Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells

Emily Bowler-Barnett; Francisco D Martinez-Garcia; Matthew Sherwood; Ahood Aleidan; Steve John; Sara Weston; Yihua Wang; Nullin Divecha; Paul Skipp; Rob M Ewing

doi:10.1371/journal.pone.0246707

. 2021 Nov 5;16(11):e0246707. doi: 10.1371/journal.pone.0246707

Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells

Emily Bowler-Barnett ^1,^¤a, Francisco D Martinez-Garcia ^1,^¤b, Matthew Sherwood ¹, Ahood Aleidan ¹, Steve John ¹, Sara Weston ¹, Yihua Wang ¹, Nullin Divecha ¹, Paul Skipp ¹, Rob M Ewing ^1,^*

Editor: Michael Klymkowsky²

PMCID: PMC8570494 PMID: 34739494

Abstract

Glycogen-specific kinase (GSK3β) is an integral regulator of the Wnt signalling pathway as well as many other diverse signalling pathways and processes. Dys-regulation of GSK3β is implicated in many different pathologies, including neurodegenerative disorders as well as many different tumour types. In the context of tumour development, GSK3β has been shown to play both oncogenic and tumour suppressor roles, depending upon tissue, signalling environment or disease progression. Although multiple substrates of the GSK3β kinase have been identified, the wider protein networks within which GSK3β participates are not well known, and the consequences of these interactions not well understood. In this study, LC-MS/MS expression analysis was performed using knockout GSK3β colorectal cancer cells and isogenic controls in colorectal cancer cell lines carrying dominant stabilizing mutations of β-catenin. Consistent with the role of GSK3β, we found that β-catenin levels and canonical Wnt activity are unaffected by knockout of GSK3β and therefore used this knockout cell model to identify other processes in which GSK3β is implicated. Quantitative proteomic analysis revealed perturbation of proteins involved in cell-cell adhesion, and we characterized the phenotype and altered proteomic profiles associated with this. We also characterized the perturbation of metabolic pathways resulting from GSK3β knockout and identified defects in glycogen metabolism. In summary, using a precision colorectal cancer cell-line knockout model with constitutively activated β-catenin we identified several of the diverse pathways and processes associated with GSK3β function.

Introduction

Glycogen synthase kinase-3 beta (GSK3β) is a serine/threonine kinase in the glycogen synthase kinase subfamily with diverse roles and interaction partners in a wide range of signalling pathways and cellular functions [1]. Wnt/ β-catenin signalling is a principle driver pathway in colorectal cancer, with evidence of mutations and aberrant signalling from an early stage of tumour development. Following pre-phosphorylation by casein kinase 1 (CK1), GSK3β phosphorylates β-catenin at serine residues 33, 37, and 41 which targets β-catenin for proteosomal degradation [2]. When Wnt signalling is activated by the family of Wnt ligands binding to receptors, such as Frizzled, GSK3β and CK1 phosphorylate low-density lipoprotein receptor-related protein 6 (LRP6) at conserved PPSPXS motifs, which then initiates the recruitment of the scaffold protein Axin. It is this interaction with the Axin/LRP6 complex that surrounds GSK3β with phosphorylated residues and directly inhibits its ability to phosphorylate β-catenin [3, 4]. Following inhibition of GSK3β, β-catenin accumulates in the cytosol and then undergoes nuclear import where it activates target transcription factors and gene transcription [4].

Although GSK3β plays a key role in regulating Wnt/ β-catenin signalling activity, its role in other oncogenic or tumour suppressor pathways is less well understood. Whether GSK3β plays a primarily oncogenic or tumour suppressor role appears to depend on the cellular context and tumour type. GSK3β has been shown to be expressed at lower levels in skin cancer tumours and knockdown of GSK3β in breast cancer mammary models results in activation of Wnt signalling and the formation of adenosquamous carcinomas [5, 6]. GSK3β has been shown to be significantly increased in non-small cell lung cancer patient samples with increased expression correlated with a poor long-term prognosis, and GSK3β was also shown to have inconsistent effects on Wnt/ β-catenin signalling depending on cell-type [7].

GSK3β has been linked to several roles in the regulation of cell adhesion and migration in keratinocytes. For example, desmoplakin plays a key role in the formation of intercellular junctions required for skin and heart integrity, and desmoplakin is post-translationally modified by GSK3β [8]. GSK3β is also known to phosphorylate Focal Adhesion Kinase resulting in a reduction in its activity and in cell motility [9]. Many studies have linked GSK3β to the proliferation of cardiomyocytes and to the fibrotic remodelling that occurs in the ischemic heart, further implicating GSK3β in the remodelling of cell-cell junctions [10].

In this study, we sought to profile the proteomic and phenotypic effects of GSK3β in Wnt/ β-catenin-dependent cancer cells to identify other GSK3β-dependent pathways and processes in addition to canonical Wnt signalling. We used an isogenic pair of colorectal cancer cell lines (HCT116-GSK3β-WT and HCT116-GSK3β-KO) and showed that canonical Wnt signalling is largely unimpaired by complete knockout of GSK3β whereas specific and dramatic effects on cell-cell adhesion proteins and metabolic pathways were observed.

Methods

Maintenance of cell lines

HCT116-GSK3β-KO and paired isogenic control cell line HCT116-GSK3β-WT are commercially (Cellectis) created cell-lines (created by integrating a ssDNA oligonucleotide containing several stop codons at the GSK3 β locus with TALEN technology). These cell lines were cultured in McCoys 5A culture medium (Gibco) supplemented with 10% FBS (Gibco) and 1% PenStrep (Gibco) and maintained in 5% CO₂ at 37°C with twice weekly passaging. For passaging, cells were disassociated from the flask following warm DPBS (Gibco) wash using 0.5ml of warm TrypLE Express enzyme 1X (ThermoFisher Scientific), and incubated for five to ten minutes. Disassociated cells were centrifuged at 1,000 x g for five minutes before reconstitution in warm McCoys 5A (Gibco) medium before seeding a tenth of the culture population in a fresh culture flask. Whole cell protein lysate extract was performed by resuspension of cells in 0.1M TEAB, 0.1% SDS and lysed by sonification on ice. The suspension was then centrifuged at 13,000 x g, for 20 minutes at 4°C, supernatant was then removed and protein concentration measured by a DirectDetect^® spectrometer.

SDS-PAGE and immunoblotting

Equal amounts (15 μg) of proteins from different samples were loaded on 8% acrylamide gel and subjected to electrophoresis (120 volts, 90 minutes). Afterwards, proteins were transferred to nitrocellulose membrane (80 volts, 0.4 Amps, 3 hours) (Whatman 10402594, Dassel, Germany) and blocked for 30 minutes using a 5% milk solution. Membranes were then incubated at 4°C and proteins identified using near-infrared fluorescent secondary antibodies (Li-Cor) and imaged using a Li-Cor Odyssey^® CLx with Image Studio Lite V5.2 software. Table 1 lists all antibodies used.

Table 1. List of antibodies used in this study.

Antibody	Product code	Vendor	Host species	Dilution
Actin	6H10D10	Cell signaling	Rabbit	1:1000
Axin2	D48G4	Cell signaling	Rabbit	1:1000
Beta-Catenin	MA1-301	ThermoFisher	Mouse	1:1000
Dvl2	3126S	Cell signaling	Rabbit	1:1000
Dvl3	3218S	Cell signaling	Rabbit	1:1000
Gsk3Beta	27C10	Cell signaling	Rabbit	1:1000
Jup	75550S	Cell signaling	Rabbit	1:1000
Pkp2	PA5-53144	ThermoFisher	Rabbit	1:1000
Pkp3	35–7600	ThermoFisher	Mouse	1:1000
Tcf3	2883S	Cell signaling	Rabbit	1:1000
Tcf4	2569S	Cell signaling	Rabbit	1:1000

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TopFlash assay measurement of Wnt signalling activity

Cells were seeded in 24 well plates at a density of 1 x 10⁵ cells per well and cultured for 24 hours in McCoys 5A culture medium (Gibco) supplemented with 10% FBS (Gibco) and 1% PenStrep (Gibco) in 5% CO2 at 37°C. Transfection was then carried out by Lipofectamin^® 3000 (ThermoFisher Scientific) according to manufacturers’ protocols. We performed TopFLASH assays as previously described [11]. TopFLASH assay constructs used; M50 Super 8x ToPFLASH (AddGene), M51 Super 8x FopFLASH (AddGene), and pcDNA3.1-ccdB-Renilla (AddGene). Cells were cultured in transfection media for 6 hours before transfection media was removed and fresh growth media was added to cells. Cells were then cultured overnight before measurement of luciferase and renilla reporter signals using a Dual-Luciferase^® Reporter assay system (Promega) and a GloMax^® Discover Microplate reader (Promega) according to manufacturers’ protocols. Results were then normalized to renilla internal transfection control, and then plotted graphically using GraphPad Prism V7.

Metabolic profiling of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells

Metabolism profiling was carried out by Agilent Seahorse XFp Cell Energy Phenotype test kit on a Seahorse XFp analyser (Agilent.com). Protocols were carried out according to manufacturer guidelines and data was analysed using Agilent Seahorse Wave desktop V2.5 https://www.agilent.com/en/solutions/cell-analysis). Metabolic pathway analysis was performed using Escher [12] in conjunction with the BiGG database [13].

Mechanical stress test of cell-cell junctions

Cell-cell adhesion strength was assessed using dispase cell-substrate detachment enzyme on cell monolayers followed by mechanical stress test, integrity of cell monolayer was then assessed via light microscopy.

HCT116-GSK3β-WT and HCT116-GSK3β-KO cells were seeded in 6-well culture plates (Corning) and grown to confluence. Media was removed from cultures and cell monolayer washed with Hanks balanced salt solution (HBSS) (14025092 ThermoFisher Scientific). Cell monolayers were then incubated with dispase enzyme (2.5 Units/ml in HBSS) (D4693 Sigma) at 37°C for 30 minutes or until monolayers detached from plate bottom. Detached monolayers were then imaged before mechanical stress test with a Leica M2 16 F microscope and S Viewer V3.0.0 software. Mechanical stress was then induced used an orbital shaker (Labscale) for 3 minutes at 300rpm (orbit of 16mm). Cell monolayer following mechanical stress was then assessed for intactness.

Cellular migration assay

HCT116-GSK3β-WT and HCT116-GSK3β-KO cells were seeded in 6-well culture plates (Corning) and grown to confluence. Cells were then treated with 2mM thymidine (Sigma) supplemented media for 24 hours prior to initiation of scratch assay. Following scratch of cell monolayer, media was aspirated and cell debris was removed by washing twice with 2mM thymidine supplemented media. Culture of cells was then continued in 2mM thymidine supplemented media with migration into scratch assessed at time points over a period of 22 hours via imaging using an EVOS XL core cell imaging system (ThermoFisher Scientific), and migration into scratch area measured by ImageJ software.

Growth and colony formation analysis

1 x 10⁵ HCT116-GSK3β-WT and HCT116-GSK3β-KO cells were seeded in 6 well plates (Corning) on day 0, and maintained in McCoys 5A culture medium (Gibco) supplemented with 10% FBS (Gibco) and 1% PenStrep (Gibco), 5% CO₂ at 37°C. Cells were disassociated using warm TrypLE Express enzyme 1X (ThermoFisher Scientific) and counted using a haemocytometer counting chamber (Neubauer improved) every 24 hours. For colony growth analysis, 5 x 10³ cells were seeded per well (12 well plate (Corning)) in a total of 500μl of 0.35% agar supplemented complete McCoys 5A medium (10% FBS, 1% PenStrep), the cultures were incubated at room temperature for 3 minutes and then incubated for 5 minutes at 4°C before continued culture at 37°C, 5% CO₂ (ThermoFisher Scientific) as described previously. Cell colonies were counted manually following visualization of the cultures using a light microscope (EVOS XL, ThermoFisher Scientific). Cell counts were recorded every 24 hours for 6 days following seeding.

Proteomic profiling and mass-spectrometry

Cell pellets (3 x 10⁶) were lysed (0.1M TEAB, 0.1% SDS) with pulsed sonication. Methanol/chloroform extraction was performed on 100ug of protein for each lysate and pellet finally re-dissolved in 100ul of 6M Urea (Sigma), 2M thiourea (Sigma), 10mM HEPES buffer (Sigma), pH7.5. Samples were reduced (1M DTT), alkylated (5.5M iodoacetamide) then diluted in 400μl of 20mM ammonium biocarbonate (Sigma) and digested with trypsin (Promega) (1/50 w/w) overnight. As an internal quantification standard, 150 fmol of enolase (Saccharomyces cerevisiae) (Waters) and Hi3 Escherichia coli standard (Waters) were added to each sample. Samples were 12xfractionated (Agilent 3100 OFFGEL fractionator) in IPG buffer, pH 3–10 (GE Healthcare) diluted 1:50 in 5% glycerol, in 13cm IPG strips, pH 3–10 (GE Healthcare) and peptides focused for 20 kVh. Each fraction was acidified to <3.0 using trifluoroacetic acid (TFA) (Sigma) and solid phase extraction performed using Empore C18 96-well solid phase extraction plate (Sigma). Samples were eluted in 150μl of 80% acetonitrile, 0.5% acetic acid, lyophilised and then stored at -20°C until re-suspension in 10μl 98% dH₂O/acetonitrile and 0.1% formic acid prior to use. Nano-LC separation was performed using the nanoAcquity UPLC 2G Trap column system (Waters) at a rate of 5ul per min and washed with buffer A (98% dH₂O/acetonitrile + 0.1% formic acid) for 5 minutes. Peptides were separated on an Acquity UPLC Peptide BEH C18 column (130Å, 1.7μm, 2.1mm x 150mm, 1/pkg (Waters)) over a 90-minute linear gradient of 5% to 40% with a flow rate of 0.3μl/min with buffer B (80% acetonitrile/dH₂O + 0.1% formic acid) and completed with a 5 minutes rinse with 85% buffer B, at a flow rate of 300nl/min. Mass Spectrometry analysis was performed on a Synapt G2-S HDMS system (Waters). Samples were sprayed directly using positive mode-ESI, and data was collected in MS^E acquisition mode, alternating between low (5v) and high (20-40V) energy scans. Glu-fibrinopeptide (m/z = 785.8426, 100 fmol/μl) was used as LockMass, and was infused at 300 nl/min and sampled every 13 seconds for calibration.

Mass-spectrometry data processing and analysis

Raw data files were processed using ProteinLynx Global Server (PLGS, Waters) version 3.0.2, and optimal processing parameters were selected by Threshold Inspector software (Waters). Data-base search (Uniprot Human reference proteome; UP000005640—July 2017 + yeast enolase P00924) was carried out using the Ion-accounting algorithm in PLGS (Waters), parameters included a false discovery rate of 4%, a maximum protein size of 500,000 Da, and trypsin cleavage with an allowable error of 1 missed cleavage with oxidation of methionine, and carbamidomethylation of cysteine modifications. For protein quantification, the Hi3 calculation method of using the top 3 most intense tryptic peptides of both the internal standards and of each protein was used. Only quantified proteins were included in further analysis, therefore all had at least 3 peptides present, indicating confidence in the identification of the protein from the spectra. Protein quantification was normalised by the sum of the quantification of total proteins per sample, and multiplied by 1x10⁶, the result of which was log₂ and averaged between replicated samples. The average for each protein within a sample was then used to calculate fold change in the expression of the proteins identified in both the HCT116-GSK3 β-WT and HCT116-GSK3 β-KO cells. A threshold value of a 2-fold change in protein expression was applied. Gene ontology (GO) analysis was performed using Gorilla (Gene Ontology enRIchment anaLysis and visuaLizAtion tool) in conjunction with GO terms, to visualize significant enrichment of the datasets against the entire human proteome, results were then visualized in REVIGO, in the TreeMap format [14, 15]. GO analysis was also performed in PANTHER to summarise the enrichment of GO terms in biological pathways of the whole dataset against the human proteome [16] and using the Pathway Studio software (Elsevier) v9.0. Protein interaction analysis was performed using STRING v10.0, a database collating protein interaction information, including experimental evidence and context and co-expression, to infer a network of protein-protein interaction partners within the dataset. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014632.

Results

Proteomic analysis of knockout GSK3β colorectal cancer cells

As a model for GSK3β knockout, we selected Talen-induced knockout cells (HCT116-GSK3β-KO) and a paired isogenic control cell line (HCT116-GSK3β-WT) in the colorectal cancer cell-line HCT116. To understand the role of GSK3β knockout in a Wnt activated (β-catenin mutant) environment we carried out whole cell proteomic profiling of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells (Fig 1A). HCT116 cells carry the oncogenic stabilized allele (ΔS45) of β-catenin that lacks the Serine residue at position 45 that is normally phosphorylated by CK1. This priming normally allows GSK3β to further phosphorylate β-catenin leading to proteasomal degradation. LC-MS/MS analysis of the wild type and knockout cells identified 6,282 proteins, 1200 of which were quantified and identified across all samples (S1 File). We identified a set of 440 proteins that were uniquely detected in HCT116-GSK3β-KO or HCT116-GSK3β-WT cells or significantly (p<0.05; Student’s T-test) differentially expressed in one of the cell lines. Gene Ontology (GO) enrichment analysis of the differentially expressed set of proteins revealed significantly enriched GO terms including “metabolic process’ (rank = 6; p-value = 3e-17). Enrichment analysis of pathways identified ‘Tricarboxylic Acid Cycle’ and ‘Glycogen Degradation’ (ranked 3rd and 4th) as highly significant. We also observed that cell-cell adhesion associated terms and pathways were significantly enriched, including ‘Actin-based cytoskeleton assembly (ranked 6th), ‘focal adhesion assembly’ (ranked 10th) and ‘hemidesmosome assembly’ (ranked 15th), we, therefore, focused on further analysis of these functional roles (see S2 File for pathway analysis).

Fig 1 — A. Overview of experimental design. HCT116 cells (with stabilizing β-catenin mutation) with wild-type (HCT116-GSK3β-WT) or knockoutGSK3β (HCT116-GSK3β-KO) were profiled using LC-MS/MS to identify associated protein networks. B. Volcano plot of protein expression from mass-spectrometry study. Plot indicates gene symbols for identified proteins with log2 fold-change greater than |2| and p-value < 0.05. C. Heat map of protein expression. Showing replicated wild-type and knockout samples showing those proteins with altered expression (with log2 fold-change greater than |2| and p-value < 0.05).

Wnt/ β-catenin signalling is not perturbed in GSK3 β knockout cells

Although the role of GSK3β in regulation of canonical Wnt signalling is well known, we first sought to characterize the HCT116-GSK3β-KO cells by analysis of key Wnt signalling proteins as shown in Fig 2. Although β-catenin protein levels are directly regulated through serine phosphorylation by GSK3β and subsequent proteasomal degradation, no significant differences were observed in β-catenin protein expression between HCT116-GSK3β-KO and HCT116-GSK3β-WT cells (Fig 2C). This finding is consistent with the understanding that GSK3β cannot regulate oncogenic β-catenin through phosphorylation because of the loss of the CK1 phosphorylation site at Serine 45. We also analysed the β-catenin destruction complex component Axin (AXIN2), the Wnt regulatory protein Dishevelled (DVL2 and DVL3), and the transcription factors Transcription Factor 3 (TCF3) and Transcription factor 4 (TCF4) that drive the Wnt transcriptional response (Fig 2D–2F), Western blot analysis shows there are no significant changes to the abundance of these key Wnt components in the absence of GSK3β. We tested the canonical Wnt-driven transcriptional activity of these cells through reporter (TopFLASH) analysis as shown in Fig 2G. TopFLASH reporter assays showed that both HCT116-GSK3β-KO and HCT116-GSK3β-WT had robust levels of TCF/ β-catenin-driven reporter activity and no statistically significant differences in the levels of that activity. Finally, we identified additional Wnt signalling components in the mass-spectrometry data (S1 File). β-catenin showed no significant difference between WT and KO cells, whilst APC (Adenomatous Polyposis Coli), an important component of the destruction complex was only identified in WT cells, indicating that loss of GSK3β may result in loss or reduced expression of APC. Finally, the Casein Kinase 1 alpha subunit (CSNK1A1) which phosphorylates β-catenin at S45 showed reduced expression in knockout cells (p-value = 0.013).

Fig 2 — (A) Schematic of the canonical Wnt signalling pathway and summary of key proteins analysed in this study. Proteins identified in the mass-spectrometry study are shown, those not analysed by Western blot are shown in grey. Western blot analysis of canonical Wnt components and GSK3β protein expression characterization in cell models. 15 μg of total protein lysates from each cell-line were analyzed on a single phase 8% SDS gel and alpha-actin used as loading control. All were replicated in at least n = 3 unless indicated otherwise. Quantifications (arbitrary intensity units) are mean values ± SEM (Mann-Whitney U test p-value indicated). (B) Protein expression of GSK3β and β-catenin in HCT116 cells (independently sourced isolate), HCT116-GSK3β-KO cells and HCT116-GSK3β-WT cells. (C) β-catenin, (D) TCF3 and TCF4, (E) Axin 2, (F) DVL2 and DVL3 (n = 2). All Westerns are representative examples, and quantifications are expressed as arbitrary intensity units. (G) TopFLASH reporter assay of canonical Wnt signalling activity in HCT116-GSK3β-WT and HCT116-GSK3β-KO cells. Cells were transfected with TopFLASH plasmids, TopFLASH luciferase activity was assessed 48 hours post-transfection following passive lysis of cells. Luciferase signaling was normalized to Renilla transfection control. p-value 0.064, n = 3.

Loss of GSK3β results in dysregulation of desmosome and hemidesmosome proteins

We analysed the enriched pathways in the differentially abundant proteins (Fig 3A) and found that pathways associated with cell-cell communication and adhesion were significantly enriched in the differentially abundant proteins identified. These include focal adhesion assembly (p = 0.005) and hemidesmosome assembly (p = 0.009). The HCT116-GSK3β-KO cells showed a specific reduction in protein abundance of desmoplakin (DSP), plakoglobin (JUP) and desmoglein (DSG), and an increase in abundance of their essential linker proteins, plakophilins (PKP2, PKP3), as well as altered expression of integrin family members integrin subunit alpha 6 (ITGA6) and integrin subunit beta 4 (ITGB4). ITGA6 and ITGB4 which mediate attachment between cells and of cells to the extracellular matrix. Western blot analysis was used to analyse the abundance of the plakophilins, plakoglobin and desmoglein and we found distinct alterations to the abundance of these proteins between the HCT116-GSK3β-KO and WT cells as shown in Fig 3B and 3C. We also analysed the abundance of ITGA6, identified in the mass-spectrometry data as differentially abundant (p = 0.042), although the Western blot did not reveal a significant difference between WT and KO cells (Fig 3D).

Given the role of desmosomes and hemidesmosomes in cell-cell adhesion and tissue structure, we investigated whether HCT116-GSK3 β-KO exhibited altered cell adhesion using a mechanical stress assay as shown in Fig 4A. Following mechanical agitation of detached monolayers of HCT116-GSK3β-KO cells the cell monolayer fragmented into numerous pieces, in contrast to the HCT116-GSK3β-WT cells in which the monolayer remained intact. This indicates that loss of GSK3β and the dramatic perturbation of desmosome and hemidesmosome proteins is associated with diminished cell-cell adherence and a reduction in mechanical strength of these cell-cell junctions. Previous findings showed that loss of Plakophilins and subsequent disruption of desmosomal plaques resulted in an increased migratory ability of cells [17, 18]. We investigated the migration of HCT116-GSK3β-KO and HCT116-GSK3β-WT cells over 24 hours on cell cycle arrested cells, as shown in Fig 4B, and found that the knockout cells exhibited an increase in migration potential. These results mirror other findings in which disruption of the desmosome complex, incurs a selective advantage on cells for migration following disruption of the primary culture and which translates into an increased metastatic ability in cancer models [19]. To investigate if this also affected the cells ability to form colonies from single cells, a soft agar colony growth assay was performed and colony number over 10 days recorded (Fig 4C). Results indicate that loss of GSK3β did not incur any colony-forming advantage to HCT116-GSK3β-KO cells.

Fig 4 — A. Mechanical stress test of cell-cell adhesion. Monolayer cultures of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells were detached from culture surface and cell adhesions stressed with mechanical agitation. B. Cell migration assay of WT and KO cells. HCT116-GSK3β-WT and HCT116-GSK3β-KO cells were grown until confluent and then cell cycle arrested with 24-hour treatment of 2mM thymidine. Migration scratch was then made in the monolayer and capacity of cells to migrate into the free space assessed by light microscopy. Movement of cell migration front was measured as area free of cells and percentage migration calculated and presented graphically. Graphical representation of cell migration is presented as percentage closure of scratch. C. Soft agar colony growth assay Cells were seeded individually in soft agar matrix supplemented with complete growth media and colony formation was assessed every 24 hours over a 10-day period by light microscopy.

Dysregulated carbon metabolism in GSK3β knockout cells

To investigate the significant enrichment of proteins involved in metabolic pathways, we mapped the set of 440 differentially expressed proteins to metabolic pathways using the Escher interface [12] as shown in Fig 5A. This analysis showed a marked distinction between the modes of energy metabolism between the HCT116-GSK3β-KO and HCT116-GSK3β-WT cells whereby the knockout cells had reduced levels of many of the proteins involved in aerobic carbon metabolism but an apparent elevation of ketogenesis. BDH1, the primary 3-Hydroxybutyrate Dehydrogenase, was substantially elevated in the knockout cells. BDH1 catalyses the interconversion of acetoacetate and hydroxybutyrate, which are the two major ketone bodies produced from fatty acids. As shown in Fig 5B, glycogen metabolism proteins were highly dysregulated between knockout and wild-type cells. The three human glycogen phosphorylase isozymes (PYGM, PYGL and PYGB) function as semi-tissue-specific enzymes catalysing the production of glucose from glycogen. All were significantly altered between knockout and wild-type cells (PYGM p-value = 0.04; PYGL p-value = 0.003; PYGB p-value = 0.02). In addition, the Glycogen synthase 1 enzyme was elevated in wild-type cells (p-value = 0.001). To further investigate these observations and their effects on cell growth and metabolism, we performed a cell energy phenotype assay (Agilent Seahorse XFp). This indicated that HCT116-GSK3β-KO cells have a reduced oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) when compared to HCT116-GSK3β-WT cells at both the basal level and under stressed conditions (Fig 5C). OCR was used as an indicator of mitochondrial respiration and ECAR was used as an indicator of glycolysis, the reduced rates of which when taken together indicate HCT116-GSK3β-KO cells have a more quiescent metabolism phenotype when compared to HCT116-GSK3β-WT cells. Although knockout of GSK3β did not affect cell viability in vitro, growth assays indicated that HCT116-GSK3β-KO cells exhibited a reduced growth rate and growth potential when grown in adherent cell culture conditions (Fig 5C).

Fig 5 — A. Map of metabolic pathway perturbations in HCT116-GSK3β-KO cells. All significantly differentially expressed proteins were mapped using the Escher tool for metabolic pathway mapping. B. Glycogen metabolism proteins significantly altered in HCT116-GSK3β-KO cells. Glycogen metabolism protein sub-network. All red or blue colored proteins were significantly (p<0.05) differentially expressed in wild-type (blue) or knockout cells (red). C. Metabolism phenotype assay of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells. Cell metabolism assessed by Agilent Seahorse XFp cell energy phenotype test, oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were used as measures of mitochondrial respiration and glycolysis respectively, under basal and stressed conditions to assess metabolic potential of cells. N = 3.

Discussion

We used proteomic and cell-biological approaches to analyse the functional consequences of loss of GSK3β in colorectal cancer cells. Since the role of GSK3β in regulation of Wnt/β-catenin signalling in colorectal cancer is comparatively well understood, we have used colorectal cancer cells with constitutively stabilized and activated β-catenin to identify other GSK3β regulated processes. We used an isogenic homozygous GSK3β knockout model derived from the model colorectal cancer cell line HCT116. We confirmed that both wild-type and GSK3β knockout HCT116 cells exhibit robust Wnt/β-catenin signalling by analysis of upstream and downstream components of the signalling pathway. Although the majority of Wnt related proteins we examined did not exhibit significant differential expression, we did observe several specific changes. Adenomatous Polyposis Coli (APC) is a key member of the β-catenin destruction complex, and in our studies was identified only in HCT116-GSK3β-WT cells (in 2 out of 3 samples), and not in HCT116-GSK3β-KO cells, indicating a likely reduction in the knockout cells. Notably, APC contains several phosphorylation consensus sequences for both GSK3β and CSK1, and GSK3β has been shown to mediate the interaction between APC and Axin in the destruction complex [20]. Although we did not observe a statistically significant alteration to β-catenin, α-catenin (CTNNA1) was significantly decreased in the HCT116-GSK3β-KO cells. GSK3β has been linked to phosphorylation of α-catenin in a double knockout phosphoproteomic study, where no phosphorylated α-catenin was detected in knockout cell models [21]. In addition, loss of α-catenin phosphorylation sites were linked to reduced strength of cell-cell adherens junctions [22]. Taken together with the Western analysis and Wnt transcriptional activity assays, these results show that Wnt/β-catenin signalling was not significantly altered in HCT116-GSK3β-KO cells.

Our proteomic analysis identified substantial alterations to the expression of plakophilins and integrins involved in desmosome and hemidesmosome formation, protein complexes that mediate cell-cell and cell-substrate adhesion respectively. Hemidesmosomes bring about the adhesion of basal epithelial cells to the underlying basement membrane and consist of a multi-protein complex [23]. This multi-protein complex is anchored by integrin α6β4, and both α6 and β4 showed significant and substantial decreases in HCT116-GSK3β-KO cells. Integrins α6 and β4 were the only integrins identified in our study as significantly differentially expressed, consistent with the observation that in epithelial cells β4 only dimerizes with α6 subunit in an exclusive association [24]. We investigated the strength of cell-cell adhesion and found that HCT116-GSK3β-KO were significantly less adherent than HCT116-GSK3β-WT cell films consistent with the proteomic data indicating major hemidesmosome disruption.

We also observed differential levels of key components of the desmosomes, including plakophilins 2 and 3 (PKP2 and PKP3) and plakoglobin (JUP), all armadillo repeat proteins, in common with β-catenin. Consistent increased or decreased expression was observed for these 3 proteins in the mass-spectrometry analysis and Western blot analysis. PKP2 and PKP3 were both increased in HCT116-GSK3β-KO whereas plakoglobin was decreased. Desmoplakin (DSP), a core component of the desmosome interacts with plakoglobin and plakophilins. DSP was identified and quantified in all mass-spectrometry samples analysed, but showed no significant differential expression between KO and WT cells. GSK3β-mediated phosphorylation of the C-terminal of desmoplakin (DSP) is crucial for binding of its interaction partners, mainly cytoskeletal proteins and plakoglobins [25]. Loss of DSP phosphorylation by knockdown or knockout of GSK3β results in the formation of desmosomes lacking a connection to the cytoskeletal intermediate filaments, reducing the strength of desmosome cell-cell junctions [25]. GSK3 and PRMT1 have been shown to cooperatively catalyse post-translational modification of desmoplakin and inhibition of GSK3 resulted in delayed formation of adherent junctions [8], although with the caveat that in the latter study the authors do not differentiate between GSK3β and GSK3α. Intriguingly, upon knockdown of GSK3, DSP aggregates at interactin filaments in the cytoplasm which is mediated by GSK3 phosphorylation of DSP at S2849, without affecting the protein abundance of DSP [8]. This relocalization of the protein results in a disconnection of the desmosomal plaque from the intracellular cytoskeleton, reducing the strength of desmosomal plaques. Relocalization of DSP may occur in the HCT116-GSK3β-KO cells as a result GSK3β loss, explaining the observations we have made.

We also observed substantial alterations to metabolic pathway utilisation in HCT116-GSK3β-KO cells. Of the 10 most statistically significant proteins that are increased in HCT116-GSK3β-KO cells, 3 of them are associated with glucose metabolism (Pyruvate kinase is the 2^nd most significantly altered protein in HCT116-GSK3β-KO cells). These findings are in line with the role that GSK3β plays in the regulation of glucose metabolism, whereby glycogen synthase is a substrate of GSK3β kinase activity [26]. A particular challenge in interpretation of the proteomic analysis is delineating the role that GSK3β plays in regulation of the observed metabolic pathways. This may be a direct role through phosphorylation as for glycogen synthase, or the perturbations observed could be indirectly associated with these direct alterations. Whilst our study does not identify direct substrates of GSK3β (which have been defined through multiple phosphoproteomic studies [21]), our quantitative proteomic analysis does identify the dysregulated protein networks, pathways and process and points to the multi-functional nature of GSK3β. Future studies that target the specific activities of GSK3β in cell-cell adhesion or regulation of metabolic activity may need more specific model systems that target specific activities of GSK3β or the activity of GSK3β in a specific protein complex or sub-cellular localisation [27].

Supporting information

S1 Raw images. Underlying western blot images.

(PDF)

Click here for additional data file.^{(1.2MB, pdf)}

S1 File. Supplementary table of protein quantification of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells by mass-spectrometry.

(XLSX)

Click here for additional data file.^{(715.5KB, xlsx)}

S2 File. Supplementary table of enriched pathways from the mass-spectrometry analysis.

(XLSX)

Click here for additional data file.^{(15.6KB, xlsx)}

Data Availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014632.

Funding Statement

RME acknowledges Medical Research Council (MR/S01411X/1) and European Commission FP7 (“Oncoprotnet”) for funding; YW acknowledges Medical Research Council (MR/S025480/1). FDMG and RME acknowledge support from the Mexican National Council for Science and Technology (CONACyT)and Technology (CONACyT) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0246707.r001

Decision Letter 0

Michael Klymkowsky

26 Mar 2021

PONE-D-20-40086

Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells

PLOS ONE

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Reviewer #1: Yes

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Reviewer #1: This manuscript may be interesting since it describes potential involvment of GSK3B in metabolism, which so far has been demonstrated only for the alpha isoform not the beta one. In addition, it shows a role for GSK3B in regulating key components of the desmosomes. However, several points have to be addressed before acceptance. In addition, also basic formal adjustment have to be made, for example how to nominate proteins and their relative abbreviations. In addition, sometimes the discussion is a little superficial. For details see the attached document

Reviewer #2: In this study the authors address GSK3β-associated signalling pathways using human colorectal cancer cells HCT116 harbouring an activating mutation in the CTNNB1 gene. Although GSK3β has regulatory functions in Wnt signalling, GSK3β-deficient HCT116 cells did not exhibit any changes in the abundance of key Wnt signalling molecules, which is in line with previously published data. Using quantitative proteomic analysis, the authors identified changes in the abundance of proteins associated with intercellular communication, adhesion, and carbon metabolism. The authors observed significant changes in the amount of proteins forming desmosomes, hemidesmosomes and focal adhesions in GSK3β-deficient cells. The practical impact of these changes is presented in several functional assays in which GSK3β-deficient cells exhibited reduced resistance to mechanical agitation and increased migratory ability. Finally, the authors show that GSK3β-deficient cells displayed reduced mitochondrial respiration, glycolysis, and growth.

The results are interesting and novel; however, there are certain shortcomings in the text and figures (see below). After revision I would recommend the study for publication.

Major issues

1. The quality of figures is insufficient – the image resolution should be higher and the text better readable (especially Figure 1b – gene names and legend; Figure 2a – protein names; Figure 4a and 4b – photo description; Figure 5c – text inside the graph).

2. The authors should be more specific when describing the generation of GSK3β knock-out in HCT116 cells. First, it is not clear whether the HCT116-GSK3β-KO cell line originated from a mixed cell culture or whether it was derived from a single cell. Second, targeting of the GSK3β locus is described insufficiently. The authors should provide sequences of the GSK3β locus and ssDNA oligonucleotide along with the information about the TALENs used (target site sequence, catalogue number of plasmids). Finally, it is not apparent what are the "replicated samples". The authors do not explain whether the replicates represent cell lines targeted by different TALENs or different cell clones targeted by the same TALEN. This should be mentioned in the methods.

3. The principle of the TopFLASH assay and metabolic profiling (Agilent Seahorse XFp cell energy phenotype test) should be described, either in the (supplementary) methods or results. Alternatively, the authors could add references to papers describing these methods.

4. On page 12, some results of the mass-spectrometry analysis are mentioned, specifically β-catenin, APC and CK1α expression, but there is no reference to any figure. I understand that the data are presented in the Supplementary Table; however, this table is very extensive and difficult to search through. I suggest that the authors create an extra table containing only expression of those proteins that are discussed in the text.

5. In Figure 2, the protein expression in Western blot 2b is shown in three samples: "WT", "KO", and "HCT116" – it is not clear what is the third column, as both the "WT" and

"KO" are HCT116 cells. Next, Western blots in 2c and 2d display protein abundance in two "WT" and two "KO", while in 2e and 2f there is only one column for "WT" and "KO". The authors should clarify whether these figures display representative or all results. Finally, graphs in 2c and 2d lack y-axis description.

6. The authors evaluate expression of the Axin protein (page 12 and Figure 2e); however, they don’t specify whether it is Axin1 or Axin2. In addition, the anti-Axin antibody should be added into methods.

7. The authors claim that they identified differential abundance of proteins included in focal adhesion assembly (p=0.005) – page 14, lines 1-3; however, there is no reference to any figure or table supporting this claim. I suggest that the authors add these results (possibly within supplementary materials).

8. On page 14, a specific reduction of several proteins is described in HCT116-GSK3β-KO with reference to Figure 3A; however, the desmoglein protein, which is mentioned in the results, is not shown in the Figure. It should be added into the graph.

9. On page 16, the authors describe differential abundance of proteins involved in glycogen metabolism (PYGM, PYGL, PYGB, glycogen synthase 1) with a reference to Figure 5B; however, the figure contains only a scheme of the glycogen metabolism with color-coded protein abundance in WT or KO cells. I would be appropriate to show a graph or table with specific protein expression values as well.

10. In Figure 2, the Western blot analysis of WT cells is displayed in the first column and for KO cells in the following one, while in Figure 3, the first column in the graph shows protein abundance in KO cells and the second in WT cells, which is confusing for the reader. I suggest that the authors unify, in all figures, the order in which they show the results for WT and KO cells.

11. There is no reference to Figure 5D in the text. In addition, the y-axis description is not clear – the authors claim that they seeded cells at density 1 × 105 (page 17); however, the numbers on the y-axis named "Cell number" are in the range 0-800, which is confusing. This should be corrected.

Minor issues

12. The language is clear and easy to follow; however, there are several typos in the text (e. g. "6-cell" – page 7; "-20C" – page 20; "CSK1" – page 18) or missing words (e. g. Western analysis – page 18); therefore, I suggest that the authors have the text corrected by a copyeditor.

13. It would be appropriate to unify some terms and descriptions. For example, "β-Catenin" should be written with lowercase "c"; in "Wnt/ β-catenin signalling", no extra space should follow the slash; in Figure 1B description, the sign of inequality "greater than" should be expressed by the symbol ">"; "P value" on page 13 and "P-value" on page 15 should be written as "p-value"; "alpha-catenin" on page 18 should be written as "α-catenin".

14. The names of the figures should describe their content, not the method used. For example in Figure 4B, instead of "Cell migration assay", more appropriate name would be "GSK3β-deficient HCT116 cells display increased migration potential". I suggest that the authors rename all the figures accordingly.

15. Some abbreviations are not explained in figure description (e.g. Figure 2A and 5B) or are explained more than once in the text (desmoplakin on page 14 and 19). I ask the authors to correct this.

16. On page 6, a Tbc1d4 antibody is listed in the table, but there is no mention of the Tbc1d4 protein in the text.

17. I ask the authors to check whether the Figure numbers correspond with references in the text – e.g. on page 12, there is a reference to Figure 1G, which should be numbered 2G.

18. According to the text on page 14, Figure 3 is divided to Figure 3A (mass spectrometry analysis) and Figure 3B (Western blot analysis). This division is lacked in the Figure itself, as the letter "B" is missing in both, the Figure and the figure description. I ask the authors to unify this.

**********

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Reviewer #1: No

Reviewer #2: Yes: Monika Stastna

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PLoS One. 2021 Nov 5;16(11):e0246707. doi: 10.1371/journal.pone.0246707.r002

Author response to Decision Letter 0

20 Sep 2021

We thank the reviewers for their insightful comments and for helping us improve the manuscript. Alongside requested changes to the manuscript we have performed new experiments. These include new Western blots of new proteins showing the dys-regulation of proteins associated with cell-cell and cell-substrate adhesion. We also have redone the soft agar colony growth assay in line with the comments of the reviewers.

We address each point in turn below (all of our responses are in bold).

List of modified figures/tables:

Figure 1B (gene symbol labels removed)

Figure 2A (shading changed)

Figure 2E (Axin->Axin2)

Figure 3A (new data)

Figure 3C (new data)

Figure 3D (new data)

Figure 4C (new data)

Figure 5C (legend/text)

Figure 5D (removed)

Supplementary Table 1 (tab added)

Supplementary Table 2 (new)

Reviewer #1

This manuscript may be interesting since it describes potential involvement of GSK3B in metabolism, which so far has been demonstrated only for the alpha isoform not the beta one. In addition, it shows a role for GSK3B in regulating key components of the desmosomes. However, several points have to be addressed before acceptance. In addition, also basic formal adjustment have to be made, for example how to nominate proteins and their relative abbreviations. In addition, sometimes the discussion is a little superficial. For details see the attached document

Page 3, Abstract

- Consistent with the role GSK3β, we found that… -> Consistent with the role of GSK3β, we found that….

- …therefore use this knockout cell model ->… therefore used this knockout cell model

- …and we characterize the phenotype and altered proteomic profiles associated with this -> ….and we characterized the phenotype and altered proteomic profiles associated with this

- We also characterize the perturbation…-> We also characterized the perturbation…

- …and identify defects -> and identified defects

Materials and methods

- Several times the 6-well plates have been indicated as 6-cell plates

Pag.12

- We tested the canonical Wnt-driven transcriptional activity of these cells through reporter (TopFLASH) analysis as shown in Figure 1G.

It’s Figure 2G

- Finally, we identified additional Wnt signaling components in the mass-spectrometry data. β-catenin showed no significant difference between WT and KO cells, whilst

- All of the above comments have been addressed.

- APC (Adenomatous Polyposis Coli), an important component of the destruction complex was only identified in WT cells, indicating that loss of GSK3β may result in loss or reduced expression of APC. Finally, the Casein Kinase 1 alpha subunit (CSNK1A1) which phosphorylates β-catenin at S45 showed reduced expression in knockout cells (p-value = 0.013).

The Authors show the western blot for all those proteins whose expression does not change in the absence of GSK3B but do not show the western blot for the only proteins whose expression is indeed changed: It’s a non-sense! So please add the blots for APC and CSNK1A1

We’ve steered the manuscript towards focusing more on the cell adhesion perturbations that we observed in the knock-out cells, in part because this is a more novel area than the role of GSK3B in Wnt signaling (which is well understood). We therefore include new Western blots for the cell-cell adhesion proteins of the desmosome and hemidesmosome, rather than expanding our analysis of the Wnt proteins. Whilst we agree with the author, that validation of proteins via Western blots that are identified by mass-spectrometry would be ideal for as many of the proteins as possible, in this instance, we feel that our efforts are best directed towards the cell-cell adhesion proteins (please see comments below for further explanation). To make this clearer for the reader, in Figure 2 we now highlight only those proteins in the Wnt pathway diagram (Figure 2A) that we have investigated by Western analysis.

Pag.14

- The HCT116- GSK3β-KO cells showed a specific reduction in protein abundance of desmoplakin (DSP), plakoglobin (JUP) and desmoglein (DSG), and an increase in abundance of their essential linker proteins, plakophilins.

We also observed the complete lack of expression in HCT116-GSK3β-KO cells of two key integrin family members (Figure 3A). ITGA6 and ITGB4

In the western blot of Fig.3 (that should be presented as a separate panel, 3B) not all of the proteins identified ad differentially expressed in KO cells by MS are validated and in the text is mentioned a protein, desmoglein, whose expression is not presented in the figure neither as MS nor as WB data. Please show the expression of all the proteins both by MS and WB. In addition, remember that there are rules as of proteins have to be indicated: full names followed by acronyms in brackets should be indicated in the text the first time a protein is mentioned and acronyms can be used in the figures but they have to explained in the legend. So please correct the text and the legend accordingly. In fact, for desmoplakin and plakoglobin the acronym is indicated in the text but for plakophilins it is not. Then the Authors report of the complete loss of expression of two key integrin family members but they don’t specify who they are….???? It’s up to the reader to guess they are ITGA6 and ITGB4 whose function is explained in the following sentence. By the way, these two proteins are indicated by acronyms without displaying the full name…. Finally, in figure 3 proteins are indicated by their full name for MS experiments and by acronyms in the WB panel!!... that are not explained in the figure legend… this is extremely confusing for the reader!

We provide new Western blots of the desmoglein protein identified in the mass-spectrometry (DSG2) and of integrin A6 (ITGA6). We have also made it clearer in the text which of the proteins we have analysed by Western, including canonical gene symbols for clarity.

- Following mechanical agitation of detached monolayers of HCT116-

GSK3β-KO cells the cell monolayer fragmented into numerous pieces, in contrast to the HCT116-GSK3β-WT cells in which the monolayer remained intact

Is it possible a quantification of the phenomena for example, by counting the fragments in a given area?

The dispase cell-cell adhesion assay allows for the qualitative assessment of the disruption of cell-cell adhesion, and is typically paired (e.g. Roberts et al, PLoS One, Vol 8, 10, 2013) with scratch/wound assays as we have done to show impairment of cell-cell adhesion. Truly quantitative analysis of the strength of cell-cell adhesion would be achieved though more sensitive single-cell techniques such as atomic force microscopy. The number of fragments could be counted per plate well and compared between the cell-lines. However, we don’t believe that this pseudo-quantitative analysis would significantly add to the qualitative analysis that we have performed, nor change our findings that the desmosome function in HCT116-GSK3KO cells are grossly altered and this has changed the cell-matrix and cell-cell adhesion properties of the cells.

Pag.15

- a soft agar colony growth assay was performed and colony number over 6

days recorded (Fig 4C). Results indicate that loss of GSK3β did not incur any colony-forming advantage to HCT116-GSK3β-KO cells

First of all, usually the results of a soft agar assay are shown as images of the cristal violet-stained welsl accompanied by the graph showing the quantification. Only the graph is not acceptable.

Second, the soft agar assay is an endpoint assay ie, colony forming in two different experimental situation are counted at the end of the experiment only, and not every day. This because, by definition, a colony is considered when it is formed by at least 50 cells: given that HCT116 have a cell cycle of approximately 24hs it means that a colony is formed between day 6 and day 7.

Therefore I request a “real”experiment of soft agar with representative images of the colonies after 7-10 days, accompanied by quantification graphically shown

We have completely redone this experiment as the reviewer has requested and now show the colony formation across the 7-10 day period with representative images.

Finally, what do you mean for:

- although we observed an increased cell-count in cultures of HCT116-GSK3β-WT as compared to HCT116-GSK3β-KO (Figure 4C).

Reading this sentence I understand that the Authors are referring to a different experiment like a growth curve (which is actually performed in cultures) not to a soft agar assay. Please explain

We’ve now clarified this by explaining the colony formation results as shown in the new Figure.

Figure 5

Legend of panel C

There’s no legend of the graph: please indicate what the colors and empty/filled squares means

We’ve now added the legend to Figure 5C.

Growth curve shown in panel D

From day 6 to day 9 a plateau for WT cells and a decrease in survival of KO cells is evident, likely to be due to nutrients exhaustion. To assess the growth potential of a cell line cells should be plated at 10% and then counted every 24 hs in the log phase that in this experiment appears to be from day 1 to 6. Why the Authors decided to carry on for 3 more days? They exposed cells to an “artificial” stress in the final part of the experiment, so the apparent reduced “growth potential” (as they indicate it at page 17) of KO cells is in reality an a decline in the number of cells due to increased death of cells in conditions of nutrient exhaustion. WT cells seem instead to cope better in face of such a stress since the decrese in the number of cells is minimal. In order to get reliable and interpretable results the Authors should repeat the experiment in better controlled conditions such as stopping the growth count at 6 days. It is clear from the data they showed that WT cells are more resistant to limited nutrient availability so I would perform more experiments in that sense ie, repeating growth curves also maintaining cells in reduced amount of serum (eg in 1% serum) or assessing their viability in absence of serum by FACS analysis or by CellTiter-Glo® Luminescent Cell Viability Assay or even by a very simple Trypan blue staining.

The reviewer points out that the main difference between the growth profiles of the WT and KO cells is the period after 6 days where there is likely to be nutrient exhaustion. During the log phase, there is little difference between the cell-lines. We have therefore removed this figure from the results since we do not believe this result will add to the overall findings.

Pag.19

- Loss of DSP phosphorylation by knockdown or knockout of GSK3β results in the formation of desmosomes lacking a connection to the cytoskeletal intermediate filaments, reducing the strength of desmosome cell-cell junctions (24). GSK3 and PRMT1 have been shown to cooperatively catalyse posttranslational modification of desmoplakin and inhibition of GSK3 resulted in delayed formation of adherent junctions (8). Intriguingly, upon knockdown of GSK3β, DSP aggregates at interactin filaments in the cytoplasm which is mediated by GSK3β phosphorylation of DSP at S2849, without affecting the protein abundance of DSP (8). This relocalization of the protein results in a disconnection of the desmosomal plaque from the intracellular cytoskeleton, reducing the strength of desmosomal plaques. Relocalization of DSP may occur in the HCT116-GSK3β-KO cells as a result GSK3β loss, explaining the observations we have made.

In this paragraph the Authors refer indiscriminately to GSK3 and GSK3β but this is not correct since the alpha and beta isoforms, although being largely redundant play also unique roles as demonstrated by gene knockout studies. In fact, GSK3A is unable to rescue the lethal phenotype of GSK3B null mice: the animals die during embryogenesis as a result of liver degeneration caused by widespread hepatocyte apoptosis, where excessive TNF-alpha-mediated cell death occurs, due to reduced

NFkB function (Hoeflich Ket al., 2000, Nature 406: 86–90). On the other hand, GSK3A null mice are viable and show metabolic defects – such as enhanced glucose and insulin sensitivity and reduced fat mass - which cannot be counteracted by the beta isofom (MacAulay K, et al.,2007, Cell Metab 6: 329–337). The reference used here to support their reasoning is vague about which isoform is capable to mediate the effects described since these Authors mostly use in their experiments LiCl which is a inhibitor of both isoforms. Therefore, it would be better to find an appropriate reference or modify the discussion.

We recognize that in the Albrecht et al paper that we cited, the authors did not use antibodies or techniques that discriminated between GSK3B and GSK3A. Given the findings though, it’s a very relevant paper for our discussion. We’ve therefore kept the citation but modified the discussion to acknowledge the point the reviewer makes.

Pag.19

- These findings are in line with the extensive role that GSK3β plays in the regulation of metabolic pathways, including the early finding that glycogen synthase is a substrate of GSK3β kinase activity (25).

Also in this case the statement is not well supported by the citation quoted. In fact, the reference is the original paper describing the glycogen synthase as a substrate of GSK3β. What about papers supporting the extensive role that GSK3β plays in the regulation of metabolic pathways? To my knowledge is more GSK3A than GSK3B that plays a role in the metabolis, given that GSK3A null mice are viable and show metabolic defects which cannot be counteracted by the beta isofom (see above). Therefore please quote the data from the literature supporting this statement or modify the discussion. Moreover, given that the data from Authors indicate a role for GSK3B in metabolic regulation can they exclude that the knockout of the beta isoform do not trigger a compensatory upregulation of GSK3A?

We’ve now altered the discussion to indicate the specific role of GSK3B in glucose metabolism, and we recognize that there is plenty of evidence of GSK3 isoforms and their role in regulation of metabolic pathways, but many of these studies may not differentiate between isoforms, or as the reviewer indicates point to GSK3A rather than GSK3B.

Reviewer #2:

In this study the authors address GSK3β-associated signalling pathways using human colorectal cancer cells HCT116 harbouring an activating mutation in the CTNNB1 gene. Although GSK3β has regulatory functions in Wnt signalling, GSK3β-deficient HCT116 cells did not exhibit any changes in the abundance of key Wnt signalling molecules, which is in line with previously published data. Using quantitative proteomic analysis, the authors identified changes in the abundance of proteins associated with intercellular communication, adhesion, and carbon metabolism. The authors observed significant changes in the amount of proteins forming desmosomes, hemidesmosomes and focal adhesions in GSK3β-deficient cells. The practical impact of these changes is presented in several functional assays in which GSK3β-deficient cells exhibited reduced resistance to mechanical agitation and increased migratory ability. Finally, the authors show that GSK3β-deficient cells displayed reduced mitochondrial respiration, glycolysis, and growth.

The results are interesting and novel; however, there are certain shortcomings in the text and figures (see below). After revision I would recommend the study for publication.

Major issues

We’ve now addressed this. Gene symbol labels have been removed from Figure 1b (there are simply too many to display in a readable way). Label size increased where possible.

We’ve now made it clearer that the HCT116-GSK3β-KO and paired isogenic control cell line HCT116-GSK3β-WT are commercially acquired cell-lines (i.e. the cell-lines were not engineered by our laboratory) – they were purchased by us.

We’ve now added an appropriate reference for a methods article for Topflash and point the reader to the Agilient website for a description of how the Agilent seahorse works.

Please see the response made to Reviewer 1’s comments concerning APC and CSNK1A. We’ve added more to the data concerning the cell-cell adhesion proteins and do not emphasize those proteins for which we have not validated via western blot. We think that this provides a better focus for the manuscript. Also, we have not added another table of MS results to the paper – but rather added an additional tab to Supplementary Table 1 where we list the key proteins referenced in the paper with all of the mas-spectrometry intensity data.

5. In Figure 2, the protein expression in Western blot 2b is shown in three samples: "WT", "KO", and "HCT116" – it is not clear what is the third column, as both the "WT" and

We’ve now clarified these points in the figure image.

It is Axin 2 as now indicated in the figure, and with the antibody listed in the table.

We’ve added an additional Supplementary table (Supplementary Table 2) with the pathay analysis and the proteins within each group.

A new Western blot showing the desmoglein protein identified in the mass-spectrometry (DSG2) has been added (Figure 3C).

We’ve now added these as an addendum to Supplementary Table 1, so that readers can see the protein expression values for the selected proteins discussed in the paper.

All Westerns are now in the order WT then KO.

Please see comments to Reviewer 1 concerning Figure 5D.

Minor issues

These issues have now been addressed.

15. Some abbreviations are not explained in figure description (e.g. Figure 2A and 5B) or are explained more than once in the text (desmoplakin on page 14 and 19). I ask the authors to correct this.

We’ve now taken care of the typos and inconsistencies, but have left the figure headings largely unchanged, because we think these shorter titles are sufficiently clear.

16. On page 6, a Tbc1d4 antibody is listed in the table, but there is no mention of the Tbc1d4 protein in the text.

17. I ask the authors to check whether the Figure numbers correspond with references in the text – e.g. on page 12, there is a reference to Figure 1G, which should be numbered 2G.

All these have now been addressed.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0246707.r003

Decision Letter 1

Michael Klymkowsky

4 Oct 2021

Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells

PONE-D-20-40086R1

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Reviewer #1: All comments have been addressed

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Reviewer #1: I feeel satisfied with the improvements made by the Authors in response to my comments, therefore I recommend the manuscript for publication

Reviewer #2: The authors edited the manuscript based on the comments from both reviewers and supplemented it with missing information. In its current form, I recommend the manuscript for publication.

One minor note: the term "glycogen-specific kinase" is used in the text, however, GSK3β acronym stands for "glycogen synthase kinase" - the authors should correct this.

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PLoS One. doi: 10.1371/journal.pone.0246707.r004

Acceptance letter

Michael Klymkowsky

20 Oct 2021

PONE-D-20-40086R1

Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells.

Dear Dr. Ewing:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Raw images. Underlying western blot images.

(PDF)

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S1 File. Supplementary table of protein quantification of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells by mass-spectrometry.

(XLSX)

Click here for additional data file.^{(715.5KB, xlsx)}

S2 File. Supplementary table of enriched pathways from the mass-spectrometry analysis.

(XLSX)

Click here for additional data file.^{(15.6KB, xlsx)}

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Data Availability Statement

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014632.

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PERMALINK

Proteomic characterization of GSK3β knockout shows altered cell adhesion and metabolic pathway utilisation in colorectal cancer cells

Emily Bowler-Barnett

Francisco D Martinez-Garcia

Matthew Sherwood

Ahood Aleidan

Steve John

Sara Weston

Yihua Wang

Nullin Divecha

Paul Skipp

Rob M Ewing

Roles

Abstract

Introduction

Methods

Maintenance of cell lines

SDS-PAGE and immunoblotting

Table 1. List of antibodies used in this study.

TopFlash assay measurement of Wnt signalling activity

Metabolic profiling of HCT116-GSK3β-WT and HCT116-GSK3β-KO cells

Mechanical stress test of cell-cell junctions

Cellular migration assay

Growth and colony formation analysis

Proteomic profiling and mass-spectrometry

Mass-spectrometry data processing and analysis

Results

Proteomic analysis of knockout GSK3β colorectal cancer cells

Fig 1.

Wnt/ β-catenin signalling is not perturbed in GSK3 β knockout cells

Fig 2.

Loss of GSK3β results in dysregulation of desmosome and hemidesmosome proteins

Fig 3.

Fig 4.

Dysregulated carbon metabolism in GSK3β knockout cells

Fig 5.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Michael Klymkowsky

Roles

Author response to Decision Letter 0

Decision Letter 1

Michael Klymkowsky

Roles

Acceptance letter

Michael Klymkowsky

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