AK4 Human

What is AK4 and what is its primary function in human cells?

Adenylate Kinase 4 (AK4) is a mitochondrial enzyme belonging to the adenylate kinase family that plays a crucial role in cellular energy metabolism. Unlike other adenylate kinases, AK4 primarily functions in maintaining the homeostasis of adenine and guanine nucleotides and regulating cellular ATP levels through phosphorylation and activation of energy sensor proteins .

AK4 has multiple biological functions in cellular metabolism including:

• Regulation of nucleotide homeostasis

• Control of cellular ATP levels

• Involvement in mitochondrial respiration and glycolytic metabolism

• Interaction with hypoxia-inducible factor-1α (HIF-1α) signaling pathways

How is AK4 expression regulated under different physiological conditions?

AK4 expression is primarily regulated by oxygen levels, with chronic hypoxia being a major inducer of AK4 expression. This regulation occurs through a hypoxia-inducible factor-1α (HIF-1α)-dependent mechanism . In pulmonary arterial smooth muscle cells (PASMCs), chronic hypoxia upregulates AK4 in a HIF-1α-dependent manner .

In immune cells, specifically macrophages, AK4 expression is regulated by inflammatory stimuli. For instance, AK4 is almost exclusively expressed in M1 (classically activated) macrophages compared to M0 (unstimulated) or M2 (alternatively activated) macrophages. Lipopolysaccharide (LPS) stimulation is the primary inducer of AK4 expression in macrophages, with interferon-gamma (IFN-γ) co-treatment further enhancing AK4 expression .

What is the role of AK4 in cancer development and progression?

AK4 has emerged as a significant player in cancer biology, exhibiting elevated expression in various cancer types and contributing to multiple hallmarks of cancer:

AK4 promotes cancer progression through several mechanisms:

• Facilitates metabolic reprogramming toward glycolysis (Warburg effect)

• Forms a positive feedback loop with HIF-1α, enhancing oncogenic signaling

• Promotes epithelial-mesenchymal transition (EMT)

• Contributes to resistance against anticancer drugs and radiotherapy

Analysis of TCGA database and immunohistochemistry assays revealed significantly higher expression levels of AK4 in human SOC tissues compared to normal samples . Experimental silencing of AK4 in cancer cells inhibits tumor growth and metastasis, suggesting its potential as a therapeutic target .

How does AK4 contribute to pulmonary hypertension pathophysiology?

AK4 operates as a key metabolic regulator in pulmonary hypertension (PH) pathogenesis. In pulmonary arterial smooth muscle cells (PASMCs), AK4 drives the pro-proliferative and glycolytic phenotype characteristic of PH through several mechanisms:

• Mediates HIF-1α-dependent responses to chronic hypoxia

• Interacts with Akt signaling pathways to promote cell proliferation

• Regulates the metabolic shift from oxidative phosphorylation to glycolysis

• Creates a feedforward loop with HIF-1α in hypoxic PASMCs

Research demonstrates that RNA interference of AK4 decreases the viability and proliferation of PASMCs under both normoxia and chronic hypoxia. Additionally, AK4 silencing augments mitochondrial respiration and reduces glycolytic metabolism in these cells .

Clinical significance is supported by elevated AK4 levels in pulmonary vessels from patients with idiopathic pulmonary arterial hypertension (IPAH), suggesting AK4 as both a biomarker and potential therapeutic target for PH .

What are the most effective methods for studying AK4 function in human cells?

When designing experiments to study AK4 function, researchers should consider a combination of the following methodological approaches:

• RNA Interference (RNAi) for Loss-of-Function Studies:

  • siRNA or shRNA targeting AK4 has been successfully employed to study its function in various cell types

  • Multiple siRNA sequences should be tested to ensure specificity and efficiency

  • Appropriate controls (scrambled sequences) are essential

• Overexpression Systems for Gain-of-Function Studies:

  • Lentiviral or plasmid-based systems can be used for stable or transient overexpression

  • Include appropriate empty vector controls

  • Consider inducible systems for temporal control of expression

• Metabolic Analysis:

  • Measure mitochondrial respiration using oxygen consumption rate (OCR)

  • Assess glycolytic metabolism via extracellular acidification rate (ECAR)

  • Analyze ATP levels and adenine nucleotide ratios

• Protein-Protein Interaction Studies:

  • Co-immunoprecipitation to detect interactions with HIF-1α and other partners

  • Proximity ligation assays for in situ detection of protein interactions

• Cellular Phenotype Assays:

  • Cell viability (MTT, XTT, or similar assays)

  • Proliferation assays (BrdU incorporation, Ki-67 staining)

  • Migration and invasion assays for cancer studies

What models are appropriate for investigating AK4 function in disease contexts?

Selecting appropriate models is crucial for studying AK4 in disease contexts:

• Cell Culture Models:

  • Primary human cells (PASMCs for pulmonary hypertension studies)

  • Patient-derived cancer cell lines

  • Induced pluripotent stem cell (iPSC)-derived neurons for cognitive studies

• Animal Models:

  • Mouse xenograft models for cancer studies

  • Lung metastasis models to evaluate AK4's role in metastasis

  • Hypoxia-induced pulmonary hypertension models

• Human Samples:

  • TCGA database for cancer expression analysis

  • Patient tissue samples for validation of findings

  • Post-mortem brain tissue for neurodegenerative studies

• iPSC Models:

  • Generation of iPSC lines from patient samples

  • Differentiation into relevant cell types (e.g., neurons, smooth muscle cells)

  • Valuable for studying person-specific differences in AK4 function

The choice of model should align with the specific research question and consider disease relevance, technical feasibility, and ethical considerations.

What omics approaches are most informative for comprehensive analysis of AK4 function?

Integrative multi-omics approaches can provide deeper insights into AK4 function:

• Transcriptomics:

  • RNA-seq analysis to identify differentially expressed genes following AK4 modulation

  • Example: RNA-seq analysis of Ak4 shRNA-treated M1 macrophages identified 145 upregulated and 268 downregulated genes, revealing AK4's role in regulating inflammation-related genes

• Proteomics:

  • Tandem mass tag (TMT) proteomics to quantify protein changes

  • Used successfully to measure AK4 protein levels in induced neurons (iNs) and correlate with cognitive reserve in human donors

  • Co-immunoprecipitation followed by mass spectrometry to identify novel interaction partners

• Metabolomics:

  • Targeted metabolomics to assess nucleotide levels and ratios

  • Untargeted metabolomics to identify novel metabolic pathways influenced by AK4

• Pathway Analysis Tools:

  • Ingenuity Pathway Analysis (IPA) for identifying canonical pathways regulated by AK4

  • Gene Set Enrichment Analysis (GSEA) for determining pathway enrichment

  • Example: IPA of AK4-regulated genes in macrophages revealed involvement in HIF-1α signaling, adhesion and diapedesis, cytokine-mediated communication, and TLR signaling

• Single-Cell Analysis:

  • Single-cell RNA-seq to address heterogeneity in AK4 expression and function

  • Particularly valuable for cancer and immune cell studies

What statistical approaches are most appropriate for analyzing AK4 expression data in clinical samples?

Statistical analysis of AK4 expression in clinical samples requires careful consideration:

• Differential Expression Analysis:

  • For RNA-seq data: DESeq2 or edgeR with appropriate thresholds (e.g., p<0.01, fold-change >2)

  • For proteomics data: limma or mixed-effects models with proper normalization

• Survival Analysis:

  • Kaplan-Meier analysis with log-rank test to evaluate the association between AK4 expression and patient outcomes

  • Cox proportional hazards models for multivariate analysis

• Correlation Analysis:

  • Pearson or Spearman correlation to assess relationships between AK4 and other molecular markers

  • Example: Correlation between AK4 expression in induced neurons and cognitive decline rates

• Study Design Considerations:

  • Power analysis to determine appropriate sample sizes

  • Adjustment for multiple testing (e.g., Benjamini-Hochberg procedure)

  • Inclusion of appropriate covariates in statistical models

• Visualization Techniques:

  • Principal Component Analysis (PCA) for visualizing sample clustering

  • Heatmaps and hierarchical clustering for identifying patterns in expression data

  • Volcano plots for highlighting differentially expressed genes

What are the key ethical considerations when conducting AK4 research with human samples?

Research involving human samples requires careful attention to ethical considerations:

• IRB Approval and Oversight:

  • All human subjects research must be reviewed by an Institutional Review Board (IRB)

  • Determine whether your research qualifies for exemption categories under 45 CFR 46

• Informed Consent:

  • Different consent requirements apply depending on the research design:

    • Written documentation of consent for patient participants

    • Verbal informed consent may be sufficient for key stakeholders/employees

  • For studies using stored biospecimens, broad consent may be required (Exemptions 7 & 8)

• Data Privacy and Security:

  • Implement highly secure data management and storage protocols

  • Consider whether identifiable private information is being used

  • De-identify data whenever possible

• Special Considerations for Vulnerable Populations:

  • Additional protections apply when working with vulnerable groups

  • Exemption categories may not apply to research involving prisoners or certain research with children

• Reporting and Publication Ethics:

  • Transparently report all methods and findings

  • Acknowledge limitations of the research

  • Declare any conflicts of interest

How does AK4 interact with other signaling pathways to influence cellular metabolism?

AK4 functions as a central node connecting multiple signaling pathways that regulate cellular metabolism:

• AK4-HIF-1α Feedforward Loop:

  • HIF-1α induces AK4 expression under hypoxic conditions

  • AK4, in turn, stabilizes HIF-1α, creating a positive feedback loop

  • This feedforward mechanism amplifies hypoxic responses and promotes glycolytic metabolism

• AK4-Akt Signaling Axis:

  • AK4 influences phosphorylation levels of protein kinase B (Akt)

  • AK4 silencing reduces phosphorylated Akt levels

  • This interaction links AK4 to cell survival and proliferation pathways

• AK4-AMPK Regulation:

  • AK4 inhibits the activation of AMP-activated protein kinase (AMPK)

  • In macrophages, this inhibition promotes pro-inflammatory gene expression

  • The AK4-AMPK axis represents a link between energy metabolism and inflammation

• Metabolic Pathway Regulation:

  • AK4 augments glycolytic metabolism while suppressing mitochondrial respiration

  • This metabolic reprogramming resembles the Warburg effect observed in cancer cells

  • The shift promotes rapid ATP generation required for proliferation

Research approaches to study these interactions should include:

• Phosphoproteomic analysis to identify phosphorylation changes in signaling proteins

• Metabolic flux analysis to quantify changes in pathway utilization

• Proximity-dependent biotin identification (BioID) to identify novel interaction partners

What explains the tissue-specific effects of AK4 in different human physiological and pathological contexts?

AK4 exhibits context-dependent functions across different tissues and disease states, which can be explained by several factors:

• Differential Expression Patterns:

  • AK4 expression varies widely across tissue types

  • In the immune system, AK4 is predominantly expressed in M1 macrophages but not M2 macrophages

  • Certain cancer types show significantly upregulated AK4 compared to normal tissues

• Cell-Type Specific Interactomes:

  • AK4's interaction partners may differ between cell types

  • These differential interactions could result in activation of distinct downstream pathways

• Metabolic Context Dependency:

  • The impact of AK4 on cell function depends on the baseline metabolic state

  • In highly glycolytic environments (cancer cells), AK4 amplifies this phenotype

  • In cells primarily using oxidative phosphorylation, AK4 may facilitate metabolic switching

• Genetic Background Influences:

  • Person-specific differences in genetic background may modify AK4 function

  • iPSC models from different donors show variable relationships between AK4 expression and phenotypes

• Microenvironmental Factors:

  • Oxygen availability, nutrient status, and inflammatory conditions all influence AK4 function

  • These microenvironmental factors vary across tissues and disease states

Methodological approaches to investigate these tissue-specific effects should include:

• Single-cell analyses to capture heterogeneity within tissues

• Comparative studies across multiple cell types under identical conditions

• Creation of tissue-specific knockout or transgenic models

• Analysis of AK4 in patient-derived organoids representing different tissues

How should researchers address contradictory findings regarding AK4's role in different systems?

Contradictory findings about AK4 functions are common in the literature. Researchers should systematically address these inconsistencies through:

• Standardization of Experimental Conditions:

  • Establish consistent cell culture conditions (oxygen levels, growth media)

  • Use standardized methods for AK4 manipulation (siRNA sequences, expression vectors)

  • Implement reporter systems to monitor AK4 activity in real-time

• Context-Specific Analysis:

  • Explicitly define the cellular context being studied

  • Avoid generalizing findings from one cell type to others

  • Example: AK4 regulates inflammation genes in M1 macrophages but does not affect M1/M2 polarization

• Multi-Method Validation:

  • Employ complementary approaches to study AK4 function:

    • Both loss-of-function (siRNA, CRISPR) and gain-of-function (overexpression) studies

    • Independent validation in multiple cell lines or primary cells

    • Combination of in vitro and in vivo models

• Transparent Reporting:

  • Document all experimental conditions in detail

  • Report negative results along with positive findings

  • Follow standardized reporting guidelines for specific experiment types

• Meta-Analysis Approaches:

  • Systematically integrate findings across multiple studies

  • Identify patterns that explain apparent contradictions

  • Use statistical methods appropriate for heterogeneous data

What are the current limitations in AK4 research and how can they be addressed?

Several limitations currently constrain AK4 research, but strategies exist to address them:

• Lack of Specific Small-Molecule Inhibitors:

  • Current limitation: No approved drugs targeting AK4 or selective inhibitors exist

  • Solution: Implement high-throughput screening of compound libraries to discover AK4 inhibitors

  • Apply structure-based drug design approaches based on AK4's known structure

• Challenges in Measuring AK4 Activity:

  • Current limitation: Difficulty distinguishing AK4 activity from other adenylate kinases

  • Solution: Develop specific activity assays for AK4

  • Create biosensors to monitor AK4 activity in living cells

• Limited Understanding of Post-Translational Modifications:

  • Current limitation: Minimal knowledge about how PTMs regulate AK4

  • Solution: Apply mass spectrometry-based approaches to map AK4 modifications

  • Investigate how modifications affect AK4 activity and interactions

• Inter-Individual Variability:

  • Current limitation: Person-specific differences in AK4 function complicate interpretation

  • Solution: Use iPSC-derived models from multiple donors to capture variability

  • Implement statistical approaches that account for inter-individual differences

• Integration of Multi-Omics Data:

  • Current limitation: Difficulty connecting transcriptomic, proteomic, and metabolomic changes

  • Solution: Apply integrated computational approaches to connect multiple data types

  • Develop mathematical models that predict AK4's impact on cellular metabolism

What emerging technologies might advance AK4 research in the coming years?

Several cutting-edge technologies hold promise for transforming AK4 research:

• CRISPR-Based Technologies:

  • CRISPR interference (CRISPRi) for precise, tunable repression of AK4

  • CRISPR activation (CRISPRa) for endogenous upregulation

  • Base editors for introducing specific AK4 mutations without double-strand breaks

  • Prime editing for precise modification of AK4 regulatory regions

• Advanced Imaging Approaches:

  • Live-cell imaging of AK4 activity using fluorescent biosensors

  • Super-resolution microscopy to visualize AK4's mitochondrial localization

  • Intravital microscopy to monitor AK4 dynamics in vivo

• Organoid and Microfluidic Technologies:

  • Patient-derived organoids to study AK4 in complex 3D environments

  • Organ-on-chip systems to model tissue-specific AK4 functions

  • Microfluidic devices to analyze AK4's role in single cells

• Artificial Intelligence and Machine Learning:

  • AI-powered prediction of AK4 interaction networks

  • Machine learning models to identify patterns in multi-omics data

  • Deep learning for image analysis of AK4 localization

• Spatial Transcriptomics and Proteomics:

  • Mapping AK4 expression and activity with spatial resolution

  • Correlating AK4 with other markers in complex tissues

  • Understanding microenvironmental influences on AK4 function

How might targeting AK4 lead to novel therapeutic approaches for cancer and other diseases?

AK4-targeted therapeutic strategies represent a promising frontier in multiple disease contexts:

• Direct AK4 Inhibition Strategies:

  • Small-molecule inhibitors of AK4 enzymatic activity

  • Peptide-based inhibitors targeting AK4-protein interactions

  • Antisense oligonucleotides or siRNA for AK4 knockdown

• Combination Therapy Approaches:

  • Combining AK4 inhibition with existing cancer therapeutics

  • Targeting AK4 to overcome drug resistance mechanisms

  • Sequential therapy to exploit metabolic vulnerabilities

• Biomarker-Guided Treatment:

  • Using AK4 expression as a predictive biomarker for treatment response

  • Stratifying patients based on AK4-related pathway activation

  • Monitoring AK4 levels during treatment to assess efficacy

• Disease-Specific Applications:

  • Cancer: Inhibiting AK4 to reverse metabolic reprogramming and reduce metastasis

  • Pulmonary hypertension: Targeting AK4 to normalize PASMC proliferation and metabolism

  • Inflammatory conditions: Modulating AK4 to reduce pro-inflammatory gene expression

• Challenges to Clinical Translation:

  • Ensuring tissue-specific delivery of AK4-targeting agents

  • Minimizing off-target effects in normal tissues

  • Developing appropriate biomarkers for patient selection

  • Designing clinical trials with relevant endpoints and stratification

Therapeutic development will require systematic preclinical validation, including demonstration of efficacy in relevant animal models, careful assessment of safety profiles, and thorough understanding of pharmacokinetic and pharmacodynamic properties of AK4-targeting agents.