gedatolisib, Go 6983, GSK J4, harmine, hexachlorophene, HTH-01-015, hydroxy camptothecine, I-BET151, 1-CBP-112, ICG-001, ICI- 199441, IMD 0354, indacaterol, sapanisertib, itraconazole, KU-0063794. LDN193189, LDN- 212854, letrozole, liothyronine, LM-22A4, lonafamib, LRRK2-IN-1, LY3023414, M344, malotilate, mechlorethamine, 6-mercaptopurine, miconazole, mirin, MK-2206, ML-297, ML- 348, MRK-560, my cophenolate, mycophenolic acid, NAV-26, NBI-27914, nelarabine, neratinib, nexturastat A, NH125, niclosamide, nifuratel. nitazoxanide, nitrofurantoin, nitroxoline, NNC-05-2090, NU6027, obatoclax. octenidine. oligomycin A. OS1-027. OSI-420. OTX015, oxaliplatin, pentamidine, PF-04691502, PF-05212384, PF-06463922, PF-477736, PHA-767491, PHTPP, pifithrin-cyclic, PIK-93, pirfenidone, piroctone, PKI-402, PLX-4720, PNU-74654, PP-121, PP242, PPT, prexasertib, PRT062607, PSB-1115, PTC -209, rapamycin, resiquimod, resminostat. RG7112. RGFP966, Ro-90-7501, RX-3117, SB415286, SB-328437, SB505124, scriptaid, securinine, serabelisib, silodosin, SN-38, STF-118804, T0901317, tannic acid, TC-G-1004, TCS-2210, telotristat etiprate, temsirolimus, tenovin-1, TG100713, thioguanine, tizoxanide, topotecan, torcetrapib, Torin-1, Torin-2, trichostatin A, triclocarban, tubastatin A, tucidinostat, tyrphostin 9, valnemulin, velneperit, vemurafenib, vilazodone.voxtalisib, VS-5584, WAY-100635, wiskostatin, WYE-125132, WYE-354, WYE-687, XL388, yoda-1. and ZSTK474. The AH-to-CH phenopushing hit compounds are also described herein as hypoxic adaptive state-inducing compounds.

[0196] Discussion

[0197] Adaptation to hypoxia has long been recognized as a powerful mechanism to alleviate hypoxic or ischemic stress. (8,10). In this study, we developed an early-stage drug discovery' platform to identify compounds that accelerate adaptation and confer functional protection against severe acute hypoxic stress. Using our platform, we captured functionally defined acute hypoxic (AH) versus chronic hypoxic (CH) states via high-content microscopy (FIG. 1), identified compounds and targets whose modulation phenopush cells from AH to CH states (FIGS. 2-3), and validated that these hit compounds functionally rescue cells under severe hypoxia or ischemia-like conditions (FIGS. 4-5).

[0198] Our approach leverages the ability to identify acute and chronically adapted hypoxia regions in a high-dimensional phenotypic space and to analyze compound effects relative to these regions. Traditional life / death screens poorly capture the complexity of hypoxia adaptation, as preventing cell death does not equate to inducing adaptation. (50-52). Alternatively, omics approaches (e.g., transcriptomics, proteomics, metabolomics) can capture high-dimensional readouts of molecular changes; however, their high cost and limited throughput hinder their use for large-scale screening. (50-52). In contrast, high-content phenotypic profiling, capable of capturing the complex phenotypes of cells as they transition from a hypoxia-stressed state to a hypoxia-adapted state, offers a scalable and efficient method to fulfill both requirements.

[0199] The concept of phenopushing can be applied to various choices of start or destination time points or conditions, as long as they are phenotypically separable. We chose the 1-day AH and 6-day CH time points out of practical considerations for performing the screen. However, in principle, the screen could be performed at time points as early as 2 hours (FIGS. 8A-8B) or at longer time points (e.g., 10 days or 14 days) (FIGS. 8C-8D). These different choices could alter the landscape of phenopushing hit compounds and targets (FIGS. 8E-8G). Additionally, the platform could have been applied to milder hypoxia conditions (5% O2), where cells show an intermediate degree of hypoxia adaptation after 6 days of exposure (FIGS. 8H-8I).

[0200] A fundamental question of any phenotypic screen is whether similarity in phenotypic space translates into similarity of underlying biology and function. In our case, the question is whether phenopushed AH cells resemble the protective, adaptive CH state. At the molecularlevel, we found that downstream components of our top inhibited targets (mTOR and BETs) were also inhibited in the CH state. Functionally, we found that inhibiting these target classes conferred functional protection not only for the survival of HepG2s (the originally screened cell type) in severe ischemia-like conditions but also for the beating and sarcomere integrity of matured iPSC-CM cells under hypoxic stress. These findings indicate that phenotypically mimicking the adapted state also mimics its protective properties and even translates to other cell types.

[0201] By applying our platform to a chemical genetic library, we identified key targets involved in hypoxia adaptation within the druggable target space. Among these, mTOR and BETs emerged as top significantly enriched target categories, representing both established mechanisms and less explored aspects of the hypoxia response. mTOR inhibition has been associated with hypoxia-induced translational repression and is primarily studied for its role in mitigating inflammation after hypoxic damage. (53-57). Our findings highlight its ability to accelerate cellular adaptation to hypoxia. Similarly. BETs inhibition, known to reduce transcriptional elongation during hypoxia, has been reported only for preventing cell death under hypoxic stress. (58,59). Our results expand its application by demonstrating its role in promoting functional hypoxia adaptation.

[0202] Materials and Methods

[0203] Cell lines. HepG2 cells (obtained from the UCSF Cell Culture Core Facility ) were maintained in DMEM with 4.5 g / dL glucose (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (Gemini Bio #100-106) and 1 % penicillin / streptomycin (Thermo Fisher Scientific). Cells were grown in a humidified 37° C incubator with 5% CO2for up to 6 weeks, during which they were passaged every ~4-5 days at -70-80% confluence with TrypLE (Thermo Fisher Scientific). Cells were exposed to hypoxia by culturing in a glovebox (InvivCh 400. Baker Ruskinn) at 1% O2, 5% CO2, and 70% humidity. Aliquots of cell lines w ere frozen in media with 10% DMSO and stored in liquid nitrogen. All cell lines w ere routinely tested for mycoplasma as described previously. (60,61).

[0204] Compound Library. All compounds were stored at -80 °C as DMSO-dissolved solutions in 384-well PP microplates (Labcyte, #pp-0200). Screened compounds included (1) Selleck FDA-approved & Passed Phase I Drug Library (L3800), (2) Selleck Kinase Inhibitor Library (L1200). (3) Selleck Apoptosis Compound Library (L3300), (4) Selleck Epigenetics Compound Library (LI 900). (5) Selleck Bioactive Library (LI 700), (6) manually curated FDA- approved & Passed Phase I drugs from other vendors, and (7) manually curated bioactivecompounds from other vendors.

[0205] Reagents. Phenotypic profiling staining solution was made of 3.24 pM Hoechst 33342(Invitrogen, H3570), 50pM BODIPY™ 493 / 503 (4,4-Difluoro-l,3,5,7,8-Pentamethyl-4- Bora-3a,4a-Diaza-s-Indacene, Invitrogen, D3922), 5.2pM Cl l-BODIPY (4,4-Difluoro- l .3.5.7.8-Penlamethyi-4-Bora-3a.4a-Diaza-s-lndacene from Image-iT™ Lipid Peroxidation Kit, Invitrogen, C 10445), and 1.9pM MitoTracker™ Deep Red FM (Invitrogen, M22426) in HBSS. CellTox™ Green Cytotoxicity Assay (G8731) was purchased from Promega. The following antibodies were used according to the dilution factors as primary antibodies in immunofluorescence experiments: HIF-1α (Cell Signaling technology, 36169, 1:200), Phospho- S6 Ribosomal Protein (Ser235 / 236) (Cell Signaling technology, 4856, 1 : 1600), Phospho-Akt (Ser473) (Cell Signaling technology , 4060, 1:400), RNA polymerase II CTD repeat YSPTSPS (pSer2) (Abeam, abl93468, 1 :100), Phospho-Akt (Thr308) (Cell Signaling technology , 13038, 1:400), Sarcomeric alpha Actinin Monoclonal Antibody (EA-53) (Invitrogen, MAI -22863, 1:200). The following antibodies were used as the secondary antibodies in immunofluorescence experiments: 488-conjugated goat anti-mouse IgG (Invitrogen, A32723, 1 :500), 488-conjugated goat anti-rabbit IgG (Invitrogen, A32731, 1:500). Alexa Fluor™ 568 Phalloidin (Invitrogen, A12380, 1:400) and Hoechst 33342 (16pM) were also added to the secondary antibody staining solution for the purpose of cell segmentation.

[0206] Immunofluorescence assay. Cells were seeded in 384-well PhenoPlate’s (PerkinElmer #6057302) at 2,500 cells per well (an empirically determined density) in 75pL media and treated with compounds and / or hypoxia as indicated in the respective experiments. Cells were then fixed with 4% paraformaldehyde for 30 min, washed with PBS once, permeabilized with 0.5% Triton X-100 in PBS for 30 mins, and incubated with blocking buffer (0.5% Triton X-100, 2% BSA in PBS) for Ihr. Primary antibody were used at dilutions indicated in the section antibodies and incubated at 4 °C overnight on a rocker. The next day, cells were washed three times with 0.5% Triton X-100 in PBS and incubated with secondary antibody. Hoechst 33342 and Alexa Fluor™ 568 Phalloidin at room temperature for Ihr on a rocker. Next, cells were washed three times with 0.5% Triton X-100 in PBS. After the final wash, 50pL of IX PBS was added to each well. Plates were sealed with adhesive foil and imaged with 20x water immersion lens at 5 fields of view, in 3 channels. GraphPad Prism (v9.4.1) was used for graphs and statistical tests (oneway ANOVA and Tukey’s correction).

[0207] Imaging and feature extraction. All imaging for the phenotypic profiling of HepG2 cells was performed on the PerkinElmer Operetta CLS System in confocal spinning-disk modewith a 20x water immersion lens (NA1.0, effective resolution 0.66pm). Each well was imaged at 5 fields of view, in 4 channels in 3 z-planes. Cell segmentation and single-cell feature extraction were performed using the Harmony™ software (v4.9, Perkin-Elmer) based on maximum intensity projections of each field of view.

[0208] High-dimensional phenotypic profiles for hypoxic response trajectory. In total, 859 features (e.g., intensity, morphology, and texture features) were extracted from each cell. Phenotypic profiles were constructed on well-level to reflect the feature distributions as previously described. (30). For each well, we compute a Kolmogorov-Smirnov (KS) statistic score for each feature, summarizing the difference between the cumulative distributions of cells in the well and cells from the control condition, defined by pooling all wells in normoxia. Differences in features across batches were assessed using the batch-to-average log fold change for each dimension. Feature categories enriched in the top fold-change dimensions, such as intensity, threshold compactness, radial mean, and axial small, were subsequently filtered out, resulting in 591 features for the subsequent analysis. KS scores for all features were then concatenated to form a phenotypic profile for the selected well. Subsequent analyses (cell state classification, PCA visualization) were based on well-level phenotypic profiles. Computations were performed using Python (v3.9).

[0209] Classification of cellular states based on phenotypic profiles. Cellular states prediction was performed on well-level phenotypic profiles by k-Nearest Neighbor (kNN) classifier. 10% of all wells at 5 different hypoxic treatment times (Normoxia, hypoxia 6hr, hypoxia 24hr, hypoxia 48hr, and hypoxia 6days) were randomly chosen as the test set. The remaining wells were used to train the kNN classifier (k=5) with hypoxic treatment time as the labels. Prediction accuracy of the testing wells was calculated by the fraction of correct label assignments for the testing set. This procedure was repeated for 100 times and the mean prediction accuracy was used to represent the prediction accuracy of cellular states.

[0210] Measurement of ATP production by Seahorse assay. HepG2 cells were seeded in 96- well Seahorse XFe96 Cell Culture Plates (Agilent, 101085-004) at a density' of 10,000 cells per well and treated with compounds about 1 hr later. Metabolic activity was measured using an Agilent Seahorse XFe96 Analyzer following the manufacturer’s protocol for Mito Stress Test and analyzed using the manufacturer-provided online analysis tool (https: / / seahorseanalytics.agilent.com / ).

[0211] AH-to-CH phenopushing compound library screen. The pnmary compound screen was performed in multiple batches. Each batch contained eight screening plates plus three plates forbatch-level QC: a normoxia control plate (DMSO only), a 6d CH control plate (DMSO only) for QC assessment of hypoxia responses, and a 24h AH reference compound plate for QC assessment of cellular responses to compounds in AH. For plate-level QC, each screening plate also included positive control wells (with bioactive compounds) and negative control wells (DMSO).

[0212] Within each batch, our screen workflow is designed to seed cells in all plates at the same time as well as to stain / fix cells at the same time. HepG2 cells were split from flasks and seeded into 384-well PhenoPlate’s (Perkin-Elmer) at about 2,500 cells per well (about 3,500 cells for the 6d hypoxia condition to account for growth rate differences) in 75 pL of media and treated with compound for 48h before cell staining.

[0213] For chronic hypoxia plates, we start with cells from a flask that had already been treated with 1% O2for 4d. Cells were then plated into the 384-well plate using media that had been preconditioned with 1% O2. All other plates were seeded with cells maintained in normoxia. Right after plate seeding, compounds and DMSO were added in duplicate at 2 doses (high dose of lOpM or 2pM and a 10-fold dilution) using the ECHO 650 liquid handling system (Beckman Coulter. Brea. CA) integrated into a Perkin Elmer EXPLORER G3 WORKSTATION to a final concentration of 0. 1% DMSO. Because the EXPLORER G3 workstation was also integrated with a 1 % O2incubator, exposure to ambient air was minimized for chronic hypoxia plates except for the brief duration of ECHO-mediated compound addition to the plate.

[0214] After compound addition: (1) the 6d CH control plate was immediately transferred to the hypoxia glovebox, (2) the normoxia control plate was returned to the normoxia incubator, and (3) the 24h AH reference compound plate and eight screening plates were incubated in normoxia for 24h before transfer to the hypoxia (1% O2) glovebox for another 24h. After a total of 48h compound treatment, cells were stained by adding lOuL of a freshly prepared 8x dye master mix in pre-warmed media (see staining reagents) and incubated at 37 °C for Ih in their corresponding incubators (N plates in normoxic incubator, AH and CH plates in hypoxic incubator). Cells were then fixed by adding 30 pL 16% paraformaldehyde (final concentration about 4%) for 30 min at room temperature. Finally, cells were washed three times with lx HBSS. sealed with adhesive foil, and light-protected until imaging. All pipetting steps were automated using the Perkin Elmer EXPLORER G3 WORKSTATION and performed by MultiFloFX and 405 TS washer (BioTek, Agilent Technologies). Plate handling steps were established using PerkinElmer's plate:: works™ (v6.2) software.

[0215] The experimental workflow in the confirmation screen was identical as in primaryscreening, except compounds were tested at 6 concentrations (lOpM, 2pM, 0.4pM, 0.08pM. 0.016pM, and 0.003pM) in 3 well replicates.

[0216] High-dimensional phenotypic profiles for compound screening. Phenotypic profiles were constructed on well-level similar as described earlier in High-dimensional phenotypic profiles for hypoxic response trajectory. For the purpose of identifying AH-to-CH phenopushing hits, the phenotypic profiles were calculated using AH DMSO condition as the control. Phenotypic profiles were calculated batch -wise: For a specific feature, the difference in cumulative distribution functions (CDF) between cells in a selected w ell and cells from control condition (pooled cells from all DMSO-treated wells in AH within this batch) were summarized by a Kolmogorov-Smirnov (KS) statistic. KS scores for all features were then concatenated to form a phenotypic profile for the selected well. Subsequent analyses (QC, AH-to-CH phenopushing hit calling) were based on w ell-level phenotypic profiles.

[0217] After summarization of well-level phenotypic profiles, feature selection was performed for batch bias removal. We first calculated PC A embeddings based on combining all well-level phenotypic profiles from all batches. Among the top 5 PCs, w e identified the PC dimension that accounted for the most variance. We further checked the feature loading scores for PC2 and identified the top 4 feature types w ith the strongest batch effects and removed them for subsequent analysis. Computations were performed using Python (v3.9).

[0218] Quality control for compound screening. Quality control for compound screening was performed on both batch-level and plate-level.

[0219] Batch-level QC. Separability between nomoxia, acute hypoxia and chronic hypoxia states (represented by DMSO-treated wells in each condition) as well as the classification accuracy of compounds within a reference library w as used as quality control for each batch. (1) Cellular states prediction using phenotypic profiles of DMSO-treated wells was performed on well-level phenotypic profiles by k-Nearest Neighbor (kNN) classifier in the same procedure as described in Classification of cellular states based on phenotypic profiles. (2) Compound category classification for reference compound set was performed on well-level phenotypic profiles in the same procedure as cell state classification, except that the phenotypic profiles of selected compound wells were first transformed by linear discriminant analysis with category names as the labels before kNN classification.

[0220] Plate-level QC. The plate-level QC was performed by analyzing the phenotypic profiles of control w ells (DMSO wells as negative control, AZD8055 or Fluvastatin treated wells as bioactive control) on AH plates. (1) For each batch, compound classification wasperformed on DMSO and bioactive (AZD8055, Fluvastatin) control wells in all AH plates. (2) The Mahalanobis distance of the phenotypic profiles of each DMSO control wells to centroid of the pooled AH DMSO control wells within the same batch were calculated (as indicated later in the AH-to-CH phenopushing hit-calling for primary screen) and compared for plate-level variability .

[0221] AH-to-CH phenopushing hit-calling for primary screen. Hit calling was performed as in the following steps: Identification of wells that move out of the AH DMSO cloud. We calculated if there was detectable alteration in phenoty pic profiles of screening wells from the control wells (DMSO-treated wells in AH condition within the same batch) batch-wise. The Mahalanobis distance of the phenotypic profile of each screening well relative to the centroid of control wells were calculated. The Mahalanobis distance of each control well towards control centroid was also calculated to build up the control background distribution. The Mahalanobis distance of a given well was compared with this background distribution. Screening wells with a significantly larger Mahalanobis distance (t-test, p-value<10-6) were identified as bioactive wells.

[0222] Distance-based AH-to-CH phenopushing calculation and hit calling. DMSO-treated wells in CH condition across all batches were combined as the destination cell state (CH cloud) for AH-to-CH phenopushing. For well i, the Mahalanobis distance (Di) of each screening well was calculated as the distance from its phenotypic profile to the centroid of the destination cell state (CCH). AS reference, the Diof DMSO-treated wells in N. AH and CH conditions were also calculated. The Diof each screening well i was normalized to acute hypoxia DMSO-treated wells within the same screening plate by Z-score: ZScorei= (Di- DDMSO) / σDMSO, where DDMSOand σDMSOrepresent the mean value and standard deviation of the Di of AH DMSO wells on the same screening plate. A tested perturbation is identified as hit perturbation if at least one of the duplicate wells is bioactive (bioactivity p-value <10-6) and has ZScorei < -3 (where lower ZScore; is “desirable”, i.e., indicates a closer distance towards the chronic hypoxia centroid).

[0223] Direction-based AH-to-CH phenopushing calculation and hit calling. The AH-CH reference vector was defined as the vector from the centroid of pooled DMSO wells in AH from all batches (CAH) to the centroid of pooled DMSO wells in CH from all batches (CCH): VCH AH = CCH - CAH. For each well i (both screening wells and DMSO-treated wells in N, AH, CH conditions), the cosine similarity between its pheno-shift vector from CAH (Vi) and the AH-CH reference vector (VCH AH) was calculated and converted to an angle (0i): 0i = cos-1(VCH AH • Vi / II VCH AH II IlVill). The 0i of all DMSO-treated wells in CH condition (θCH) across all batcheswere combined and ranked from smallest to largest to form a positive control distribution of angles. A tested perturbation is identified as hit perturbation if at least one of the duplicate wells is bioactive (bioactivity p-value <1 O-6) and has 0i within the top 90%tile of the θCHpopulation (0; < P90( θCH)).

[0224] Hit perturbations were then matched onto compounds. The union of hit compounds identified from distance- and / or direction- based hit-calling constituted the primary phenopushing hit compounds.

[0225] Hit-calling for multi-dose confirmation screen. Bioactivity was calculated using the DMSO wells (negative control) under acute hypoxia within the corresponding batch. For phenopushing calculations, the destination cell state was defined by combining DMSO wells under chronic hypoxia from both the primary' screen and the confirmation screen. The Diand Oi for all test wells and DMSO-treated wells at N, AH. CH conditions were calculated. The same AH-to-CH phenopushing hit-calling cutoff was used as described for Hit-calling for primary screen. A perturbation (compound + dose) was identified as a hit perturbation if at least 2 out of 3 well replicates meet the hit criteria. The list of confirmed phenopushing hit compounds reflected all compounds with hit perturbations at any dose.

[0226] Target overrepresentation analysis. Target profiles for screening compounds were summarized based on compound activities in the Chembl database (chembl32) and then subjected to the following criteria: specific target type (single protein, protein complex), organism (Human), standard type (IC50. EC50. Kd), and more than 3 reported activity observations. For each compound-target pair, the median value of all reported observations was calculated as the activity' value. For each screened compound, the targets with median Pactivityrepresenting the -log10 of the median activity values) >6 were curated as the active targets. Each target was then mapped onto the screened compounds for that target. We applied hypergeometric distribution overrepresentation analysis for each target’s association with phenopushing hits. The corresponding P-values were adjusted for false discovery' rate (Benjamini-Hochberg procedure). Targets with an adjusted P-value <0.05 were considered overrepresented targets for phenopushing.

[0227] Hit rate vs potency calculation. Tested compounds for a specific target were separated into different potency ranges based on their activity values (see "Target overrepresentation analysis”). For BETs, the median activity over multiple BRD isoforms (BRD2. BRD3. BRD4. BRDT) was used. For PI3K, the median activity over all reported PI3K isoforms (PIK3C2A, PIK3C2B, PIK3C3, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3CG / PIK3R5,PIK3R1 / PIK3CA, PIK3R1 / PIK3CB, PIK3R1 / PIK3CD. PIK3R1 / PIK3CG) was used. Hit rate for a specific target within a potency range was calculated as the percentage of confirmed hits identified (from either distance-based or direction-based hit-calling) among the subset of screened compounds whose activity value fell within the potency range for that target (FIGS. 3B-3D)

[0228] Hit evaluation in ischemia-like conditions. HepG2 cells were seeded into 384-well PhenoPlate’s (Perkin Elmer) at an empirically determined density (2500 cells / well) in 75 pL of media per well. Compounds were added to each well (4 replicates per compound) using the ECHO 650 liquid handling system (Beckman Coulter) integrated with an automation system (Perkin Elmer EXPLORER G3 WORKSTATION with PerkinElmer's plate:: works™ (v6.2) software). After 48hr of compound treatment, media was replaced with media containing CellTox™ Green according to the manufacturers protocol. Cells were then subjected to hypoxia (1% O2) with deprivation of glucose and FBS (to mimic ischemia-like stress) or without nutrient deprivation. The duration of exposure to ischemia-like stress was calibrated so that only -10% of the DMSO cells remained (~48-72hr in ischemic conditions, FIG. 4B). Next, cells were washed and fixed with paraformaldehyde (final concentration 4%) for 30 min at room temperature. Plates were then stained with Hoechst 33332, washed with lx HBSS (Invitrogen), sealed with adhesive foil, and light-protected until imaging. All pipetting steps were automated using the Perkin Elmer EXPLORER G3 WORKSTATION and performed by MultiFloFX and 405 TS washer (BioTek, Agilent Technologies). Imaging w as performed on the Operetta CLS confocal spinning-disk high-content analysis system (Perkin Elmer) using a lOx objective and appropriate filter settings. The full area of each well was imaged. Cell segmentation and singlecell feature extraction were performed using the Harmony ™ (v4.9) software (Perkin Elmer, Waltham, MA). Live-cell count was calculated by applying a threshold to the mean nuclear CellTox™ Green intensity. Cell survival was normalized to the mean survival of DMSO-treated cells.

[0229] Human iPSC-derived cardiomyocyte differentiation. Human induced pluripotent stem cell (iPSC)-derived cardiomyocytes were generated from a GCaMP6-expressing cell line in the WTC background (obtained from the Gladstone Institute Stem Cell Core facility; this cell line also harbors an inducible dCas9-KRAB cassette) by combining previously published protocols. (47-49). Briefly, the iPSCs were dissociated into a single cell suspension using TrypLE (Thermo Fisher Scientific) and seeded into Geltrex™ LDEV-Free Reduced Grow th Factor Basement Membrane Matrix-coated 12 well-plates (Thermo Fisher Scientific) at 75k cells per well in E8 media (Thermo Fisher Scientific) with 10 pM Y-27632. E8 media (without Y-27632) wasexchanged daily. We initiate the differentiation when the hiPSCs reached 80-90% confluence (~48h) by addition of RPMI-1640 with B27 (without insulin) and 7.5 pM CHIR99021. After 48h, the media was replaced with RPMI-1640 with B27 (without insulin) and 7.5 pM IWP2. After an additional 48h, the media was replaced with RPMI-1640 with B27 (without insulin). Two days later, the media was changed to RPMI-1640 with B27 containing insulin and 1% penicillin / streptomycin and replenished every 2-3 days. Spontaneous beating was typically observed 8-10 days after the addition of CHIR99021. Only iPSC-CM’s from wells that were estimated to show spontaneous beating in >80% of the surface area were used for subsequent lactate enrichment. After 6 days of enrichment, iPSC-CM’s were seeded into Geltrex coated 72cm2flasks (cells from one 12 well per flask) and cultured in RPMI-1640 with B27 with insulin and 2 pM CHIR99021 until they reached confluence. Finally. iPSC-CM’s were replated into Geltrex coated 384-well PhenoPlate’s (Perkin Elmer) and switched to maturation medium for 10 days. These matured iPSC-CM’s were then used for all subsequent experiments.

[0230] Hit evaluation using iPSC-derived cardiomyocytes. Compounds were added directly to each well (4 replicates per compound) of matured iPSC-CM’s in 384-well Phenoplate’s (Perkin Elmer) and incubated at 1% oxygen in a glovebox (InvivO2400, Baker Ruskinn) 48h after drug addition. Once cardiomyocyte beating was lost in most DMSO wells (~48-72h in hypoxia), GFP signal (reflecting Ca2+transients) was monitored using a EVOS M7000 Imaging System set at 37 C, 80% humidity, 5% CO2and 1% O2(Thermo Fisher Scientific) for 30 seconds at a frame rate of 30 Hz in the GFP channel (excitation max 488 nm) using a 20x objective (one FoV) for a total processing time of ~12h. Normoxic iPSC-CM’s from the same batch of cells was imaged after the hypoxic experiments were complete. Subsequently, cells were fixed with paraformaldehyde (final concentration 4%) for 30 mm at room temperature. To assess intactness of sarcomere structure, immunofluorescence staining w as performed as described above using a sarcomeric a-actinin monoclonal antibody (EA-53, Invitrogen #MA 1-22863). In addition, the actin cytoskeleton was stained using phalloidin. Plates were then sealed with adhesive foil and light-protected until immunofluorescence confocal imaging on the Operetta CLS (Perkin Elmer) at 20x. Each well was imaged at 5 fields of view, in 3 channels in 3 planes.

[0231] Analysis of iPSC-derived cardiomyocyte Ca2+transients. Mean fluorescence intensities for each field of view were extracted using the scikit-image (vl.2.2) module. The signal was baseline-corrected using the Python package BaselineRemoval (v0. 1 .3), and the mean signal intensity of frames 250-850 (~20secs) w as plotted for each compound. Peaks were detected from the resulting time series data and converted into peak feature vectors using a custom analysis pipeline in Python. The similarity of beating patterns was quantified by calculating theManhatan distance of replicate-averaged peak feature vectors compared to DMSO-treated iPSC-CM’s in normoxia and min-max scaled.

[0232] Analysis of iPSC-derived cardiomyocyte sarcomere structure. Subsets of image pixels were manually labeled as either background (no cells), intact sarcomere structure, or deteriorated sarcomere structure using the Python package Label Studio (vl.9). Image features of the labeled regions were extracted using the scikit-image (vl.2.2) module “multiscale_basic_features” and used to train a random forest classifier. New images were then subjected to the same feature extraction procedure, and the trained random forest classifier was used to predict the labels for all pixels. The resulting predicted labels were manually spot checked. The percentage of all imaged pixels labeled as intact sarcomere was used for all downstream analysis.

[0233] Comparison of transcriptomics data from GTEx healthy human tissues and CCLE cancer cell lines. The Genotype-Tissue Expression (GTEx) and Cancer Cell Line Encyclopedia (CCLE) RNA-seq datasets, comprising 53 healthy human tissues and 934 cancer cell lines, respectively, were downloaded as FPKM values from EMBL-EBI database22,23. First, the expression data w ere scaled to ensure consistent library sizes. Next, gene expression was Z- score transformed across all cell lines (for CCLE data) or tissues (for GTEx data). Then, the top 200 expressed genes were identified in each cell line or tissue, and the number of overlapping genes between cell lines or tissues was used to measure their similarity. Pairwise comparisons were made between each tissue and each cell line (i.e., 49,502 comparisons) to determine the number of shared top 200 expressed genes (FIG. 6). The best-matching tissue-cell line pair (defined as the highest number of shared top 200 expressed genes) was liver and HepG2. The same pair was identified when examining top 500 or top 1000 expressed genes.

[0234] RNA-seq and data processing. HepG2 cells were cultured in 6-well plates and treated with hypoxia for the indicated time. Approximately one million cells were collected by trypsinizing. The cell pellet was washed with PBS and stored at -80 °C until extraction with RNeasy Plus Mini kit (Qiagen #74134) according to manufacturer instructions. Preparation of sequencing libraries (NEBNext Ultra II RNA Library Prep Kit), sequencing (Illumina HiSeq 2xl50bp paired end), and demultiplexing (Illumina bcl2fastq v2.20) were conducted by Azenta Life Sciences (Genewiz). After mapping reads to GRCh38 using STAR (version 2.7.9a). the count matrix was obtained using featureCounts (version 2.0.1) with reference gene annotations from Gencode v38. Count data w ere normalized using DESeq2 (version 1.44.0). For differential expression analysis, DESeq2 was used to compare hypoxia conditions against normoxia. Log2 fold change and p-values were obtained for each gene in each comparison. PCA analysis wasperformed with median-normalized read counts scaled using StandardScalar in scikit-leam (version 1.5.2). Pathway enrichment was performed using Gprofiler (version 1.0) on genes that have |log2FC| >1.5 and padj<0.05. The RNAseq data were deposited in the Gene Expression Omnibus (GEO) repository (GSE283995).

[0235] Data availability. RNA-seq data from GTEx and CCLE FPKM datasets can be accessed from the EMBL-EBI database under the accession numbers E-MT AB-5214 and E- MT AB-2770, respectively. Processed cell line data relevant to each figure has been deposited in CSV format on Zenodo at the following link: https: / / zenodo.org / records / 14406186.

[0236] Code availability. The code necessary to replicate the results has been made available in Jupyter Notebooks and can be accessed on Zenodo at the following link: https: / / zenodo.org / records / 14406186.

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Claims

CLAIMSWhat is claimed is:

1. A method of identifying a compound that accelerates a transition of a cell from an acute hypoxic state to a hypoxic adaptive state, the method comprising:(i) contacting a test compound with the cell in the acute hypoxic state, and(ii) detecting the transition of the cell from the acute hypoxic state to the cell in the hypoxic adaptive state and measuring the length of time of the transition; wherein the length of time of the transition is shorter in the presence of the test compound relative to the absence of the test compound, thereby identifying the compound that accelerates the transition.

2. The method of claim 1, wherein detecting the transition from the cell in the acute hypoxic state to the cell in the hypoxic adaptive state comprises detecting a cell hypoxic stress marker in the cell in the hypoxic adaptive state.

3. The method of claim 2, wherein the cell hypoxic stress marker is an oxidative phosphorylation marker, a glycolysis marker, an ATP production marker, HIF-1α protein expression, an imaging marker, or a combination of two or more thereof4. The method of claim 2, wherein the cell hypoxic stress marker is an oxidative phosphorylation marker; and wherein step (ii) comprises detecting oxygen consumption rate or mitochondrial membrane potential.

5. The method of claim 2. wherein the cell hypoxic stress marker is a glycolysis marker; and wherein step (ii) comprises detecting extracellular acidification rate or glucose uptake.

6. The method of claim 2, wherein the cell hypoxic stress marker is an ATP production marker; and wherein step (ii) comprises detecting ATP amount or ATP production rate.

7. The method of claim 2. wherein the cell hypoxic stress marker is HIF-1α protein expression; and wherein step (ii) comprises detecting a decrease in HIF-1α protein expression.

8. The method of claim 2, wherein the cell hypoxic stress marker is a imaging marker selected from the group consisting of lipid droplets, lipid peroxidation, mitochondrial structure, nuclear morphology, nucleolus markers, endoplasmic reticulum / Golgi complex, actin cytoskeleton, plasma membrane, or a combination of two or more thereof.

9. The method of claim 8, wherein the imaging marker is lipid droplets; and wherein step (ii) comprises detecting an increase in number and intensity of punctuate structures, a decrease in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

10. The method of claim 8, wherein the imaging marker is lipid peroxidation; and wherein step (ii) comprises detecting an increase in number and intensity of punctate structures, an increase in diffuse staining across the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.1 1 . The method of claim 8, wherein the imaging marker is mitochondrial structure; and wherein step (ii) comprises detecting an increase in perinuclear staining, a decreased staining throughout the cell body, or a combination thereof in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

12. The method of claim 8. wherein the imaging marker is nuclear morphology; and wherein step (ii) comprises detecting a decrease in staining in the cell in the hypoxic adaptive state compared to the cell in the acute hypoxic state.

13. The method claim 1, further comprising before step (i): culturing a cell in a normoxic state under acute hypoxic conditions to transition the cell in the normoxic state to the cell in the acute hypoxic state.

14. The method claim 1, further comprising after step (i): further culturing the test compound with the cell in the acute hypoxic state under acute hypoxic conditions.

15. A method of treating acute hypoxia in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute hypoxia.

16. A method of treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleep apnea, interstitial lung disease, cyanide poisoning in a patient in need thereof in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute respiratory distress syndrome, anemia, asthma, pulmonary embolism, pneumothorax, pulmonary edema, pulmonary fibrosis, bronchitis, chronic obstructive pulmonary disease, emphysema, pneumonia, congestive heart failure, sleepapnea, interstitial lung disease, cyanide poisoning in a patient in need thereof.

17. A method of treating acute hypoxia in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute hypoxia.

18. A method of treating acute ischemia in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating acute hypoxia.

19. A method of treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof in a patient in need thereof, the method comprising administering to the patient an effective amount of hypoxic adaptive state-inducing compound, thereby treating stroke, acute limb ischemia, myocardial ischemia, myocardial infarction, intestinal ischemia, or mesenteric ischemia in a patient in need thereof.

20. A method of transitioning a cell in an acute hypoxic state to a cell in a hypoxic adaptive state ex vivo or in vivo, the method comprising contacting the cell with a hypoxic adaptive state-inducing compound, thereby transitioning the cell in the acute hypoxic state to the cell in the hypoxic adaptive state.

21. The method of claim 15, wherein the hypoxic adaptive state-inducing compound is a mTOR inhibitor.

22. The method of claim 21, wherein the mTOR inhibitor is sirolimus, everolimus, temsirolimus, ridaforolimus, Torin-1, Torin-2, PP242, rapamycin, AZD8055, niclosamide, omipalisib, KU-0063794, RMC-5552, WYE-687, ETP -46464, alpelisib, apitolisib, bimiralisib, buparlisib, copanlisib, dactolisib, duvelisib, gedatolisib. idelalisib, umbralisib, voxtalisib, PI- 103, VS-5584, PKI-402, BGT226, SF1126, PKI-587, PF-04691502, or a pharmaceutically acceptable salt of any one of the foregoing.

23. The method of claim 15, wherein the hypoxic adaptive state-inducing compound is a BET inhibitor.

24. The method of claim 23, wherein the BET inhibitor is mivebresib, BAY 1238097. amredobresib, BMS-986158, pelabresib, FT-1101, alobresib. molibresib. GSK2820151 , INCB054329, birabresib, PLX51107, R06870810, ZEN003694, or a pharmaceutically acceptable salt of any one of the foregoing.

25. The method of claim 15, wherein the hypoxic adaptive state-inducing compound is sirolimus, omipalisib, RMC-5552, alpelisib, apitolisib, copanlisib, duvelisib. idelalisib, umbralisib, PI-103, BGT226, SF1126, PKI-587, mivebresib, BAY 1238097, amredobresib, BMS-986158, pelabresib, FT-1101, alobresib, molibresib, GSK2820151, INCB054329, birabresib, PLX51107, R06870810, ZEN003694, (-)-MK 801, (+)-JQl, 7 -methoxy tacrine, 9- aminoacridine, ABT-737, AG-1024. Akt inhibitor VIII, AMG-458, AMG-548, amitriptyline, amonafide. aprepitant. AR-A014418, arctigenin, tivantinib. arteether. artesunate. Aurora A Inhibitor I, axitinib, AZ20, azacitidine, azaguanine-8, azathioprine, AZD2014, AZD2858, AZD8055, bedaquiline, belotecan, benzalkonium chloride, benzethonium chloride, dactolisib, bimiralisib, brigatinib, bromocriptine, broxyquinoline, buparlisib. BYL719, camptothecin, carmofur, CC-115. CC-223, CGP-37157, CHIR-124, chlorquinaldoL clotrimazole, cloxiquine. CO-101244, COTI-2, CP-99994, crizotinib, defactinib, ridaforolimus, di chlorophene, digoxigenin, digoxin, dihydroartemisinin, eltrombopag, entinostat, epetraborole, erastin, eravacy cline, erlotinib, ethacridine, ethidium, ETP-46464, everolimus, 5-fluoracil, fosaprepitant, G1T38, GDC-0084, GDC-0349. GDC-0980, gedatolisib, Go 6983, GSK J4, harmine, hexachlorophene, HTH-01-015, hydroxy camptothecine, I-BET151, 1-CBP-112, ICG-001, ICI- 199441, IMD 0354, indacaterol, sapanisertib, itraconazole, KU-0063794, LDN193189, LDN- 212854, letrozole, liothyronine, LM-22A4, lonafamib, LRRK2-IN-1, LY3023414, M344, malotilate, mechlorethamine, 6-mercaptopurine, miconazole, mirin, MK-2206, ML -297, ML- 348, MRK-560, my cophenolate, mycophenolic acid, NAV-26, NBI-27914. nelarabine. neratinib, nexturastat A, NH125, niclosamide, nifuratel, nitazoxanide, nitrofurantoin, nitroxoline, NNC-05-2090, NU6027, obatoclax, octenidine, oligomycin A, OSI-027, OSI-420, OTX015, oxaliplatin, pentamidine, PF-04691502, PF-05212384, PF-06463922, PF-477736, PHA-767491, PHTPP. pifithrin-cyclic, PIK-93, pirfenidone, piroctone. PKI-402. PLX-4720. PNU-74654, PP-121, PP242, PPT, prexasertib, PRT062607, PSB-1115, PTC -209, rapamycin, resiquimod, resminostat, RG7112, RGFP966, Ro-90-7501, RX-3117, SB415286, SB-328437, SB505124, scriptaid, securinine, serabelisib, silodosin, SN-38, STF-118804, T0901317, tannic acid, TC-G-1004, TCS-2210, telotristat etiprate, temsirolimus, tenovin-1. TG100713, thioguanine, tizoxanide, topotecan, torcetrapib, Torin-1, Torin-2, trichostatin A, triclocarban, tubastatin A, tucidinostat, tyrphostin 9, valnemulin, velneperit, vemurafenib, vilazodone, voxtalisib, VS-5584, WAY-100635, wiskostatin, WYE-125132, WYE-354, WYE-687, XL388, yoda-1. ZSTK474, or a pharmaceutically acceptable salt of any one of the foregoing.

26. A method of identifying a compound that induces a hypoxic adaptive state in a population of cells from a mammalian cell line or mammalian cell source of interest, the method comprising:(a) distributing cells of the population to individual compartments to provide a subpopulation of the cells in each compartment for compound treatment;(b ) culturing the subpopulations of cells in the individual compartment under acute hypoxic conditions for a predetermined amount of time that is sufficient to induce a cellular phenotypic profile of an acute hypoxic state in the cell line or cell source of interest;(c) adding one or more test compounds to each individual compartment;(d) further culturing the subpopulations of cells under acute hypoxic conditions for a predetermined length of time shorter than a predetermined amount of time that induces the phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest;(e) detecting at least one morphological feature of cells from each compartment, wherein the morphological feature is a phenotypic characteristic of a phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest;(f) performing image analysis to obtain a phenotypic profile for the at least one morphological feature for each of the treated subpopulation of cells; and(g) identifying a compound that induces a shift of the phenotype of the at least one morphological feature towards the phenotypic profile of the hypoxic adaptive state in the subpopulation of cells treated with the compound, thereby identifying a compound that induces a hypoxic adaptive state.

27. A method of identifying a compound that induces a hypoxic adaptive state in a population of cells from a mammalian cell line or mammalian cell source of interest, the method comprising:(a) distributing cells of the population to individual compartments to provide a subpopulation of the cells in each compartment for compound treatment;(b) culturing the subpopulations of cells in the individual compartment under acute hypoxic conditions for a predetermined amount of time that is sufficient to induce a cellular phenotypic profile of an acute hypoxic state in the cell line or cell source of interest;(c) adding one or more test compounds to each individual compartment;(d) further culturing the subpopulations of cells under acute hypoxic conditions for a predetermined length of time shorter than a predetermined amount of time that induces a phenotypic profile of a hypoxic adaptive state in the cell line or cell source of interest;(e) detecting at least two different morphological features of cells from each compartment, wherein the morphological features are phenotypic characteristics of a phenotypic profile of the hypoxic adaptive state in the cell line or cell source of interest;(f) performing image analysis to obtain a phenotypic profile for the at least two different morphological features for each of the treated subpopulation of cells; and(g) identifying a compound that induces a shift of the phenoty pes of the at least two morphological features towards the phenotypic profile of the hypoxic adaptive state in the subpopulation of cells treated with the compound, thereby identifying a compound that induces a hypoxic adaptive state.

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