A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2-induced stomatal closure. In this work, we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the model's potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.

Enhanced Resistance to Viruses in Nicotiana edwardsonii 'Columbia' Is Dependent on Salicylic Acid, Correlates with High Glutathione Levels, and Extends to Plant-Pathogenic Bacteria and Abiotic Stress.

Our earlier research showed that an interspecific tobacco hybrid (Nicotiana edwardsonii 'Columbia' [NEC]) displays elevated levels of salicylic acid (SA) and enhanced resistance to localized necrotic symptoms (hypersensitive response [HR]) caused by tobacco mosaic virus (TMV) and tobacco necrosis virus (TNV), as compared with another interspecific hybrid (Nicotiana edwardsonii [NE]) derived from the same parents. In the present study, we investigated whether symptomatic resistance in NEC is indeed associated with the inhibition of TMV and TNV and whether SA plays a role in this process. We demonstrated that enhanced viral resistance in NEC is manifested as both milder local necrotic (HR) symptoms and reduced levels of TMV and TNV. The presence of an adequate amount of SA contributes to the enhanced defense response of NEC to TMV and TNV, as the absence of SA resulted in seriously impaired viral resistance. Elevated levels of subcellular tripeptide glutathione (GSH) in NEC plants in response to viral infection suggest that in addition to SA, GSH may also contribute to the elevated viral resistance of NEC. Furthermore, we found that NEC displays an enhanced resistance not only to viral pathogens but also to bacterial infections and abiotic oxidative stress induced by paraquat treatments. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach (named EA) that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN.

Context-specific regulation of lysosomal lipolysis through network-level diverting of transcription factor interactions.

Plasticity in multicellular organisms involves signaling pathways converting contexts-either natural environmental challenges or laboratory perturbations-into context-specific changes in gene expression. Congruently, the interactions between the signaling molecules and transcription factors (TF) regulating these responses are also context specific. However, when a target gene responds across contexts, the upstream TF identified in one context is often inferred to regulate it across contexts. Reconciling these stable TF-target gene pair inferences with the context-specific nature of homeostatic responses is therefore needed. The induction of the Caenorhabditis elegans genes lipl-3 and lipl-4 is observed in many genetic contexts and is essential to survival during fasting. We find DAF-16/FOXO mediating lipl-4 induction in all contexts tested; hence, lipl-4 regulation seems context independent and compatible with across-context inferences. In contrast, DAF-16-mediated regulation of lipl-3 is context specific. DAF-16 reduces the induction of lipl-3 during fasting, yet it promotes it during oxidative stress. Through discrete dynamic modeling and genetic epistasis, we define that DAF-16 represses HLH-30/TFEB-the main TF activating lipl-3 during fasting. Contrastingly, DAF-16 activates the stress-responsive TF HSF-1 during oxidative stress, which promotes C. elegans survival through induction of lipl-3 Furthermore, the TF MXL-3 contributes to the dominance of HSF-1 at the expense of HLH-30 during oxidative stress but not during fasting. This study shows how context-specific diverting of functional interactions within a molecular network allows cells to specifically respond to a large number of contexts with a limited number of molecular players, a mode of transcriptional regulation we name "contextualized transcription."

pystablemotifs: Python library for attractor identification and control in Boolean networks.

SUMMARY: pystablemotifs is a Python 3 library for analyzing Boolean networks. Its non-heuristic and exhaustive attractor identification algorithm was previously presented in Rozum et al. (2021). Here, we illustrate its performance improvements over similar methods and discuss how it uses outputs of the attractor identification process to drive a system to one of its attractors from any initial state. We implement six attractor control algorithms, five of which are new in this work. By design, these algorithms can return different control strategies, allowing for synergistic use. We also give a brief overview of the other tools implemented in pystablemotifs. AVAILABILITY AND IMPLEMENTATION: The source code is on GitHub at https://github.com/jcrozum/pystablemotifs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Whole community invasions and the integration of novel ecosystems.

The impact of invasion by a single non-native species on the function and structure of ecological communities can be significant, and the effects can become more drastic-and harder to predict-when multiple species invade as a group. Here we modify a dynamic Boolean model of plant-pollinator community assembly to consider the invasion of native communities by multiple invasive species that are selected either randomly or such that the invaders constitute a stable community. We show that, compared to random invasion, whole community invasion leads to final stable communities (where the initial process of species turnover has given way to a static or near-static set of species in the community) including both native and non-native species that are larger, more likely to retain native species, and which experience smaller changes to the topological measures of nestedness and connectance. We consider the relationship between the prevalence of mutualistic interactions among native and invasive species in the final stable communities and demonstrate that mutualistic interactions may act as a buffer against significant disruptions to the native community.

synergy: a Python library for calculating, analyzing and visualizing drug combination synergy.

SUMMARY: Combinations of multiple pharmacological agents can achieve a substantial benefit over treatment with single agents alone. Combinations that achieve 'more than the sum of their parts' are called synergistic. There have been many proposed frameworks to understand and quantify drug combination synergy with different assumptions and domains of applicability. We introduce here synergy, a Python library that (i) implements a broad array of popular synergy models, (ii) provides tools for evaluating confidence intervals and conducting power analysis and (iii) provides standardized tools to analyze and visualize drug combinations and their synergies and antagonisms. AVAILABILITY AND IMPLEMENTATION: synergy is available on all operating systems for Python >=3.5. It is freely available from https://pypi.org/project/synergy, and its source code is available at https://github.com/djwooten/synergy. This software is released under the GNU General Public License, version 3.0 or later. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies.

Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 17 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

Structure-based approach to identifying small sets of driver nodes in biological networks.

In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) by node-state override forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories-centrality measures, propagation measures, and cycle-based measures. Using each measure, every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system toward or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without the knowledge of the network dynamics.

Reactive Oxygen Species Contribute to Symptomless, Extreme Resistance to Potato virus X in Tobacco.

Here we show that in tobacco (Nicotiana tabacum cultivar Samsun NN Rx1) the development of Rx1 gene-mediated, symptomless, extreme resistance to Potato virus X (PVX) is preceded by an early, intensive accumulation of the reactive oxygen species (ROS) superoxide (O2·-), evident between 1 and 6 h after inoculation and associated with increased nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activities. This suggests a direct contribution of this ROS to virus restriction during symptomless, extreme resistance. Superoxide inhibition in PVX-inoculated leaves by infiltration of antioxidants (superoxide dismutase [SOD] and catalase [CAT]) partially suppresses extreme resistance in parallel with the appearance of localized leaf necrosis resembling a hypersensitive resistance (HR) response. F1 progeny from crosses of Rx1 and ferritin overproducer (deficient in production of the ROS OH·) tobaccos also display a suppressed extreme resistance to PVX, because significantly increased virus levels are coupled to HR, suggesting a role of the hydroxyl radical (OH·) in this symptomless antiviral defense. In addition, treatment of PVX-susceptible tobacco with a superoxide-generating agent (riboflavin/methionine) results in HR-like symptoms and reduced PVX titers. Finally, by comparing defense responses during PVX-elicited symptomless, extreme resistance and HR-type resistance elicited by Tobacco mosaic virus, we conclude that defense reactions typical of an HR (e.g., induction of cell death/ROS-regulator genes and antioxidants) are early and transient in the course of extreme resistance. Our results demonstrate the contribution of early accumulation of ROS (superoxide, OH·) in limiting PVX replication during symptomless extreme resistance and support earlier findings that virus-elicited HR represents a delayed, slower resistance response than symptomless, extreme resistance.

A new discrete dynamic model of ABA-induced stomatal closure predicts key feedback loops.

Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated. We integrated and processed hundreds of disparate observations regarding ABA signal transduction responses underlying stomatal closure into a network of 84 nodes and 156 edges and, as a result, established those relationships, including identification of a 36-node, strongly connected (feedback-rich) component as well as its in- and out-components. The network's domination by a feedback-rich component may reflect a general feature of rapid signaling events. We developed a discrete dynamic model of this network and elucidated the effects of ABA plus knockout or constitutive activity of 79 nodes on both the outcome of the system (closure) and the status of all internal nodes. The model, with more than 1024 system states, is far from fully determined by the available data, yet model results agree with existing experiments in 82 cases and disagree in only 17 cases, a validation rate of 75%. Our results reveal nodes that could be engineered to impact stomatal closure in a controlled fashion and also provide over 140 novel predictions for which experimental data are currently lacking. Noting the paucity of wet-bench data regarding combinatorial effects of ABA and internal node activation, we experimentally confirmed several predictions of the model with regard to reactive oxygen species, cytosolic Ca2+ (Ca2+c), and heterotrimeric G-protein signaling. We analyzed dynamics-determining positive and negative feedback loops, thereby elucidating the attractor (dynamic behavior) repertoire of the system and the groups of nodes that determine each attractor. Based on this analysis, we predict the likely presence of a previously unrecognized feedback mechanism dependent on Ca2+c. This mechanism would provide model agreement with 10 additional experimental observations, for a validation rate of 85%. Our research underscores the importance of feedback regulation in generating robust and adaptable biological responses. The high validation rate of our model illustrates the advantages of discrete dynamic modeling for complex, nonlinear systems common in biology.

Model-driven discovery of calcium-related protein-phosphatase inhibition in plant guard cell signaling.

The plant hormone abscisic acid (ABA) promotes stomatal closure via multifarious cellular signaling cascades. Our previous comprehensive reconstruction of the stomatal closure network resulted in an 81-node network with 153 edges. Discrete dynamic modeling utilizing this network reproduced over 75% of experimental observations but a few experimentally supported results were not recapitulated. Here we identify predictions that improve the agreement between model and experiment. We performed dynamics-preserving network reduction, resulting in a condensed 49 node and 113 edge stomatal closure network that preserved all dynamics-determining network motifs and reproduced the predictions of the original model. We then utilized the reduced network to explore cases in which experimental activation of internal nodes in the absence of ABA elicited stomatal closure in wet bench experiments, but not in our in silico model. Our simulations revealed that addition of a single edge, which allows indirect inhibition of any one of three PP2C protein phosphatases (ABI2, PP2CA, HAB1) by cytosolic Ca2+ elevation, resolves the majority of the discrepancies. Consistent with this hypothesis, we experimentally show that Ca2+ application to cellular lysates at physiological concentrations inhibits PP2C activity. The model augmented with this new edge provides new insights into the role of cytosolic Ca2+ oscillations in stomatal closure, revealing a mutual reinforcement between repeated increases in cytosolic Ca2+ concentration and a self-sustaining feedback circuit inside the signaling network. These results illustrate how iteration between model and experiment can improve predictions of highly complex cellular dynamics.

Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers.

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

Structure-based control of complex networks with nonlinear dynamics.

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Resveratrol as Inducer of Autophagy, Pro-Survival, and Anti-Inflammatory Stimuli in Cultured Human RPE Cells.

Purpose: To investigate the mechanism by which resveratrol acts upon retinal pigment epithelial (RPE) cells and to characterize its effect upon autophagy, survival, and inflammation, with consequent implications to treatment for age-related macular degeneration (AMD). METHODS: Cultured ARPE-19 cells were exposed to 10 and 50 µM resveratrol. Cell survival/death was determined by annexin-FITC/propidium iodide using flow cytometry, while autophagy was studied by detecting autophagic vacuoles formation (acridine orange and transmission electron microscopy), as well as LC3II/I ratio and p62 expression by Western blot. In addition, time-lapse confocal microscopy of a pDENDRA-LC3 expression vector was performed to detect autophagy in transfected ARPE-19 cells under the different treatment conditions. Inhibition of proteasomal and autophagy-lysosomal fusion was carried out by MG-132 and chloroquine, respectively, while induction of autophagy was achieved by rapamycin treatment. Detection of secreted cytokines by ARPE-19 cells using Human XL Cytokine Array was performed under oxidative stress (H2O2) and resveratrol treatments, respectively. RESULTS: Resveratrol induced autophagy in ARPE-19 cells as determined by augmented presence of autophagic vacuoles, increased LC3II/I ratio and decreased p62 expression, as well as time-lapse confocal microscopy using pDENDRA-LC3 expression vector. Resveratrol acted similarly to proteasomal inhibition and downstream of mammalian target of rapamycin (mTOR), since upstream inhibition of autophagy by 3-methyladenine could not inhibit autophagy in ARPE-19 cells. Co-treatmeant by rapamycin and/or proteasome inhibition showed no additive effect upon autophagy induction. ARPE-19 cells treated by resveratrol showed lower cell death rate compared to untreated controls. Resveratrol induced a specific anti-inflammatory response in ARPE-19 cells. CONCLUSIONS: Resveratrol can induce autophagy, pro-survival, and anti-inflammatory stimuli in ARPE-19 cells, properties which could be plausible to formulate future treatment modalities for AMD.

Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition.

We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.

Identifying (un)controllable dynamical behavior in complex networks.

We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system "decision point", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the system's repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.

Edgetic perturbations to eliminate fixed-point attractors in Boolean regulatory networks.

The dynamics of complex biological networks may be modeled in a Boolean framework, where the state of each system component is either abundant (ON) or scarce/absent (OFF), and each component's dynamic trajectory is determined by a logical update rule involving the state(s) of its regulator(s). It is possible to encode the update rules in the topology of the so-called expanded graph, analysis of which reveals the long-term behavior, or attractors, of the network. Here, we develop an algorithm to perturb the expanded graph (or, equivalently, the logical update rules) to eliminate stable motifs: subgraphs that cause a subset of components to stabilize to one state. Depending on the topology of the expanded graph, these perturbations lead to the modification or loss of the corresponding attractor. While most perturbations of biological regulatory networks in the literature involve the knockout (fixing to OFF) or constitutive activation (fixing to ON) of one or more nodes, we here consider edgetic perturbations, where a node's update rule is modified such that one or more of its regulators is viewed as ON or OFF regardless of its actual state. We apply the methodology to two biological networks. In a network representing T-LGL leukemia, we identify edgetic perturbations that eliminate the cancerous attractor, leaving only the healthy attractor representing cell death. In a network representing drought-induced closure of plant stomata, we identify edgetic perturbations that modify the single attractor such that stomata, instead of being fixed in the closed state, oscillates between the open and closed states.

Self-sustaining positive feedback loops in discrete and continuous systems.

We consider a dynamic framework frequently used to model gene regulatory and signal transduction networks: monotonic ODEs that are composed of Hill functions. We derive conditions under which activity or inactivity in one system variable induces and sustains activity or inactivity in another. Cycles of such influences correspond to positive feedback loops that are self-sustaining and control-robust, in the sense that these feedback loops "trap" the system in a region of state space from which it cannot exit, even if the other system variables are externally controlled. To demonstrate the utility of this result, we consider prototypical examples of bistability and hysteresis in gene regulatory networks, and analyze a T-cell signal transduction ODE model from the literature.

Cultivation and characterisation of the surface markers and carbohydrate profile of human corneal endothelial cells.

BACKGROUND: The study aims to characterise human corneal endothelial cell (HCEnC) cultures generated by the peel-and-digest method based on their surface protein/carbohydrate expression pattern. METHODS: Quantitative polymerase chain reaction was used to compare expression of vimentin, CD90, Cytokeratin-19, ZO-1 and Claudin 14 in cultured HCEnC and cell line B4G12 versus stromal cells. Fluorescence-activated cell sorting was used to assess surface protein distribution of cultured and uncultured HCEnC. Distribution of surface proteins/carbohydrates was visualised by immunofluorescent and lectin staining. RESULTS: Human corneal endothelial cell and B4G12 showed lower expression level for vimentin, CD90, Cytokeratin-19 compared with stromal cells; while ZO-1 was expressed in endothelial cells, Claudin 14 was detected in B4G12 only. Fluorescence-activated cell sorting analyses revealed CD166, CD47, CD44, CD54, CD73, CD90, CD105, CD106, CD112, CD146 and CD325 to be present, with CD34 to be absent from cultured HCEnC. Freshly isolated, non-cultivated HCEnCs were CD90, CD73, CD146 and CD325 positive. Carbohydrates were detected by lectins LCA, PHA E, PHA L, PSA, sWGA, Con A, RCA 120 and WGA, but cultured HCEnC showed negative for GSL I, SBA, DBA, PNA and UEA I. CONCLUSION: Cultures established by the peel-and-digest method are probably not prone to stromal contamination, but the cells are likely to undergo endothelial-to mesenchymal transition as suggested by apparent morphological changes.