A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva

Genetic analysis of Caenorhabditis elegans pry-1/Axin suppressors identifies genes involved in reproductive structure development, stress responses, and aging

The Axin family of scaffolding proteins regulates a wide array of developmental and post-developmental processes in eukaryotes. Studies in the nematode Caenorhabditis elegans have shown that the Axin homolog PRY-1 plays essential roles in multiple tissues. To understand the genetic network of pry-1, we focused on a set of genes that are differentially expressed in the pry-1-mutant transcriptome and are linked to reproductive structure development. Knocking down eight of the genes (spp-1, clsp-1, ard-1, rpn-7, cpz-1, his-7, cdk-1, and rnr-1) via RNA interference efficiently suppressed the multivulva phenotype of pry-1 mutants. In all cases, the ectopic induction of P3.p vulval precursor cell was also inhibited. The suppressor genes are members of known gene families in eukaryotes and perform essential functions. Our genetic interaction experiments revealed that in addition to their role in vulval development, these genes participate in one or more pry-1-mediated biological events. Whereas four of them (cpz-1, his-7, cdk-1, and rnr-1) function in both stress response and aging, two (spp-1 and ard-1) are specific to stress response. Altogether, these findings demonstrate the important role of pry-1 suppressors in regulating developmental and post-developmental processes in C. elegans. Given that the genes described in this study are conserved, future investigations of their interactions with Axin and their functional specificity promises to uncover the genetic network of Axin in metazoans.

A Computational Model of the Endothelial to Mesenchymal Transition

Endothelial cells (ECs) form the lining of lymph and blood vessels. Changes in tissue requirements or wounds may cause ECs to behave as tip or stalk cells. Alternatively, they may differentiate into mesenchymal cells (MCs). These processes are known as EC activation and endothelial-to-mesenchymal transition (EndMT), respectively. EndMT, Tip, and Stalk EC behaviors all require SNAI1, SNAI2, and Matrix metallopeptidase (MMP) function. However, only EndMT inhibits the expression of VE-cadherin, PECAM1, and VEGFR2, and also leads to EC detachment. Physiologically, EndMT is involved in heart valve development, while a defective EndMT regulation is involved in the physiopathology of cardiovascular malformations, congenital heart disease, systemic and organ fibrosis, pulmonary arterial hypertension, and atherosclerosis. Therefore, the control of EndMT has many promising potential applications in regenerative medicine. Despite the fact that many molecular components involved in EC activation and EndMT have been characterized, the system-level molecular mechanisms involved in this process have not been elucidated. Toward this end, hereby we present Boolean network model of the molecular involved in the regulation of EC activation and EndMT. The simulated dynamic behavior of our model reaches fixed and cyclic patterns of activation that correspond to the expected EC and MC cell types and behaviors, recovering most of the specific effects of simple gain and loss-of-function mutations as well as the conditions associated with the progression of several diseases. Therefore, our model constitutes a theoretical framework that can be used to generate hypotheses and guide experimental inquiry to comprehend the regulatory mechanisms behind EndMT. Our main findings include that both the extracellular microevironment and the pattern of molecular activity within the cell regulate EndMT. EndMT requires a lack of VEGFA and sufficient oxygen in the extracellular microenvironment as well as no FLI1 and GATA2 activity within the cell. Additionally Tip cells cannot undergo EndMT directly. Furthermore, the specific conditions that are sufficient to trigger EndMT depend on the specific pattern of molecular activation within the cell.

Attractor Stability in Finite Asynchronous Biological System Models

We present mathematical techniques for exhaustive studies of long-term dynamics of asynchronous biological system models. Specifically, we extend the notion of [Formula: see text]-equivalence developed for graph dynamical systems to support systematic analysis of all possible attractor configurations that can be generated when varying the asynchronous update order (Macauley and Mortveit in Nonlinearity 22(2):421, 2009). We extend earlier work by Veliz-Cuba and Stigler (J Comput Biol 18(6):783-794, 2011), Goles et al. (Bull Math Biol 75(6):939-966, 2013), and others by comparing long-term dynamics up to topological conjugation: rather than comparing the exact states and their transitions on attractors, we only compare the attractor structures. In general, obtaining this information is computationally intractable. Here, we adapt and apply combinatorial theory for dynamical systems from Macauley and Mortveit (Proc Am Math Soc 136(12):4157-4165, 2008. https://doi.org/10.1090/S0002-9939-09-09884-0 ; 2009; Electron J Comb 18:197, 2011a; Discret Contin Dyn Syst 4(6):1533-1541, 2011b. https://doi.org/10.3934/dcdss.2011.4.1533 ; Theor Comput Sci 504:26-37, 2013. https://doi.org/10.1016/j.tcs.2012.09.015 ; in: Isokawa T, Imai K, Matsui N, Peper F, Umeo H (eds) Cellular automata and discrete complex systems, 2014. https://doi.org/10.1007/978-3-319-18812-6_6 ) to develop computational methods that greatly reduce this computational cost. We give a detailed algorithm and apply it to (i) the lac operon model for Escherichia coli proposed by Veliz-Cuba and Stigler (2011), and (ii) the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva precursor cells proposed by Weinstein et al. (BMC Bioinform 16(1):1, 2015). In both cases, we uncover all possible limit cycle structures for these networks under sequential updates. Specifically, for the lac operon model, rather than examining all [Formula: see text] sequential update orders, we demonstrate that it is sufficient to consider 344 representative update orders, and, more notably, that these 344 representatives give rise to 4 distinct attractor structures. A similar analysis performed for the C. elegans model demonstrates that it has precisely 125 distinct attractor structures. We conclude with observations on the variety and distribution of the models' attractor structures and use the results to discuss their robustness.

On the role of sparseness in the evolution of modularity in gene regulatory networks

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Modular systems have performance and design advantages over non-modular systems. Thus, modularity is very important for the development of a wide range of new technological or clinical applications. Moreover, modularity is paramount to evolutionary biology since it allows adjusting one organismal function without disturbing other previously evolved functions. But how does modularity itself evolve? Here I analyse the structure of regulatory networks and follow simulations of network evolution to study two hypotheses for the origin of modules in gene regulatory networks. The first hypothesis considers that sparseness, a low number of interactions among the network genes, could be responsible for the evolution of modular networks. The second, that modules evolve when selection favours the production of additional gene activity patterns. I found that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, it enhances the effects of selection for multiple gene activity patterns. While selection for multiple patterns may be decisive in the evolution of modularity, that sparseness is widespread across gene regulatory networks suggests that its contributions should not be neglected.

Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks

Boolean networks are important models of biochemical systems, located at the high end of the abstraction spectrum. A number of Boolean gene networks have been inferred following essentially the same method. Such a method first considers experimental data for a typically underdetermined “regulation” graph. Next, Boolean networks are inferred by using biological constraints to narrow the search space, such as a desired set of (fixed-point or cyclic) attractors. We describe Griffin, a computer tool enhancing this method. Griffin incorporates a number of well-established algorithms, such as Dubrova and Teslenko's algorithm for finding attractors in synchronous Boolean networks. In addition, a formal definition of regulation allows Griffin to employ “symbolic” techniques, able to represent both large sets of network states and Boolean constraints. We observe that when the set of attractors is required to be an exact set, prohibiting additional attractors, a naive Boolean coding of this constraint may be unfeasible. Such cases may be intractable even with symbolic methods, as the number of Boolean constraints may be astronomically large. To overcome this problem, we employ an Artificial Intelligence technique known as “clause learning” considerably increasing Griffin's scalability. Without clause learning only toy examples prohibiting additional attractors are solvable: only one out of seven queries reported here is answered. With clause learning, by contrast, all seven queries are answered. We illustrate Griffin with three case studies drawn from the Arabidopsis thaliana literature. Griffin is available at: http://turing.iimas.unam.mx/griffin.

A Network Model to Explore the Effect of the Micro-environment on Endothelial Cell Behavior during Angiogenesis

Angiogenesis is an important adaptation mechanism of the blood vessels to the changing requirements of the body during development, aging, and wound healing. Angiogenesis allows existing blood vessels to form new connections or to reabsorb existing ones. Blood vessels are composed of a layer of endothelial cells (ECs) covered by one or more layers of mural cells (smooth muscle cells or pericytes). We constructed a computational Boolean model of the molecular regulatory network involved in the control of angiogenesis. Our model includes the ANG/TIE, HIF, AMPK/mTOR, VEGF, IGF, FGF, PLCγ/Calcium, PI3K/AKT, NO, NOTCH, and WNT signaling pathways, as well as the mechanosensory components of the cytoskeleton. The dynamical behavior of our model recovers the patterns of molecular activation observed in Phalanx, Tip, and Stalk ECs. Furthermore, our model is able to describe the modulation of EC behavior due to extracellular micro-environments, as well as the effect due to loss- and gain-of-function mutations. These properties make our model a suitable platform for the understanding of the molecular mechanisms underlying some pathologies. For example, it is possible to follow the changes in the activation patterns caused by mutations that promote Tip EC behavior and inhibit Phalanx EC behavior, that lead to the conditions associated with retinal vascular disorders and tumor vascularization. Moreover, the model describes how mutations that promote Phalanx EC behavior are associated with the development of arteriovenous and venous malformations. These results suggest that the network model that we propose has the potential to be used in the study of how the modulation of the EC extracellular micro-environment may improve the outcome of vascular disease treatments.

Mechanism of all-transretinoic acid increasing retinoblastoma sensitivity to vincristine

OBJECTIVE

To explore the mechanism of all-transretinoic acid (ATRA) increasing retinoblastoma (RB) sensitivity to vincristine, and the inhibiting effect of vincristine combined with ATRA treatment on the SO-RB50 cell proliferation.

METHODS

SO-RB50 cells were cultivated by routine culture method. Different concentrations of vincristine or ATRA were added into culture solution. After 48 h, cell counting kit-8 was used to detect the median inhibitory concentration (IC50) of vincristine combined with ATRT treatment to SO-RB50 cells. SO-RB50 cells were divided into drug combination group, vincristine group, ATRA group and control group. Different drugs were added into the culture solution respectively for cell culture based on the IC50 value. Cell counting kit-8 was used to detect the cell proliferation every 24-h cultivation. After continuous determination for 6 d, data was processed to draw the cell growth curve. After drug use for 72 h, flow cytometry was used to detect the proportion of different cell cycles of SO-RB50 cells in each group. After drug use for 48 h, annexin V/propidium iodide method was used to detect the SO-RB50 cell apoptosis in each group.

RESULTS

The IC50 value of vincristine treatment on the SO-RB50 cells was 0.11 μmol/L, and ATRT was 12.84 μmol/L. The cell growth curve in control group rose gradually along with the extended culture time, but after vincristine and ATRA treatment, the cell growth curve was smooth and steady. The cell increment was the least in drug combination group and its cell growth curve was the smoothest. There was significant difference in A450 48 h and 72 h after treatment (Fgrouping = 77.316, P < 0.001; Ftime = 86.985, P < 0.001). Compared with control group, A450 value in drug combination group, vincristine group, ATRA group was significant lower (P < 0.001). Compared with control group, the G2/M phase cell proportion in vincristine group was significantly increased, while the G0/G1 phase cell proportion was significantly decreased; the G0/G1 phase cell proportion in ATRA group was significantly increased, while the S phase cell proportion was significantly decreased (FG0/G1 = 85.878, Fs = 56.455, FG2/M = 85.878, P < 0.001). After 48 h, there was significant difference in SO-RB50 cell apoptosis rate among groups (F = 11.312, P < 0.05). The apoptosis rate in drug combination group was significantly higher than that of other groups (P < 0.001).

CONCLUSIONS

ATRA can increase the sensitivity of SO-RB50 cells to vincristine. Vincristine combined with ATRA treatment can significantly increase the inhibiting effect on SO-RB50 cells, which may be related with promoting cell apoptosis and involving in cell cycle control.