VEGF Human, His

What are the main isoforms of human VEGF-A and how do they differ structurally?

Human VEGF-A exists in several isoforms generated through alternative splicing of exons 6 and 7, with VEGF-A₁₂₁ and VEGF-A₁₆₅ being the two major isoforms in mammals. All isoforms contain exons 1-5 and 8, with a hydrophobic signal sequence essential for secretion encoded within exon 1 and a small region of exon 2. The vascular homology domain (VHD) is encoded by exons 3 and 4 .

The key structural differences between the major isoforms include:

• VEGF-A₁₂₁ lacks both exons 6 and 7

• VEGF-A₁₆₅ lacks exon 6 but contains exon 7

These structural differences directly impact their biological roles, as VEGF-A₁₂₁ (the main isoform in circulating blood) plays a minor role in angiogenesis but a major role in vascular permeability. In contrast, the heavier VEGF-A₁₆₅ has higher mitogenic potential and appears to induce angiogenesis more effectively .

Exon 6 encodes a heparin-binding domain, while exons 7 and 8 encode a neuropilin-1 (NRP1)/heparin-binding domain. With the exception of VEGF-A₁₂₁, all isoforms bind to heparin. Additionally, VEGF-A₁₆₅ binds to both NRP1 and NRP2 co-receptors, whereas VEGF-A₁₄₅ binds only to NRP2 .

How is VEGF physiologically distributed across different human tissues?

VEGF distribution varies significantly across different tissues and body compartments, a crucial consideration for both basic research and therapeutic development. Within the blood compartment, VEGF is predominantly concentrated in platelets, with significant portions also found in leukocytes .

Contrary to common assumptions, even in cancer patients, tumors are not the largest source of VEGF in the body. Other tissues, particularly skeletal muscle, contain large reservoirs of VEGF, which is an important consideration in research design and therapeutic targeting. These findings suggest that a significant amount of VEGF is stored intracellularly in healthy tissues .

The concentration of VEGF also varies between plasma and serum measurements, with serum typically showing higher concentrations due to the release of VEGF from platelets during the clotting process. This distribution pattern is critical for understanding the physiological roles of VEGF and for properly interpreting research findings across different sample types .

Understanding this distribution is particularly important when developing anti-VEGF therapies for cancer, as systemic administration of anti-VEGF antibodies may have to overcome the effects of a large non-tumor-derived VEGF reservoir .

What functional differences exist between His-tagged recombinant VEGF and native human VEGF?

His-tagged recombinant human VEGF contains a polyhistidine tag (typically 6-10 histidine residues) that is not present in the native protein. This tag is engineered to facilitate protein purification through metal affinity chromatography and enable easier detection in experimental settings, but it introduces several important functional differences:

• Molecular weight and structure: His-tagged VEGF has a slightly higher molecular weight due to the additional histidine residues, which may affect its mobility in analytical techniques like gel electrophoresis.

• Binding kinetics: While the His-tag is designed to have minimal impact on protein function, it may subtly alter binding kinetics with receptors, particularly in interactions involving the protein's N-terminal or C-terminal regions (depending on tag placement).

• Solubility and stability: The His-tag can influence protein solubility and stability. In some cases, it may enhance solubility, but in others, it might promote aggregation or alter thermal stability profiles.

• Biological activity: For most research applications, His-tagged VEGF retains comparable biological activity to native VEGF, but researchers should validate this in their specific experimental system, especially for quantitative studies.

• Immunogenicity: In certain applications, particularly in vivo studies, the His-tag might elicit an immune response that wouldn't occur with native VEGF, potentially confounding experimental results.

When using His-tagged VEGF for research, it's essential to consider whether the tag should be removed for specific applications, especially in studies focusing on precise structural interactions or in vivo applications where tag artifacts could influence outcomes.

How do different VEGF isoforms interact with their receptors and co-receptors?

VEGF isoforms exhibit differential binding patterns to receptors and co-receptors, which contributes to their diverse biological effects in both physiological and pathological contexts:

• VEGFR1 and VEGFR2: All major VEGF-A isoforms bind to these primary signaling receptors, but with different affinities. VEGF-A has higher affinity for VEGFR1 than VEGFR2, though VEGFR2 is the primary mediator of angiogenic signaling in most contexts.

• Neuropilins (NRPs): VEGF-A₁₆₅ binds to both NRP1 and NRP2, whereas VEGF-A₁₄₅ binds only to NRP2. Importantly, VEGF-A₁₂₁ does not bind to either neuropilin due to the absence of exon 7 . The interaction with neuropilins enhances VEGFR2 signaling and contributes to isoform-specific biological activities.

• Heparan sulfate proteoglycans (HSPGs): With the exception of VEGF-A₁₂₁, all VEGF isoforms bind to heparin and interact with HSPGs on the cell surface. This interaction restricts their diffusion and creates concentration gradients vital for directed vessel growth.

• Soluble VEGFR1 (sVEGFR1): This naturally occurring truncated version of VEGFR1 lacks the transmembrane and intracellular signaling domains and functions as an inhibitory factor by sequestering VEGF ligands or forming dominant-negative heterodimers with membrane-bound VEGFRs .

These complex interaction patterns create a finely tuned system where the ratio of different isoforms and their spatial distribution regulate vascular development and homeostasis. Computational models have been developed to understand these dynamic systemic distributions of VEGF and sVEGFR1, providing insights into how these molecules maintain angiogenic balance in normal physiology .

What are the molecular mechanisms underlying VEGF-A-mediated human organ rejuvenation?

Recent research has identified VEGF-A as a master pathway for human organ rejuvenation. The molecular mechanisms involved include:

• Rejuvenation cascade: VEGF-A treatment initiates a molecular rejuvenation cascade that improves key aging parameters in human skin. This cascade involves the upregulation of angiogenesis- and hypoxia-related pathways, particularly VEGF-A itself and HIF1A, which are most upregulated in rejuvenated human skin .

• Improvement of aging markers: VEGF-A treatment has been shown to improve several key aging parameters including p16ink4a, SIRT1, PGC1α, collagen 17A, and MMP1, which are important molecular markers and regulators of aging .

• Direct tissue effects: Importantly, VEGF-A treatment improves aging parameters even in isolated, organ-cultured aged human skin, in the absence of functional skin vasculature, neural, or murine host inputs. This suggests that VEGF-A has direct effects on tissue rejuvenation beyond its known role in angiogenesis .

• Amplification mechanism: The rejuvenation cascade appears to be initiated by murine VEGF-A, which then up-regulates VEGF-A expression and secretion within aged human skin, creating a positive feedback loop .

• Delivery mechanisms: Intradermally injected VEGF-loaded nanoparticles are sufficient to induce a molecular rejuvenation signature in aged human skin, suggesting a potential practical approach for clinical applications .

These findings identify VEGF-A as the first pharmacologically pliable master pathway for human organ rejuvenation in vivo and demonstrate the potential for developing VEGF-A-based interventions for age-related conditions .

How should researchers interpret contradictory findings on VEGF levels across different studies?

Interpreting contradictory findings on VEGF levels requires a systematic approach that considers multiple methodological and biological factors:

• Isoform-specific analysis: Different VEGF isoforms may show opposing patterns in the same condition. For example, in stress-induced exhaustion disorder (SED), sVEGF₁₂₁ is elevated while sVEGF₁₆₅ may not show significant changes compared to controls . Studies not specifying isoforms may miss these differential patterns.

• Sample type considerations: The choice between plasma and serum significantly impacts results. Measuring circulating extracellular VEGF in plasma is often more accurate than measuring it in serum because platelets store VEGF and release it during clotting, potentially leading to artificially elevated levels in serum .

• Processing methodology: Centrifugation time and speed can significantly affect VEGF concentrations. Studies use varying speeds (1000-3000 × g) and durations (7-20 minutes) . Storage conditions and freeze-thaw cycles also influence stability.

• Assay variability: Different protein assays can yield significantly different results. While 74% of studies use ELISA, there are at least 5 other assay types employed, and even within ELISA-based studies, kits from 15 different companies are used .

• Disease heterogeneity: Disease subtypes and progression stages can show varying VEGF patterns. For example, in major depressive disorder (MDD), VEGF levels may vary with disease severity and treatment status .

Previous studies on VEGF in stress-related disorders have produced conflicting results - some found higher VEGF levels, others found similar levels to healthy controls, and some found lower levels. These contradictions may be explained by measuring different isoforms, using different assay methods, or examining different disease stages .

What are the optimal methods for measuring VEGF levels in different biological samples?

The measurement of VEGF levels requires careful consideration of sample type, preparation methods, and analytical techniques. Based on meta-analysis of methodological approaches, the following protocols represent current best practices:

Sample collection and preparation:

For plasma:

• Collect blood in tubes containing anticoagulants (trisodium citrate or EDTA)

• Allow samples to stand for 0-30 minutes before centrifugation

• Centrifuge at 1000-3000 × g at 4-21°C for 10-20 minutes

• Aliquot plasma and store at -20 to -80°C before assay

For serum:

• Collect blood in sterile silicone-coated tubes without additives or serum separator/clot activator tubes

• Allow samples to clot for 30-120 minutes before centrifugation

• Centrifuge at 1000-3000 × g at 4-21°C for 7-15 minutes

• Aliquot serum and store at -20 to -80°C before assay

For tissue cytosol:

• Immediately freeze tissue samples in liquid nitrogen after collection

• Dilute samples in an appropriate buffer

• Homogenize, pulverize, or dice tissues using consistent methodology

• Centrifuge homogenized samples at 800-105,000 × g for 15-60 minutes

• Store the resulting supernatant (cytosol) at -70 to -80°C until analysis

Analytical methods:

Enzyme-linked immunosorbent assay (ELISA) is the most commonly used method (74% of studies), with R&D Systems kits being the most frequently used (40% of ELISA-based studies) . Other validated methods include quantitative sandwich enzyme immunoassay, chemiluminescence immunosorbent assay, and immunofluorometric assay.

Key considerations include: (1) selecting plasma over serum for more accurate measurements of circulating VEGF, (2) choosing assays capable of detecting specific VEGF isoforms of interest, (3) maintaining consistent methodology for comparing results across studies, and (4) ensuring assays recognize His-tagged VEGF without interference when analyzing recombinant variants .

What experimental models are most reliable for studying human VEGF function?

Several experimental models have been developed for studying human VEGF function, each with specific advantages for different research questions:

In vitro models:

• Human endothelial cell cultures (HUVECs, HMVECs):

  • Useful for studying direct effects on endothelial cell proliferation, migration, and tube formation

  • Appropriate for initial screening of VEGF variants and receptor interactions

  • Limited in recapitulating the complex in vivo microenvironment

• 3D organoid cultures:

  • Better mimic tissue architecture and cell-cell interactions

  • Allow for the study of vascular network formation

  • More physiologically relevant than 2D cultures for studying morphogenetic processes

Ex vivo models:

• Organ culture systems:

  • Human skin organ culture has been validated for studying VEGF-mediated rejuvenation effects

  • Maintains tissue architecture and cellular diversity

  • Allows for the study of VEGF effects in the absence of systemic factors

In vivo models:

• Humanized mouse models:

  • Xenografts of human tissue (e.g., skin) onto immunodeficient mice

  • Enable the study of human tissue responses in a living organism

  • Have demonstrated potential for clinically relevant aging research

• Computational models:

  • Multi-tissue compartment models recapitulate the dynamic systemic distributions of VEGF and sVEGFR1

  • Help understand complex interactions between VEGF ligands and receptors across different body compartments

The choice of model should align with specific research questions. For studying basic VEGF signaling mechanisms, cell culture systems may be sufficient. For aging research, the humanized mouse model has proven valuable . For understanding systemic effects, computational models combined with in vivo studies provide the most comprehensive insights .

How should His-tagged human VEGF be purified and validated for experimental use?

Purification of His-tagged human VEGF requires careful attention to maintain protein integrity and biological activity. The following protocol represents current best practices:

Expression system selection:

• Mammalian expression systems (CHO or HEK293 cells) are preferred for human VEGF expression as they provide proper folding and post-translational modifications

• E. coli systems may be used for certain applications but often require refolding procedures

Purification protocol:

• Immobilized Metal Affinity Chromatography (IMAC):

  • Equilibrate Ni-NTA or Co-NTA affinity resin with binding buffer (10-20 mM imidazole)

  • Apply sample and allow binding

  • Wash with buffer containing 20-50 mM imidazole

  • Elute His-tagged VEGF with buffer containing 250-500 mM imidazole

• Further purification steps:

  • Size exclusion chromatography to remove aggregates

  • Ion exchange chromatography for removal of charged contaminants

  • Endotoxin removal for in vivo applications

Validation assays:

• Physical characterization:

  • SDS-PAGE and Western blotting to confirm purity and identity

  • Mass spectrometry to verify molecular weight and integrity

• Functional validation:

  • Endothelial cell proliferation assay

  • Receptor binding assays

  • Phosphorylation of downstream signaling proteins

• Stability assessment:

  • Thermal stability analysis

  • Aggregation propensity evaluation

  • Activity retention after storage

Storage considerations:

• Store purified His-tagged VEGF in small aliquots at -80°C

• Add carrier proteins like BSA (0.1%) for dilute solutions

• Avoid repeated freeze-thaw cycles

• Consider adding stabilizers like trehalose or glycerol

For applications requiring native-like VEGF, researchers should consider enzymatic cleavage of the His-tag using specific proteases if a cleavage site was incorporated between the tag and the VEGF sequence.

How do VEGF isoform levels correlate with various pathological conditions?

VEGF isoform levels show complex correlations with various pathological conditions, with distinct patterns emerging for different isoforms:

Mental disorders:

• Stress-induced exhaustion disorder (SED): Patients show significantly higher concentrations of soluble VEGF₁₂₁ (sVEGF₁₂₁) compared to healthy controls (p = 0.043)

• Major depressive disorder (MDD): Patients have significantly lower levels of sVEGF₁₆₅ than patients with SED (p = 0.004) or healthy controls (p = 0.037)

• Total sVEGF levels are significantly higher in patients with SED than in patients with MDD (p = 0.021) and healthy controls (p = 0.040)

Biomarker correlations:

• In patients with SED specifically, levels of sVEGF₁₂₁ positively correlate with levels of astrocyte-derived extracellular vesicles (p = 0.0128)

• Similarly, total sVEGF levels correlate with astrocyte-derived EVs only in SED patients (p = 0.0046)

• These correlations are not observed in patients with MDD or healthy controls

Cancer:

• While tumors are significant sources of VEGF in cancer patients, they are not the largest reservoir of VEGF in the body

• Skeletal muscle and other tissues contain large VEGF reservoirs that must be considered in therapeutic approaches

• Within the blood compartment, VEGF is concentrated in platelets and leukocytes

These findings suggest that measuring specific VEGF isoforms, rather than total VEGF, provides more meaningful clinical correlations and mechanistic insights. The differing patterns across various conditions likely reflect distinct pathophysiological processes that could inform diagnostic and therapeutic approaches .

What evidence supports VEGF-A as a therapeutic target for human tissue rejuvenation?

Recent research has provided compelling evidence for VEGF-A as a therapeutic target for human tissue rejuvenation:

Direct evidence from human tissue studies:

• Molecular rejuvenation signatures: VEGF-A treatment improves key aging parameters in aged human skin, including markers like p16ink4a, SIRT1, PGC1α, collagen 17A, and MMP1 .

• Direct tissue effects: VEGF-A treatment improves aging parameters even in isolated, organ-cultured aged human skin, in the absence of functional skin vasculature or neural inputs. This indicates direct rejuvenation effects beyond angiogenesis .

• Pathway analysis: Angiogenesis- and hypoxia-related pathways, namely VEGF-A and HIF1A, are most up-regulated in rejuvenated human skin, supporting their central role in the rejuvenation process .

Delivery methods with therapeutic potential:

• Nanoparticle delivery: Intradermally injected VEGF-loaded nanoparticles induce a molecular rejuvenation signature in aged human skin, suggesting a practical delivery approach .

• Self-amplifying mechanism: The rejuvenation cascade initiates a positive feedback loop where VEGF-A upregulates its own expression and secretion within aged human skin, potentially enhancing therapeutic efficacy .

Mechanistic support:

• Master pathway identification: VEGF-A has been identified as "the first pharmacologically pliable master pathway for human organ rejuvenation in vivo" .

• Specificity confirmation: The rejuvenation cascade can be prevented by VEGF-A–neutralizing antibodies, confirming the specificity of the effect .

• Translational model: A humanized mouse model has demonstrated potential for clinically relevant aging research, providing a platform for testing VEGF-A-based rejuvenation strategies before clinical translation .

These findings collectively position VEGF-A as a promising therapeutic target for addressing age-related tissue changes, with potential applications extending beyond skin to other human organs.

What methodological factors affect VEGF measurements in clinical studies?

Several critical methodological factors affect VEGF measurements in clinical studies, contributing to the variability observed across research:

Sample collection and processing:

• Sample type selection:

  • Plasma vs. serum: VEGF levels in serum are typically 2-7 times higher than in plasma due to platelet release during clotting

  • Measuring VEGF in plasma is generally more accurate for assessing circulating levels

• Processing timeline:

  • Time between collection and centrifugation (0-30 minutes for plasma; 30-120 minutes clotting time for serum)

  • Delay in processing can affect VEGF release from cellular components

• Centrifugation parameters:

  • Speed (1000-3000 × g) and duration (7-20 minutes) vary across studies

  • Temperature during centrifugation (4-21°C) can affect stability

Assay methodology:

• Assay selection:

  • ELISA (74% of studies) vs. other assay types

  • Commercial kit variability: studies use kits from at least 15 different companies

• Isoform detection:

  • Different assays have varying specificities for VEGF isoforms

  • Some detect all isoforms while others are specific to certain variants

Clinical variables affecting interpretation:

• Treatment status:

  • Antidepressants can affect VEGF levels in psychiatric disorders

  • A 2020 meta-analysis showed significant elevation of blood VEGF only in MDD patients treated with antidepressants

• Disease stage:

  • VEGF levels may vary across the course of a disorder

  • Timing of measurement relative to disease onset is crucial

• Comorbidities:

  • Conditions affecting vascular function can influence VEGF levels

  • Control for confounding conditions is essential

To improve reliability, clinical studies should standardize methodology, clearly report all processing details, specify VEGF isoforms measured, and account for treatment status and disease stage when interpreting results .

How should researchers analyze VEGF data from different sample types?

Proper analysis of VEGF data from different sample types requires understanding their inherent differences and applying appropriate analytical approaches:

Sample-specific normalization:

• Plasma samples:

  • Normalize to total plasma protein for concentration comparisons

  • Consider platelet counts as potential confounders

  • Report anticoagulant used (EDTA vs. citrate)

• Serum samples:

  • Account for platelet contribution to serum VEGF (serum levels typically 2-7× higher than plasma)

  • Consider clotting time as a variable affecting VEGF release

  • Normalize to platelet counts when comparing across studies

• Tissue samples:

  • Normalize to total protein content (pg/mg protein)

  • Alternatively, normalize to cell number (pg/10^6 cells)

  • Specify extraction method in detail for comparison purposes

Statistical approaches:

When comparing across studies, use weighted averages based on sample size:
Weighted average = Σ(n𝑖 × x𝑖) / Σn𝑖
where n𝑖 is the number of subjects in each study and x𝑖 is the VEGF level reported .

For studies with different methodologies, calculate standardized mean differences rather than comparing absolute values. When meta-analyzing studies using different units (pg/ml, pg/mg protein), perform appropriate unit conversions before analysis .

Isoform-specific analysis:

Separate analyses should be performed for different VEGF isoforms rather than pooling all VEGF measurements. In conditions like stress-related disorders, different isoforms show distinct patterns - VEGF₁₂₁ may be elevated while VEGF₁₆₅ shows different patterns .

What are common pitfalls in the experimental use of His-tagged human VEGF?

Researchers working with His-tagged human VEGF should be aware of several common pitfalls that can affect experimental outcomes:

Protein functionality concerns:

• Tag interference with activity:

  • The His-tag may occasionally interfere with protein folding or receptor binding

  • Solution: Validate biological activity of the tagged protein against native VEGF using functional assays like endothelial cell proliferation

• Aggregation issues:

  • His-tagged proteins can form aggregates, particularly at high concentrations

  • Solution: Use size exclusion chromatography to ensure monomeric protein; add stabilizers if necessary

• Metal ion effects:

  • Residual metal ions from purification can affect VEGF activity or interfere with certain assays

  • Solution: Include an EDTA dialysis step after purification to remove metal ions

Experimental design pitfalls:

• Improper controls:

  • Failure to include appropriate controls for the His-tag itself

  • Solution: Include control proteins with the same tag but without VEGF activity

• Endotoxin contamination:

  • Bacterial expression systems can introduce endotoxins that activate inflammatory pathways

  • Solution: Use endotoxin removal steps and test final preparations, especially for cell culture or in vivo use

• Isoform confusion:

  • Different VEGF isoforms (VEGF₁₂₁ vs. VEGF₁₆₅) have distinct biological properties

  • Solution: Clearly specify which isoform is being used and don't extrapolate findings between isoforms

Understanding these pitfalls allows researchers to design more robust experiments and generate more reliable data when working with His-tagged human VEGF, particularly when studying specific isoform functions or receptor interactions.

How can computational models enhance VEGF research?

Computational models offer powerful tools to enhance VEGF research by integrating complex data and predicting system-level behaviors:

Multi-scale compartmental modeling:

• Physiologically-based multi-scale compartmentalization:

  • Models can represent different tissues and organs as distinct compartments

  • Allows simulation of VEGF distribution across the human body

  • Helps predict effects of therapeutic interventions

• Inter-compartmental transport processes:

  • Models incorporate vascular permeability and lymphatic drainage

  • Simulate how VEGF and sVEGFR1 move between compartments

  • Predict concentration gradients that drive biological effects

• Molecular interaction networks:

  • Detailed binding interactions between VEGF isoforms and their receptors

  • Include signaling receptors (VEGFR1, VEGFR2), co-receptors (NRP1), and soluble receptors (sVEGFR1)

  • Model competition between different binding partners

Applications in research:

• Predicting therapeutic outcomes:

  • Assessing the stability of circulating VEGF and sVEGFR1 levels in plasma

  • Evaluating their reliability as indicators of tissue-level angiogenic signaling

  • Predicting effects of anti-VEGF therapies

• Testing hypotheses:

  • In silico experiments can test mechanisms difficult to isolate experimentally

  • For example, simulations showed that sVEGFR1 acting as a diffusible VEGF sink alone (without sVEGFR1-VEGFR heterodimerization) did not significantly lower inter-compartmental VEGF levels

• Integrating multi-omics data:

  • Computational models can integrate transcriptomic, proteomic, and functional data

  • Help identify key regulatory nodes in VEGF signaling networks

  • Guide the design of more targeted experiments

By leveraging computational approaches, researchers can gain insights into VEGF biology that would be difficult to obtain through experimental methods alone, particularly for understanding complex systemic effects and predicting therapeutic outcomes across different tissues and disease states.