extfieldsofstudy: Medicine Biology

text:
Multiple Delivery of siRNA against Endoglin into Murine Mammary Adenocarcinoma Prevents Angiogenesis and Delays Tumor Growth

Endoglin is a transforming growth factor-β (TGF- β) co-receptor that participates in the activation of a signaling pathway that mediates endothelial cell proliferation and migration in angiogenic tumor vasculature. Therefore, silencing of endoglin expression is an attractive approach for antiangiogenic therapy of tumors. The aim of our study was to evaluate the therapeutic potential of small interfering RNA (siRNA) molecules against endoglin in vitro and in vivo. Therapeutic potential in vitro was assessed in human and murine endothelial cells (HMEC-1, 2H11) by determining endoglin expression level, cell proliferation and tube formation. In vivo, the therapeutic potential of siRNA molecules was evaluated in TS/A mammary adenocarcinoma growing in BALB/c mice. Results of our study showed that siRNA molecules against endoglin have a good antiangiogenic therapeutic potential in vitro, as expression of endoglin mRNA and protein levels in mouse and human microvascular endothelial cells after lipofection were efficiently reduced, which resulted in the inhibition of endothelial cell proliferation and tube formation. In vivo, silencing of endoglin with triple electrotransfer of siRNA molecules into TS/A mammary adenocarcinoma also significantly reduced the mRNA levels, number of tumor blood vessels and the growth of tumors. The obtained results demonstrate that silencing of endoglin is a promising antiangiogenic therapy of tumors that could not be used as single treatment, but as an adjunct to the established cytotoxic treatment approaches.

Introduction
Antiangiogenic therapies of cancer represent a promising approach to the conventional systemic treatments of cancer. Namely, tumors depend on angiogenesis to grow beyond a few mm 3 , as they are unable to grow in a lack of oxygen and nutrients. Endothelial cells in angiogenic tumor vasculature proliferate much faster than in normal vasculature, therefore they represent an ideal therapeutic target. The fast proliferation is also due to increased expression of certain endothelial cell markers [1,2]. One of these markers is endoglin (CD105), a TGF-b co-receptor, which participates in the activation of a complex signaling pathway that mediates cell proliferation, migration and adhesion [3,4]. Endoglin expression is highly elevated in proliferating vascular endothelial cells within and in the surrounding of the tumors [5][6][7]. Due to the increased expression of endoglin in angiogenic endothelium, antibodies targeting endoglin have already been used for tumor imaging [8][9][10][11] and endoglin was proposed to be used as prognostic tumor marker [12][13][14]. Moreover, several in vitro studies showed that different anti-endoglin antibodies inhibit proliferation and tube formation of human endothelial cells [15][16][17], which was a strong experimental support for the usage of antiendoglin antibodies for vascular targeted therapy in vivo. Most of these studies were done in immunodeficient SCID mice bearing human MCF7 mammary carcinoma. When repetitive treatments with toxin-conjugated or radiolabelled anti-endoglin antibodies were performed, a long-lasting regression of tumor growth was obtained, whereas naked antibodies had less pronounced effect on tumor growth [18][19][20][21]. Anti-tumor effectiveness and anti-metastatic activity of naked anti-endoglin antibodies were also reported in immunocompetent BALB/c mice bearing 4T1 mammary carcinoma or colon-26 colon carcinoma [22][23][24].
Silencing of endoglin mRNA by RNA interference is another attractive approach for endoglin targeting. This approach has been studied in vitro and demonstrated that silencing of endoglin reduces the proliferation of mouse embryonic endothelial cells [25]. However, the effect of silencing of endoglin on in vitro antiangiogenic potential and in vivo tumor growth reduction has not been studied yet. Therefore, the aim of our study was to evaluate the therapeutic potential of siRNA molecules against endoglin in vitro and in vivo. Firstly, the expression level of endoglin in endothelial cell lines in vitro and mammary tumors in vivo after endoglin silencing was assessed. Secondly, the effect of silencing of endoglin on proliferation and tube formation of endothelial cells was determined in vitro. Finally, the therapeutic potential of siRNA molecules against endoglin was investigated in vivo in BALB/c mice bearing TS/A mammary adenocarcinoma.

Endoglin expression in endothelial cells, tumor cells and tumors
Endoglin expression in HMEC-1 human endothelial cells, 2H11 murine endothelial cells, TS/A tumor cells and TS/A subcutaneous tumors from BALB/c mice was determined with quantitative real time polymerase chain reaction (qRT-PCR). HMEC-1 and 2H11 cells expressed high levels of endoglin. In TS/ A tumor cells the expression of endoglin was very low. In contrast, TS/A tumors induced in BALB/c mice express higher levels of endoglin, indicating that the elevated endoglin level originate from the endothelial cells present in the tumor blood vessels rather than from the tumor cells themselves (Table 1).

Endoglin expression was reduced after the transfection of endothelial cells with siRNA molecules
Two days after the transfection of HMEC-1 and 2H11 cells with siRNA molecules targeting different sections of coding sequences of endoglin, the endoglin mRNA level in these cells was statistically significantly lower in comparison to endoglin mRNA level of cells treated with Lipofectamine RNAiMAX only and negative control siRNA. In human HMEC-1 endothelial cells, all three tested siRNA molecules (h_siRNA 529, h_siRNA 240, h_siRNA 241) reduced the level of endoglin mRNA for the approximately same level, ranging from 88 to 96% ( Figure 1A). In murine endothelial cell line, two tested siRNA molecules (m_siRNA 868, m_siRNA 869) were equally effective in reducing the level of endoglin mRNA ( Figure 1B), while m_siRNA 150 only marginally reduced endoglin mRNA level.
Besides endoglin mRNA level, the silencing effect of siRNA molecules on endoglin expression was also determined by immunofluorescence staining of endoglin and flow cytometry analysis of endoglin positive cells. In HMEC-1 cells, silencing of endoglin with all three tested siRNAs (h_siRNA 529, h_siRNA 240, h_siRNA 241) was demonstrated by a reduced immunofluorescence staining for endoglin ( Figure 1C). In 2H11 cells, immunofluorescence staining of 2H11 cells showed that m_siRNA 868 and m_siRNA 869 reduce amount of endoglin to a greater extent as m_siRNA 150, as was also demonstrated at the mRNA level by qRT-PCR at the mRNA level ( Figure 1D).
Flow cytometry analysis of endoglin positive endothelial cells confirmed the results obtained by immunofluorescence staining. Mean fluorescence intensity of HMEC-1 cells was statistically significantly decreased for,50% after transfection with all 3 siRNAs compared to cells that were treated with Lipofectamine RNAiMAX only (Figure;1E). On the other hand, mean fluores-cence intensity of 2H11 cells was statistically significantly reduced for m_siRNA 868 and m_siRNA 869 (for,25%), while m_siRNA 150 had minor effect on endoglin protein level as already demonstrated by qRT-PCR and immunofluorescence staining ( Figure 1F).

Proliferation of endothelial cells was reduced after the transfection with siRNA molecules against endoglin
The inhibitory effect of silencing of endoglin with different siRNA molecules on proliferation of HMEC-1 (doubling time: 55.0 h) and 2H11 cells (doubling time: 18.6 h) with high endoglin expression was followed for 4-6 days. In HMEC-1 cells, the transfection with h_siRNA 529 resulted in a statistically significant decrease of cell proliferation for,60% at day 6 after the transfection in comparison to the proliferation of HMEC-1 cells treated with Lipofectamine RNAiMAX only. The other 2 siRNA molecules, h_siRNA 240 and h_siRNA 241 also had effect on cell proliferation, but not statistically significant. In addition, siRNA Ctrl did not significantly reduced cell proliferation (Figure 2A). Due to the fast growth of 2H11 cells, the proliferation was followed for 4 days only. M_siRNA 869 reduced cell proliferation for,45% at day 4 after the transfection in comparison to the proliferation of 2H11 cells treated with Lipofectamine RNAiMAX only. The other 2 siRNA molecules m_siRNA 868 and m_siRNA 150 as well as siRNA Ctrl did not significantly reduce cell proliferation ( Figure 2B).
In addition, we demonstrated that the transfection of siRNA molecules targeting murine endoglin does not reduce the proliferation of murine mammary adenocarcinoma cells TS/A, which express very low levels of endoglin mRNA ( Figure 2C).

Tube formation of endothelial cells was reduced after the transfection with siRNA molecules against endoglin
To determine the antiangiogenic potential of siRNA molecules against endoglin, the endothelial cell tube formation assay (in vitro angiogenesis assay) was performed. Two and 3 days after the transfection, h_siRNA 529 inhibited tube formation of HMEC-1 cells. The other 2 siRNA molecules, h_siRNA 240 and h_siRNA 241 as well as siRNA Ctrl did not significantly affect tube formation of HMEC-1 cells, thus confirming the results obtained by cell proliferation ( Figure 3A, 3B). The analysis of binary images showed that h_siRNA 529 statistically significantly decreased the total length of tubular complexes, the total size of tubular complexes and the number of junctions at day 2 and 3 posttransfection ( Table 2). The decrease was more pronounced at day 2 indicating that the effect of endoglin silencing is short lived. The other two siRNA molecules (h_siRNA 240 and h_siRNA 241) had no significant effect on the measured properties of tubular complexes.
Tube formation of 2H11 cells was determined two days after transfection, according to the results obtained in HMEC-1 cells. M_siRNA 869 molecule significantly inhibited tube formation. The other 2 siRNA molecules, m_siRNA 868 and m_siRNA 150 as well as siRNA Ctrl, did not significantly affect tube formation of 2H11 cells, thus confirming the results determined by cell proliferation ( Figure 3C, 3D). The analysis of binary images showed that m_siRNA 869 statistically significantly decreased the total length of tubular complexes for,16%, the total size of tubular complexes for,13% and the number of junctions for,22% at the day 2 post-transfection. The other two siRNA molecules (m_siRNA 868 and m_siRNA 150) had no significant effect on the measured properties of tubular complexes. For in vivo experiments on silencing of endoglin in tumor endothelial cells, the murine TS/A mammary adenocarcinoma growing in immunocompetent BALB/c mice was used, since the TS/A cells themselves express almost undetectable levels of endoglin mRNA and our aim was to target support cells within the tumors. In addition, the proliferation of TS/A cells was not altered after transfection with siRNAs against endoglin in vitro. Due to the low transfection efficiency of lipofection in vivo in tumors, electrotransfer was used for in vivo experiments [26]. After a single electrotransfer of m_siRNA 869 in TS/A tumors in vivo, no statistically significant reduction of tumor growth was observed ( Figure 4A) even though endoglin mRNA level was statistically significantly reduced for ,50% in comparison to the untreated control tumors at day 2 after the treatment ( Figure 4B). After repetitive treatments (triple electrotransfer of m_siRNA 869 in three consecutive days) a statistically significant reduction of tumor growth compared to all other groups was observed in the first 6 days after the beginning of the therapy ( Figure 4C) as well as at the level of tumor tripling time (Table S1). However, after this reduction lasting for 6 days, the growth rate of the treated tumors was similar to growth rate of tumors in control group. Other, pertinent control treatments did not affect tumor growth, although in all these treatment groups the tumors received injection of either H 2 O (control and EP group) or m_siRNA Ctrl (m_siRNA Ctrl and m_siRNA Ctrl +EP group) 3 times. The tumors grew at the same growth rate as the tumors, which received single treatments. In addition, the endoglin mRNA level was statistically significantly reduced for more than 80% in comparison to the untreated control tumors at day 2 after the treatments ( Figure 4D). Three repetitive intratumoral injections of m_siRNA 869 only also had a minor effect on tumor growth and on endoglin mRNA level, which was reduced for ,60%, but the difference was not statistically significant. These results, together with the results after single electrotransfer, indicate that the reduction of endoglin mRNA level has to reach at least 60% for an evident tumor response. In addition, the weight of the animals did not change during the observation period indicating that there was no systemic toxicity due to the therapy.

Number of blood vessels was reduced after the triple transfection of TS/A tumors with siRNA molecules in vivo
Histological analysis was performed 2 days after the triple electrotransfer of m_siRNA 869. In the viable part of tumors there were no morphological changes in endothelial cells or blood vessels wall thickness between different groups. In the group treated with triple electrotransfer of m_siRNA 869 blood vessels with larger lumens were observed. In addition, small blood vessels were not as pronounced as in the control or other groups. Therefore, because endoglin is involved in the activation of endothelial cells, thus sprouting of blood vessels, the CD31 positive blood vessels of the diameter less than 30 mm were counted to determine the antiangiogenic effect of silencing of endoglin. According to the appearance of blood vessels in the histological sections, this cut off point was selected as a representative for tumor capillaries. The triple electrotransfer of m_siRNA 869 significantly reduced the number of blood vessels in tumors. The application of electric pulses alone and the treatment with m_siRNA 869 alone also reduced the number of tumor blood vessels, but to a lesser extent. Treatment of tumors with control siRNA Ctrl alone or in combination with electric pulses did not affect the number of tumor blood vessels ( Figure 5A, 5C). Additional staining of tumors for Ki-67 to assess the proliferation of tumor cells after the therapy, demonstrated that the fraction of proliferating tumor cells did not differ between the treatment groups. These results support the results obtained by CD31 staining, demonstrating that the effect on tumor growth is due to the prevention of growth of small vessels and not by direct acting on proliferation of tumor cells ( Figure 5B, 5D).

Discussion
In this study, we showed that siRNA molecules against endoglin have a good antiangiogenic therapeutic potential in vitro and in vivo. Namely, the expression of endoglin mRNA and protein levels in endothelial cells after the transfection were efficiently reduced, which resulted in an inhibition of endothelial cell proliferation and tube formation in vitro. In vivo, the silencing effect was evident at the endoglin mRNA level and in the reduced number of tumor blood vessels which resulted in a significantly reduced growth of TS/A tumors after a repetitive electrotransfer of m_siRNA 869.
The overexpression of endoglin in proliferating vascular endothelial cells during tumor angiogenesis and vascular development [3,5,27,28] makes endoglin targeting a promising antiangiogenic therapeutic approach in cancer treatment. Several studies    using anti-endoglin antibodies have already been performed. However, to our best knowledge, our study is the first comprehensively evaluating the effect of silencing of endoglin expression with siRNA molecules on antiangiogenic potential in vitro and antitumor effectiveness in vivo. In the first part of our study, the antiangiogenic potential of silencing of endoglin in vitro in human and murine endothelial cell lines was assessed by cell proliferation and in vitro tube formation. Our results showed that h_siRNA 529 as well as m_siRNA 869 are effective therapeutic molecules, as the transfection of endothelial cells with these siRNAs resulted in a reduced endoglin mRNA and protein levels as well as in a decreased cell proliferation and endothelial cell tube formation. Proliferation of endothelial cells was reduced up to ,60%, which is in agreement with the results obtained in other studies using siRNA against mouse endoglin, or specific antibodies and nanobodies. Lebrin et al. studied the molecular mechanisms by which endoglin regulates endothelial cell functions through TGF-b receptor complex signaling and they demonstrated that silencing of endoglin in the presence of TGF-b reduced the proliferation of mouse embryonic endothelial cells for approximately 60% [25]. Maier et al. demonstrated that antiendoglin antibody TEC-11 reduced the proliferation of human umbilical vein endothelial cells (HUVEC) and human dermal microvascular endothelial cells (HDMEC) after 3 days of incubation for,60 and,75%, respectively [16]. In the study of She et al., 4 different SN6 series antibodies inhibited proliferation of HUVEC cells in the range of 26-55% after 3 days of incubation [17]. Ahmadvand et al., who characterized a new nanobody (single monomeric variable domain of antibodies devoid of light chains) targeting endoglin, also observed,40% inhibition of proliferation of primary HUVEC cells after 3 days of incubation [15].
In vitro tube formation assays provide a rapid assessment of antiangiogenic potential of therapy through measurement of the ability of endothelial cells to form capillary-like tubular structures [29]. In our study, the silencing of endoglin in endothelial cells resulted in antiangiogenic effect, since the transfection of HMEC-1 cells with h_siRNA 529 reduced endothelial cell tube formation in vitro for,30%, while transfection of 2H11 cells with m_siRNA 869 resulted in less evident effect, which can be attributed to the very fast growth of 2H11 cells and consequently to the dilution of siRNAs from transfected cells. Similar to our results with siRNA, the inhibitory effect on tube formation was obtained also with antiendoglin nanobodies [15] and antisense DNA against endoglin mRNA in the presence of TGF-b in primary HUVEC cells [30]. Therefore, our approach using siRNA molecules for silencing of endoglin proved to be effective in vitro and comparable to other strategies targeting endoglin with antibodies or DNA oligonucleotides. Based on encouraging in vitro results, the in vivo study was conducted.
For the in vivo experiments, instead of lipofection, clinically validated method electrotransfer was chosen for delivery of m_siRNA 869 into the subcutaneous tumors. Electrotransfer yields higher transfection efficiency compared to lipofection of tumors [26]. In addition, it is effective and safe method for delivery of different molecules to various types of cells in vitro and tissues in vivo [31][32][33]. It is also in use in the clinical setting combined with chemotherapeutic drugs bleomycin or cisplatin or with plasmid DNA encoding interleukin-12 for treatment of tumors [34][35][36][37]. Reports on electrotransfer of siRNA to tumors are scarce; so far electrotransfer of siRNAs has been only used for silencing of reporter green fluorescent protein in a stably transfected tumor cells B16F10 and for silencing of vascular endothelial growth factor VEGF and Rho GTPase Rac1 genes [38][39][40]. In the study of Golzio et al., similarly to our results, where mRNA level of endoglin were reduced for 50% after one electrotransfer of m_siRNA 869, the mRNA level of GFP was reduced for 52% [38]. In addition, in that study it was shown that the effect of electrotransfered siRNAs is short lived and lasts less than 5 days [38]. Therefore, to demonstrate the therapeutic potential of siRNA against endoglin in vivo, a repetitive (3-times) treatment was utilized and compared to a single treatment. Repetitive approach was used also in other studies on the electrotransfer of therapeutic siRNAs [39,40]. In our study, a single treatment of tumors with m_siRNA 869 electrotransfer or multiple treatment with m_siRNA 869 alone were sufficient to silence endoglin for up to 60%, but this was not or only slightly reflected in the reduction of tumor growth. On the other hand, a triple electrotransfer of m_siRNA 869 reduced the endoglin mRNA level for up to 80% which resulted in a pronounced antitumor effect and significantly delayed tumor growth. Our results are similar to the results of other in vivo studies, where systemic administration of anti-endoglin antibodies was used for the therapy. In contrast to our 3 times local therapy, multiple administrations (4 or 5-times) of antibodies were necessary for the significant effect on tumor growth. In the study of Tsujie et al., significant reduction in tumor growth was obtained in colon-26 and 4T1 tumors after the treatment with anti-endoglin antibodies SN6j; 5 times treatment was used in colon-26 and 4 times in 4T1 breast cancer model [23]. Therefore, our approach, although local, can represent a viable alternative to antibody treatments, especially because it did not result in any changes of animals' weight, indicating that the therapy does not induce systemic toxicity. However, multiple administrations would be required for pronounced antitumor effect. Therefore, this treatment approach, as in the case with antibodies, could be foreseen as an adjuvant treatment to standard chemotherapy or radiotherapy. Furthermore, the systemic delivery of siRNA, which is more acceptable than intratumoral injection is currently accompanied with technical hurdles, as the clearance of siRNA from blood stream is very quick and thus it lacks therapeutic effect [41].
Other antiangiogenic treatment approaches using electrotransfer as a delivery system, utilized siRNAs against Rac1 and VEGF [39,40]. Rac1 is Rho GTPase involved in VEGF signaling pathway, while endoglin is a TGF-b co-receptor and its expression is highly elevated in tumor blood vessels. Therefore, silencing of endoglin could represent a new approach in antiangiogenic therapies by targeting an alternative signaling pathway to VEGF [42,43]. In addition, it was shown that TGB-b can stimulate VEGF synthesis in vascular smooth muscle cells, therefore targeting of endoglin might also reduce VEGF in tumors and consequently VEGF-mediated angiogenesis [44]. The in vivo results of our study demonstrate that endoglin is an appropriate target as its silencing resulted in a similar antitumor effectiveness as silencing of Rac1. Furthermore, we observed reduced formation of blood vessels in the treated tumors.
In conclusion, the results of our study show that endoglin targeting is a promising antiangiogenic therapy that might represent an alternative to present clinically used anti-VEGF therapies, which are associated with several undesired side effects, such as kidney dysfunction and intracranial hemorrhage [45,46]. SiRNA molecules showed a good antiangiogenic potential in vitro and pronounced antiangiogenic and antitumor effect in vivo. Due to the short half-life of siRNA molecules in vivo, further studies in this area may continue by using plasmid DNA encoding shRNA or miRNA molecules constructed from the sequence of the siRNA used in our experiments. On the other hand, the stability of siRNA molecules could be also improved by the usage of chemically modified LNA-based oligonucleotides.
Female BALB/c mice, 6-8-week old, purchased from Institute of Pathology, Faculty of Medicine, University of Ljubljana, Slovenia were used in the experiments. Mice were maintained under specific pathogen-free conditions at a constant room temperature and humidity and a 12 h light/dark cycle. Food and water were provided ad libitum. Animals were subjected to an adaptation period of 2 weeks before experiments.
For induction of subcutaneous tumors, a suspension of 2610 6 TS/A cells, prepared from cell culture in vitro in 0.1 ml of physiological solution, was injected into the right flank of mice. When the largest diameter of tumors reached 3 mm (in 4-5 days after subcutaneous injection of cells) animals were randomly divided into experimental groups and subjected to a specific experimental protocol.

Ethics Statement
Animal studies were carried out in accordance with the guidelines for animal experiments of the EU directives and the permission from the Veterinary Administration of Ministry of Agriculture and the Environment of the Republic of Slovenia (permission No.: 34401-12/2009/6). All efforts were made to minimize suffering.

siRNA molecules
Three siRNA duplexes targeting different sections of coding sequences of human or murine endoglin were chosen on the basis of Invitrogen's program »BLOCK-iT TM RNAi DESIGNER » (Invitrogen, Carlsbad, CA) ( Table 3).
The negative control siRNA duplexes were, by manufacturer's assurance, designed in a way of no homology to any known vertebrate transcript. The siRNA duplexes were obtained as ready-annealed, purified duplexes and diluted in a sterile diethylpyrocarbonate (DEPC) treated H 2 O to a concentration of 20 mM.

Transfection
In vitro lipofection. Cells were trypsinized and harvested from a monolayer to a cell suspension in antibiotics-free medium. Cells (1.4610 6 ) were plated on 24-well ultra-low attachment plate (Corning Inc., Corning, NY) in 1.5 ml of particular medium. Complexes of siRNA duplexes and Lipofectamine RNAiMAX (Invitrogen) were prepared as follows:5 ml of 20 mM siRNA was diluted in 500 ml Opti-MEM I medium (Gibco) to which 5 ml of Lipofectamine RNAiMAX was added. After 15 min incubation of this mixture, the complexes were added to each well. Cells were incubated at 37uC in a 5% CO 2 humidified incubator for 5 h and then plated on Petri dishes of diameter 10 cm for further analysis: qRT-PCR, immunofluorescence staining, flow cytometry, proliferation assay and tube formation assay.
In vivo electrotransfection. In vivo electrotransfection was performed once or 3 times on each consecutive day. Tumors were treated with intratumoral injection of 40 ml m_siRNA 869 and immediately thereafter electric pulses were applied (8 square wave pulses of amplitude 240 V (amplitude over distance ratio 600 V/ cm), duration 5 ms at frequency 1 Hz) (m_siRNA 869+EP group). Electric pulses were generated by electric pulse generator GT-01 (Faculty of Electrical Engineering, University of Ljubljana, Slovenia) and delivered through 2 parallel stainless steel electrodes with 4 mm distance between them. Due to the relatively large volume of intratumoral injection of m_siRNA 869, the solution also escaped out of the tumor. Therefore, tissues around the tumor that were encompassed between the electrodes were also electrotransfected with m_siRNAs, including tumor surrounding blood vessels, where expression of endoglin is elevated [5][6][7]. Pertinent control groups were: intratumoral injection of H 2 O alone (control group) or combined with application of electric pulses (EP group), intratumoral injection of m_siRNA 869 (m_siRNA 869 group), intratumoral injection of negative control siRNA (siRNA Ctrl group) and electrotransfer of siRNA Ctrl (m_siRNA Ctrl+EP group).

RNA extraction and qRT-PCR analysis
RNA extraction and qRT-PCR analysis were used for to determine the endoglin expression level and extent of silencing of endoglin at the mRNA level 2 days after transfection in vitro and in vivo. For in vitro experiments, cells were trypsinized and harvested from a monolayer to a cell suspension and then centrifuged. Afterwards the total RNA was extracted from the cells. For in vivo experiments, mice were sacrificed and 2 to 9 tumors per group were excised and frozen in liquid nitrogen. Frozen tumors were homogenized in a mortar with a pestle and thereafter the total RNA was extracted from the samples.
The total RNA was extracted with TRIzol Plus RNA Purification System (Invitrogen) according to the manufacturer's instructions. Concentrations of RNA were quantified by a spectrophotometer at 260 nm (Epoch, Biotek, Winooski, VT). The purity of RNA was determined by measuring the ratio of absorbance at A 260 nm/280 nm and A 260 nm/230 nm . One mg of total RNA was reverse transcribed into cDNA using SuperScript VILO TM cDNA Synthesis Kit (Invitrogen), according to the manufacturer's instructions. After reverse transcription, 106diluted and 1006diluted mixtures were used as a template for the qRT-PCR using TaqMan Gene Expression Master Mix (Applied Biosystems, Carlsbad, CA) and TaqMan Gene Expression Assay (Applied Biosystems). Taqman Gene Expression Assay contained a pair of primers and TaqMan probes to amplify the fragment of human endoglin cDNA (Hs00923996_m1) or murine endoglin cDNA (Mm00468256_m1). As an internal control TaqMan probes were used to amplify human RNA polymerase II cDNA (Hs00172187_m1) or murine 18S ribosomal RNA (Mm03928990_g1). qRT-PCR was performed on 7300 System (Applied Biosystems) as follows: activation of Uracil-DNA Glycosylase (2 min at 50uC), hot start activation of AmpliTaq Gold Enzyme (10 min at 95uC), 45 cycles of denaturation (15 s at 95uC), annealing and extension (1 min 60uC). The qRT-PCR products were analyzed using 7300 System SDS software (Applied Biosystems). The level of endoglin expression in different endothelial and tumor cell lines and TS/A tumors was presented as the threshold cycle value. The level of mRNA of endoglin after therapy was normalized against the reference genes.

Immunofluorescence staining
Two days after the transfection, the extent of silencing of endoglin at the protein level was determined by immunofluorescence staining. Cells were trypsinized and 5610 4 cells were plated on Lab-Tek Chamber Slide System (Nalge Nunc International, Naperville, IL) in an appropriate medium containing 10% fetal bovine serum and antibiotics. Attached cells were fixed in 4% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA) for 15 min. Fixed cells were then permeabilized with 0.5% Tween 20 (Sigma) for 10 min. Nonspecific binding was blocked with 10% donkey serum (Sigma). Afterwards, cells were incubated with primary antibodies overnight at 4uC. For HMEC-1 cells, mouse monoclonal antibodies against endoglin were used (P3D1: sc-18838, dilution 1:50, Santa Cruz Biotechnology Inc., Santa Cruz, CA). After incubation with primary antibodies, cells were incubated with fluorescein-conjugated donkey anti-mouse secondary antibodies for 1 h (1:100, Jackson ImmunoResearch Laboratories, Inc., West Grove, PA). For 2H11 cells, goat polyclonal antibodies against endoglin were used (AF1320, dilution 1:12.5, R&D Systems Inc., Minneapolis, MN). After incubation with primary antibodies, cells were incubated with fluorescein-conjugated donkey anti-goat secondary antibodies for 1 h (1:100, Jackson ImmunoResearch Laboratories, Inc.). Between each step, the cells were washed 3-times with phosphate buffered saline. After incubation with secondary antibodies, slides were mounted with Prolong Gold Antifade Reagent (Invitrogen). Images were captured with a DP72 CCD camera (Olympus, Hamburg, Germany) connected to a BX-51 microscope (Olympus). The exposure time and acquisition settings were determined on control cells and kept constant throughout the aquisition.

Flow cytometry
For quantification of silencing of endoglin at the protein level, flow cytometry analysis was performed. Two days after transfection, cells were trypsinized and 1610 6 cells were resuspended in phosphate buffered saline. Cells were fixed in 4% paraformaldehyde for 15 min and permeabilized with 0.5% Tween 20 for 10 min. The HMEC-1 cells were then incubated with primary fluorescein-conjugated mouse monoclonal antibodies (P3D1 FITC: sc-18838, 1:50, Santa Cruz Biotechnology, Inc.) overnight at 4uC. The next day, primary antibody solution was removed and cells were resuspended in 300 ml of phosphate buffered saline. Mouse 2H11 cells were after permeabilization first incubated with primary goat anti-mouse polyclonal antibodies (1:50) overnight at 4uC. The next day, cells were incubated with fluoresceinconjugated donkey anti-goat secondary antibodies for 1 h (1:100). Between each step, the cells were washed 3-times with phosphate buffered saline. The measurements were performed on at least 20.000 cells per sample using FACSCanto II flow cytometer (BD Biosciences, San Jose, CA). A 488-nm laser (aircooled, 20 mW solid state) and 530/30-nm band-pass filter were used for the excitation and detection of FITC fluorescence, respectively. Firstly, the cell population gate was determined from the biparametric logarithmic plot defined by forward and side scatter to eliminate debris. Secondly, the histogram of gated cells against their fluorescence intensity was recorded. The mean fluorescence intensity of the gated cells was determined for each experimental group (software: BD FACSDiva V6.1.2).

Proliferation assay
After the transfection, 1.5610 3 HMEC-1, 3610 2 2H11 cells or 2610 2 TS/A cells were plated for proliferation assay in 0.1 ml of particular media on 96-well plates (Corning) containing 10% fetal bovine serum and antibiotics. Cells were incubated at 37uC in a 5% CO 2 humidified incubator. Cell viability was measured every second day with Presto Blue assay (Invitrogen). Ten ml of Presto Blue reagent was added to each well and fluorescence intensity in the well was measured after 1.5 h of incubation at 37uC in a 5% CO 2 humidified incubator with a microplate reader (Infinite 200, Tecan, Mä nnedorf, Switzerland).

Tube formation assay
To determine the effect of silencing of endoglin on endothelial cell tube formation, cells were incubated at 37uC in a 5% CO 2 humidified incubator for different period of time after the transfection. At days 1, 2 and 3 after transfection, 1.5610 4 HMEC-1 cells were plated on 96-well plates (Corning) covered with BD Matrigel TM Basement Membrane Matrix Growth Factor Reduced, Phenol Red Free (BD Biosciences) and incubated for 6 h until the formation of tubular complexes. The tubular complexes were fixed and stained with Crystal violet dye (Sigma). For assessment of tube formation of 2H11 cells, an improved system for assessment of tube formation was used [48]. Cells (1.6610 4 ) were plated two days after the transfection on m-Slide Angiogenesis (Ibidi, Munich, Germany) covered with BD Matrigel TM Basement Membrane Matrix, Phenol Red Free (BD Biosciences) and incubated for 2 h until the formation of tubular complexes. The tubular complexes were stained with calcein AM (Sigma). Images were captured with a DP72 CCD camera (Olympus) connected to an IX-70 inverted microscope (Olympus). AxioVision program (Carl Zeiss Microscopy GmbH, Jena, Germany) was used to convert raw images into binary masks, which were quantified with AngioQuant image analysis program [49]. The total length of tubular complexes, the total size of tubular complexes and the total number of junctions were quantified.

Tumor growth
The therapeutic potential of electrotransfer of m_siRNA 869 molecules against murine endoglin was assessed by measuring the tumor size every day after the therapy using digital Vernier caliper. Tumor volume was calculated according to the formula for ellipsoid: V = a6b6c6 p/6, where a, b and c represent perpendicular tumor diameters. From the tumor growth curves, the tumor tripling time (the time in which the tumor volume at the start of the treatment triplicates) was determined. The weight of the mice was followed as a general index of systemic toxicity. Each group consisted of 6 mice.

Histology
From each experimental group, 2 or 3 mice were sacrificed at day 2 post-treatment and the tumors were excised. The tumors were fixed in IHC zinc fixative (BD Pharmingen, BD Biosciences) and embedded in paraffin. Three consecutive 5-mm thick sections were cut from each paraffin block. The first section was stained with hematoxylin and eosin and the second and the third were used for immunohistochemical staining. The immunohistochemical staining sections were incubated with rabbit polyclonal antibodies against murine CD31 (ab28364, Abcam, Cambridge, MA) at dilution 1:100 or Ki-67 (clone SP6, Thermo Fisher Scientific, Cambridge, MA) at dilution 1:150. A peroxidaseconjugated streptavidin-biotin system (Rabbit specific HRP/DAB detection IHC kit, ab64261, Abcam) was used as the colorogenic reagent followed by hematoxylin counterstaining. The immunohistochemicaly stained slides were observed under light microscopy and from each slide 6 images of randomly selected areas of viable tumor tissue were captured with a DP72 CCD camera (Olympus) connected to a BX-51 microscope (Olympus). On the acquired images the number of Ki-67 positive cells and CD31 positive blood vessels with the diameter of less than 30 mm was determined.

Statistical analysis
All data were tested for normality of distribution with the Shapiro-Wilk test. The differences between the experimental groups were statistically evaluated by one-way analysis of variance (one-way ANOVA) followed by a Holm-Sidak test for multiple comparison. A P-value of less than 0.05 was considered to be statistically significant. SigmaPlot Software (Systat Software, Chicago, IL) was used for statistical analysis and graphical representation.

Supporting Information
Table S1 Tripling time of TS/A tumors treated with triple electrotransfer of siRNA against endoglin. After repetitive treatment of tumors with m_siRNA 869 a statistically significant tumor growth delay was observed at the level of tumor tripling time (related to Figure 4). (DOC)


Thermodynamic modeling of a phase transformation in protein filaments with mechanical function

Protective egg capsules from whelks (intertidal marine gastropods) were recently demonstrated to derive their impressive mechanical behavior—reminiscent of the pseudoelastic behavior in some alloy systems from a reversible phase transition of component protein building blocks from a compact α-helical conformation to a more extended softer conformation, called β*. This behavior was previously modeled under equilibrium conditions, demonstrating that the transition from the α- to β*-phase could account for the pronounced yield plateau and reversibility; however, a theoretical understanding of the non-equilibrium behaviors of the egg capsule (e.g. strain rate dependence and hysteresis) requires a new approach. Here, we modify the previously proposed model in order to address the time-dependent behaviors of the whelk egg capsule biopolymer. Our results indicate that hysteresis during cyclic loading originates from a mismatch between the speed of the mechanical driving force and the rate at which the phase transition occurs. Furthermore, the characteristic curved shape of the stress–strain plot arises from a nonlinear relationship between the transformation rate and the amount of applied load. These results have important implications for our understanding of the mechanics of biological polymers and may have implications for the design of biomimetic pseudoelastic polymers.

Introduction
The forces experienced by organisms living on the rocky seashore among crashing waves have been compared in the literature to being strapped to the nose of a supersonic jet [1]. Therefore, it is not altogether surprising that species inhabiting the intertidal zone dedicate significant resources to strategies and materials for protecting soft and vulnerable tissues. Along these lines, many marine organisms construct hard and tough mineralized parts such as the nacreous shells of abalone or the carapace of crabs; however, this strategy is not ideal for all applications, for example, adhesive attachment or encapsulation of embryos. An alternate strategy is to create softer organic structures capable of dissipating large amounts of mechanical energy via reversible deformation (e.g. the byssal threads of marine mussels [2] or various marine egg capsules [3,4]). The egg capsules of marine whelks are particularly interesting due to their characteristic pseudoelastic mechanical behavior.
The marine whelk Busycotypus canaliculatus lays its eggs in disc-like egg capsules arranged in a string, which can be found lining beaches along the east coast of North America (figure 1) [5]. Mechanical studies on the whelk egg capsule (WEC) material reveal unusual properties for a biopolymer, including pseudoelastic behavior and high hysteresis, which is completely reversible [3,[6][7][8]. Figure 1 shows a stress-strain curve of a cyclically loaded strip of egg case material. The material is initially stiff; however, at ∼5% engineering strain, it yields and then proceeds to elongate without a marked increase in stress, after which it eventually stiffens until it ruptures and fails [3,8]. If the egg case polymer is, instead, unloaded prior to failure, a large hysteresis loop is observed as the material returns to its initial length. Immediately upon unloading, it can be reloaded and will exhibit a practically identical stress-strain curve as the first cycle, and this can be repeated over numerous loading cycles.
As mentioned, this behavior is reminiscent of the pseudoelastic mechanical behavior of certain metal alloys, albeit with higher strain values and lower stiffness. Aside from certain metal alloys, similar mechanical behavior is also observed in some rubber-like materials, sometimes referred to as the 'Mullins effect' [9][10][11][12]. This is described within the framework of network models, assuming two interpenetrating networks of different strength. Additionally, hysteretic mechanical behaviors have been observed in other biological systems and described as diffusionless or displacive transformations (often termed 'martensitic' if strain energy is involved in the transformation). A well-characterized example is the length change within the tail sheath of a T4-bacteriophage, in which the cylindrical protein crystal comprising the capsid Protein structural transformations dictate WEC mechanics. Marine whelks lay long chains of disc-like egg capsules. When strips of the WEC tissue are cyclically loaded, there is a large hysteresis, as observed in the above stress-strain curve. The mechanical performance of the material has been correlated with the reversible transformation of α-helical protein subunits (lowstrain phase, LS) into an unstructured, extended conformation (high-strain phase, HS). of the virus is reduced to ∼35% of its initial length during injection of viral DNA into a bacterial host [13,14]. The associated change in the helical arrangement of proteins on the cylinder surface was described as a martensitic phase transformation [13,14]. The transformation from the extended, initial configuration to the contracted, final configuration is irreversible and driven only by the surplus of chemical free energy in the parent phase, which is partially consumed by the deformation of the cylindrical crystal. The transformation in the WEC material during cyclic loading, on the other hand, is completely reversible, leading researchers to investigate the molecular level structure-property relationships in more detail.
It was recently determined that the notable mechanical behavior of the WEC arises from load-bearing α-helical proteins, which undergo a reversible structural transformation during cyclic loading [6,7,15]. Transitions of α-helical proteins to a β-sheet conformation via mechanical stretching have also been observed in materials such as hair, intermediate filaments and in hagfish slime fibers [15][16][17][18][19]; however, in these cases the complete reversibility that characterizes the pseudoelastic behavior of the WEC is not observed. An equilibrium-state model of the WEC mechanical behavior was developed based on pseudoelastic theory, in which the structural changes of the protein building blocks were modeled as a diffusionless or displacive transformation between a low-strain and a high-strain phase, with the coexistence of both phases at intermediate strains [6]. The low-strain phase (α-phase) was determined to behave elastically, whereas the high-strain phase (extended β * -phase) was modeled using a worm-like chain equation [20][21][22] (figure 1). Consistent with experimental observation, the transition from α to β * structure was predicted to occur at a constant stress, following a classical phase transition. While this model fits well with much of the observed structural and mechanical data, it is an equilibrium model and thus it is unable to account for the viscoelastic properties of the material, including the notable hysteresis. In order to address the hysteresis of the WEC material, we present here a modified version of the earlier model [6], which considers specifically non-equilibrium states. This strategy has proven valuable in modeling the mechanical behaviors of other protein-based fibrous materials such as silk [23] and in shape memory polymers [24] or even shape memory alloys [25], such as Ni-Ti [26]. Moreover, models have been developed [27] to describe protein unfolding based on single-molecule experiments including titin [28], tenascin [29] or ubiquitin [30]. Previously, the nonlinear mechanical behavior of the WEC was modeled using a continuum model in which a homogeneous nucleation of the new phase is assumed to occur along the whole chain [31]; however, this macroscopic approach does not incorporate recent biochemical and biophysical evidence for the discontinuous behavior of the protein building blocks during cyclic loading. Based on our new model, our conclusions are two-fold. Firstly, the origin of hysteresis in the WEC appears to stem from a mismatch between the speed at which the molecular phase transformation occurs and the rate at which the material is loaded. Secondly, the curved shape of the WEC stress-strain loading cycle likely originates from a nonlinear relationship between the driving force and the rate of transformation between the two phases (i.e. transformation is slower at higher stress levels). The results provide several experimentally testable hypotheses that should be further pursued.

Deformation and stress state
The transformation takes place within protein fibers, which can be viewed as parallel stringlike arrangements of molecular segments, which-in the low-strain phase-correspond to α-helices, and in the high-strain phase as an extended polypeptide chain. A bundle of these parallel molecular filaments (figure 2) constitute a parallel fiber bundle whose cross-section does not depend on the amount of longitudinal strain. We, therefore, represent the material by a one-dimensional (1D) continuum. We call p the force applied to a single molecular filament, which relates to the stress σ applied to the fiber through the relation where ρ is the number of molecular filaments per cross-sectional area of the fiber (see figure 2). The length of a molecular segment in the reference configuration without load is denoted as s 0 , its length in the actual (deformed) configuration as s and its maximum length after complete extension of the protein (contour length) as L. As a simple measure of strain, we introduce the local strain ε (often denoted as engineering strain or shortly 'strain') as Sketch of a WEC protein fiber during mechanical deformation. It consists of a number ρ of parallel molecular filaments per unit area of fiber crosssection (green dots). Each molecular filament consists of a series of segments that might be either in the low-strain (L) or in the high-strain (H) conformation dependent on tissue deformation.
Given that the largest possible length of the protein is L, the maximum possible strain in this model is given by Of course, one could work also with the natural strain ε n = ln (1 + ε). However, since the following stress and energy terms are measured and expressed in ε and not in ε n , we opt to work with ε. Two phases are distinguished, the 'low-strain phase' (L) and the 'high-strain phase' (H), when the fiber is subjected to a stress σ withσ being the corresponding dimension-free stress, σ = σ/C, and C being a (constant) reference stress used to make the variable dimensionless. The stress σ is not influenced by the cross-sectional area, since it depends only on the number Parameters defining the two models [33,34] used to describe the highstrain phase. The tensile force per molecule is denoted as f. In both models the force is proportional to k B T , the product of the Boltzmann constant and the absolute temperature. The Z-model has bending fluctuations according to a persistence length l p and the M-model has kinking fluctuations with a total number ν of kinks for an extended (contour) length of L.
of molecules in the fiber cross-section (according to (1a)). Consequently, σ is also denoted as engineering stress, which we hereafter refer to just as 'stress'. The string can only transfer a tensile stress, p > 0 (or σ > 0). The following stress-(local) strain relations can be observed for constant temperature, expressed by ε as The quantityk = k/C is a dimension-free elasticity coefficient; F (ε) refers to the mechanical behavior of the high-strain phase, which corresponds to an extended conformation of the molecule [6]. Different models are conceivable to describe the force-extension curve of a fluctuating filament [32].
Here we consider two very simple analytical models yielding nonlinear elastic behaviors for F (ε), namely that according to Misof et al [34], denominated as F M and listed below as F M (ε), and to Zhang et al [33], denominated as F Z and listed below as F Z (ε), as (see figure 3) where the indices Z and M refer to the worm-like chain model [20] in the formulation by Zhang et al [33] and the kinked molecule model by Misof et al [34]. Figure 3 shows sketches of the underlying assumptions and gives a definition of the physical parameters involved. Note that both models have the property that the stress diverges when the strain ε is approaching its limiting value ε * . The reason for this divergence is that we consider the covalent bonds in the polypeptide chain to be inextensible. This implies that the models cannot be used for strains coming too close to ε * . The reference stress C is in both models just the product of the thermal energy k B T (k B being the Boltzmann constant and T the temperature), the molecular density ρ in the fiber (as defined in figure 2) and an inverse length scale characterizing the rigidity of the polypeptide chain. This length scale is the persistence length l p in the worm-like chain model or the contour length divided by the number of kinks L/ν in the other model. Then we have for C z and C M where the labels 'Z' and 'M' point to the material types, as defined in figure 3. The numerical value of ρ has not been confidently determined experimentally for the WEC and was set in the calculations so that the calculated stress values matched the experimentally determined values.

Energetics
We define a Helmholtz free energyW (ε), a dimension-free function of ε, describing the sum of chemical and elastic energy of the molecular segments (see figure 2) in both phases: According to equation (2), we have for the low-strain L-phase Note that with respect to chemistry we address a zero reference chemical energy to the (lowstrain) L-phase. Considering equation (5), one finds forW (ε,s 0 ) in the (high-strain) H-phase HereW 0 stands for the sum of the chemical contribution and the contribution due to the prestress. The integration on the right side of (7) can be performed analytically and we obtain, using (4a) and (4b), respectively, The quantitiesW 0M ,W 0Z are constants and their values are reported below.
Here it should be mentioned that the formulations ofW M ,W Z are in accordance with continuum mechanics. The equilibrium process was discussed previously by Harrington et al [6] and is only summarized here. The equilibrium energy for the complete system is the convex envelope of the energiesW H andW L as a function of the strain ε. At small strains, the total energy isW L (ε). Starting from a lower critical strain ε * L and straining up to a higher critical strain ε * H , the energỹ W T (ε) is given as the common tangent to the two energy curvesW L (ε) andW H (ε) (see figure 4) asW The critical strains ε * L and ε * H can be found by solving the set of nonlinear equations 1 2k Finally, above ε * H , the energy isW H (ε).

Transformation condition and kinetics
We now study the kinetics of the transformation from the L to the H phase (on increasing strain) or from the H to the L phase (on decreasing strain), which was first worked out by Eshelby [35] and later by several researchers (for references see [36]). Our model considers a sharp interface between the regions in the H and L phase, with a thermodynamic force acting on it. The experimental data do not allow us to decide whether there is actually just one or several interfaces as one would expect if there were multiple nucleation sites. Hence, we stick to the simplest hypothesis here. It is also worth mentioning that other theoretical approaches study the movement of an interfacial zone using either a variational principle [37] or a levelset concept [38]. In the current treatment, we prefer the simpler sharp-interface model. If we consider a likely mechanical challenge of the egg capsule such as crashing waves or perhaps biting of a predator, then a stress-controlled process is more likely than a strain-controlled process. We thus consider a stress that increases linearly with time t from t = 0 to t m and then decreases again linearly to zero until time t f : whereσ m is the maximum dimension-free applied stress at t = t m .
From the point of view of thermodynamics, the dimension-free thermodynamic forcef (i.e. the force per unit area of interface, normalized by the constant C Z or C M , with labels LH and HL depending on the direction of the transformation) is acting at the transformation front or, in other words, at the interface between the L and H phases. The thermodynamic forcẽ f LH refers to a transformation from L to H,f HL from H to L. Eshelby [35] was the first who introduced the thermodynamic force as the projection of the so-called energy momentum tensor in the normal direction to the interface. The difference of the thermodynamic forces yields the resultant (driving) thermodynamic force. A very detailed derivation of the thermodynamic force on an interface can be found in Simha et al [39]. We follow the derivation by Abeyaratne and Knowles [36], who previously worked out an expression forf in a small-strain 1D setting formulated forf LH as in which ε L and ε H are the strain values in the L and H phases, respectively, corresponding to a given value of the applied stressσ , according to the relations given in equations (2) and (3), respectively. This makes an inversion of F (ε) necessary, which can be performed analytically for both F M and F Z , but for the sake of convenience, can also be done numerically. Note that no physical interface is formed, and therefore it is unnecessary to consider the interface energy. The advancement of the transformation is characterized by the volume fraction of transformed phase ξ , 0 ξ 1, and its time rateξ . Note that ξ takes the role of an internal variable. Then the rateξ is the conjugate variable to the thermodynamic forcef yielding, according to the thermodynamics of continua, the local dissipation fξ . However, since thermodynamics does not provide a kinetic (or evolution) equation for the internal variable (in our caseξ ), we assume, as a simple and standard assumption in non-equilibrium thermodynamics, a linear kinetic equation such aṡ The quantity κ LH is proportional to the speed of the transformation from L to H with the dimension [κ LH ] = 1 s −1 . Since we assume that no dissipation occurs both in the L phase and in the H phase, the only dissipative term arises from the interface motion, which corresponds, as already mentioned, to the product of interface velocity (represented byξ LH ) and thermodynamic force (f LH ). The dissipation term per unit area of interfaced LH , [d LH ] = 1 s −1 , which is again normalized by the constants C Z or C M , follows as To ensure positive dissipation, the mobility κ LH must be positive. Of course, it does not need to be constant (as considered here for simplicity) but may depend onf LH or its rateḟ LH , see e.g. the discussion in [36]. The dissipation can be considered as the power necessary to break bonds during loading or, alternatively, the energy per time unit extracted from the system to form bonds during unloading.
The total strain ε tot of the molecular filament depends on ξ LH in the form which makes it necessary to integrate equation (10) with respect to time. This is easiest performed by replacing time t with the instantaneous stressσ (dt = dσ /σ LH or dt = dσ /σ HL withσ LH =σ m /t m ,σ HL = −σ m /(t f − t m ), respectively, with the definitions at the beginning of section 2.3), yielding The process starts withf LH = 0 atσ =σ start and continues withf LH > 0. The quantityσ start follows from setting equation (10) to zero and solving the equation with respect toσ start with ε * L = ε L (σ start ) and ε * H = ε H (σ start ). This amounts to solving equation (9b), whereσ start = kε * L = ∂W H ∂ε (ε * H ). The strain at the interface jumps at the stress valueσ start from the Lequilibrium configuration ε * L to the H-equilibrium configuration ε * H . The difference is denoted as transformation strain ε T = ε * H − ε * L . The correspondingσ − ε tot diagram shows a horizontal line, the so-called Maxwell line according toσ start , which is called Maxwell stress. For a further interpretation, see [6].
The above set of equations (9)-(13) can be also used to describe the transformation path from the H to the L phase (in our case the 'unloading' process). Only the labels L and H have to be exchanged to H and L. The mobility κ HL can be different from κ LH . The back-transformation starts atσ =σ start and stops when ξ HL has reached the value 1.
We would like to mention that the so-called Jarzynski equation [40] is often used to describe the pulling of proteins. If we reformulate the thermodynamic force asf LH =g L −g H withg =W −σ ε L , whereg is the (specific) Gibbs (free) energy, then this transformation proceeds forf LH = g =g L −g H > 0. This formulation is in fact equivalent to the Jarzynski equation.

Modeling and experiments
The model introduced in section 2 is formulated in a dimension-free way. All kinetic quantities, such as energies, dissipation and thermodynamic forces, as well as stress, are related to a reference stress C, namely C M or C Z , which means that all these dimension-free quantities must be multiplied by C M or C Z to get to physical quantities.
We now model the transformation process in WEC protein reported in an earlier experimental paper [6]. The relevant parameters are taken from this paper: we consider a molecular segment with the initial length As the free parameter, we use the kinetic parametersσ LH /κ LH andσ HL /κ HL , respectively. Figure 5 demonstrates the stress-strain hysteresis for three different ratiosσ LH /κ LH for material types Z according to Zhang and Evans [33] and M according to Misof et al [34] in comparison with a stress-strain hysteresis measured by Harrington et al [6], figure 1(b).
One can easily recognize that at least semiquantitative agreement of the hysteresis from the model with the experimental hysteresis can be obtained. It should be mentioned that the rather strange behavior of material type Z leading to negative values of ε on the unloading path has Hysteresis for material type Z using the enhanced kinetics equation (14). Numbers 1, 2, 3 designate curves corresponding to the kinetic parameteṙ σ LH /((1 + α) κ LH ) = 5 together with α = 9, 3, 0, respectively. The Maxwell stress is marked by a dashed line.
its origin in the fact that the H-phase covers a certain pre-stress in the amount of 0.78 MPa at strain ε = 0. This part of the curve is not depicted in figure 5(b), since this part is of no physical relevance.
As a further possibility of modeling, we study a so-called enhanced kinetics by applying instead off LH on the rhs of equation (11a) the quantityf LH · ξ −α , α 0, ensuring also the second law (see the text passage in context with equations (11a) and (11b)) and yielding Obviously, the significantly increased transformation rate in the start phase influences the hysteresis remarkably. The results are depicted in figure 6. Curves 1 and 2 are calculated for the enhanced kinetics (see the figure caption). Note that the unloading path above the Maxwell line is nearly the same for all three curves. It is very interesting to note that the enhanced kinetics leads to a hysteresis, whose shape is much closer to the observed one, see figure 5(c).

Discussion
From a biological perspective, the properties of materials such as the WEC are shaped via natural selection to handle cyclic mechanical perturbances by undergoing large extensions and dissipating mechanical energy, observable at the macroscale in the large hysteresis loop. Experimental evidence informs us that mechanical hysteresis stems from reversible transitions in the conformational structure of the protein building blocks, which manifests itself during material yield as a classical phase transition between an ordered α-helical protein structure (α) and an unstructured protein structure (β * ) [6,7]. The current work provides a theoretical framework for understanding the kinetics of this process and how this is manifested in timedependent viscoelastic mechanical behavior at the macroscopic level.
Based on this work, three important conclusions can be drawn that add to the understanding of WEC mechanics and of martensitic behaviors in biopolymers, in general. The first is that modeling the biomolecular subunits as a worm-like chain versus a kinked molecule has only a small influence on the outcome of the model. Only the relative magnitude of the changes differed, but the shapes of the resultant stress-strain curves were preserved. The second observation is that development of hysteresis in the cyclic loading curve, reflecting a dissipative process, arises when the loading rate in the biomolecules is larger than the rate at which the LS phase can transform into the HS phase. Only at very low loading rates (i.e. equilibrium) would one expect no dissipation and, consequently, that the course of the stress-strain curve would follow along the Maxwell line for both the loading and unloading paths. The third observation from our study is that while the simple assumption of a linear relation between thermodynamic driving force and rate of phase transformation (thermodynamic flux) generates hysteresis in the model, it cannot account for the shape of the WEC deformation curve. By considering an 'enhanced' kinetics, where the proportionality constant in the linear relation is replaced by a positive function of the internal variable (i.e. the volume fraction of transformed matter), we can modify the shape of the hysteresis and arrive at some of the experimentally observed features. In the current case, one learns from the nonlinear kinetics that the transformation process occurs much more intensively in the starting phase than in the finishing phase, an observation that is also well known for some shape memory materials [25,26]. These collective observations provide testable hypotheses, namely that there should be a temperature and strain rate dependence in the amount of hysteresis observed in the WEC. In fact, Miserez et al [7] observed that increasing temperature does result in decreased stiffness, yield stress and hysteresis; however, other experiments show that the hysteresis does not depend on stress rate [8]. This second observation is in contrast to our modeling results where the hysteresis is predicted to increase with stress rate. This disparity may have several causes: one is the comparatively small-strain amplitude in the experiment [8] or the frequency range of the loading in comparison to the mobility. Moreover, our model assumes a constant value of the mobility κ LH , which shows the essence of the physical behavior of the fiber. In a real material, κ LH may depend onf LH and, thus, indirectly on the loading speedσ LH . This may influence the way in which hysteresis depends on frequency in cyclic loading.
Experimental measurements on α-helical proteins with single-molecule force spectroscopy provide a biophysical basis for understanding some of the observations of the models in this study. Single-molecule load-extension curves of fibrinogen, a coiled-coil α-helical protein, reveal a characteristic sawtooth pattern indicative of molecular energy dissipation [41,42], whereas myosin exhibits elastic loading behavior lacking notable hysteresis [43]. It was suggested that the relative differences in mechanical behavior between these two α-helical proteins stem from variation in the refolding rate based on the relative structural complexity of the two proteins, i.e. fibrinogen has many disruptions in the α-helical motif sequence, whereas the myosin helix is relatively uninterrupted. Implicit in this result is the fact that the rate of transformation between the L phase and the H phase is dependent on the protein sequence and structure, and therefore the mechanical behavior (and therefore hysteresis) of materials based on α-helical proteins is tunable by molecular augmentation processes such as natural selection or genetic engineering [44]. Finally, as discussed above, stress-controlled processes are typical for natural events. However, strain-controlled processes are usually performed in the laboratory. For the sake of completeness, a corresponding algorithm is presented in the appendix.

Appendix. Strain-controlled process
We start with the strain-controlled process from the L phase to the H phase with given quantities ε tot andε tot . Considering equation (12) one finds and for the rateε tot using (A.1) Sinceσ acts in both phases, one can express ε L by ε H with equation (3) as With F (ε H ) the derivative dF (ε H )/dε H is denoted. We can now expressf LH , equation (10), as a function of ε H by using the first equation of (A.3) and equation (10) as Note that ε tot andε tot are known quantities, so equation (A.5a) represents a first-order ordinary differential equation for ε H (t). As the initial condition we have for t = t start the stressσ start , see the discussion below equation (13), yielding with equation (2) ε H,start = F −1 (σ start ) for t = t start , (A.5b) which enforces inverting F (ε) to F −1 (ε). Then we have to solve the initial value problem for ε H = ε H (t) until ξ LH becomes 1, yielding ε H = ε H,final . Then further straining occurs only in the H phase. The backtransformation starts when ε H has reached ε H (σ start ). Then the strain-controlled backtransformation process from the H phase to the L phase can also be described with the differential equation (A.5a), however, by replacing κ LH by −κ HL with κ HL > 0. Note thatε tot is now a negative quantity. The same start condition as (A.5b) is also valid. The process stops when ξ HL has reached the value 1. Then only the L phase exists again.


s2fieldsofstudy: Biology Medicine

text:
Chromogranin A, Ki-67 index and IGF-related genes in patients with neuroendocrine tumors

Chromogranin A (CgA) and the Ki-67 proliferation index are considered as important biochemical and pathological markers for clinical behaviour of gastroenteropancreatic neuroendocrine tumors (GEP NETs), respectively. The IGF system has been suggested as an important regulator of GEP NET proliferation and differentiation. A possible relationship between serum CgA (sCgA), Ki-67 proliferation index, and expression of IGF-related genes in patients with GEP NETs has not been demonstrated yet. This study investigates the relationship between sCgA, the Ki-67 proliferation index, and the expression of IGF-related genes in GEP NET tissues and their relation with 5-year survival. Tumor and blood samples from 22 GEP NET patients were studied. Tumoral mRNA expression of IGF-related genes (IGFs: IGF1, IGF2; IGF receptors: IGF1R, IGF2R; insulin receptors: subtype A (IR-A) and B (IR-B); IGF-binding proteins (IGFBPs): IGFBP1, IGFBP2, IGFBP3, and IGFBP6) was measured using quantitative RT-PCR. Ki-67 proliferation index was determined using immunohistochemistry. sCgA was measured with ELISA. Five-year survival in patients with nonelevated sCgA (n=11) was 91 vs 46% in patients with elevated sCgA (n=11) (P=0.006). IR-A mRNA expression was significantly higher in tumors obtained from patients with elevated sCgA than in those from patients with nonelevated sCgA (6.42±2.08 vs 2.60±0.40; P=0.04). This data suggests that sCgA correlates well with 5-year survival of GEP NET patients, and that IR-A mRNA expression correlates well with tumor mass in GEP NET patients.

Introduction
Gastroenteropancreatic neuroendocrine tumors (GEP NETs) are rare and heterogeneous tumors which may vary according to their biological, functional, and clinical behavior (1). Chromogranin A (CgA) and the Ki-67 proliferation index are considered as important biochemical and pathological markers, respectively, for GEP NET clinical behaviour. The insulin-like growth factor (IGF) system has been suggested as an important regulator of GEP NET proliferation and differentiation (2). Up to present, a possible relationship between serum CgA (sCgA), the cellular expression of the Ki-67 protein, and the IGF-related genes has not been studied in GEP NETs.
Deregulation of the IGF system, a complex network involved in cell growth and metabolic functions in normal Endocrine Connections

Research
Open Access R C S van Adrichem et al.
CgA, Ki-67 index and IGF system in GEP NETs tissues and tumors, plays an important role in the pathophysiology of GEP NETs (2). The IGF system consists  of different IGF-related genes: two ligands (IGF1 and IGF2),  two IGF receptors (IGF1R and IGF2R), two insulin receptors  (IR-A and IR-B), and six IGF-binding proteins (IGFBPs). Upon binding to the IGF1R and IR-A, IGFs predominantly generate mitogenic effects. Binding to IR-B predominantly exerts metabolic effects (3,4). Almost all IGFs are bound to one of the six high-affinity IGFBPs which all differ with regard to their IGF inhibiting and potentiating actions (4,5,6,7). The functions of IGFBP1, IGFBP2, IGFBP3, and IGFBP6 have been well characterized (6). The Ki-67 proliferation index is generally used for grading of NETs (8,9,10). The ENETS/AJCC/WHO 2010 grading system consists of three categories: Grade 1 (G1)Z Ki-67 proliferation index %2%, G2ZKi-67 proliferation index 3-20%, and G3ZKi-67 proliferation index O20% (8,9,11,12). This grading system has been shown to have relevant prognostic consequences and has been used for decision making with regard to therapeutic options in GEP NET patients (13,14).
Another important characteristic of GEP NETs is the presence of the CgA protein. CgA is co-secreted by GEP NET cells in the bloodstream with other hormones or peptides (15). CgA is the best available circulating parameter in the follow-up of tumor mass in GEP NET patients (16).
The main aim of our research was to investigate relationships between sCgA levels in GEP NET patients, cellular Ki-67 proliferation index, and the mRNA expression of IGF-related genes in their GEP NET tissues and to correlate this with their 5-year survival.

Subjects and methods
Patients with a GEP NET and tissue samples A total of 22 GEP NET tissue samples from 22 nonconsecutive GEP NET patients were collected before the start of any nonsurgical therapy. The diagnosis of GEP NET was based on clinical, biochemical, radiological, and histopathological characteristics. After tumor excision or biopsy, these tissue samples were immediately frozen in liquid nitrogen and stored at K80 8C. The other tissues were obtained from the Erasmus MC Tissue Bank. These specimens were stored according to a standard procedure (17).
All patients gave written informed consent before inclusion in the studies, which were approved by the Medical Ethics Committee of the Erasmus MC, Rotterdam.

Biochemical parameters
Blood samples for the determination of sCgA were obtained at the time of diagnosis of the GEP NET (baseline). The sCgA levels were measured using a commercially available ELISA method (CIS Bio International, Gif-sur-Yvette cedex, France; upper limit of normal (ULN) 94 mg/l).
'Nonelevated' sCgA was defined as %2! the ULN (%188 mg/l), and 'elevated' sCgA was defined as O2!ULN (O188 mg/l). These definitions were based on a previous publication and were selected to maximally exclude other confounding factors which might have caused (slight) elevations of sCgA (18).

Ki-67 immunohistochemical staining
Immunohistochemical analysis for Ki-67 was performed on 4 mm thick paraffin-embedded tissue sections according to the standardized and optimized benchmark procedure (Benchmark Ultra, Ventana, Tucson, AZ, USA). Pretreatment was performed with CC1 buffer for 64 min at 97 8C. Primary monoclonal mouse antibodies against Ki-67 (clone MIB-1, 1:200 dilution; Dako, Glostrup, Denmark) were incubated for 32 min at 36 8C, and were detected by a high-sensitive detection kit (UltraView Universal DAB Detection kit).
The Ki-67 proliferation index in GEP NET samples was expressed as the percentage of Ki-67 immunopositive NET cells. The counting procedure was performed by three experienced investigators according to the published guidelines (8,9,12).

Real-time quantitative PCR
For mRNA expression experiments, total RNA of GEP NET tissues was isolated with the ready-to-use High Pure RNA Isolation Kit (Roche Diagnostics). The cDNA synthesis and real-time quantitative PCR (RT qPCR) were performed according to previously published methods (19). Sequences and concentrations of primer-probe sets for all above-mentioned genes are listed in the Supplementary Table 1, see section on supplementary data given at the end of this article. The relative expression of IGF-related genes was calculated using the comparative threshold method, 2 KDC t , after efficiency correction of target and reference gene transcripts (HPRT ) (20,21).

Statistical analysis
Data were analyzed using SPSS software (version 17 for Windows; SPSS, Inc.). Comparative statistical evaluations were performed by Mann-Whitney U tests. Correlation analysis was accomplished using Spearman's rank correlation tests. Survival rates were calculated using the Kaplan-Meier method, and groups were compared using the log rank test. Kaplan-Meier curves were plotted using overall survival data. The mRNA expression data are reported as meanGS.E.M.

Patient characteristics
Patient characteristics are shown in Table 1. Fifty percent of the patients had nonelevated sCgA levels (nZ11) and the others had elevated sCgA (nZ11) with median values of 121 and 894 mg/l respectively. As compared to the elevated sCgA group, there were more female patients in the nonelevated sCgA group, these patients were younger, their primary tumor origins were less often in the small intestine and less distant metastases were found.

Tumor characteristics
In the nonelevated sCgA group, there were four G1 and six G2 patients and, there was one G3 patient. In the elevated sCgA group there were eight G1 and three G2 patients ( Table 2).
In the nonelevated sCgA group, four patients were classified as ENETS stage IIIB and the other seven patients were classified as ENETS stage IV. In the elevated sCgA group, two patients were classified as ENETS stage IIIB and the other nine patients were classified as ENETS stage IV.

Five-year survival of GEP NET patients
In Fig. 1, the 5-year survival of 22 GEP NET patients categorized according to nonelevated and elevated sCgA is shown. There was a significant shorter 5-year survival in the elevated sCgA group as compared with the nonelevated sCgA group (46 vs 91%; PZ0.006).
In the elevated and nonelevated sCgA groups, no statistical significant correlations could be found between the mRNA expression levels of the different IGF-related genes and 5-year survival. Also, no statistical significant correlation could be demonstrated between the Ki-67 proliferation index and the 5-year survival in these two groups (data not shown).

Tumoral mRNA expression of IGF-related genes in GEP NET samples
In Table 3, the tumoral mRNA expression levels of IGFrelated genes in the nonelevated and elevated sCgA groups are shown. There was a significant higher tumoral mRNA expression for IR-A in the elevated sCgA group compared with the nonelevated sCgA group (2.60G0.40 vs 6.42G2.08, PZ0.04).

Correlation between IGF-related genes and Ki-67 proliferation index
No statistical significant relationship between the Ki-67 proliferation index and mRNA expression of IGF-related genes could be demonstrated (data not shown).

Discussion
To our knowledge, this is the first study in which the relationship between sCgA levels, the tumoral Ki-67 proliferation index, and the tumoral expression of IGFrelated genes has been evaluated in GEP NET patients. Survival analysis showed a significantly shorter 5-year survival in patients with elevated sCgA levels compared with those with nonelevated sCgA levels. sCgA levels generally correlate well with tumor mass. These findings have already been confirmed by other groups (22,23).
In the elevated and nonelevated sCgA groups, no statistical significant correlations could be found between the mRNA expression levels of the different IGF-related genes and 5-year survival. Also, no statistical significant correlation could be demonstrated between the Ki-67 proliferation index and the 5-year survival in these two groups.
However, other studies have shown a significant shorter survival in Grade 3 GEP NET patients (Ki-67 index O20%) (24,25). A possible explanation for our discrepant results could be the very small sample size of these heterogeneous tumor entities and the short follow-up.
Our study showed significant higher tumoral mRNA expression of the insulin receptor A (IR-A) in GEP NET patients with elevated sCgA compared with those patients with nonelevated sCgA. Increased expression of the IR-A, a mitogenic variant of the IR, is also found in tumors arising in the colon, breast, thyroid, prostate, and fibrous tissues (26,27,28,29,30,31,32). Until present, these findings have not been reported for GEP NETs.
As sCgA levels correlate well with tumor bulk, our data therefore suggest that tumor mass correlates to tumoral IR-A expression in patients with GEP NETs.
No significant difference in tumoral mRNA expression levels was observed for all other IGF-related genes between patients with nonelevated vs patients with elevated sCgA. Although we have no obvious explanation for these findings, we suggest that IR-A expression has predominantly tumor-stimulating functions in more advanced tumors in contrast to other IGF-related genes, which are involved in the pathophysiology of GEP NETs regardless of the tumor stage.
In conclusion, our study could not demonstrate a relationship between IGF-related genes and the Ki-67 proliferation index in GEP NET tissues. We could confirm previous observations supporting a negative correlation between sCgA levels and 5-year survival. We could not demonstrate a relationship between the tumoral Ki-67 proliferation index and sCgA. However, our study results showed a relation between cellular IR-A mRNA expression and tumor mass.