Biomimicry of quorum sensing using bacterial lifecycle model
25-29 July 2012

Ben Niu
Hong Wang
Qiqi Duan
Li Li
Biomimicry of quorum sensing using bacterial lifecycle model

From The 2012 International Conference on Intelligent Computing (ICIC 2012) Huangshan
China25-29 July 2012P R O C E E D I N G S Open Access
Background: Recent microbiologic studies have shown that quorum sensing mechanisms, which serve as one of the fundamental requirements for bacterial survival, exist widely in bacterial intra-and inter-species cell-cell communication. Many simulation models, inspired by the social behavior of natural organisms, are presented to provide new approaches for solving realistic optimization problems. Most of these simulation models follow population-based modelling approaches, where all the individuals are updated according to the same rules. Therefore, it is difficult to maintain the diversity of the population.Results: In this paper, we present a computational model termed LCM-QS, which simulates the bacterial quorumsensing (QS) mechanism using an individual-based modelling approach under the framework of Agent-Environment-Rule (AER) scheme, i.e. bacterial lifecycle model (LCM). LCM-QS model can be classified into three main sub-models: chemotaxis with QS sub-model, reproduction and elimination sub-model and migration submodel. The proposed model is used to not only imitate the bacterial evolution process at the single-cell level, but also concentrate on the study of bacterial macroscopic behaviour. Comparative experiments under four different scenarios have been conducted in an artificial 3-D environment with nutrients and noxious distribution. Detailed study on bacterial chemotatic processes with quorum sensing and without quorum sensing are compared. By using quorum sensing mechanisms, artificial bacteria working together can find the nutrient concentration (or global optimum) quickly in the artificial environment. Conclusions: Biomimicry of quorum sensing mechanisms using the lifecycle model allows the artificial bacteria endowed with the communication abilities, which are essential to obtain more valuable information to guide their search cooperatively towards the preferred nutrient concentrations. It can also provide an inspiration for designing new swarm intelligence optimization algorithms, which can be used for solving the real-world problems.

Background

Many agent-based models, inspired by biological phenomenon, have been formulated to provide new approaches for solving realistic optimization problems especially complex NP problems [1]. Two different types of agent-based models, population-based and individual-based models, have been classified based on the viewpoint of biological simulation. In population-based models such as the particle swarm optimization algorithm [2], all individuals have unique characters and follow the same evolutionary rules.

Nevertheless, in individual-based models (IBM) [3], an individual is regarded as a discrete entity endowed with its own attributes, states and behaviors. Every heterogeneous entity can communicate with each other and then make group decisions by social intelligence.

Early in 1988, Kreft and his colleagues proposed an individual-based model termed BacSim to simulate the evolution process of Escherichia coli (E. coli) from an individual bacterium to a group. As he says, we can see a macroscopic world in the microscopic object [4]. An E-CELL model was illustrated by Tomita et al. in 1999, inspired by developmental processes of Mycoplasma genitalium [5]. Ginovart et al. (2002) designed a discrete IBM called INDISIM to simulate the growth of bacterial cultures [6]. An alternative model based on the COS-MIC system to simulate the artificial bacterial interaction and evolution was shown by Paton et al. in 2004 [7]. Soon after, Emonet et al. (2005) developed an IBM termed AgentCell to simulate bacterial chemotactic processes at the single-cell level [8]. Another individualbased model of low-population bacteria cultures in the lag stage was presented by Prats et al. in 2006 [9]. Recently, an IBM termed iDynoMiCS, which employs new bacterial biofilm modelling approaches, was formulated by Lardon et al. (2011) [10].

In our previous work, we formulated a lifecycle model (LCM) guided by the Agent-Environment-Rule architecture to simulate the bacterial evolution in 2008 [11]. LCM mainly focuses on microscopic and macroscopic evolution processes of bacteria in different growth phases. Three main developmental phases of E. coli including the lag, dynamic and decline phases are studied. Compare with the population-based computational model, the individual-based LCM has improved flexibility where the behaviors of every individual could be investigated and controlled. The original LCM, however, is in its infancy [11]. Recent studies have demonstrated that quorum sensing (QS) systems generally exist in bacteria acting as communication with and between groups [12]. Incorporating intra-and inter-species QS mechanisms into LCM is the primary aim and work of this paper.

Methods

Lifecycle model (LCM)

A bio-inspired lifecycle model (LCM), according to bacterial evolution processes during their lifecyle, was proposed as a new inspiration to solve optimization problems in 2008 [11]. Behaviors of E. coli in different life phases are concentrated on in LCM. In biological science, behaviors of E. coli have been intensively studied for more than 150 years and four key behavioral patterns of E. coli, i.e. chemotaxis, reproduction, migration and elimination, have been detailed described in [13].

In absence of gradient information about attractant or repellent chemical concentration, a bacterium runs in a straight line using flagella as propellers for a few seconds, and then tumbles with random directions. The run-tumble-run cycle will be repeated during the whole bacterial lifecycle. A bacterium with gradient information shows distinctly different behaviors. It has been suggested that the bacteria can possess the memory ability so that it can compare current gradient information with previous ones [14]. If the concentration of attractant chemicals raises or the density of repellent chemicals reduces, the frequency of run increases. Otherwise, the frequency of tumble increases. The run-tumble-run cycle is the essential property of bacterial chemotactic behaviors.

When a bacterium obtains sufficient energy from the environment, it has a chance to reproduce. The healthy bacterium splits into two identical daughter cells in the same spatial position. Those with poor nutrients intake may undergo extinction or migrate to new niches for survival [15]. Of course, the population varies between different life phases. In the lagging phase, most bacteria absorb rich nutrients and thus the population of bacteria grows exponentially. The increasing of population size leads to the intensified competition for nutrients. During dynamic phase, the population size of bacterial colony fluctuates markedly in the early stage and then gradually stagnates in a relatively stable state. With the food depleted gradually, some bacteria are not able to find sufficient food sources, which will be eliminated or migrate to new places with good nutrient concentrations, the population size reduces at the decline stage.

In LCM, each bacterium has different characters and is independent from each other. Therefore, it is also considered as the artificial individual possessing an ability of autonomous. LCM consists of three underlying components. The most important component is the artificial bacteria, which possesses plenty of attributes and behavioral features [16]. N-dimension environment with gradient information where artificial bacteria undertake metabolisms is the second key component. The most complex component is interaction rules between artificial bacteria and environment. The original LCM model is presented in Figure 1.

Although the theoretical modelling based on individual bacterial behaviors is insufficient. The flexible structure of LCM allows it to retain the potential of incorporating quorum-sensing (or cell-cell communication) mechanisms. This paper chiefly concentrates on the integration of QS systems into LCM for achieving a more accurate biological simulation model. A brief description of QS mechanisms is given as follows.

Quorum-sensing (QS) mechanisms

New microbial discoveries have illustrated that though as the simple unicellular microbes on earth, bacteria can utilize cell-to-cell communication to make group decisions, synthesize beneficial molecules for themselves and so on [17]. Information about other bacteria and the environment can be acquired by an individual bacterium, and interpreted in a 'meaningful' way which finally results in sharing of knowledge [18]. Possessing the sophisticated linguistic communication abilities, bacteria are able to take on some advanced features of social intelligence, such as cooperative foraging and creating complex niches [19]. Such communication process via chemical signals is termed quorum sensing (QS).

We now know that QS systems have been found in both Gram-positive and Gram-negative bacteria [20]. For example, the first known QS mechanism was discovered in bioluminescent bacteria called Vibrio fischeri. It resides in light-producing organs of the squid for reproduction. AI-1 synthase produced by Vibrio fischeri diffuses AHL molecules to increase the cell-population concentration. As long as the cell-population density exceeds a threshold level, luciferase operons will be activated and result in the generation of light [21]. More and more studies have shown that the AI-1 QS system is mainly responsible for local interactions between colony members [22]. In contrast, another famous AI-2 QS system functions as interspecies communication [23]. According to different QS mechanisms, two kinds of topologic schemas are presented in Figure 2.

Two main kinds of QS systems in individual-wide and population-wide scales are considered and simulated in the paper [24]. It is clear that via the use of intrinsic QS mechanisms, global behaviors of bacterial species are coordinated for maximizing group benefits as well as individual benefits [25].

LCM with QS mechanisms

Lifecycle model with QS mechanisms (LCM-QS) not only involves microscopic objects such as the run-tumble-run cycle, but also includes macroscopic entities such as the interspecies communication. LCM-QS is more in accordance with the natural metabolic processes compared with the original LCM. LCM-QS is made up of five core components, i.e., chemotaxis, quorum sensing, reproduction, elimination and migration, as shown in Figure 3.

The bacterial movement of chemotaxis is run through in the whole lifecyle. During each movement of run or tumble, each bacterium pursues nutrients or avoid noxious. Bacteria with high energy intake will broadcast their search information to other bacteria with low energy level by QS mechanism. When a mass of bacteria congregate together and the local environment becomes overcrowded, they compete with each other instead of cooperated with others. Some bacteria with strong foraging abilities accumulate sufficient nutrients for reproduction. Others that lack competitive edges are easily eliminated. In the proposed model, these dead bacteria are replaced by copies of bacteria possessing the opportunity of reproduction. The rest of bacteria, which have a little energy and average foraging capacities, will migrate to a new region together through interspecies communication. To reduce computational complexity, the lifecycle model with QS mechanisms is divided into three sub-models, which are presented in detail as follows.

Chemotaxis with QS sub-model

In the primary LCM, bacterial movement of runs and tumbles with no information exchange within and between bacterial strains, which is not in accord with recent biological discoveries. In fact, the chemotactic behavior is always accompanied with intra-species and inter-species communication via QS systems in the whole bacterial optimization processes. Microbial chemotactic behaviors are mainly influenced by personal previous experiences, information exchange and random direction choices, which are shown in Figure 4. The chemotactic behavior with QS mechanisms prolongs motion towards a favorable orientation and restrains movement in an adverse direction [26].

The specific formulae about bacterial motion are presented below. Equation 1 indicates chemotactic behaviors of simulated bacteria in the absence of QS mechanisms. On the contrary, artificial bacterial movement with QS systems is characterized in Equation 2. P i (k) donates position of the i-th bacterium in the k-th iteration step. R indicates a random number. C i is the run length of i-th bacterium. randDir(i, k) stands for a random orientation choice of the i-th bacterium in the k-th iteration step. Gbest donates current optimal location with richest nutrients.
P i (k + 1) = P i (k) + R * C i * randDir(i, k)(1)P i (k + 1) = P i (k) + R 1 * C i * randDir(i, k) + R 2 * (Gbest − P i (k))(2)
It is expected that the integration of QS mechanisms into chemotaxis will facilitate the cooperative search of the global optimum and accelerate the convergence rate. Subsequently, the reproduction and elimination submodel will been conducted until the number of run and tumble reaches a certain value (N re or N eli ). Note that the number of chemotaxis step equals the total number of iterations (iterMax). if J i > J threshold && iter > N re , then i ∈ healthy and reproduce i (3) if J i < J threshold && iter > N eli , then i ∈ unhealthy and eliminate i (4) where J i is fitness value of ith bacterium, J threshold is a predefined threshold. The asexual reproduction of healthy bacteria doubles the population of the group. Nevertheless, the colony population size may shrink rapidly owing to the sudden death of a mass of unhealthy bacteria. Hence, the total number of artificial bacteria in the proposed model remains unchanged. From the viewpoint of computation, the reproduction and elimination progress may disturb chemotactic processes in the next iteration step. But more importantly, it could improve the computational speed and possibly find the global optimum.

Reproduction and elimination sub-model

Migration sub-model

Owing to the increment of bacteria in a given region, the competition for nutrients becomes more and more    intensive. The nutrient-rich food sources will not satisfy the requirements of all bacteria. Naturally, some bacteria with average foraging capacities but poor energy level are more inclined to migrate to new areas with expected richer nutrient concentrations rather than die directly. From a perspective of optimization, the long-distance migration to new random niches, described in Figure 5, is able to keep the diversity of colony and avoid being trapped into local optimum.

A core formula of the migration sub-model is presented in Equation 5.
P i (d, k + 1) = lb + rand * (d, k) * (ub − lb)(5)
where lb indicates the lower boundary and ub donates the upper boundary, d is the number of the dimensions, k is the current chemotatic step. The values of lower and upper boundaries are always determined according to the constraints defined in the realistic optimization problems.

To simplify LCM-QS, it is suggested that the entire migration process will be conducted only if certain migration conditions are satisfied. For instance, if the chemotactic steps reach a predefined threshold value, the migration process will be performed.

Implementation of LCM-QS

Some population-based optimization models, such as BFO [13], utilize a nested loop structure, which requires more computational time and thus influences the convergence rate. However, LCM-QS adapts a sequential implementation structure to reduce computational time. In our proposed LCM-QS, a bridge between individual behaviors and group interactions is built and a right balance between computational simplicity and model complexity is maintained. The implementation procedure of LCM-QS is presented in Figure 6.

It should be noted that some initial parameters, such as boundary conditions and the bacterial initial position, are under user control. Table 1 gives a detailed description of the optimization processes of LCM-QS. It is obvious that the reproduction and elimination submodel as well as the migration sub-model are condition-dependent.

Results and discussion

To measure the search performance of artificial bacteria using the proposed new model, simulation studies have conducted in a 3-D environment with nutrient-noxious distribution. As illustrated above, information exchange mechanism is one of key indicators in swarm optimization, our experimental studies are conducted with four types of information exchange scenarios.

A: Bacterial chemotaxis without information exchange; B: Bacterial chemotaxis with group information exchange;

C: Bacterial chemotaxis with individual information exchange; D: Bacterial chemotaxis with individual and group information exchange.

The nutrient distribution of 3-D environment is set by the function as Equation (6), which is also illustrated in Figure 7.

(6)

A: Bacterial chemotaxis without information exchange

In this section, bacterial chemotaxis will be operated without considering information exchange between individuals and groups. Bacterium runs and tumbles to nutrition area by stochastic turbulence. Figure 8 shows the bacterial optimization process with the chemotaxis step N c ranging from 1 to 2000. In LCM-QS, chemotaxis goes along with entire optimization process. From Figure 8, the bacterial colonies have to spend more than 500 chemotaxis steps to find the global optimum. After a long time of chemotaxis (without communication), reproduction and elimination process, the final fitness value of each bacterium have been shown in Figure 9. Figure 10 and Figure 11 indicate the individual best position (θ 1 and θ 2 are two dimensional vectors) obtained by each bacterium during 2000 chemotactic step. Figure 10 points out that not all of bacteria in group find the global optimum after the maximal iterations satisfied, which is also confirmed by Figure 11. From Figure 11, chemotaxis process is divided into four stages and every stage has 500 chemotatic steps. But even in the fourth stage, some of bacteria cannot find the global optimum.

B: Bacterial chemotaxis with group information exchange

Section A suggests that the bacterial chemotaxis without information exchange fails to find global optimum efficiently. Therefore, bacterial chemotaxis with group information exchange is considered for getting better search performance in this section. Similar to the above section, Figure 12 shows the average optimal fitness obtained by bacterial colonies over 2000 chemotactic steps. The bacterial colonies approach the best nutrient concentrations with a faster convergence rate compared to the case of no information exchange. The individual best position found by each bacterium during 2000 chemotactic step is shown in Figure 14 and Figure 15. From the figures, we can find that most bacteria can reach the global optimum position in the first stage. Because most of the them can locate in the global optimum in the first stage, the best position obtained by bacterial colonies keep nearly unchanged at the later If (given probability >random)

Share information with surrounding neighbours
Tumble (Pi(k + 1) = P i (k) + R 1 * C i * randDir(i, k) + R 2 * (Gbest − P i (k)) )
While (current function value Fc< previous function value Fp)

Swim (using Equation 2)

End While    three stages. Specifically, in Figure 13, the fitness of the bacteria mostly assemble between -3.986 and -3.987 with little difference, and only a few of individuals cannot arrive at the global optimum but run very close to it.

C: Bacterial chemotaxis with individual information exchange

Section B aims at improving the search efficiency of bacterial chemotaxis through group information exchange. In this section, individual information exchange is incorporated as one of communication mechanisms to speed up the search speed of global optimum. Figure 16 confirms that the participating of individual information exchange indeed helps a lot in guiding the bacteria to approach the nutrient area. After maximum iterations reached, all bacteria can find the global optimum shown in Figure 17. Figure 18 and Figure 19 once again illustrate that the individual communication mechanism is favorable to orient bacteria colony to global optimum. With the help of the individual information exchange between bacteria, the bacterial colony can find the global optimum in the first 500 chemotatic steps.

D: Bacterial chemotaxis with individual and group information exchange

The average fitness values obtained by bacterial colony with individual and group information exchange are shown in Figure 20. It converges in a high speed in the early iterations (chemotatic step), but a relatively slow convergence rate in the later iterations. When approach Figure 11 The process of finding the global optimum without information exchange.         the promising area (near global optimum), many iterations are used to fine-tune the local search. Finally, all the bacteria are able to find the global optimum when the maximum iterations reached. Figure 22 and Figure 23 inform that that bacteria can search for global optimum quickly with help of individual and group information exchange. Figure 22 even shows that the bacteria can find the global optimum no more than 100 iterations. There is no doubt that these two communication mechanisms have increasingly improved the search efficiency of original model with no information exchange or individual information exchange.

The results presented in the above figures indicate the emergent behavior of comparative search from a macroscopic view. LCM-QS model is in relating the macroscopic effects of bacterial colony to microscopic behavior of single bacterial cell. Figure 24 and Figure 25 describe optimization process in bacterial micro-communities, where four bacteria are selected. Those four bacteria are randomly distributed in the artificial environment. After 100 iterations, all of them located the global optimum with the best nutrient concentrations. Chemotaxis process of a single bacterium during four different stages (25 chemotatic steps in one stage) is shown in Figure 26. From the figure we can find that the bacterium located in the global optimum position with the best nutrient concentrations after 25 steps. The overall search procedure during 100 chemotactic steps is illustrated in Figure 27.

Conclusions

In this paper, a new computational model, termed LCM-QS (lifecycle model with quorum sensing mechanism) is proposed to simulate emergent behaviour of bacterial quorum sensing. The communication mechanism is the most important factor to indicate a swarm intelligence system. The artificial bacteria are endowed with communication ability by using the principle of swarm intelligence. Additionally, reproduction, elimination and migration are all viewed as optimization strategies to build the LCM-QS model. To illustrate the performance of the proposed model, four types of communication schemes between individuals or groups are studied by adapting a 3-D artificial environment with nutrient-noxious distribution. The results show that by using quorum sensing mechanism artificial bacteria are able to response quickly to the complex environment and can find the global optimum in a short time.

The primary goal of this paper concentrates on developing a novel individual-based modelling approach to simulate the quorum sensing mechanism among bacterial colonies. Meanwhile, the LCM-QS model is expected to give an inspiration to present a new swarm intelligence optimization algorithm. However, little consideration is given to other factors such as varying population, dynamic environment. Therefore, in our future work, these issues will be focused on and some real-world applications will be considered, such as the design of new evolutionary neural networks [27][28][29][30].    Intelligent Computing (ICIC 2012). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/ supplements/14/S8.

Figure 1
1Lifecycle model under the agent-environment-rule architecture.

After long-time chemotactic steps, some bacteria with higher energy level (represented by nutrient concentrations) have more opportunities to reproduce and maximize lifespan, whereas other bacteria with lower energy level are faced with being eliminated. Bacteria accumulate enough nutrients can propagate by binary fission and produce two identified daughter cells at the same position. Besides, if an artificial bacterium move out of the restricted area, it should be deleted and replaced by a new bacterium for better control of the model. Detailed conditions of reproduction or elimination are presented below in Equations 3 and 4.

a) QS system in individual-wide scales (b) QS system in population-wide scales

Figure 2 Figure 3
23Two kinds of topologic schemas of QS. LCM with quorum-sensing systems (LCM-QS).

Figure 4
4Chemotactic behaviors with an intra-species QS mechanism.

F
(x) =5 * exp −0.1((x1−15) 2 +(x2−20) 2 ) − 2 * exp −0.08((x1−20) 2 +(x2−15) 2 ) + 3 * exp −0.08((x1−25) 2 +(x2−10) 2 ) + 2 * exp −0.1((x1−10) 2 +(x2−10) 2 ) − 2 * exp −0.5((x1−5) 2 +(x2−10) 2 ) − 4 * exp −0.1((x1−15) 2 +(x2−5) 2 ) − 2 * exp −0.5((x1−8) 2 +(x2−25) 2 ) − 2 * exp −0.1((x1−21) 2 +(x2−25) 2 ) + 2 * exp −0.5((x1−25) 2 +(x2−16) 2 ) + 2 * exp −0.5((x1−5) 2 +(x2−14) 2 )

Figure 5
5Long-distance migration mechanism.

Figure 6
6Flow chart of simulation processes.

ElseFigure 7
7Share the information with two random-choice bacteria Tumble (using Equation 2) While (current function value Fc< previous function value Fp) Nutrient-noxious environments.

Figure 8
8The average fitness values during 2000 iterations without information exchange.

Figure 9
9The final optimal function values of each individual without information exchange.

Figure 10 2
10-D position during 2000 iteration process without information exchange.

Figure 12
12The average fitness values during 2000 iterations with group information exchange.

Figure 13
13The final optimal function values of each individual with group information exchange.

Figure 14 2
14-D position during 2000 iteration process with group information exchange.

Figure 17
17The final optimal function values of each bacterium with individual information exchange.

Figure 18 2
18-D position during 2000 iteration process with individual information exchange.

Figure 19
19The process of finding the global optimum with individual information exchange.

Figure 20
20The average fitness values during 2000 iterations with both individual and group information exchange.

Figure 15
15The process of finding the global optimum with group information exchange.

Figure 16
16The average fitness values during 2000 iterations with individual information exchange.

Figure 21
21The final optimal function values of each bacterium with both individual and group information exchange.

Figure 22 2
22-D position during 2000 iteration process with both individual and group information exchange.

Figure 23
23The process of finding the global optimum with both individual and group information exchange.

Figure 24
24Global optimum found by four bacteria during 100 chemotactic steps.

Declarations
This article has been published as part of BMC Bioinformatics Volume 14 Supplement 8, 2013: Proceedings of the 2012 International Conference on

Figure 25
25Optimal process of four bacteria during 100 chemotactic steps.

Figure 26
26Global optimum found by single bacterium during 100 chemotactic steps.

Figure 27
27Optimal process of one bacterium during 100 chemotactic steps.

Table 1
1Implementation of LCM-QSFor (each loop or iter<iterMax)

// chemotaxis process
For (each bacterium )

© 2013 Niu et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
AcknowledgementsThis work is partially supported by The National Natural ScienceAuthors' contributions NB. defined the research question and initiated the ideas. NB, WH designed and conducted simulation experiments. NB, WH, DQQ drafted the manuscript. LL defined the final manuscript. All authors contributed to and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.Submit your next manuscript to BioMed
Tutorial on agent-based modelling and simulation. C M Macal, M J North, Journal of Simulation. 4Macal CM, North MJ: Tutorial on agent-based modelling and simulation. Journal of Simulation 2010, 4:151-162.

Individual-based modelling: an essential tool for microbiology. J Ferrer, C Prats, D Lopez, Journal of Biological Physics. 34Ferrer J, Prats C, Lopez D: Individual-based modelling: an essential tool for microbiology. Journal of Biological Physics 2008, 34:19-37.

A novel full structure optimization algorithm for radial basis probabilistic neural networks. J X Du, D S Huang, G J Zhang, Z F Wang, Neurocomputing. 70Du JX, Huang DS, Zhang GJ, Wang ZF: A novel full structure optimization algorithm for radial basis probabilistic neural networks. Neurocomputing 2006, 70:592-596.

BacSim, a simulator for individualbased modelling of bacterial colony growth. J U Kreft, G B Booth, Jwt Wimpenny, Microbiology. 144Kreft JU, Booth GB, Wimpenny JWT: BacSim, a simulator for individual- based modelling of bacterial colony growth. Microbiology 1998, 144:3275-3287.

E-CELL: software environment for whole-cell simulation. M Tomita, K Hashimoto, K Takahashi, T S Shimizu, Y Matsuzaki, F Miyoshi, K Saito, S Tanida, K Yugi, J C Venter, C A Hutchison, Bioinformatics. 151Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter JC, Hutchison CA: E-CELL: software environment for whole-cell simulation. Bioinformatics 1999, 15(1):72-84.

INDISIM, an individual-based discrete simulation model to study bacterial cultures. M Ginovart, D Lopez, J Valls, Journal of Theoretical Biology. 214Ginovart M, Lopez D, Valls J: INDISIM, an individual-based discrete simulation model to study bacterial cultures. Journal of Theoretical Biology 2002, 214:305-319.

Evolvable social agents for bacterial systems modeling. R Paton, R Gregory, C Vlachos, J Saunders, H Wu, IEEE Transactions on Nanobioscience. 33Paton R, Gregory R, Vlachos C, Saunders J, Wu H: Evolvable social agents for bacterial systems modeling. IEEE Transactions on Nanobioscience 2004, 3(3):208-216.

AgentCell: a digit single-cell for bacterial chemotaxis. T Emonet, C M Macal, M J North, C E Wickersham, P Cluzel, Bioinformatics. 2111Emonet T, Macal CM, North MJ, Wickersham CE, Cluzel P: AgentCell: a digit single-cell for bacterial chemotaxis. Bioinformatics 2005, 21(11):2714-2721.

Individual-based modeling of bacterial cultures to study the microscopic causes of the lag phase. C Prats, D Lopez, A Giro, J Ferrer, J Valls, Journal of Theoretical Biology. 241Prats C, Lopez D, Giro A, Ferrer J, Valls J: Individual-based modeling of bacterial cultures to study the microscopic causes of the lag phase. Journal of Theoretical Biology 2006, 241:939-953.

IDynoMiCS: next-generation individual-based modelling of biofilms. L A Lardon, B V Merkey, S Martins, A Dotsch, C Picioreanu, J U Kreft, B F Smets, Environmental Microbiology. 139Lardon LA, Merkey BV, Martins S, Dotsch A, Picioreanu C, Kreft JU, Smets BF: IDynoMiCS: next-generation individual-based modelling of biofilms. Environmental Microbiology 2011, 13(9):2416-2434.

A lifecycle model for simulating bacterial evolution. B Niu, Y L Zhu, X X He, H Shen, Q H Wu, Neurocomputing. 72Niu B, Zhu YL, He XX, Shen H, Wu QH: A lifecycle model for simulating bacterial evolution. Neurocomputing 2008, 72:142-148.

The evolution of social behavior in microorganisms. B J Crespi, Trends in Ecology and Evolution. 164Crespi BJ: The evolution of social behavior in microorganisms. Trends in Ecology and Evolution 2001, 16(4):178-183.

Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine. K M Passino, 22Passino KM: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 2002, 22(3):52-67.

J Adler, Chemotaxis in bacteria. 44Adler J: Chemotaxis in bacteria. Annual Review of Biochemistry 1975, 44:341-356.

An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. X H Yan, Y L Zhu, H Zhang, H Chen, B Niu, Discrete Dynamics in Nature and Society. 12Yan XH, Zhu YL, Zhang H, Chen H, Niu B: An adaptive bacterial foraging optimization algorithm with lifecycle and social learning. Discrete Dynamics in Nature and Society 2012, 12:1-20.

A rule-based approach to the modelling of bacteria ecosystems. C Vlachos, R C Paton, J R Saunders, Q H Wu, Biosystems. 84Vlachos C, Paton RC, Saunders JR, Wu QH: A rule-based approach to the modelling of bacteria ecosystems. Biosystems 2005, 84:49-72.

Bacterial quorum-sensing network architectures. W L Ng, B L Bassler, Annual Review of Genetics. 43Ng WL, Bassler BL: Bacterial quorum-sensing network architectures. Annual Review of Genetics 2009, 43:197-222.

Bacterial linguistic communication and social intelligence. E B Jacob, I Becker, Y Shapira, H Levine, Trends in Microbiology. 128Jacob EB, Becker I, Shapira Y, Levine H: Bacterial linguistic communication and social intelligence. Trends in Microbiology 2004, 12(8):366-372.

Quorum sensing, communication and cross-kingdom signalling in the bacterial world. P Williams, Microbiology. 153Williams P: Quorum sensing, communication and cross-kingdom signalling in the bacterial world. Microbiology 2007, 153:3923-3938.

Quorum sensing: cell-to-cell communication in bacteria. C M Waters, B L Bassler, Annual Review of Cell and Development Biology. 21Waters CM, Bassler BL: Quorum sensing: cell-to-cell communication in bacteria. Annual Review of Cell and Development Biology 2005, 21:319-346.

Quorum sensing in bacteria: the luxR-luxI family of cell density-responsive transcriptional regulators. W C Fuqua, S C Winans, E P Greenberg, Journal of Bacteriology. 1762Fuqua WC, Winans SC, Greenberg EP: Quorum sensing in bacteria: the luxR-luxI family of cell density-responsive transcriptional regulators. Journal of Bacteriology 1994, 176(2):269-275.

Cell-to-cell signalling in eshrichia coli and salmonella enterica. Bmm Ahmer, Molecular Microbiology. 524Ahmer BMM: Cell-to-cell signalling in eshrichia coli and salmonella enterica. Molecular Microbiology 2004, 52(4):933-945.

LuxS quorum sensing: more than just a numbers game. K B Xaveier, B L Bassler, Current Opinion in Microbiology. 6Xaveier KB, Bassler BL: LuxS quorum sensing: more than just a numbers game. Current Opinion in Microbiology 2003, 6:191-197.

Bacteria are small but not stupid: cognition, natural genetic engineering and socio-bacteriology. J A Shapiro, Studies in History and Philosophy of Biological and Biomedical Sciences. 38Shapiro JA: Bacteria are small but not stupid: cognition, natural genetic engineering and socio-bacteriology. Studies in History and Philosophy of Biological and Biomedical Sciences 2007, 38:807-819.

Microbial communication, cooperation and cheating: quorum sensing drives the evolution of cooperation in bacteria. T Czaran, R F Hoekstra, Public Library of Science46655Czaran T, Hoekstra RF: Microbial communication, cooperation and cheating: quorum sensing drives the evolution of cooperation in bacteria. Public Library of Science 2009, 4(8):e6655.

Novel bacterial foraging optimization with timevarying chemotaxis step. B Niu, Y Fan, H Wang, International Journal of Artificial Intelligence. 7Niu B, Fan Y, Wang H: Novel bacterial foraging optimization with time- varying chemotaxis step. International Journal of Artificial Intelligence 2011, 7:257-273.

Radial basis probabilistic neural networks: model and application. D S Huang, International Journal of Pattern Recognition and Artificial Intelligence. 137Huang DS: Radial basis probabilistic neural networks: model and application. International Journal of Pattern Recognition and Artificial Intelligence 1999, 13(7):1083-1101.

D S Huang, A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Transactions on Neural Networks. 15Huang DS: A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Transactions on Neural Networks 2004, 15(2):477-491.

Zeroing polynomials using modified constrained neural network approach. D S Huang, Hsi Horace, Chi Z , IEEE Transactions on Neural Networks. 163Huang DS, Horace HSI, Chi Z: Zeroing polynomials using modified constrained neural network approach. IEEE Transactions on Neural Networks 2005, 16(3):721-732.

A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. D S Huang, J X Du, 10.1186/1471-2105-14-S8-S8IEEE Transactions on Neural Networks. 1912Huang DS, Du JX: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Transactions on Neural Networks 2008, 19(12):2099-2115. doi:10.1186/1471-2105-14-S8-S8

Biomimicry of quorum sensing using bacterial lifecycle model. Niu, BMC Bioinformatics. 88Cite this article as. SupplCite this article as: Niu et al.: Biomimicry of quorum sensing using bacterial lifecycle model. BMC Bioinformatics 2013 14(Suppl 8):S8.


Vasohibin inhibits angiogenic sprouting in vitro and supports vascular maturation processes in vivo

Johann Kern email@example.com
Division of Internal Medicine V, Tumor Biology & Angiogenesis Laboratory
Medical University Innsbruck
Innrain 66A-6020InnsbruckAustria

Michael Steurer email@example.com
Division of Internal Medicine V, Laboratory for Molecular Genetics
Medical University Innsbruck
Anichstrasse 35A-6020InnsbruckAustria

Günther Gastl
Division of Internal Medicine V, Tumor Biology & Angiogenesis Laboratory
Medical University Innsbruck
Innrain 66A-6020InnsbruckAustria

Eberhard Gunsilius firstname.lastname@example.com
Division of Internal Medicine V, Tumor Biology & Angiogenesis Laboratory
Medical University Innsbruck
Innrain 66A-6020InnsbruckAustria

Gerold Untergasser firstname.lastname@example.com
Division of Internal Medicine V, Tumor Biology & Angiogenesis Laboratory
Medical University Innsbruck
Innrain 66A-6020InnsbruckAustria

BMC Cancer

Vasohibin inhibits angiogenic sprouting in vitro and supports vascular maturation processes in vivo
10.1186/1471-2407-9-284BioMed Central Page 1 of 11 (page number not for citation purposes) Research article * Corresponding author †Equal contributors
Background:The murine homologue of human vasohibin (mVASH1), a putative antiangiogenic protein, was investigated for its effects on in vitro and in vivo angiogenesis.Methods:Cell growth and migration were analyzed in murine fibroblasts, smooth muscle cells and endothelial cells. Angiogenic sprouting was studied in human umbilical vein endothelial cells (HUVECs) in the spheroid sprouting assay. In vivo effects on blood vessel formation were investigated in the chorioallantoic membrane (CAM) assay and in the C57BL/6 melanoma xenograft model.Results:Purified murine and human VASH1 protein induced apoptosis of murine fibroblasts in vitro, but not of vascular aortic smooth muscle cells (AoSMC) or endothelial cells. Adenoviral overexpression of murine and human VASH1 inhibited capillary sprouting of HUVECs in the spheroid assay. Administration of recombinant murine and human VASH1 inhibited growth of large vessels in the CAM assay and promoted the formation of a dense, fine vascular network. Murine VASH1-overexpressing B16F10 melanomas displayed a reduction in large vessels and vascular area. Moreover, tumors showed more microvessels that stained positive for the mural cell markers αsmooth muscle cell actin (ASMA) and proteoglycan (NG2).Conclusion:Our data imply that murine VASH1 causes angiogenic remodelling by inhibiting angiogenic sprouting and large vessel growth, thereby supporting the formation of a vascular bed consisting predominantly of mature microvessels.

Background

The human vasohibin 1 protein (hVASH1) has been postulated as an important negative feedback inhibitor of VEGF signaling and is thus a novel candidate for antiangiogenic therapies [1,2]. Despite the high degree of conservation and homology (>90%) of the vertebrate VASH1 gene, mouse and chicken have a shorter 289 aa protein lacking the N-terminal 76 aa (HomoloGene # 8941; http:/ /www.ncbi.nlm.nih.gov). Vasohibin-1 (hVASH1) transcription in human endothelial cells has been shown to be upregulated by vascular endothelial growth factor (VEGF) [2,3], one of the key players in angiogenic proc-esses [4]. The hVASH1 protein harbors a putative nuclear localization sequence, but has no signal peptide sequence within its 76 aa N-terminus. Thus, transfected cells show a cytoplasmic and nuclear accumulation of the protein under standard culture conditions [5]. Moreover, a shorter variant lacking the C-terminus (hVASH1B) has been identified in human endothelial cells [6]. This hVASH1B isoform has been shown to be a potent antiangiogenic protein in in vitro and in vivo angiogenesis assays [5]. The VASH1 gene seems to be expressed in a variety of tissues and organs during embryogenesis of mouse and chicken [7]. In particular, high transcriptional activity was found in the developing brain, eye, ear and joints. Hitherto, function of the shorter murine VASH-1 protein has not been studied.

In this study we analyzed whether the lack of the N-terminal protein domain affects angiogenic/antiangiogenic function. Due to the lack of specific and sensitive antibodies directed against murine VASH1 we generated a FLAGtagged protein for detection of the recombinant protein after adenoviral or transposon-based overexpression. The affinity-purified recombinant FLAG-tag protein was tested in the angiogenic sprouting assay and the chicken chorioallantoic membrane assay. Moreover, overexpression of mVASH1 was analyzed in endothelial cells and in the B16F10 mouse melanoma model to study inhibitory effects on tumor growth and vascularization.

Methods

Cell culture

The murine B16F10 melanoma cell line was purchased from ATCC (American Type Culture Collection). Cells were grown in RPMI 1640 supplemented with 10% fetal calf serum, 1 × 10 5 IU/L penicillin, 100 mg/L streptomycin, 2-mmol/L glutamine in the presence of 5% CO 2 . Mouse vascular aortic smooth muscle cells (mASMC) were purchased from Dominion Pharmakine and cultivated in DMEM (Gibco BRL) with 10% fetal calf serum. Mouse embryonic fibroblasts (mFB) obtained from C129 mice were cultivated in IMDM (Gibco BRL) containing 10% fetal calf serum. HUVECs were isolated and propagated as described elsewhere [8]. Human microvascular endothelial cells (HMECs) were isolated from foreskin tissue according to the protocol of the CD31 Microbead Kit (Miltenyi Biotech) after overnight digestion in dispase II followed by a 1 h digestion step in collagenase I (both Sigma Biochemicals). All endothelial cells were cultured in EGM-2 (Cambrex) on type I collagen (Sigma-Aldrich) coated flasks.

Cloning and generation of mVASH1 expression constructs

The mVASH1 cDNA was cloned from a mouse brain library of C57BL/6 mice by specific primers to exon 1 (for: 5-GTCAAGCTTACTGCCAGTGGATGAG) and exon 7 (rev: 5-TTATCTAGACACCCGGATCTGGTAC) of the genomic reference sequence on chromosome 12 (NT 039551.7). The amplified cDNA was subcloned in the pCRScript vector (Stratagene), and the nucleotide sequence verified by double strand sequencing. The cloned mVASH1 cDNA corresponded to the clone NM_177354 of the NCBI Entrez Nucleotide Data base http://www.ncbi.nlm.nih.gov. Due to the lack of specific antibodies against murine VASH1 the cDNA was cloned in frame with a short FLAGtag (p3xFLAG CMS-14 vector, Sigma Biochemicals). This tag allows sensitive and specific detection of fusion proteins.

Western/Dot Blot analysis

Cells were harvested and lysed in a buffer containing 10 mM Tris-HCl, pH 7, 0.2% Triton X-100 and protease inhibitors (Complete Mini EDTA-free; Roche Applied Science). Total protein (20 μg) was separated on 4%-20% SDS-PAGE and transferred to an Immuno-Blot™ polyvinylidene difluoride (PVDF) membrane (Bio-Rad). After blocking the membrane in 3% skim milk powder/PBS, it was probed with primary antibodies directed against FLAG-tag (0.1 μg/mL, Sigma Biochemicals, mouse monoclonal), human vasohibin (1 μg/mL, R&D systems, goat polyclonal anti human VASH1), carbonic anhydrase IX (CA9, 2 μg/mL, Santa Cruz Biotechnology, goat polyclonal anti human CA9), vascular endothelial growth factors (1 μg/mL, R&D systems, goat polyclonal anti mouse VEGF) or tubulin alpha (0.1 μg/mL, Sigma Biochemicals, mouse monoclonal anti human tubulin) and then detected with a 1: 2,500 dilution of an HRP-conjugated rabbit anti-mouse IgG or a 1:2,500 dilution HRP-conjugated rabbit anti-goat IgG. After washing, a chemoluminescent substrate (Super Signal West dura extended, Pierce) was added to the membrane, which was then exposed to the ECL Hyperfilm (GE Healthcare; Amersham Biosciences).

For Dot Blot analysis 1 μg total protein was spotted on a nitrocellulose membrane (BioRad). Blocking and detection were performed as described for the Western Blot.

Purification of recombinant protein

Recombinant Flag-tagged mVASH1 was purified from cytosolic extracts according to the manufacturer's instructions using liquid chromatography columns and anti-FLAG M2 agarose beads (all SIGMA Biochemicals). The purified protein was quantified with a standardized FLAG-BAP fusion protein (SIGMA Biochemicals) by Western Blot analysis on the Chemidoc XRS using Quantity One software (BioRad). Human VASH1 (VASH1A, 365 aa) was produced and purified as described elsewhere [5].

Adenoviruses

Replication-defective adenoviruses were generated with the AdEasy adenoviral vector system (Stratagene) according to the manufacturer's instructions. In brief, the mVASH1-FLAG cDNA was subcloned in the pShuttle CMV GFP-1 vector. Recombinant adenoviral DNA was generated in BJ5183 bacteria cells by means of a double-recombination event between co-transformed adenoviral backbone plasmid vector, pAdEasy-1, and a shuttle vector carrying the gene of interest. For generation of replicationdefective adenovirus recombinant DNA was transfected into HEK293 cells by Lipofectamin 2000 (Invitrogen), after which cytosolic extracts were prepared. The human VASH1-and GFP-overexpressing adenoviruses were generated as described elsewhere [5,9]. All viral titres were determined by qPCR for the gene coding for the encapsulation signal (for:5-cgacggatgtggcaaaagt, rev: 5-cctaaaaccgcgcgaaaa) and the respective viral plasmid DNA standards.

Flow cytometry

Cells were incubated with 10 nM solutions of the purified proteins and apoptosis and cell viability determined after 48 h. In brief, cells were detached by means of trypsin and then stained with FITC-labelled Annexin-V and 7-AAD (Beckman Coulter). After washing, cells were analyzed in a Cytomics-FC-500 cytometer using Cytomics RXP software (Beckman Coulter).

Cell migration assay: Scratch assay

Cells were seeded into 6-well cell culture plates. Once at confluence, cells were serum-starved in medium containing 0.5% FCS over night, and then scratch injury was applied using a disposable pipette tip or rubber cell scraper (1-mm width). After injury, the monolayer was gently washed with PBS, and the medium was replaced with medium containing 1% FCS and 10 nM purified proteins or control FLAG-tag purified extract. Cell migration from the edge of the injured monolayer was examined and photographed 6 hours after scratching. Migrated cells were counted in 10 randomly selected high-power fields (HPF) adjacent to the scratch injury and were expressed as cells/mm 2 .

Spheroid sprouting assay

The assay was performed as described elsewhere [10] with following modifications. HUVEC spheroids where generated overnight in hanging-drop culture consisting of 400 cells in EBM-2 medium, 2% FCS and 20% methylcellulose (Sigma Biochemicals). Spheroids were embedded in collagen type I from rat tail (BD) and stimulated with 25 ng/mL bFGF (Immunotools) or VEGF 165 (Sigma Biochemicals) in the presence or absence of 10 nM purified recombinant proteins. Adenoviral transfection was performed 48 h before embedding of the spheroids to allow robust expression of the gene of interest.

BrDU-incorporation assay

Five thousand cells/well were seeded in triplicates into a 96 well plate (Nunc) in culture medium and were allowed to adhere overnight before the culture medium was changed. Thereafter, cells were transfected with the adenovirus with a multiplicity of infection (MOI) of 10 virus/ cell. After 2 day cells were pulsed for 24 h with a 10 μM dilution of BrDU (GE Healthcare) in culture medium. Then cells were washed, fixed and incorporated BrDU detected by a specific ELISA (GE Healthcare) in a ELISA reader (EL808, BioTek).

Chorioallantoic membrane assay

The chick chorioallantoic membrane (CAM) assay has been used as an established in vivo model for screening for proand antiangiogenic proteins and drugs [11]. In brief, fertilized white leghorn chicken eggs (SPF eggs, each group n = 6) were purchased from Charles River (Kiesslegg) and incubated in an egg incubator at 37°C and 70% humidity (Compact S84, Grumbach) for four days. Subsequently, a window was cut in each eggshell and the underlying membrane. Eggs were incubated again for 4 h with the windows sealed (Durapor™ tape). Then, a Thermanox™ Ring (Nunc) was placed on the CAM and a 10 mM Tris-Glycine solution (pH 7.4) containing the purified recombinant protein (total 30 ng) or control extracts was added. Eggs were sealed and incubated for a further period of three days. Then they were opened and the CAM with the Thermanox™ ring was analyzed and photographed under a stereomicroscope with connected digital camera and flexible cold light (Olympus SZ51, Olympus E410). Blood vessels were counted inside the ring area (20 mm 2 ). The allantoic vascular plexus was subclassified in large vessels (diameter > 50 μm) and microvessels (diameter < 50 μm).

RT-PCR and qPCR

RNA was purified by cell lysis and nucleic acid extraction using the RNeasy Kit (Qiagen). Extracted total RNA was digested with DNAse I and then transcribed to cDNA using oligo-dT and hexanucleotide random primers (dN6) and the AMV-Reverse Transcriptase (all Promega). For analysis in the quantitative PCR 20 ng of each cDNA was used, 5 μL Sybrgreen Mix (Bio-Rad) and 10 pMol of each primer: vasohibin (mVASH1 for: 5'-cccataccaagtgtgcctac, rev: 5'-agcctctttggtcatttcct), actin beta (ACTB for: 5-aagagctatgagctgcctga; rev: 5tacggatgtcaacgtcacac), vascular growth factor (VEGF for: 5ttactgctgtacctccacc; rev: 5-acaggacggcttgaagatg), transforming growth factor alpha (TGFA for: 5-tggctgtcctcattatcacc, rev: 5tgggatcttcagaccactgt), angiopoietin 2 (ANGPT2 for: 5-gagcaaaccaccttcagaga; rev: 5-atcttctcggtgttggatga) basic fibroblast growth factor (bFGF for: 5-tccaagcagaagagagagga; rev: 5tcagtgccacataccaactg), platelet-derived groth factor beta (PDGFB for: 5-ttccttcctctctgcta; rev: 5-tgagctttccaactcgactc)and angiopoietin 1 (ANGPT1 for: 5-aaatgcgcttctatgctaac, rev: 5-cagctttctttgcagctttc) Analysis was performed within 50 cycles in the Bio-Rad iCycler (Bio-Rad). Data were collected and analyzed with the iCycler software.

Generation of VASH1-overexpressing B16F10 cells

The generated cDNA-FLAG fusion protein sequence of the p3xFLAG-CMV14 vector (Sigma Biochemicals) was subcloned in the pT2 neo CIMS vector [9]. Murine B16F10 cells were transfected with the pT2 neo CMV mVASH1 FLAG vector. Cotransfection was performed with the pCMV SB11 vector encoding the sleeping beauty (SB) transposase. Clones resistant to neomycin were selected and propagated in ClonaCell medium (Stem Cell Technologies Inc.). All used VASH1-overexpressing clones (VH#1, VH#2, VH#4), and control clones (co#10, co#11, co#12) were analyzed for mVASH1 gene and protein expression before in vivo use.

Mouse melanoma model

Animal experiments were performed after review and approval by the Austrian Government and the local committee for animal studies and established standards for animal handling. Female C57BL/6 mice, 5-6 weeks old, were purchased from Harlan Germany. Mice were subcutaneously inoculated in the right flank with 1 × 10 6 B16F10 cells dissolved in PBS. Experiments were performed using three mice per cell clone (total 18 animals). After one week tumors were measured each day using a caliper. After 14 days animals were sacrificed, the tumor weight determined and cryosections prepared for immunofluorescence staining. Moreover, after homogenization of the tissue mRNA and protein were extracted from all tumors to verify mVASH1 gene/protein expression in vivo.

Immunofluorescence staining of blood vessels

Microvessel density (MVD) has been shown to correlate with angiogenic activity and tumor progression [12]. Thus, cryosections of B16 melanoma were prepared after freezing the removed tumor tissue in Tissue-Tek (Sakura Finetech) using methylbuthanol and liquid nitrogen. Thereafter, cryosections of 5 μm were cut and fixed in acetone. Sections were blocked in 3% BSA/PBS and incubated with a 1 μg/mL rat anti-mouse CD31 (PECAM-1, BD Biosciences), mouse anti-smooth muscle cell actin (ASMA; SIGMA Biochemicals) or rabbit anti mouse proteoglycan NG2 (Chemicon-Millipore). Detection was performed with an Alexa-488-labelled goat anti-rat (Molecular Probes, 1 μg/mL), Texas Red-labeled goat antimouse (Molecular Probes, 2 μg/mL) or TRITC-labeled swine anti-rabbit antibody (Dako Cytomation, 4 μg/mL). Nuclei were counterstained with DAPI (100 ng/mL, Molecular Probes). Tumors were examined with the Axiovert 200 M fluorescence microscope and Axiovert software (both Zeiss). Microvessel density (MVD; diameter < 50 μm) was determined by counting ten randomly chosen high-power fields (HPF 200×) from each tumor. Vascular lumens were traced and luminal areas were analyzed with the NIH ImageJ software.

Statistical analyses

Statistical analyses were performed with GraphPad Prism™ software for Windows. Student's t Test and the Mann-Whitney U Test were used to study differences between two groups. Quantitative PCR data were calculated according to the delta Ct method described by Pfaffl et al. [13]. In brief, cDNAs of all tumor samples were normalized by the internal house-keeper gene actin beta (ACTB) and all cDNAs diluted to the same concentrations. Relative gene expression for each proangiogenic factor was calculated by the delta Ct method and the use of the mean Ct value of all control tumors (n = 9, relative expression of 1 ± standard error of the mean).

Results

Murine VASH1 induces apoptosis in embryonic fibroblasts

Both human and mouse VASH1-FLAG protein were stably overexpressed in murine B16F10 cells for recombinant protein production (Figure 1a, left image). Flag-tagged protein was purified from whole cell extracts by affinity purification and quantified by Western Blot analysis with a monoclonal antibody specific for the C-terminal FLAG (Figure 1a, right image). Control FLAG extracts were generated by purifying extracts of the wild type B16F10 cells without Flag-tag fusion protein expression, i.e. traces of not specifically bound and eluted proteins. In addition to the full length protein, shorter proteolytic fragments were also purified.

Both VASH1 isoforms significantly increased cell apoptosis of mouse fibroblasts (Figure 1b, p < 0.05). Induction of apoptosis was observed only in murine fibroblasts (mFB) and not in mouse aortic smooth muscle cells (mASMC), human umbilical vein (HUVECs), or human microvascular endothelial cells (HMECs). Both, murine and human VASH1 significantly inhibited cell migration of mFB, but had no inhibitory effect on mASMC. Moreover, both vasohibins were tested on human endothelial cells were they showed an opposite effect on short-time cell migration (Figure 1c). Human VASH1 supported migration and murine VASH1 inhibited migration of human endothelial cells within 6 hours of incubation. Due to these effects on cell migration in the scratch assay, we tested both vasohibins in a more complex angiogenic sprouting assay based on three-dimensional cell-cell contacts and extracellular matrix degradation with proteases.

Vasohibin inhibits angiogenic sprouting of endothelial cells in vitro

A three-dimensional angiogenic sprouting assay in rat collagen gel was performed to analyze the effect both vasohibin isoforms, human and mouse on VEGF-and bFGFinduced angiogenic sprouting. Both vascular growth factors induced strong angiogenic responses 24 h after stimulation when incubated with FLAG-control extract ( Figure  Effects  2a). This was not observed after application of 10 nM murine or human VASH1 (Figure 2b). Application of recombinant human and murine VASH1 significantly inhibited bFGF-induced sprouting of spheroids ( Figure  2c). The inhibitory effect was even more pronounced in VEGF-treated spheroids (Figure 2d).

The intracellular effects of VASH1 were tested after adenoviral overexpression of mouse and human VASH1. HUVECs showed strong expression of human and mouse VASH1 48 h after transfection (Figure 3a). Moreover, transfected cells were analyzed for inhibitory effects on cell proliferation. In comparison to GFP-transfected cells, both vasohibins did not significantly inhibit DNA synthesis in HUVECs (Figure 3b). Transfected cells were used to form spheroids and then stimulated with VEGF or bFGF for 24 h. In comparison to GFP-transfected cells, VASH1transfected cells showed a reduction in bFGF- (Figure 3c) and VEGF- (Figure 3d) induced angiogenic sprouting.

Vasohibin inhibits large-vessel growth and supports microvessel formation in the CAM assay

On the basis of our in vitro data in the HUVEC spheroid sprouting assay, we decided to test the murine vasohibin protein in direct comparison to the human isoform in the chicken chorioallantoic membrane (CAM) assay ( Figure  4a). Application of 30 ng purified human VASH1/ring resulted in strong inhibition of large vessel formation after three days (p < 0.05, Figure 4b). Moreover, the number of visible blood-conducting microvessels signifi-cantly increased in human VASH1 treated CAMs (p < 0.05, Figure 4c). These effects were more pronounced after application of murine VASH1.

Murine VASH1 supports microvessel maturation in the B16F10 melanoma model

Murine B16F10 melanoma cells with stable overexpression of mVASH1 were generated. These cells were used to study the effects of mVASH1 on subcutaneous tumor growth and vascularization without any immunological interference due to overexpression of a xenoprotein. Interestingly, B16F10 cells themselves have weak endogenous VASH1 and strong VEGF and ANGPT2 gene expression (data not shown). VASH1 overexpression was retained in all three different cell clones (VH#1, VH#2, VH#4) clones analyzed within 14 days of in vivo growth (Figure 5a). Within the observed short time of two weeks, mVASH1overexpressing melanomas did not show a significant decrease in tumor size (data not shown), but histologically large blood vessels with a diameter > 50 μm were nearly absent (Figure 5b, Figure 6a). Moreover, the vascular area in the tumors was strongly reduced (Figure 5c). Moreover, generated tumors showed immunoreactive Flag-tagged murine VASH1 protein (Figure 5d). Hypoxic regions in tumors were analyzed by the use of carbonic anhydrase 9 (CA9), one of the most sensitive endogenous sensors of HIF-1 activity. Analysis of tumor tissue revealed an increase of CA9 protein levels in mVASH1 overexpressing melanoma (figure 5d), whereas VEGF protein levels were not significantly altered.

Moreover. proangiogenic gene expression was analyzed in VASH1 overexpressing tumors (Figure 5e). Remarkably, mVASH1 overexpressing tumors showed a significant reduction of transforming growth factor -alpha (TGFA) gene expression, whereas bFGF, VEGF, ANGPT1, ANGPT2 and PDGFB gene expression differed not significantly. The downregulation of TGFA gene expression was also observed in all in vitro cultivated mVASH1 overexpressing cell clones (data not shown).

However, when microvessels in all tumors were analyzed with an antibody together with the mural cell markers ASMA or NG-2 (Figure 6b), mVASH1-overexpressing melanomas showed a significant increase in CD31+, NG2+/CD31+ and ASMA+/CD31+ microvessels (p < 0.05; Figure 6c).

Discussion

VASH1 has been identified as a target gene of VEGF in human umbilical vein endothelial cells [2,3]. Moreover, the gene has been demonstrated to be strongly upregulated by VEGF in the mouse retinopathy model in vivo [14]. Despite the clear induction by VEGF, vasohibin has been shown to inhibit in vitro and in vivo angiogenesis Effects of recombinant VASH1 proteins on blood vessel for-mation in the CAM assay Figure 4 Effects of recombinant VASH1 proteins on blood vessel formation in the CAM assay. (A) Comparison of CAMs treated with 30 ng purified mVASH1 (n = 6), co-FLAG extract (n = 6) or 30 ng human VASH1 (n = 6). All blood vessels within the ring area (20 mm 2 ) were counted. (B) Reduction of large vessels (diameter > 50 μm) in human and murine VASH1 treated CAMs. (C) Microvessels (diameter < 50 μm) were significantly increased in human and murine VASH1-treated CAMs. Stars indicate p values < 0.05. Controls indicate untreated CAMs (n = 6).

after exogenous application and proteolytic modification [2,5]. Thus, it has been assumed that VASH1 functions as a negative feedback inhibitor of angiogenesis and might be a promising candidate structure for antiangiogenic therapies. The mechanism of action is still unclear, but vasohibin protein fragments might interfere in integrin/ FAK signaling or inhibit angiogenic signaling pathways by interfering in ligand-receptor interactions. Hitherto, no receptor binding specifically VASH1 or its fragments has been identified. Additionally, vasohibin might be deposed in the extracellular matrix and thereby affect blood vessel formation and maturation [15] or be released into the blood circulation as shown for endostatin [16] and thereby inhibit angiogenic sprouting processes of endothelial cells.

This study compares the shorter murine VASH1 protein directly with the longer human homologue for antiangiogenic activities in a variety of in vitro and in vivo assays. Administration of recombinant mVASH1 neither inhibited proliferation nor induced apoptosis of endothelial cells in vitro. In line with our previous in vitro data with human VASH1A on HUVECs [5] the unprocessed human isoform either supported migration of human endothelial cells in vitro. This was not observed with the shorter murine VASH1, that slightly inhibited cell migration of human endothelial cells. Presumably, the N-terminal region modulates the function of human VASH1 and requires proteolytical removal to unmask an inhibitory domain responsible for inhibition of cell migration.

However, both VASH1 proteins induced apoptosis and inhibited cell migration in fibroblasts. Thus, VASH1 released from endothelial cells or cells of the immune system [5] could inhibit fibroblast proliferation in the tumor stroma and inhibit pro-angiogenic tissue remodeling in the reactive stroma of the tumor and support differentiation processes of mural cells.

Interestingly, exogenously applied recombinant protein as well as intracellular overexpression of the mVASH1 gene strongly inhibited angiogenic sprouting of HUVEC spheroids in the three-dimensional collagen gel. This observation is a novel finding and indicates that in order to act VASH1 needs to interact with a complex extracellular matrix, proteases and/or three-dimensional cell-cell contacts.

Exogenous administration of recombinant murine and human VASH1 in the CAM assay strongly induced a remodeling of the developing embryonic allantoic vascular plexus. In comparison to controls, large vessel formation was strongly inhibited and the number of bloodconducting microvessels was significantly increased. These in vivo data and the in vitro observation that VASH1 inhibited spheroid sprouting processes might indicate that VASH1 has additional functions. The protein might acts as a morphogen in vascular remodeling by inhibiting the formation of larger vessels in favor of a vascular bed Effects of stable overexpression of mVASH1 on tumor growth and large vessel growth Figure 5 Effects of stable overexpression of mVASH1 on tumor growth and large vessel growth. B16F10 cell clones with mVASH1 overexpression (VH#1, 2, 4) or control clones (co#10, 11,12) were subcutaneously transplanted in C57BL/6 mice and melanomas removed after 14 days. (A) VASH1 gene expression was analyzed in all tumor samples by RT-PCR. Actin beta (ACTB) served as housekeeping gene. (B) Statistical analysis of large vessels (diameter>50 μm) in controls and mVASH1-overexpressing tumors. (C) Analysis of vasular area in controls and mVASH1-overexpressing tumors (D) Western blot analysis of tissue protein extracts of a VASH1 overexpressing and control melanoma. Immunoreactive VASH1 protein was present in high amounts and also carbonic anhydrase 9 (CA9) was higher expressed in VASH1 overexpressing melanomas. VEGF protein was not altered. Tubulin alpha served as internal loading control. (E) Proangiogenic gene expression was analyzed by qPCR in controls and mVASH1-overexpressing tumors. Proangiogenic gene expression of VASH1 overexpressing tumors (n = 9) was quantified relative to the mean of all control tumors (n = 9). Stars indicate p values < 0.05 and bars mean ± SEM.

Effects of stable overexpression of mVASH1 on tumor microvessel growth and maturation consisting primarily of small capillaries. Whether the observed increases of microvessels are the consequence of collapsing large vessels or hypoxia in the CAM after VASH1 induced destabilization of larger vessels remains to be investigated in future studies.

Apart from its effects on physiological angiogenesis murine VASH1 was also studied in a mouse tumor model to analyze its effects on tumor growth and vascularization. This was done in the B16 melanoma model, a tumor model with a strong proangiogenic phenotype, i.e. production and secretion of VEGF and angiopoietin-2 [17].

In agreement with Watanabe et al. [2] we found no influence on in vitro proliferation of tumor cells but a significant reduction in large vessels and vascular area in mVASH1-overexpressing melanomas. Simultaneously, we observed an increase of microvessels in mVASH1-overexpressing tumors. This might be due to hypoxia after large vessel regression, since Hosaka et al. observed a positive correlation between VASH1 expression in blood vessels and HIF-1α expression in tumor cells of human nonsmall cell lung carcinoma [18]. At least in the final stages of our melanoma we found an increases of carbonic anhydrase protein levels, an enzyme induced by hypoxia and a sensor of HIF1α transcription factor activity [19]. However VEGF, an other important target of hypoxia was not increased in the final stages of mVASH1 overexpressing melanomas. These observations can be explained that either hypoxia did not always provoke VEGF upregulation in tumors [20] or that VEGF was upregulated in early stages and normalized at later stages of tumor progression.

Moreover, microvessels were more mature, i.e. they had more mural cell coverage as shown by costaining for pericytes (NG2) and smooth muscle cells (ASMA). These observation is in line with the recent observations in VASH1 knock-out mice, that show defects in blood vessel maturation of Lewis lung carcinoma xenografts [18,21].

Presumably, the fast-growing subcutaneous melanoma model prevented us from noticing a reduction in tumor size, as Watanabe et al. [2] observed in the Lewis lung carcinoma xenograft model overexpressing the human VASH1A. Therefore, the efficiency of the antiangiogenic effect of VASH1 might strongly depend on the tumor type analyzed. Additional tumor models have to be studied to identify tumor types that are sensitive to an antiangiogenic therapy consisting of gene therapy or systemically administered VASH1 protein or peptides.

Conclusion

Although vasohibin has been proposed to be primarily an antiangiogenic protein, we could further demonstrate that it acts also as a morphogen in the vessel maturation process in vivo. Overexpression in tumor xenografts led to a reduction of larger tumor vessels and supported the formation of a dense microvessel network with more mature vessels covered by mural cells, such as pericytes and smooth muscle cells.

of recombinant vasohibins on cell viability of fibroblasts, smooth muscle and endothelial cells Figure 1 Effects of recombinant vasohibins on cell viability of fibroblasts, smooth muscle and endothelial cells. (A) Affinity purification of FLAG-tagged vasohibin proteins. Cell extracts of B16F10 cells with stable expression of human and murine VASH1 in comparison to wild-type cells (WT). Recombinant human and murine VASH1 proteins were analyzed by Western Blot after purification from cell extracts by affinity chromatography. Unspecific proteins of WT cells binding to the column are indicated as control. (B) Embryonic mouse fibroblasts (mFB), mouse aortic smooth muscle cells (mASMC), human umbilical vein (HUVEC) and microvascular endothelial cells (HMEC) were stimulated with 10 nM murine or human VASH1 protein or control extract (FLAG), after which apoptotic cells were counted by flow cytometry after Annexin-V/7AAD staining. Untreated cells were used as control. (C) Cell migration was analyzed in the scratch assay 6 h after stimulation with 10 nM mouse or human VASH1 or control protein. Stars indicate p values < 0.05.

Effects of recombinant vasohibins on angiogenic sprouting of endothelial cellsFigure 2 Effects of recombinant vasohibins on angiogenic sprouting of endothelial cells. (A) HUVEC spheroids were stimulated for 24 h with VEGF and bFGF in the presence of control FLAG protein to induce angiogenic sprouting into the collagen gel. (B) Angiogenic sprouting was inhibited by administering of 10 nM recombinant murine VASH1. (C) Effects of recombinant human and murine VASH1 (10 nM) on bFGF-induced angiogenic sprouting. (D) Effects of recombinant human and murine VASH1 (10 nM) on VEGF-induced angiogenic sprouting. FLAG indicates the control extract of the affinity purification. CSL indicates the cumulative sprout length of the spheroid and stars indicate p values < 0.05. Effects of adenoviral overexpression of vasohibins on cell pro-liferation and sprouting of endothelial cells Figure 3 Effects of adenoviral overexpression of vasohibins on cell proliferation and sprouting of endothelial cells. (A) Dot Blot analysis of VASH1 protein expression 48 h after adenoviral transfection of HUVECs. The used MOI was 100 virus/cell. A monoclonal antibody directed against the FLAGtag (FLAG) was used to detect FLAG proteins. Tubulin alpha (Tub) served as internal loading control. Note: the polyclonal antibody directed against human VASH1 (hVASH1) does not recognize murine VASH1. (B) Cell proliferation was analyzed after adenoviral overexpression of human and human vasohibins or GFP. BrDU incorporation was measured 48 h post transfection. (C) HUVEC spheroids were stimulated with bFGF to induce angiogenic sprouting two days after adenoviral transfection. CSL indicates cumulative sprout length of the spheroid. GFP was used as control for unspecific adenoviral effects. (D) Angiogenic sprouting induced by the growth factor VEGF was significantly inhibited by VASH1 overexpression. Stars indicate p values < 0.05.

Figure 6
6Effects of stable overexpression of mVASH1 on tumor microvessel growth and maturation. (A) Representative image of CD31 stained blood vessels in controls and VASH1 overexpressing melanomas. Bars indicate 50 μm. (B) Cryosections of all tumors were analyzed by co-staining for the endothelial marker CD31 together with the pericyte marker NG2 or the smooth muscle cell marker ASMA. Bars indicate 20 μm (C) Statistical analysis of microvessels in controls and mVASH1-overexpressing tumors. HPF indicates high power field (200×).
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AcknowledgementsThe authors wish to thank Cornelia Heis, Monika Bauer and Martina Zimmermann for their excellent assistance. This study was supported by "Verein zur Förderung der Krebsforschung an der Universitätsklinik Innsbruck", "Krebshilfe Tirol", Tilak GmbH and the "Oncotyrol" scientific consortium.Competing interestsThe authors declare that they have no competing interests.Authors' contributionsGU conceived the study, abstracted data, drafted and revised the manuscript. JK carried out all angiogenic assays. MS performed the statistical analysis. EG and GG participated in discussion of results. The final version of the manuscript was read and approved by all authors.Publish with Bio Med Central and every scientist can read your work free of chargePre-publication historyThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/9/284/pre pub
R S Kerbel, Vasohibin: the feedback on a new inhibitor of angiogenesis. 114Kerbel RS: Vasohibin: the feedback on a new inhibitor of ang- iogenesis. Journal of Clinical Investigation 2004, 114:884-886.

Vasohibin as an endothelium-derived negative feedback regulator of angiogenesis. K Watanabe, Y Hasegawa, H Yamashita, K Shimizu, Y Y Ding, M Abe, Journal of Clinical Investigation. 114Watanabe K, Hasegawa Y, Yamashita H, Shimizu K, Ding YY, Abe M, et al.: Vasohibin as an endothelium-derived negative feedback regulator of angiogenesis. Journal of Clinical Investigation 2004, 114:898-907.

cDNA microarray analysis of the gene expression profile of VEGF-activated human umbilical vein endothelial cells. M Abe, A Sato, Angiogenesis. 4Abe M, Sato A: cDNA microarray analysis of the gene expres- sion profile of VEGF-activated human umbilical vein endothelial cells. Angiogenesis 2001, 4:289-298.

Angiogenic and cell survival functions of vascular endothelial growth factor (VEGF). A M Byrne, D J Bouchier-Hayes, J H Harmey, J Cell Mol Med. 9Byrne AM, Bouchier-Hayes DJ, Harmey JH: Angiogenic and cell survival functions of vascular endothelial growth factor (VEGF). J Cell Mol Med 2005, 9:777-794.

Alternative splicing of vasohibin-1 generates an inhibitor of endothelial cell proliferation, migration, and capillary tube formation. J Kern, M Bauer, K Rychli, J Wojta, A Ritsch, G Gastl, Arterioscler Thromb Vasc Biol. 28Kern J, Bauer M, Rychli K, Wojta J, Ritsch A, Gastl G, et al.: Alterna- tive splicing of vasohibin-1 generates an inhibitor of endothe- lial cell proliferation, migration, and capillary tube formation. Arterioscler Thromb Vasc Biol 2008, 28:478-484.

Gene regulation of a novel angiogenesis inhibitor, vasohibin, in endothelial cells. K Shimizu, K Watanabe, H Yamashita, M Abe, H Yoshimatsu, H Ohta, Biochemical and Biophysical Research Communications. 327Shimizu K, Watanabe K, Yamashita H, Abe M, Yoshimatsu H, Ohta H, et al.: Gene regulation of a novel angiogenesis inhibitor, vaso- hibin, in endothelial cells. Biochemical and Biophysical Research Communications 2005, 327:700-706.

Expression pattern of Vasohibin during chick development. S Nimmagadda, P Geetha-Loganathan, F Prols, M Scaal, B Christ, R Huang, Dev Dyn. 236Nimmagadda S, Geetha-Loganathan P, Prols F, Scaal M, Christ B, Huang R: Expression pattern of Vasohibin during chick devel- opment. Dev Dyn 2007, 236:1358-1362.

Culture of human endothelial cells derived from umbilical veins. Identification by morphologic and immunologic criteria. E A Jaffe, R L Nachman, C G Becker, C R Minick, J Clin Invest. 52Jaffe EA, Nachman RL, Becker CG, Minick CR: Culture of human endothelial cells derived from umbilical veins. Identification by morphologic and immunologic criteria. J Clin Invest 1973, 52:2745-2756.

The Dickkopf-homolog 3 is expressed in tumor endothelial cells and supports capillary formation. G Untergasser, M Steurer, M Zimmermann, M Hermann, J Kern, A Amberger, Int J Cancer. 122Untergasser G, Steurer M, Zimmermann M, Hermann M, Kern J, Amberger A, et al.: The Dickkopf-homolog 3 is expressed in tumor endothelial cells and supports capillary formation. Int J Cancer 2007, 122:1539-1547.

Tensional forces in fibrillar extracellular matrices control directional capillary sprouting. T Korff, H G Augustin, J Cell Sci. 112Korff T, Augustin HG: Tensional forces in fibrillar extracellular matrices control directional capillary sprouting. J Cell Sci 1999, 112(Pt 19):3249-3258.

In vivo models of angiogenesis. K Norrby, J Cell Mol Med. 10Norrby K: In vivo models of angiogenesis. J Cell Mol Med 2006, 10:588-612.

New molecular mediators in tumor angiogenesis. W D Beecken, W Kramer, Jonas D , J Cell Mol Med. 4Beecken WD, Kramer W, Jonas D: New molecular mediators in tumor angiogenesis. J Cell Mol Med 2000, 4:262-269.

A new mathematical model for relative quantification in real-time RT-PCR. M W Pfaffl, Nucleic Acids Res. 2945Pfaffl MW: A new mathematical model for relative quantifica- tion in real-time RT-PCR. Nucleic Acids Res 2001, 29:e45.

Vasohibin is up-regulated by VEGF in the retina and suppresses VEGF receptor 2 and retinal neovascularization. J Shen, X Yang, W H Xiao, S F Hackett, Y Sato, P A Campochiaro, FASEB J. 20Shen J, Yang X, Xiao WH, Hackett SF, Sato Y, Campochiaro PA: Vasohibin is up-regulated by VEGF in the retina and sup- presses VEGF receptor 2 and retinal neovascularization. FASEB J 2006, 20:723-725.

The extracellular matrix and blood vessel formation: not just a scaffold. J M Rhodes, M Simons, J Cell Mol Med. 11Rhodes JM, Simons M: The extracellular matrix and blood ves- sel formation: not just a scaffold. J Cell Mol Med 2007, 11:176-205.

Angiogenic factors. J Folkman, M Klagsbrun, Science. 235Folkman J, Klagsbrun M: Angiogenic factors. Science 1987, 235:442-447.

Responses of vascular endothelial cells to angiogenic signaling are important for tumor cell survival. S Shan, N D Robson, Y Cao, T Qiao, C Y Li, C D Kontos, FASEB J. 18Shan S, Robson ND, Cao Y, Qiao T, Li CY, Kontos CD, et al.: Responses of vascular endothelial cells to angiogenic signal- ing are important for tumor cell survival. FASEB J 2004, 18:326-328.

Vasohibin-1 Expression in Endothelium of Tumor Blood Vessels Regulates Angiogenesis. T Hosaka, H Kimura, T Heishi, Y Suzuki, H Miyashita, H Ohta, Am J Pathol. 175Hosaka T, Kimura H, Heishi T, Suzuki Y, Miyashita H, Ohta H, et al.: Vasohibin-1 Expression in Endothelium of Tumor Blood Ves- sels Regulates Angiogenesis. Am J Pathol 2009, 175:430-439.

Hypoxia-inducible expression of tumor-associated carbonic anhydrases. C C Wykoff, N J Beasley, P H Watson, K J Turner, J Pastorek, A Sibtain, Cancer Res. 60Wykoff CC, Beasley NJ, Watson PH, Turner KJ, Pastorek J, Sibtain A, et al.: Hypoxia-inducible expression of tumor-associated car- bonic anhydrases. Cancer Res 2000, 60:7075-7083.

Expression of vascular endothelial growth factor, hypoxia inducible factor 1alpha, and carbonic anhydrase IX in human tumours. A M Jubb, T Q Pham, A M Hanby, G D Frantz, F V Peale, T D Wu, J Clin Pathol. 57Jubb AM, Pham TQ, Hanby AM, Frantz GD, Peale FV, Wu TD, et al.: Expression of vascular endothelial growth factor, hypoxia inducible factor 1alpha, and carbonic anhydrase IX in human tumours. J Clin Pathol 2004, 57:504-512.

Distinctive localization and opposed roles of vasohibin-1 and vasohibin-2 in the regulation of angiogenesis. H Kimura, H Miyashita, Y Suzuki, M Kobayashi, K Watanabe, H Sonoda, Blood. 113Kimura H, Miyashita H, Suzuki Y, Kobayashi M, Watanabe K, Sonoda H, et al.: Distinctive localization and opposed roles of vaso- hibin-1 and vasohibin-2 in the regulation of angiogenesis. Blood 2009, 113:4810-4818.