Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2024 Oct 8:2024.09.05.611545.
doi: 10.1101/2024.09.05.611545.

HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility

Dávid Deritei  1 , Wardatul Jannat Anamika  1 , Xiaobo Zhou  1 , Edwin K Silverman  1 , Erzsébet Ravasz Regan  2 , Kimberly Glass  1
Affiliations

HHIP's Dynamic Role in Epithelial Wound Healing Reveals a Potential Mechanism of COPD Susceptibility

Dávid Deritei et al. bioRxiv. .

Abstract

A genetic variant near HHIP has been consistently identified as associated with increased risk for Chronic Obstructive Pulmonary Disease (COPD), the third leading cause of death worldwide. However HHIP's role in COPD pathogenesis remains elusive. Canonically, HHIP is a negative regulator of the hedgehog pathway and downstream GLI1 and GLI2 activation. The hedgehog pathway plays an important role in wound healing, specifically in activating transcription factors that drive the epithelial mesenchymal transition (EMT), which in its intermediate state (partial EMT) is necessary for the collective movement of cells closing the wound. Herein, we propose a mechanism to explain HHIP's role in faulty epithelial wound healing, which could contribute to the development of emphysema, a key feature of COPD. Using two different Boolean models compiled from the literature, we show dysfunctional HHIP results in a lack of negative feedback on GLI, triggering a full EMT, where cells become mesenchymal and do not properly close the wound. We validate these Boolean models with experimental evidence gathered from published scientific literature. We also experimentally test if low HHIP expression is associated with EMT at the edge of wounds by using a scratch assay in a human lung epithelial cell line. Finally, we show evidence supporting our hypothesis in bulk and single cell RNA-Seq data from different COPD cohorts. Overall, our analyses suggest that aberrant wound healing due to dysfunctional HHIP, combined with chronic epithelial damage through cigarette smoke exposure, may be a primary cause of COPD-associated emphysema.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest In the past three years, EKS has received grant support from Bayer and Northpond Laboratories.

Figures

Figure 1:
Figure 1:. Network representation of the Boolean model of the interaction between Hedgehog signaling and EMT.
Colors represent the different regulatory modules. Links ending in arrows represent directed activation (sufficient or necessary); links ending in T represent inhibition. The details of each interaction (literature source, interaction type, cell type, etc.) are documented in Supplementary Table 1. Dashed lines represent documented interactions that are not directly incorporated in the logical functions.
Figure 2:
Figure 2:. The dynamic behavior of the Boolean model reproduces the expected wild-type behavior and predicts full, irreversible EMT in the case of HHIP knock-out.
Dynamic trajectories of relevant variables; average of 5000 simulations using general asynchronous update from the same initial conditions with the same perturbations. A) Wild-type wound healing behavior: with the introduction of the wound the GLI-HHIP negative feedback loop leads to the oscillation of the HH pathway and most EMT drivers, which results in intermediary concentrations corresponding to partial EMT. Once the wound input is removed the system reverts to the initial epithelial state. B) HHIP knock-out: the wound input results in a transition to an irreversible mesenchymal attractor, due to the lack of negative feedback from HHIP. C) Correlation between the predicted and empirical change in output for the perturbation experiments listed in Supplementary Table 3. The blue line is the fitted linear regression model.
Figure 3.
Figure 3.. Boolean model of SHH signaling linked to mechanosensitive EMT regulation and cell cycle progression.
Modular network representation of our Boolean model expanded from (30). light gray: inputs representing factors in the microenvironment of the modeled cell; gray: Matrix (feedback control of ECM composition; new); blue: Adhesion signals; red: TGFβ signaling; maroon: SHH signaling (new); light blue: Contact Inhibition; yellow: EMT switch; light orange: Migration; remaining modules noted in filled rounded squares are shown in detail in Supplementary Figure 1; black links between molecules: → : activation; –| : inhibition; links between modules: color: source module; → : activation; –| : inhibition; –● : complex influence. (See Supplementary Table 4 for details on all nodes and links, and Supplementary File 2 for Boolean model in .dmms (53), .SBML (54) and .BooleanNet (55) formats.)
Figure 4.
Figure 4.. Model linking EMT to contact inhibition and cell cycle predicts that HHIP haploinsufficiency upregulates full EMT during lung re-epithelialization, leading to the loss of adherens junctions and ECM remodeling.
A-B) Dynamics of the expression/activity of a select set of regulatory molecules in a cell at high confluency (first interval), exposed to the edge of a wound (middle interval), then transitioning back to high confluency (last interval) (A) wild-type (HHIP+/+) cells and (B) heterozygous (HHIP+/−) cells (HHIP is inactive in 50% of time-steps, modeling the stochastic weakening of its effects at reduced levels) exposed to 75% saturating mitogenic signals but no external Shh or TGFβ. X axis: time-steps (synchronous update); y axis: nodes organized in regulatory modules; red circle: HHIP. yellow/blue/gray: ON/OFF/forced OFF; vertical red line: start / end of wound; labels: relevant phenotype changes (Supplementary Figures 4A–B show dynamics of all modules). C) Stacked bar charts showing the average % time cells exposed to a gap (at a monolayer’s edge) spend in the Epithelial (yellow), Hybrid E/M (dark yellow) and Mesenchymal (mustard) states in consecutive 25-minute intervals (75% of saturating mitogen level, no external Shh or TGFβ). Sample size: 1000 independent runs; Update: synchronous (Supplementary Figure 3C shows results with biased asynchronous update).
Figure 5:
Figure 5:. HHIP knock-down in A549 pulmonary epithelial cell line derived from a human alveolar cell carcinoma shows enrichment for EMT markers.
A) representation of migration assays over time in HHIP KD and control (see Supplemental Figure 8), B) Gene expression in the cells collected from the scratch assays (3 repeats) measured by RT-PCR, represented as 2 −ΔΔCt values (control values normalized to 1). Significance determined by paired t-test ( “ * ” : p<0.05 ; “ ** “: p<0.01)
Figure 6:
Figure 6:. Bulk RNA-seq from lung tissue and scRNA-Seq in epithelial cells shows enrichment for EMT markers.
A) Violin plots of bulk RNA-Seq data from the LTRC for case and two control groups. The selection criteria for the COPD/emphysema and two control groups are detailed in Methods. The significance of the differences between expression levels was estimated using a Mann–Whitney U test. B) Violin plots of single cell RNA-Seq data of epithelial cells in COPD and controls. Significance levels were estimated using nonparametric Mann–Whitney U test.
Figure 6:
Figure 6:. Bulk RNA-seq from lung tissue and scRNA-Seq in epithelial cells shows enrichment for EMT markers.
A) Violin plots of bulk RNA-Seq data from the LTRC for case and two control groups. The selection criteria for the COPD/emphysema and two control groups are detailed in Methods. The significance of the differences between expression levels was estimated using a Mann–Whitney U test. B) Violin plots of single cell RNA-Seq data of epithelial cells in COPD and controls. Significance levels were estimated using nonparametric Mann–Whitney U test.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., NIHR RESPIRE Global Respiratory Health Unit, Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med 10, 447–458 (2022). - PMC - PubMed
    1. Kheradmand F., Zhang Y., Corry D. B., Contribution of adaptive immunity to human COPD and experimental models of emphysema. Physiol. Rev. 103, 1059–1093 (2023). - PMC - PubMed
    1. Agustí A., Hogg J. C., Update on the Pathogenesis of Chronic Obstructive Pulmonary Disease. N. Engl. J. Med. 381, 1248–1256 (2019). - PubMed
    1. Hogg J. C., Chu F., Utokaparch S., Woods R., Elliott W. M., Buzatu L., Cherniack R. M., Rogers R. M., Sciurba F. C., Coxson H. O., Paré P. D., The nature of small-airway obstruction in chronic obstructive pulmonary disease. N. Engl. J. Med. 350, 2645–2653 (2004). - PubMed
    1. Zhou X., Baron R. M., Hardin M., Cho M. H., Zielinski J., Hawrylkiewicz I., Sliwinski P., Hersh C. P., Mancini J. D., Lu K., Thibault D., Donahue A. L., Klanderman B. J., Rosner B., Raby B. A., Lu Q., Geldart A. M., Layne M. D., Perrella M. A., Weiss S. T., Choi A. M. K., Silverman E. K., Identification of a chronic obstructive pulmonary disease genetic determinant that regulates HHIP. Hum. Mol. Genet. 21, 1325–1335 (2012). - PMC - PubMed

Publication types

LinkOut - more resources