HIF1A Human (85 a.a.)

What is the functional significance of HIF1A in cellular response to hypoxia?

HIF1A is a member of the basic helix-loop-helix PAS superfamily that plays a crucial role in cellular and developmental response to low oxygen concentration (hypoxia) . The protein comprises four functional regions: basic helix-loop-helix domain, PAS domain, stability determining domain, and trans-activating domain . As a metabolic regulator, HIF1A induces glycolysis in macrophages, promotes M1 polarization and activation that enhances inflammatory gene expression, bacterial killing, and cell migration . Additionally, HIF1A is involved in tumor growth, survival, and metastasis, serving as a marker for poor clinical outcomes in certain cancers like lung cancer .

How is HIF1A protein regulated under normoxic versus hypoxic conditions?

Under normoxic conditions, HIF1A protein is rapidly degraded through hydroxylation by prolyl hydroxylases (PHDs), marking it for ubiquitination and proteasomal degradation. In hypoxic conditions, PHD activity is inhibited due to limited oxygen availability, resulting in HIF1A stabilization. Experimental approaches demonstrate this regulation, as treatment with dimethyloxalylglycine (DMOG), which inhibits PHDs, stabilizes HIF1α protein even in normoxic conditions . This stabilization leads to increased expression of downstream targets including inflammatory cytokines and enzymes like IL-1β, IL-6, TNF-α, and iNOS .

What standard methods are used to detect and measure HIF1A expression in research settings?

Based on the research data, several standard methods are employed to study HIF1A:

• Western blotting for protein expression analysis of HIF1A and downstream targets like iNOS and Nox2

• Quantitative real-time PCR for analyzing mRNA expression of HIF1A and target genes

• ELISA for measuring production of cytokines regulated by HIF1A (IL-1β, IL-6, TNF-α)

• Three-dimensional spheroid culture as an in vitro model of hypoxia to study HIF1A function

• Genetic manipulation using shRNA targeting HIF1α to study loss-of-function effects

• Pharmacological manipulation using DMOG to stabilize HIF1α protein

These complementary approaches allow researchers to comprehensively assess HIF1A expression, stability, and transcriptional activity in various experimental contexts.

How does HIF1A form regulatory feedback loops with other molecular partners?

Research data reveals that HIF1A participates in crucial molecular feedback loops that regulate cellular responses to hypoxia and inflammation. A significant finding is the positive feedback loop formed between HIF1A and Adenylate Kinase 4 (Ak4) in M1 macrophages:

• Suppressing HIF1α expression with shRNA results in downregulation of Ak4

• Conversely, treating M1 cells with DMOG (which stabilizes HIF1α) upregulates Ak4 expression

• Ak4 not only stabilizes HIF1α protein but also enhances its transcription

• This feedback loop positively regulates the expression of inflammatory genes including IL-1β, IL-6, TNF-α, Nox2, and iNOS

The mechanism appears to operate through ATP level regulation and ADP/ATP ratio, suggesting a connection between energy metabolism and inflammatory response . Additionally, AMPK activation is enhanced in cells treated with Ak4 shRNA, and AMPK agonists further reduce the expression of inflammatory markers, indicating a complex regulatory network involving HIF1A, Ak4, and AMPK signaling pathways .

Why do studies show contradictory results regarding HIF1A's effect on cell proliferation?

The research results highlight interesting contradictions regarding HIF1A's effect on cell proliferation:

These contradictions may be explained by different experimental models (3D spheroid cultures versus 2D monolayer cultures), varying oxygen conditions, cell-type specific responses, and context-dependent effects. This highlights the complexity of HIF1A's function and the importance of clearly defining experimental conditions when studying its effects on cell proliferation.

How does the 85 a.a. region of human HIF1A contribute to its interaction with cofactors?

While the search results don't specifically address an 85 a.a. region of HIF1A, computational analysis has identified extensive cofactor interactions that may involve specific domains:

• A systematic study discovered 201 potential HIF1A cofactors across eight cancer cell lines

• Among these, 21 of 29 known HIF1A cofactors from public databases were identified

• Of the top 37 cofactors in the study, 19 were directly validated in the literature while 18 were novel

• These cofactors were statistically and biologically significant

The specific 85 a.a. region may be part of a functional domain (basic helix-loop-helix, PAS, stability determining, or trans-activating) that mediates these protein-protein interactions. The extensive network of cofactors demonstrates that HIF1A functions within complex transcriptional complexes that likely contribute to its diverse cellular functions in different contexts.

What is the relationship between HIF1A and malignant phenotypes in cancer?

Research indicates that HIF1A significantly promotes malignant phenotypes in cancer cells:

• In cervical cancer (HeLa cells), blocking HIF1α resulted in a significant decrease in cell proliferation and invasion, and an increase in cell apoptosis in three-dimensional culture

• HIF1A serves as a regulator of adaptive processes that promote tumor cell malignant phenotypes, including proliferation, anti-apoptosis, and invasive ability

• In non-small cell lung cancer, HIF1A acts as a marker for metastasis and poor clinical outcome

• The protein's role is particularly pronounced in three-dimensional spheroid cultures that better mimic the tumor microenvironment than traditional monolayer cultures

These findings demonstrate that HIF1A is a central regulator of cancer cell adaptation to hypoxic conditions, which are common in the tumor microenvironment, promoting aggressive phenotypes that contribute to disease progression.

What are the optimal experimental models for studying HIF1A function?

Based on the research results, several experimental models have proven valuable for studying HIF1A function:

• Three-dimensional spheroid culture:

  • Described as "an ideal model of hypoxia in vitro"

  • Creates a natural oxygen gradient with hypoxic conditions in the core

  • Enables the study of HIF1A's effects on growth, apoptosis, and invasion without artificial hypoxia induction

• Genetic manipulation systems:

  • Anti-HIF1α plasmid transfection to downregulate expression

  • shRNA-mediated knockdown of HIF1A

  • Comparison between wild-type and HIF1A-deficient cells

• Chemical modulation:

  • DMOG treatment to stabilize HIF1α even under normoxic conditions

  • AMPK agonists (A-769662, metformin) to study interaction with HIF1A pathways

• Cell type selection:

  • Macrophage models for inflammatory responses (M0, M1, M2 polarization states)

  • Cancer cell lines for malignancy studies (HeLa, lung cancer)

  • Multiple cancer cell lines (eight different lines used in cofactor studies)

The choice of model depends on the specific research question, with spheroid cultures being particularly valuable for studying tumor biology and macrophage models for investigating inflammatory responses.

How can researchers effectively identify and validate HIF1A cofactors?

The research results describe a systematic approach to identify HIF1A cofactors:

• Computational motif mining tools:

  • SIOMICS (Systematic Identification Of Motifs In Co-regulated Sequences) was used to analyze HIF1A cofactors across eight cancer cell lines

  • This approach enabled the discovery of 201 potential HIF1A cofactors

• Validation strategies:

  • Statistical significance testing of identified cofactors

  • Biological significance assessment

  • Comparison with known cofactors in public databases (21 of 29 known cofactors were identified)

• Ranking and prioritization:

  • Among the top 37 cofactors identified, 19 were directly validated in the literature

  • 18 were novel cofactors that could be prioritized for experimental validation

This computational approach demonstrates the power of bioinformatics tools in predicting protein-protein interactions and transcriptional networks. By combining computational predictions with experimental validation, researchers can efficiently discover new cofactors that may be therapeutically relevant in hypoxia-related diseases.

What techniques are most effective for studying the HIF1A-mediated inflammatory response?

The research results suggest several effective techniques for studying HIF1A-mediated inflammatory responses:

• Gene expression analysis:

  • Quantitative real-time PCR to analyze expression of inflammatory genes (Il1b, Il6, Tnfa, Nos2, Nox2, and Hif1a)

  • Western blotting to examine protein levels of HIF1α, iNOS, Nox2, and Ak4

• Cytokine quantification:

  • ELISA to measure production of IL-1β, IL-6, and TNF-α

  • Provides quantitative assessment of inflammatory output

• Genetic manipulation:

  • shRNA targeting HIF1α or Ak4 to study their interdependence

  • siRNA against AMPKα1/2 to investigate the role of AMPK pathway

• Pharmacological interventions:

  • DMOG treatment to stabilize HIF1α

  • AMPK agonists (A-769662, metformin) to modulate AMPK activity

These approaches help establish the molecular mechanisms by which HIF1A regulates inflammatory responses and identify potential therapeutic targets for inflammatory diseases.

How can researchers address data contradictions in HIF1A research?

To address contradictions in HIF1A research data, researchers should implement several methodological strategies:

• Standardize experimental conditions:

  • Use consistent oxygen concentrations to define normoxia and hypoxia

  • Standardize exposure times for hypoxic conditions

  • Control for cell density and passage number

• Compare multiple model systems:

  • Directly compare 2D monolayer cultures with 3D spheroid models

  • Use multiple cell lines to identify cell type-specific effects

  • Verify findings across different experimental platforms

• Assess multiple parameters simultaneously:

  • Measure proliferation, apoptosis, and invasion within the same experimental setup

  • Correlate phenotypic changes with HIF1A expression/activity levels

• Examine signaling context:

  • Investigate interactions with other pathways (e.g., AMPK signaling)

  • Consider the metabolic state of the cells being studied

By implementing these approaches, researchers can better understand context-dependent effects of HIF1A and resolve apparent contradictions in research findings.

How does manipulation of HIF1A affect inflammatory markers?

Table 1: Effects of HIF1A and Ak4 manipulation on inflammatory mediators

This data demonstrates the regulatory relationship between HIF1A and inflammatory mediators . Specifically, downregulation of HIF1α reduces pro-inflammatory cytokine production and iNOS expression, while DMOG-mediated stabilization of HIF1α enhances these inflammatory markers . The restoration of inflammatory marker expression in Ak4 shRNA-treated cells by DMOG treatment supports the existence of a feedback loop between Ak4 and HIF1α in regulating inflammation .

How does HIF1A affect cellular phenotypes in cancer models?

Table 2: Effects of HIF1A inhibition on cancer cell phenotypes

What is the landscape of HIF1A cofactors in cancer?

Table 3: HIF1A cofactors identified through computational analysis

This computational analysis reveals the extensive network of HIF1A cofactors, with 201 potential interaction partners identified across eight cancer cell lines . The identification of 21 out of 29 known cofactors validates the approach, while the discovery of 18 novel cofactors opens new avenues for research . These cofactors likely contribute to the diverse functions of HIF1A in different cellular contexts and may represent potential therapeutic targets in hypoxia-related diseases.