The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mechanistic computational modeling of sFLT1 secretion dynamics
Open accessPreprint — published as 10.1371/journal.pcbi.1013324 · Paper page
Abstract
Constitutively secreted by endothelial cells, soluble FLT1 (sFLT1 or sVEGFR1) binds and sequesters extracellular vascular endothelial growth factors (VEGF), thereby reducing VEGF binding to VEGF receptor tyrosine kinases and their downstream signaling. In doing so, sFLT1 plays an important role in vascular development and in the patterning of new blood vessels in angiogenesis. Here, we develop multiple mechanistic models of sFLT1 secretion and identify a minimal mechanistic model that recapitulates key qualitative and quantitative features of temporal experimental datasets of sFLT1 secretion from multiple studies. We show that the experimental data on sFLT1 secretion is best represented by a delay differential equation (DDE) system including a maturation term, reflecting the time required between synthesis and secretion. Using optimization to identify appropriate values for the key mechanistic parameters in the model, we show that two model parameters (extracellular degradation rate constant and maturation time) are very strongly constrained by the experimental data, and that the remaining parameters are related by two strongly constrained constants. Thus, only one degree of freedom remains, and measurements of the intracellular levels of sFLT1 would fix the remaining parameters. Comparison between simulation predictions and additional experimental data of the outcomes of chemical inhibitors and genetic perturbations suggest that intermediate values of the secretion rate constant best match the simulation with experiments, which would completely constrain the model. However, some of the inhibitors tested produce results that cannot be reproduced by the model simulations, suggesting that additional mechanisms not included here are required to explain those inhibitors. Overall, the model reproduces most available experimental data and suggests targets for further quantitative investigation of the sFLT1 system. Author Summary: Proteins that are typically found outside cells are initially made inside cells, and later secreted into extracellular space. Many of these secreted proteins have important functions outside the cell that are well-studied; however, usually much less is known about the pre-secretion life of these molecules. Many computational models only represent the extracellular versions of secreted proteins, reducing all production and secretion steps into a single modeled process. Here, we develop a mechanistic model of the production and secretion of a specific secreted protein, sFLT1, which inhibits blood vessel growth by acting as an extracellular sponge for another set of secreted proteins, the vascular endothelial growth factors. We compare several models to existing experimentally-measured sFLT1 data, and we show that the data are most simply explained by including a delay between intracellular sFLT1 production and sFLT1 transport or degradation. This is consistent with the biology of the cell’s secretory pathway, where immature proteins are gradually processed into mature forms over minutes to hours. Our approach could be incorporated into improved models for any pathway involving secreted proteins, including sFLT1-regulated models of blood vessel biology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures


![Fig 3 Comparing candidate models of sFLT1 secretion. (A) Structure of candidate model equations and equation term options for each process when present or absent in a given model. In the production decay term, the value of [chase] is 1 during the chase phase of pulse-chase simulations and 0 otherwise. (B) Top: Processes included (filled) or excluded (empty) in each model. ε: internalization, κ: production decay, τ: maturation delay. Middle: Cost (goodness of fit) of each model, defined as the sum of squared differences (SSD) between experimental data points and the lowest cost simulation of each model, with colors indicating the contribution of each experimental dataset. X: extracellular sFLT1, I: intracellular sFLT1. Bottom: Corrected Akaike Information Criterion (AICC) scores for each model. All models are fit to the same number of observations (n=26), while the number of parameters k varies by model. (C) Impact of additional processes on the best-fit cost of the candidate models. Model nodes (points) are plotted as minimum model cost versus number of parameters k. Edges (arrows) represent transitions between candidate model structures by adding the labeled process (τ (maturation delay): pink arrows, κ (production decay): blue arrows, ε (internalization): gray arrows. The vertical height of each arrow represents the decrease in cost from adding the labeled process to a given model.](/paper-figures/6c3437630d7e44b8a33aca4afdfa1834/mechanistic-computational-modeling-of-sflt1-secretion-dynamics-figure-3.jpg)



![Fig 7 Simulated chemical inhibition and genetic downregulation of sFLT1 secretion. (A) Simulation timeline schematic for chemical inhibition (top) and genetic downregulation (bottom) of constitutive secretion. p=p′ represents change of parameter values to apply inhibitor effect. (B) Effects of chemical inhibition of individual parameters by 90% on extracellular sFLT1 (X) and intracellular sFLT1 (I) at 18h and 72h, expressed as log2 fold changes relative to the control (uninhibited) case. (C) Time courses for absolute concentrations of extracellular (X) and intracellular (I) sFLT1 with 90% chemical inhibition of each parameter. The impact of inhibiting each process is how different each line is from the prediction of the median, unmodified parameter set (black line). The inset magnifies the trend for inhibition of α. Black points, experimental ELISA data for the uninhibited case [29]. (D) Annotated curves for chemical inhibition of β (red) and γ (orange) reproduced from (C). Horizontal lines indicate experimentally observed relative changes in extracellular (X18h) and intracellular (I18h) sFLT1 18 hours after drug addition. Vertical lines indicate the fraction inhibition of the target parameter required to reproduce experimental results. (E) Effects of genetic downregulation of individual parameters by 90%, as described in (B). (F) Time courses for absolute concentrations of extracellular (X) and intracellular (I) sFLT1 with 90% genetic downregulation of each parameter, as described in (C). (G) Annotated curves for genetic downregulation of β (red) and γ (orange), as described in (D).](/paper-figures/be973c1d211c4e0480ae438157d8ab4d/mechanistic-computational-modeling-of-sflt1-secretion-dynamics-figure-7.jpg)
![Fig 8 Simulating chemical and genetic inhibition of sFLT1 secretion from different initial parameter sets. (A) Annotated curves for chemical inhibition of β (secretion, first 3 columns) and γ (intracellular degradation, rightmost column) at 18 hours. Horizontal lines indicate experimentally observed relative changes in extracellular (X18h) and intracellular (I18h) sFLT1 18 hours after addition of brefeldin, chlorpromazine, TATNSF700, or chloroquine. Colored vertical lines mark the fraction inhibition required to match the observed change in sFLT1 when starting from the parameter set with the matching β value. (B) Annotated curves for genetic inhibition of β (secretion rate constant) at 18 hours post media change. Horizontal lines indicate experimentally observed relative changes in extracellular (X18h) and intracellular (I18h) sFLT1 after 18 hours after media change in cells transfected with siRNA targeting the listed secretion-linked protein 48 hours previously [13]. Colored vertical lines mark the fraction inhibition required to match the observed change in sFLT1 when starting from the parameter set with the matching β value.](/paper-figures/63acc5f76ab9404fa72c832d0d1a0f70/mechanistic-computational-modeling-of-sflt1-secretion-dynamics-figure-8.jpg)
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Research works printed but not linked: 55
- 10.1126/science.1260419 · DOI · Paper page
- 10.1126/science.1096303 · DOI
- 10.1126/scisignal.aaz0274 · DOI · Paper page
- 10.1016/S0962-8924(97)01197-5 · DOI
- 10.1242/jcs.258564 · DOI
- 10.1038/onc.2013.1 · DOI
- 10.1016/S0021-9258(18)37798-6 · DOI
- 10.1016/j.yexcr.2008.12.020 · DOI
- 10.1038/nrm2748 · DOI · Paper page
- 10.1016/S0014-4827(08)00404-7 · DOI · Paper page
- 10.1073/pnas.90.22.10705 · DOI
- 10.1006/bbrc.1996.1355 · DOI
- 10.1007/s10456-023-09893-6 · DOI
- 10.1073/pnas.95.16.9349 · DOI · Paper page
- 10.1101/cshperspect.a006452 · DOI
- 10.1111/j.1582-4934.2009.00941.x · DOI
- 10.1038/nature05249 · DOI · Paper page
- 10.1016/j.devcel.2009.07.011 · DOI
- 2011, Computational modeling of interacting VEGF and soluble VEGF receptor concentration gradients, Front. Physiol, 2, 62
- 10.1083/jcb.200709114 · DOI
- 10.1172/JCI200317189 · DOI
- Jung, J.-J. et al. Secretion of Soluble Vascular Endothelial Growth Factor Receptor 1 (sVEGFR1/sFlt1) Requires Arf1, Arf6, and Rab11 GTPases. PLoS ONE 7, e44572 (2012). · DOI
- 10.1371/journal.pcbi.1011798 · DOI
- 10.1016/j.phrs.2015.06.002 · DOI
- 10.1038/s41467-021-21700-8 · DOI
- 10.1152/physiolgenomics.00031.2009 · DOI
- 10.1152/ajpheart.00365.2009 · DOI
- 10.1371/journal.pcbi.1005445 · DOI
- 2018, A computational analysis of pro-angiogenic therapies for peripheral artery disease, Integr. Biol. Quant. Biosci. Nano Macro, 10, 18
- 10.1038/labinvest.3780050 · DOI
- 10.1038/nmeth.2019 · DOI · Paper page
- 10.1126/science.1199784 · DOI
- 10.1038/s41467-018-07391-8 · DOI · Paper page
- 10.1007/978-1-4612-1694-0_15 · DOI · Paper page
- 10.1007/BF02294361 · DOI · Paper page
- 2011, Autocrine activity of soluble Flt-1 controls endothelial cell function and angiogenesis. Vasc, Cell, 3, 15
- 10.1038/s41580-020-0250-z · DOI · Paper page
- 10.1182/blood-2010-06-291690 · DOI
- 10.1172/JCI107470 · DOI
- 10.1529/biophysj.106.101501 · DOI
- 10.1186/1752-0509-3-13 · DOI
- 10.1371/journal.pone.0014790 · DOI
- 2017, Agent-based computational model of retinal angiogenesis simulates microvascular network morphology as a function of pericyte coverage, Microcirc. N. Y. N, 1994, 24
- 2022, Mechanistic characterization of endothelial sprouting mediated by pro-angiogenic signaling, Microcirc. N. Y. N, 1994, e12744
- 10.1093/cvr/cvw091 · DOI
- 2015, Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks, Integr. Biol. Quant. Biosci. Nano Macro, 7, 987
- 10.1038/s41598-021-96964-7 · DOI
- 10.1016/j.febslet.2013.05.038 · DOI
- 10.1201/9781420011432 · DOI · Paper page
- 10.1158/0008-5472.CAN-08-2905 · DOI
- 10.1006/excr.1998.4039 · DOI
- 10.1006/bbrc.1999.1580 · DOI
- 10.1161/CIRCRESAHA.110.239665 · DOI
- 10.1242/jcs.00673 · DOI
- 10.1038/s41581-019-0129-4 · DOI · Paper page
Metadata
- Authors
- Gill, A., Kinghorn, K., Bautch, V. L., & Mac Gabhann, F.
- Affiliations
- Johns Hopkins University (US)
- University of North Carolina at Chapel Hill (US)
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
- Corresponding author
- Gill, Amy
- Venue
- PLoS Computational Biology
- Publisher
- openRxiv
- Published
- 2025-02-17
- Volume
- 21
- Issue
- 8
- Pages
- e1013324-e1013324
- ISSN
- 1553-734X (ISSN-L)
- PMC
- PMC12370208
- OpenAlex
- W4413314500
- Cited by
- 1
- References
- 1
- Citations by year
- 2025: 1
- FWCI
- 0.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UNC Libraries
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R35HL139950, R01HL101200)
- National Institute of General Medical Sciences (R01GM129074)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Angiogenesis and VEGF in Cancer
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- soluble Flt-1, delay differential equations, angiogenesis, endothelial cells, parameter estimation, chemical inhibitors, gene perturbation, mechanistic modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.