Video_2_Satellite tagging confirms long distance movement and fast dispersal of Patagonian toothfish (Dissostichus eleginoides) in the Southwest Atlantic.mp4

IntroductionTo better understand Patagonian toothfish (Dissostichus eleginoides) movement and habitat in the Southwest Atlantic, fifty popup satellite archival tags (PSATs) were deployed off Davis Bank on North Scotia Ridge between 2019 and 2020 on individuals ranging from 97-139 cm total length.MethodsPSATs (18 Lotek Wireless PSATFLEX and 32 Wildlife Computers MiniPAT) were programmed to detach after completing 1 to 16-month missions recording pressure (depth) and water temperature.ResultsSix tags failed to report, and among the remaining 44 reporting tags, 34 reported on schedule, up to 487 days at sea – the longest electronic tag deployment for this species to date. Although the majority of PSATs reported within 50 km from the release sites, confirming high site fidelity, 12% of tags reported more than 200 km away, showing connectivity to Shag Rocks and South Georgia in the Southern Ocean. Toothfish moved across the Antarctic Polar Front through/to areas with no fishing activities, and hence, explained the absence of any previous conventional tag recapture. A 1-month transit to the Falkland/Malvinas Plateau Basin also revealed that toothfish can attain a surprisingly high movement rate of 33 km day-1.DiscussionFishery independent examples of toothfish presence and their movement capabilities are inviting us to broaden our examination on how toothfish move around their Scotia Arc habitats and link up different regional aggregation sites in the South Atlantic.</p

Video_1_Satellite tagging confirms long distance movement and fast dispersal of Patagonian toothfish (Dissostichus eleginoides) in the Southwest Atlantic.mp4

IntroductionTo better understand Patagonian toothfish (Dissostichus eleginoides) movement and habitat in the Southwest Atlantic, fifty popup satellite archival tags (PSATs) were deployed off Davis Bank on North Scotia Ridge between 2019 and 2020 on individuals ranging from 97-139 cm total length.MethodsPSATs (18 Lotek Wireless PSATFLEX and 32 Wildlife Computers MiniPAT) were programmed to detach after completing 1 to 16-month missions recording pressure (depth) and water temperature.ResultsSix tags failed to report, and among the remaining 44 reporting tags, 34 reported on schedule, up to 487 days at sea – the longest electronic tag deployment for this species to date. Although the majority of PSATs reported within 50 km from the release sites, confirming high site fidelity, 12% of tags reported more than 200 km away, showing connectivity to Shag Rocks and South Georgia in the Southern Ocean. Toothfish moved across the Antarctic Polar Front through/to areas with no fishing activities, and hence, explained the absence of any previous conventional tag recapture. A 1-month transit to the Falkland/Malvinas Plateau Basin also revealed that toothfish can attain a surprisingly high movement rate of 33 km day-1.DiscussionFishery independent examples of toothfish presence and their movement capabilities are inviting us to broaden our examination on how toothfish move around their Scotia Arc habitats and link up different regional aggregation sites in the South Atlantic.</p

DataSheet_1_Satellite tagging confirms long distance movement and fast dispersal of Patagonian toothfish (Dissostichus eleginoides) in the Southwest Atlantic.docx

IntroductionTo better understand Patagonian toothfish (Dissostichus eleginoides) movement and habitat in the Southwest Atlantic, fifty popup satellite archival tags (PSATs) were deployed off Davis Bank on North Scotia Ridge between 2019 and 2020 on individuals ranging from 97-139 cm total length.MethodsPSATs (18 Lotek Wireless PSATFLEX and 32 Wildlife Computers MiniPAT) were programmed to detach after completing 1 to 16-month missions recording pressure (depth) and water temperature.ResultsSix tags failed to report, and among the remaining 44 reporting tags, 34 reported on schedule, up to 487 days at sea – the longest electronic tag deployment for this species to date. Although the majority of PSATs reported within 50 km from the release sites, confirming high site fidelity, 12% of tags reported more than 200 km away, showing connectivity to Shag Rocks and South Georgia in the Southern Ocean. Toothfish moved across the Antarctic Polar Front through/to areas with no fishing activities, and hence, explained the absence of any previous conventional tag recapture. A 1-month transit to the Falkland/Malvinas Plateau Basin also revealed that toothfish can attain a surprisingly high movement rate of 33 km day-1.DiscussionFishery independent examples of toothfish presence and their movement capabilities are inviting us to broaden our examination on how toothfish move around their Scotia Arc habitats and link up different regional aggregation sites in the South Atlantic.</p

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy

<div><p>During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (<i>RAS</i>)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.</p></div

Effect of HGF stimulation on treatment responses.

<p>(A) Heat-map of cell viability changes due to HGF stimulation (log₂ (treated with HGF/untreated)-log₂ (treated without HGF/untreated)). An unbiased hierarchical clustering separated the cell population into 6 different clusters (indicated by colors on the top of heat-map). Each row indicates a treatment, and each column indicates an in silico cell. Gray to red: no change to increase due to HGF. (B) Model validation. Comparisons between experimental data (gray bars) and model predictions (red bars) after three different combinations (AKTi/MEKi, EGFRi /MEKi, and EGFRi/AKTi). Relative change of cell viability in treated-with-HGF condition to one in treated-without-HGF condition (no HGF) is reported. (C) Chord diagrams and weighted network diagrams that visualize weights between two protein nodes in the representative in silico cell <b>a</b> and <b>b</b>. The numerical data used in Fig 5 are included in the fourth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RSK, ribosomal S6 kinase.</p

Effect of homogeneous, resource-limited microenvironment on treatment outcomes and heterogeneity.

<p>(A) The growth dynamics of all in silico cells treated with various therapies for three months in a homogeneous and resource-limited microenvironmental condition. A color line at each treatment indicates the most dominant cell after a given therapy. Color-shaded boxes indicate treatments selecting for the same dominant in silico cell. Orange box: dominant cell (cell ID: 342) after treatments of EGFRi/METi, EGFRi/AKTi, METi/AKTi, EGFRi/RAFi, and AKTi/RAFi. Red box: dominant cell (ID: 417) after <i>RAS_m</i>i/METi and AKTi. Yellow box: dominant cell (ID: 337) after EGFRi/MEKi, METi/MEKi, and AKTi/MEKi. Blue box: dominant cell (ID: 36) after RAFi/MEKi, MEKi, RAFi/ERKi, and MEKi/ERKi. Purple box: dominant cell (ID: 156) after EGFRi/ERKi, METi/ERKi, and AKTi/ERKi. Green box: dominant cell (ID: 113) after ERKi and ERKi/<i>RAS_mi</i>. (B) Linear relationship between average relative cell viability (log2 scale) and post-treatment Shannon indexes. Linear correlation constant: 0.65. Square: monotherapy; circle: combination therapy. Red: EGFRi alone or combination with METi, <i>RAS_mi</i>, AKTi, RAFi, MEKi, or ERKi. Blue: METi alone or combination with <i>RAS_m</i>i, AKTi, RAFi, MEKi, or ERKi. Pink: <i>RAS_m</i>i alone or combination with RAFi, AKTi, MEKi, or ERKi. Green: AKTi alone or combination with RAFi, MEKi, or ERKi. Gray: RAFi, RAFi/MEKi, RAFi/ERKi. Yellow: MEKi, MEKi/ERKi. Orange: ERKi. (C) Linear relationship between average relative cell viability (log2 scale) and post-treatment (with HGF stimulation) Shannon indexes. Linear correlation constant: 0.63. Square: monotherapy; circle: combination therapy. Color definitions are the same as panel B. (D) Therapies affected by HGF significantly. Each arrow starts from a point without HGF stimulation to a point with HGF stimulation. Gray: RAFi/ERKi and RAFi/MEKi. Blue: <i>RAS_m</i>i, <i>RAS_m</i>i/AKTi, <i>RAS_m</i>i/ERKi. Red: EGFRi, EGFRi/<i>RAS_m</i>i, EGFRi/AKTi, EGFRi/MEKi, and EGFRi/ERKi. The numerical data used in Fig 6 are included in the fifth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; corr, linear correlation; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS.</p

Model prediction and validation.

<p>(A) Histograms of relative cell viabilities (log2 scale) of all in silico cells after treatments of EGFRi, METi, <i>RAS_m</i>i, AKTi, RAFi, MEKi, and ERKi. First, a bimodal distribution EGFRi-treated in silico cells (one mode: −1.25; second mode: −0.5). Second, a skewed distribution after METi (a skew toward 0; 0: no change). Third, a uniform distribution in response to <i>RAS_m</i>i (almost uniform distribution from −1.5 to 1.5). Fourth, a slight bimodal distribution after AKTi. Fifth, a distributional response after RAFi. Sixth, a normal distribution in response to MEKi. Seventh, a normal distribution after ERKi. (B) Histograms of relative cell viabilities of all in silico cells in log2 scale. First: EGFRi only (blue), EGFRi/ERKi (orange), and EGFRi/MEKi (yellow). Second: METi only (blue), METi/ERKi (orange), and METi/MEKi (yellow). Third: <i>RAS_m</i>i only (blue), <i>RAS_m</i>i/RAFi (green), <i>RAS_m</i>i/ERKi (orange), and METi/MEKi (yellow). Fourth: AKTi only (blue), AKTi/RAFi (green), AKTi/ERKi (orange), and AKTi/MEKi (yellow). Fifth: RAFi only (blue), RAFi/ERKi (orange), and RAFi/MEKi (yellow). (C) Validation. Model predicted relative cell viabilities (red bars) and experimental data (gray bars) after 10 different treatments. First: EGFRi, EGFRi/MEKi, and EGFRi/ERKi. Second: METi, METi/MEKi, METi/ERKi. Third: AKTi, AKTi/RAFi, AKTi/MEKi, AKTi/ERKi. The numerical data used in Fig 3 are included in the second sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DMSO, Dimethyl sulfoxide (control); EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma.</p

Effect of microenvironmental heterogeneity on a targeted therapy outcome and combination therapy.

<p>(A) Heterogeneous EGFR activity in a lung squamous cell carcinoma. Representative image of red foci showing EGFR:GRB2 proximity. Tumor cells (green) are stained with a cytokeratin antibody demarcating epithelial origin. Nuclei (blue) are stained with DAPI. Image was acquired at 200x. (B) HGF distribution. The Eq (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.e006" target="_blank">3</a>) was solved assuming the following parameters: <i>γ</i> = 1.0, diffusion rate <i>D</i> = 0.04, and decay rate <i>λ</i> = 0.001. To be consistent with the choice of parameter in the pathway model (Eq [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.e001" target="_blank">1</a>]), we use a scaling factor <i>ω</i> (<i>ω</i> ≡ 10) (i.e., HGF input in the pathway model <i>x</i>₂ = <i>ωH</i>(<i>x</i>,<i>t</i>), <i>H</i>(<i>x</i>,<i>t</i>): solution obtained using the assumed parameters). (C) First, an initial randomized configuration of cells (domain size: 50 cells × 38 cells). Color represents different in silico cells. Second, a snapshot of <i>RAS_m</i> inhibitor simulation (day 180). (D) Simulated protein activity after 180 days of <i>RAS_m</i> inhibition. The activities of MET, EGFR, RAS_w, and RAF are heterogeneous (yellow to blue color), while those of AKT, MEK, ERK, and RSK are less heterogeneous (blue color). (E) Comparison of combination therapies for 400 days. First, a snapshot of simulation (day 400) after a sequential therapy of <i>RAS_m</i>i for the first 200 days and then RAFi for the rest 200 days (200 days of <i>RAS_m</i>i → 200 days of RAFi). Color represents different in silico cells. Second, a snapshot of simulation (day 400) after a sequential therapy of RAFi for the first 200 days, then <i>RAS_m</i>i for the remaining 200 days (200 days of RAFi → 200 days of <i>RAS_m</i>i). Third: the number of cells over time during the two sequential therapies of <i>RAS_m</i>i and RAFi (blue: <i>RAS_m</i>i → RAFi, red: RAFi → <i>RAS_m</i>i) and a concurrent therapy of the two (green: <i>RAS_m</i>i/RAFi). The numerical data used in Fig 7 are included in the sixth sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DAPI, 4',6-diamidino-2-phenylindole; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; GRB2, growth factor receptor bound protein 2; HGF, hepatocycte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RSK, ribosomal S6 kinase.</p

Signaling pathway model development and model calibration.

<p>(A) Simplified Signaling Network Model. Two inputs (growth factor and HGF), signaling protein nodes (EGFR, MET, <i>RAS_m</i>, RAS_w, PI3K/AKT, RAF, MEK, ERK, RSK), and one output (cell viability). Of note, <i>RAS_m</i> indicates a mutant <i>RAS</i>, while RAS_w indicates a wild-type RAS. A green line represents a positive relation (stimulation) and red line represents a negative relation (inhibition). (B) pMET (Y1234/5), pMEK, pERK, pAKT (both T308 and S473), and pRSK (T359) expression after different inhibitors (1 μM), MET inhibitor (METi, PHA665752), EGFR inhibitor (EGFRi, Erlotinib), RAF inhibitor (RAFi, LY3009120), MEK inhibitor (MEKi, GDC0623), ERK inhibitor (ERKi, SCH772984), and AKT inhibitor (AKTi, MK2208) in both control medium (DMSO) and after 2-hour stimulation-by-HGF (50 ng/mL) condition (DMSO plus HGF). (C) Relative cell viabilities after treatments. Cells were treated inhibitors (1 μM) for 72 hours. Cell viabilities were assessed by CellTiter-Glo assay (Promega). Representative triplicates (± SD) are presented, which showed similar results at least three times. (D) The western blots were quantified using ImageJ and relative changes (log2 scale) are reported. Average values of relative cell viabilities are also reported in log2 scale. All the data are normalized to the treatment-naïve control condition. pAKT (T308) readouts are quantified and used in the model. Of note, we didn’t quantify total protein levels because our primary interest was protein activity (protein phosphorylation). The effects of all inhibitors are modeled by assuming very small activity of a target protein (i.e., 1/16 of control, Methods section), and therefore pMET under METi is set be a very small number (i.e., 1/16 of control) for consistency. (E) Comparison between model predictions (gray box plots) and experimental data (black dots). A log2 fold change of pMET, pMEK, pAKT, pERK, pRSK, and cell viability after treatments of different inhibitors (METi, EGFRi, MEKi, ERKi, AKTi) in both a control medium and HGF-stimulated conditions. RMSE of each protein is following. pMET: 0.03; pMEK: 0.49; pAKT:0.33; pERK: 0.96; pRSK: 0.57; and cell viability: 0.47. The numerical data used in Fig 2 are included in the first sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; DMSO, Dimethyl sulfoxide (control); EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RMSE, root-mean-squared-error; RSK, ribosomal S6 kinase.</p

Model predicted combination therapy effect.

<p>(A) Hierarchical clustering and heat-map of relative cell viabilities after 28 different treatments. Simulated treatment responses (cell viability changes) were clustered using an unbiased hierarchical method with a Euclidian distance function, resulting in 7 different clusters (pink, black, blue, green, purple, cyan, and orange color) indicated by color bar on the top of heat-map. Each row indicates an individual therapy. Each column indicates an individual in silico cell. Blue to yellow bars: decrease to increase. The asterisk [*] indicates relative cell viability after a combination therapy of AKTi with MEKi. (B–C) Chord diagrams and weighted network diagrams of the representative in silico cells <b>a</b> and <b>b</b>. Chord diagram: each node in the circle represents each protein node in the network model, represented by different colors. The thickness of chord between two protein nodes represents a weight between two protein nodes (weight, <i>w</i>_<i>ij</i>). The chords are directed, colored by originating sector color. For example, the interaction between <i>RAS_m</i> and RAF is depicted as a light blue chord because the direction is from <i>RAS_m</i> to RAF (<i>RAS_m</i> → RAF; color of <i>RAS_m</i> sector: light blue). Weighted network diagram: the width of each edge represents the weight. A thicker edge represents a larger weight between two proteins. The numerical data used in Fig 4 are included in the third sheet <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2002930#pbio.2002930.s013" target="_blank">S1 Data</a>. AKT (PKB), protein kinase B; EGFR, epidermal growth factor receptor; ERK, extracellular receptor kinase; HGF, hepatocyte growth factor; MEK, mitogen-activated protein kinase kinase; MET (c-MET), tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR); PI3K, phosphoinositide 3-kinase; RAF, rapidly accelerated fibrosarcoma; RAS, rat sarcoma; RAS_m, mutated RAS; RAS_w, wild-type RAS; RSK, ribosomal S6 kinase.</p