iWiW_town_network

The data is stored in two csv files. 1. iWiW_town_nodes.csv includes characteristics of 2558 towns. The file is structured into 7 coloumns as follows: “id” – key to match node characteristics into edgelist. “town” – name of towns. “user” – number of registered iWiW users in the town. “pop” – number of inhabitants in the town. “latitude” – geo-coordinates (latitude) of the town. “longitude” – geo-coordinates (longitude) of the town. “code” – unique identifier of the town in databases of the Hungarian Statistical Office. This key can be used to match the data with further town characteristics. 2. iWiW_town_edges.csv includes “id1” – identifier of town i, which can be used as a key to match node characteristics into edgelist. “id2” – identifier of town j, which can be used as a key to match node characteristics into edgelist. “connect” – the number of friendship ties between towns i and j

Network modularity.

<p>Few large communities are identified in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network compared to the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network. The five runs of the Louvain method finds exactly the same community structure in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network, therefore we report the result of only one run. The community finding algorithm produced distinct community structures in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network; therefore we report all five runs (the number after the hyphen denotes the sequence of the run). The pair-wise Cramer’s V index in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network is always above 0.95.</p><p>Network modularity.</p

Spatial structure and modularity.

<p>(A) The strongest 0.3% of all edges are depicted in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network (4,081 edges). (B) The Louvain method finds 5 modules in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network; towns belonging to the same module are depicted with same colors. (C) The strongest 0.3% of all edges are depicted in <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network (4,081 edges). (D) The illustrated community structure contains 14 modules in the <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> network. This community structure has the highest modularity index out of the Louvain algorithm runs we present in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137248#pone.0137248.t002" target="_blank">Table 2</a>. Towns belonging to the same module are depicted with same colors. Created by own data, with base map of OpenStreetMap cartography licensed as CC BY-SA.</p

The probability of links as function of distance.

<p>Probability <i>P</i>(<i>d</i>) is plotted as function of distance on a log-log scale. The straight line depicts a power-law <i>d</i>^-0.6.</p

Edge weight and node strength distributions.

<p>(A) The distribution of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> weights is unimodal and with a maximum at the value of 1. The tail ends at 7.68 indicating that a large fraction of the ties represent very low number of connections compared to a small set of high links indicating large number of personal ties. (B) Node strength <i>s</i>⁽¹⁾ rises as population increases. The capital, Budapest with its 2 million inhabitants is an outlier. (C) The heat map of the density of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> as a function of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> and the distance between towns <i>i</i> and <i>j</i> shows a complex distribution that is dominated by a large number of weak ties between distant locations. (D) <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> has a unimodal distribution with values between –2.61 and 3.29 and with a modus at –0.77. (E) Node strength <i>s</i>⁽²⁾ decreases as population increases but fluctuation is high across large towns. Budapest is again an outlier. (F) The heat map of the density of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> as a function of <math><mrow><msubsup><mi>w</mi><mrow><mi>i</mi><mi>j</mi></mrow><mrow><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow></mrow></msubsup></mrow></math> and the distance between towns <i>i</i> and <i>j</i> illustrates that the highest edge weights are between towns that are in geographical proximity.</p

Aggregation of the iWiW network to a town-level.

<p>Data for the user-level network take into account all iWiW links. Data for the town-level network are aggregated: whenever there is at least one user-level link between two persons in different towns there is a link between those towns. As there are always intra-town links, the number of loops in the town network equals with the population of towns.</p><p>Aggregation of the iWiW network to a town-level.</p

Protein expression and phosphorylation analysis of the used pancreatic cancer cell lines.

<p>(A) EGFR and pY1068 EGRF, Akt and pS473 Akt, ERK1/2 and pT202/Y204 ERK were analyzed with SDS page/Western blot method. (B) The expression and phosphorylation of all proteins were compared to the KRAS wild type cell line, BxPC3. α-tubulin was used as loading control. (Original Western blot images: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185687#pone.0185687.s001" target="_blank">S1A and S1B Fig</a>).</p

Response of pancreatic cancer cell lines to trametinib treatment.

<p>Total Akt level and Akt activation status (pS473) were analyzed by Western blot. A representative blot and graphic evaluation of 3 independent experiments. α-tubulin was used as loading control. (Original Western blot image: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185687#pone.0185687.s001" target="_blank">S1C Fig</a>).</p

Results of viability assays on pancreatic cancer cell lines.

<p>(A) IC₅₀ concentrations of MEK, EGFR and Akt inhibitors measured on MiaPaCa2, BxPC3 and Panc1 cell lines (B) IC₅₀ curves of MEK inhibitor (trametinib), Akt inhibitor (triciribine) and EGFR inhibitor (afatinib) treatment and MEK+Akt (1:1) -/MEK+EGFR (1:1) inhibitor combination therapy on BxPC3 and Panc1 cell lines, curves were generated with GraphPad Prism version 7.00 for Mac (La Jolla, CA, USA) software (C) IC₅₀ concentration of different drug combinations applied in constant ratio (1:1) on BxPC3 and Panc1 cell lines and combination indexes of the same drug combinations calculated with Calcusyn.</p

The in vitro pancreatic cancer cell line model.

<p>This model is based on our protein expression and phosphorylation measurements and viability assays. MiaPaCa2 cell line with KRAS G12C mutation and low EGFR level is highly sensitive to trametinib treatment, combination with other drugs is not necessary and only increases drug toxicity. In case of BxPC3 cell line with wild type KRAS and high EGFR level/phosphorylation the feedback activation of EGFR/PI3K/Akt to trametinib treatment has a great impact, therefore combination of the MEK inhibitor with EGFR or Akt inhibitor both results drug synergism. Panc1 shows resistance to MEK inhibitors and the combination with the EGFR inhibitor afatinib does not decrease its IC₅₀ concentration to an appropriate level. Our model shows, that the presence of G12D mutation (which activates PI3K/Akt pathway) and the high expression of Akt protein both indicate the use of MEK+Akt inhibitor combination.</p