Biochemical Signaling Networks Research Articles

Absolute concentration robustness: Algebra and geometry

Motivated by the question of how biological systems maintain homeostasis in changing environments, Shinar and Feinberg introduced in 2010 the concept of absolute concentration robustness (ACR). A biochemical system exhibits ACR in some species if the steady-state value of that species does not depend on initial conditions. Thus, a system with ACR can maintain a constant level of one species even as the initial condition changes. Despite a great deal of interest in ACR in recent years, the following basic question remains open: How can we determine quickly whether a given biochemical system has ACR? Although various approaches to this problem have been proposed, we show that they are incomplete. Accordingly, we present new methods for deciding ACR, which harness computational algebra. We illustrate our results on several biochemical signaling networks.

Dynamical Information Synergy in Biochemical Signaling Networks.

Biological cells encode information about their environment through biochemical signaling networks that control their internal state and response. This information is often encoded in the dynamical patterns of the signaling molecules, rather than just their instantaneous concentrations. Here, we analytically calculate the information contained in these dynamics for a number of paradigmatic cases in the linear regime, for both static and time-dependent input signals. When considering oscillatory output dynamics, we report on the emergence of synergy between successive measurements, meaning that the joint information in two measurements exceeds the sum of the individual information. We extend our analysis numerically beyond the scope of linear input encoding to reveal synergetic effects in the cases of frequency or damping modulation, both of which are relevant to classical biochemical signaling systems.

A computational bridge between traction force microscopy and tissue contraction.

Arterial wall active mechanics are driven by resident smooth muscle cells, which respond to biological, chemical, and mechanical stimuli and activate their cytoskeletal machinery to generate contractile stresses. The cellular mechanoresponse is sensitive to environmental perturbations, often leading to maladaptation and disease progression. When investigated at the single cell scale, however, these perturbations do not consistently result in phenotypes observed at the tissue scale. Here, a multiscale model is introduced that translates microscale contractility signaling into a macroscale, tissue-level response. The microscale framework incorporates a biochemical signaling network along with characterization of fiber networks that govern the anisotropic mechanics of vascular tissue. By incorporating both biochemical and mechanical components, the model is more flexible and more broadly applicable to physiological and pathological conditions. The model can be applied to both cell and tissue scale systems, allowing for the analysis of in vitro, traction force microscopy and ex vivo, isometric contraction experiments in parallel. When applied to aortic explant rings and isolated smooth muscle cells, the model predicts that active contractility is not a function of stretch at intermediate strain. The model also successfully predicts cell-scale and tissue-scale contractility and matches experimentally observed behaviors, including the hypercontractile phenotype caused by chronic hyperglycemia. The connection of the microscale framework to the macroscale through the multiscale model presents a framework that can translate the wealth of information already collected at the cell scale to tissue scale phenotypes, potentially easing the development of smooth muscle cell-targeting therapeutics.

Biomimetic Approaches in Cardiac Tissue Engineering: Replicating the Native Heart Microenvironment.

Cardiovascular diseases, including heart failure, pose significant challenges in medical practice, necessitating innovative approaches for cardiac repair and regeneration. Cardiac tissue engineering has emerged as a promising solution, aiming to develop functional and physiologically relevant cardiac tissue constructs. Replicating the native heart microenvironment, with its complex and dynamic milieu necessary for cardiac tissue growth and function, is crucial in tissue engineering. Biomimetic strategies that closely mimic the natural heart microenvironment have gained significant interest due to their potential to enhance synthetic cardiac tissue functionality and therapeutic applicability. Biomimetic approaches focus on mimicking biochemical cues, mechanical stimuli, coordinated electrical signaling, and cell-cell/cell-matrix interactions of cardiac tissue. By combining bioactive ligands, controlled delivery systems, appropriate biomaterial characteristics, electrical signals, and strategies to enhance cell interactions, biomimetic approaches provide a more physiologically relevant environment for tissue growth. The replication of the native cardiac microenvironment enables precise regulation of cellular responses, tissue remodeling, and the development of functional cardiac tissue constructs. Challenges and future directions include refining complex biochemical signaling networks, paracrine signaling, synchronized electrical networks, and cell-cell/cell-matrix interactions. Advancements in biomimetic approaches hold great promise for cardiovascular regenerative medicine, offering potential therapeutic strategies and revolutionizing cardiac disease modeling. These approaches contribute to the development of more effective treatments, personalized medicine, and improved patient outcomes. Ongoing research and innovation in biomimetic approaches have the potential to revolutionize regenerative medicine and cardiac disease modeling by replicating the native heart microenvironment, advancing functional cardiac tissue engineering, and improving patient outcomes.

Abstract 359: Multiscale Model Reveals Importance Of Cumulative Vascular Smooth Muscle Cell Responses In Aortic Hypercontractility

Diabetes more than doubles the risk of cardiovascular disease (CVD), and the chronic hyperglycemia leads to vascular maladaptations prior to the diagnostic threshold. While endothelium-independent vascular smooth muscle cell (VSMC) hypercontractility is well-established at the tissue-scale, the mechanical phenotype is inconsistently observed at the cell-scale. Our strategy consisted of experimental characterization at the tissue-scale via an isometric ring contraction assay of WT and OGT KO aortas, the latter an established chronic hyperglycemia model, from male mice and at the cell-scale via published cellular microbiaxial stretching data, coupled with the construction of a multiscale computational model. We hypothesize that the biochemical signaling changes due to increased blood glucose are sufficient to predict VSMC hypercontractility. The multiscale model operates off a biochemical signaling network that determines the activation and contraction of actin stress fibers. The stress fibers are connected in either 2D (cell-scale) or 3D (tissue-scale) cytoskeletal networks where for the tissue-scale, the cytoskeletal mechanics are fit to a macroscale constrained mixture model. The multiscale model correctly predicts the cell-scale euglycemic contractility, however the hyperglycemic condition is too subtle to be resolved at this scale. At the tissue-scale, the model correctly predicts both euglycemic and hyperglycemic active force generation. The model does not currently consider ECM alterations, so the changes in passive tissue mechanics cannot be predicted. The model predictions suggest that while the impetus of hyperglycemia-driven hypercontractility occurs at the cell-scale, these small-scale changes must be amplified across length scales to be experimentally-observable. These results demonstrate the power of the multiscale model to connect cell- and tissue-scale vascular mechanical phenotypes due to chronic hyperglycemia.

A structural bio-chemo-mechanical model for vascular smooth muscle cell traction force microscopy.

Altered vascular smooth muscle cell (VSMC) contractility is both a response to and a driver for impaired arterial function, and the leading experimental technique for quantifying VSMC contraction is traction force microscopy (TFM). TFM involves the complex interaction among several chemical, biological, and mechanical mechanisms, making it difficult to translate TFM results into tissue-scale behavior. Here, a computational model capturing each of the major aspects of the cell traction process is presented. The model incorporates four interacting components: a biochemical signaling network, individual actomyosin fiber bundle contraction, a cytoskeletal network of interconnected fibers, and elastic substrate displacement due to cytoskeletal force. The synthesis of these four components leads to a broad, flexible framework for describing TFM and linking biochemical and biomechanical phenomena on the single-cell level. The model recapitulated available data on VSMCs following biochemical, geometric, and mechanical perturbations. The structural bio-chemo-mechanical model offers a tool to interpret TFM data in new, more mechanistic ways, providing a framework for the evaluation of new biological hypotheses, interpolation of new data, and potential translation from single-cell experiments to multi-scale tissue models.

Multi‐scale Computational Model of Endothelial Cell‐Pericyte Coupling in Idiopathic Pulmonary Fibrosis

IntroductionMicrovascular stability is highly dependent on endothelial cell‐pericyte coupling. In fibrotic diseases, such as idiopathic pulmonary fibrosis (IPF), extracellular matrix stiffening and elevated concentrations of profibrotic factors, such as TGF‐β, can disrupt this cell‐cell communication. Treatment of IPF with the antifibrotic drug Nintedanib, which targets PDGF‐βR, FGF‐R, and VEGF‐R signaling, may counteract these effects to strengthen endothelial cell‐pericyte coupling and promote microvessel stability1,2. We have developed a multi‐scale computational model that simulates intracellular signaling in endothelial cells and pericytes, heterotypic cell‐cell communication, and the dynamic lung microenvironment to study how Nintedanib2 and other drugs affect microvascular network remodeling in IPF. We hypothesize that blocking PDGF‐βR and FGF‐R signaling with Nintedanib is sufficient to rescue TGF‐β‐induced endothelial cell‐pericyte decoupling.Materials and MethodsThe multi‐scale model is comprised of logic‐based ordinary differential equations representing intracellular signaling networks in both endothelial cells and pericytes integrated into an agent‐based model representing the lung environment wherein the simulated cells interact with one another, sensing and dynamically altering their microenvironment (Figure 1). The logic‐based network signaling models were developed using the Netflux3 toolkit in MATLAB. The agent‐based model of the spatiotemporal 2D lung environment was constructed in NetLogo4. The multi‐scale model was created by linking the logic‐based network models with the agent‐based model using the NL4Py5 package in Python.Results and DiscussionEndothelial cell‐pericyte decoupling was signified by decreased N‐cadherin expression in pericytes, increased αSMA and Col1mRNA (indicating possible pericyte‐to‐myofibroblast transition), and increased physical separation and distances between endothelial cells and pericytes. The multi‐scale model predicted that increasing TGF‐β concentrations significantly elevated αSMA and Col1mRNA expression in simulated pericytes. N‐cadherin levels did not change in response to TGF‐β or Nintedanib treatment in the network model of the pericyte alone but were affected by the presence of endothelial cells in the agent‐based model, highlighting the importance of endothelial cell‐pericyte interactions in determining pericyte behaviors.ConclusionsOur multi‐scale model demonstrates the importance of considering endothelial cell‐pericyte communication in predicting cellular responses to pro‐fibrotic environments. By representing intracellular biochemical signaling networks, heterotypic cell interactions, and the dynamic multi‐cell microenvironment, we were able to demonstrate a potential mechanism through which Nintedanib treatment affects endothelial cell‐pericyte coupling in IPF.References1) Hanumegowda, C., et al.; Chest. 2012 2) Wollin, L., et al.; Eur Respir J. 2015 3) Kraeutler, MJ., et al.; BMC Syst Biol. 2010 4) Sklar, E., Artif. Life 2007 5) Gunaratne, C.; 2018. NL4Py.

From the Belousov–Zhabotinsky reaction to biochemical clocks, traveling waves and cell cycle regulation

In the last 20 years, a growing army of systems biologists has employed quantitative experimental methods and theoretical tools of data analysis and mathematical modeling to unravel the molecular details of biological control systems with novel studies of biochemical clocks, cellular decision-making, and signaling networks in time and space. Few people know that one of the roots of this new paradigm in cell biology can be traced to a serendipitous discovery by an obscure Russian biochemist, Boris Belousov, who was studying the oxidation of citric acid. The story is told here from an historical perspective, tracing its meandering path through glycolytic oscillations, cAMP signaling, and frog egg development. The connections among these diverse themes are drawn out by simple mathematical models (nonlinear differential equations) that share common structures and properties.

Theoretical approaches to determination of optimal cryopreservation regimens for cell spheroids of different cultivation terms

Background: Three-dimensional culture systems are unique platforms for studying complex biological processes in vitro. Cell-cell and cell-extracellular matrix interactions form a communication network of biochemical and mechanical signals, bring spheroids (SP) closer to native tissues and significantly distinguish them from monolayer cultures. It is important for cell technologies to develop methods for cryopreservation of 3D cultures, that allows creating the stocks of valuable cell samples, save time and materials, and prevent the loss of cultures due to technical failures, contamination, phenotype drift and aging. Objectives: Development of approaches to cellular spheroids cryopreservation. Determination of the permeability parameters of L929 cells spheroids at different cultivation periods for the theoretical assessment of optimal freezing regimens. Materials and methods: We have used L929 cells, which form SPs of different diameters and can be maintained for a long time in 3D conditions. To determine the integral filtration Lp and permeability for DMSO kp coefficients for SP at different periods of cultivation, the volumetric method was used. The study of the changes in the spheroids volume in time was carried out with a confocal microscope LSM 510 META. The numerical values of the integral SF permeability coefficients were determined by approximating the experimental data on the change in the relative volume of the SP versus the exposure time in the test solution with theoretical curves calculated on the basis of a physical and mathematical model for passive mass transfer between the spheroid and the environment, provided that they coincide as much as possible. Prediction of the osmotic behavior of spheroids under cooling conditions was carried out based on the differential equation describing the kinetics of changes in the relative cell volume during extracellular crystallization of a cryoprotective solution, substituting determined values of integral permeability coefficients Lp and kp and activation energies EAL and EAk into the model equations. The kinetics of changes in the extracellular solution concentration during freezing was set analytically by approximating the phase melting diagram of the DMSO solution. Results: The filtration and permeability for DMSO molecules coefficients in SP were determined and their significant decrease with a cultivation duration was shown. The activation energy values for the penetration of water and DMSO molecules into the SP were calculated and their dependence on the cultivation time was determined. Proceeding from the determined parameters of permeability, the dynamic of changes in the volume of SPs for different periods of cultivation at different rates of cooling was calculated. Conclusions: The optimal cooling modes of SP from L929 cells were in theory determined: for 7 days of cultivation — 1,5-2 °C/min with cooling to -80°C and subsequent immersion in nitrogen; for 14 and 21 days of cultivation — 0.5 °C/min to -40°C and subsequent immersion in nitrogen.

Multicellular mechanochemical hybrid cellular Potts model of tissue formation during epithelial‐mesenchymal transition

AbstractEpithelial‐mesenchymal transition (EMT) is the transdifferentiation of epithelial cells to a mesenchymal phenotype, in which cells lose epithelial‐like cell–cell adhesions and gain mesenchymal‐like enhanced contractility and mobility. EMT is crucial for tissue regeneration and is also implicated in pathological conditions, such as cancer metastasis. Prior work has shown that transforming growth factor‐1 (TGF‐1) is a potent inducer of this biological process. In this study, we develop a computational model coupling mechanical and biochemical signaling in a multicellular tissue undergoing EMT. Specifically, we utilize a recently developed formulation that integrates a multicellular cellular Potts model (CPM), a lattice‐based stochastic model governing cell movement; a first moment of area model, governing cellular traction and junctional forces; a finite element model, which defines extracellular matrix (ECM) substrate strains; an intracellular signaling TGF‐1‐mediated EMT model that governs cellular phenotype; and an extracellular signaling component governing ECM and TGF‐1 signaling. In this study, we modeled the spatial cellular patterns that occur in tissue and the ECM during EMT. Our model predicts that EMT often initially occurs at a tissue boundary due to mechanochemical coupling, which results in transdifferentiation to progress inwards toward the center. Variation in model parameters demonstrated conditions enhancing and suppressing EMT, especially to drive EMT in the absence of TGF‐1 and inhibit EMT in the presence of TGF‐1. Specifically, enhancing the mechanochemical feedback typically promoted EMT, whereas greater assembled ECM degradation suppressed EMT. Simulated scratch test experiments illustrate that ECM composition can impact closure directly through EMT signaling. In conclusion, we integrated mechanical, biochemical, and extracellular signaling networks in a novel hybrid computational model to reproduce tissue formation dynamics of EMT.

HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks.

Signaling networks mediate many aspects of cellular function. The conventional, mechanistically motivated approach to modeling such networks is through mass-action chemistry, which maps directly to biological entities and facilitates experimental tests and predictions. However such models are complex, need many parameters, and are computationally costly. Here we introduce the HillTau form for signaling models. HillTau retains the direct mapping to biological observables, but it uses far fewer parameters, and is 100 to over 1000 times faster than ODE-based methods. In the HillTau formalism, the steady-state concentration of signaling molecules is approximated by the Hill equation, and the dynamics by a time-course tau. We demonstrate its use in implementing several biochemical motifs, including association, inhibition, feedforward and feedback inhibition, bistability, oscillations, and a synaptic switch obeying the BCM rule. The major use-cases for HillTau are system abstraction, model reduction, scaffolds for data-driven optimization, and fast approximations to complex cellular signaling.

Heavy metal detoxification mechanisms by microalgae: Insights from transcriptomics analysis

Heavy metal pollution in ecosystem is a global threat. The associated toxicity and carcinogenic nature of heavy metals/metalloids such as mercury, cadmium, lead, and arsenic are imposing a severe risk to both ecological diversity and human lives. Harnessing the adaptive feature of microalgae for remediating toxic heavy metal has reached a milestone in past few decades. Transcriptomics analyses have provided mechanistic insights to map the dynamics of cellular events under heavy metal stress, thus deciphering the strategic responses of microalgae. Here, the present review comprehensively addresses the elicited molecular responses of microalgae to detoxify the heavy metal stress. The review highlights the intricate role of biochemical components and signaling networks mediating stress responsive transitions of microalgae at physiological level. Furthermore, the differential gene expression signifying the transporters involved in uptake, distribution/sequestration, and efflux of heavy metal has also been reviewed. In a nutshell, this study provided a comprehensive understanding of the molecular mechanisms adopted by microalgae at transcriptome level to nullify the oxidative stress while detoxifying the heavy metals.

Transforming Growth Factor Beta-1 (TGF-β1) Regulates Assembly of Cardiac Spheroids

Cells of all tissues in human body interact with their neighboring cells and components of the extracellular matrix thereby creating a unique 3D microenvironment. These interactions are realized through a complex network of biochemical and mechanical signals that are important in maintaining normal cellular homeostasis. Numerous attempts have been undertaken during the last two decades to develop 3D models for studying their properties and understanding the mechanisms of regulation of cell microenvironment in vivo. Cardiac spheroids (cardiospheres) are one these models of cardiac microenvironment. In this study we demonstrate that unique microenvironment formed in cardiospheres consists of stem/progenitor and mesenchymal cells surrounded by extracellular matrix proteins synthesized by these cells. TGF-β1 participates in the regulation of contraction of cells forming cardiospheres, promotes activation of the epithelial-mesenchymal transition and self-organization of cells, which leads to the formation of larger spheroids. Thereby, the effect of TGF-β1 on the cells of cardiospheres can serve as a model for studying the mechanisms of regulation of cardiac microenvironment.

Interactions of a Signal Transduction Protein Investigated by Fluorescence Stopped-Flow Kinetics.

To understand cellular processes such as biochemical pathways and signaling networks, we need to understand binding and reaction rates of often competing reactions, their dependence on cellular concentrations of participating molecules, and the regulation of these rates through allostery, posttranslational modifications, or other mechanisms. To do so, we break these systems down into their elementary steps, which are almost invariably either unimolecular or bimolecular reactions that frequently occur on sub-second, often sub-millisecond, time scales. Rapid mixing techniques, which generally achieve mixing in less than 2ms, are generally suitable for the study of such reactions. The application of these techniques to the study of enzyme mechanisms is described in several excellent texts (Cornish-Bowden, Fundamentals of enzyme kinetics, 1995; Gutfreund, Kinetics for the life sciences. Receptors, transmitters and catalysis, 1995); flow techniques are used to study individual steps by monitoring the approach to equilibrium (the pre-steady state) under single turnover conditions.The individual steps in complex biochemical reaction schemes determine how fast systems can respond to incoming signals and adapt to changed conditions[1, 2]. This chapter is concerned with in vitro techniques that have been developed to study fast reactions in solution, and we present the study of various interactions of calmodulin as an example. The kinetic information obtained with these techniques is indispensable for understanding the dynamics of biochemical processes and complements the static structural and thermodynamic information available from X-ray crystallography, NMR, and equilibrium binding studies.

Deciphering the Relevance of Bone ECM Signaling.

Bone mineral density, a bone matrix parameter frequently used to predict fracture risk, is not the only one to affect bone fragility. Other factors, including the extracellular matrix (ECM) composition and microarchitecture, are of paramount relevance in this process. The bone ECM is a noncellular three-dimensional structure secreted by cells into the extracellular space, which comprises inorganic and organic compounds. The main inorganic components of the ECM are calcium-deficient apatite and trace elements, while the organic ECM consists of collagen type I and noncollagenous proteins. Bone ECM dynamically interacts with osteoblasts and osteoclasts to regulate the formation of new bone during regeneration. Thus, the composition and structure of inorganic and organic bone matrix may directly affect bone quality. Moreover, proteins that compose ECM, beyond their structural role have other crucial biological functions, thanks to their ability to bind multiple interacting partners like other ECM proteins, growth factors, signal receptors and adhesion molecules. Thus, ECM proteins provide a complex network of biochemical and physiological signals. Herein, we summarize different ECM factors that are essential to bone strength besides, discussing how these parameters are altered in pathological conditions related with bone fragility.

Configuration barrier towards parity-time symmetry in randomly connected mesoscopic sets on a graph

We address the issue of dissipative vs. non-dissipative behavior in a mesoscopic set of coupled elements such as oscillators, with one half having gain and the other half having losses. We introduce a graph with coupling as the graph edges in given fixed number and gain/loss elements as its nodes. This relates to parity-time symmetry, notably in optics, e.g. set of coupled fibers, and more generally to the issue of taming divergence related to imaginary parts of eigenvectors in various network descriptions, for instance biochemical, neuronal, ecological. We thus look for the minimization of the imaginary part of all eigenvalues altogether, with a collective figure of merit. As more edges than gain/loss pairs are introduced, the unbroken cases , i.e., stable cases with real eigenvalues in spite of gain and loss, become statistically very scarce. A minimization from a random starting point by moving one edge at a time is studied, amounting to investigate how the hugely growing configuration number impedes the attainment of the desired minimally-dissipative target. The minimization path and its apparent stalling point are analyzed in terms of network connectivity metrics. We expand in the end on the relevance in biochemical signaling networks and the so-called “stability-optimized circuits” relevant to neural organization.

The Role of TGFβ in Clinical Cancer Response

The role of transforming growth factor beta (TGFβ) is context dependent. Even within the specific context of cancer, the functional significance of TGFβ varies according to environmental conditions. Therapies targeting TGFβ capitalize upon recent discoveries in canonical SMAD-dependent biochemical signaling networks as well as non-canonical SMAD-independent biochemical signal transduction pathways and mechanobiological signal transduction pathways. TGFβ clinical trials utilizing vaccine, small molecule inhibitor, antibody, and aptamer strategies support TGFβ as a viable anticancer target for a variety of cancer diagnoses. Combination therapies incorporating TGFβ inhibition and immune checkpoint blockade provide additional avenues for future TGFβ-based clinical intervention.

Stochastic activation and bistability in a Rab GTPase regulatory network

The eukaryotic endomembrane system is controlled by small GTPases of the Rab family, which are activated at defined times and locations in a switch-like manner. While this switch is well understood for an individual protein, how regulatory networks produce intracellular activity patterns is currently not known. Here, we combine in vitro reconstitution experiments with computational modeling to study a minimal Rab5 activation network. We find that the molecular interactions in this system give rise to a positive feedback and bistable collective switching of Rab5. Furthermore, we find that switching near the critical point is intrinsically stochastic and provide evidence that controlling the inactive population of Rab5 on the membrane can shape the network response. Notably, we demonstrate that collective switching can spread on the membrane surface as a traveling wave of Rab5 activation. Together, our findings reveal how biochemical signaling networks control vesicle trafficking pathways and how their nonequilibrium properties define the spatiotemporal organization of the cell.

Molecular mechanisms of mechanotransduction in psoriasis.

Psoriasis is an immune disease of the skin that frequently develops upon triggering events of mechanical nature and leads to increased proliferation and damaged differentiation of keratinocytes of the epidermis. Mechanical forces are mediated through mechanotransduction, which is the process that translates physical cues into biochemical signaling networks. Latest updates underline the role of mechanotransduction during the acquisition of aberrant properties by the keratinocytes of the skin, therefore implying a potential contribution that promotes psoriasis pathogenesis. The present review discusses the mechano-induced signaling pathways and individual molecules that become activated in psoriasis and in keratinocytes, along with mechano-based putative treatment strategies. We also suggest emerging mechanosensitive molecules for further investigation with potential diagnostic and therapeutic utility in psoriasis.

Biochemical and Functional Insights into the Integrated Regulation of Innate Immune Cell Responses by Teleost Leukocyte Immune-Type Receptors.

Across vertebrates, innate immunity consists of a complex assortment of highly specialized cells capable of unleashing potent effector responses designed to destroy or mitigate foreign pathogens. The execution of various innate cellular behaviors such as phagocytosis, degranulation, or cell-mediated cytotoxicity are functionally indistinguishable when being performed by immune cells isolated from humans or teleost fishes; vertebrates that diverged from one another more than 450 million years ago. This suggests that vital components of the vertebrate innate defense machinery are conserved and investigating such processes in a range of model systems provides an important opportunity to identify fundamental features of vertebrate immunity. One characteristic that is highly conserved across vertebrate systems is that cellular immune responses are dependent on specialized immunoregulatory receptors that sense environmental stimuli and initiate intracellular cascades that can elicit appropriate effector responses. A wide variety of immunoregulatory receptor families have been extensively studied in mammals, and many have been identified as cell- and function-specific regulators of a range of innate responses. Although much less is known in fish, the growing database of genomic information has recently allowed for the identification of several immunoregulatory receptor gene families in teleosts. Many of these putative immunoregulatory receptors have yet to be assigned any specific role(s), and much of what is known has been based solely on structural and/or phylogenetic relationships with mammalian receptor families. As an attempt to address some of these shortcomings, this review will focus on our growing understanding of the functional roles played by specific members of the channel catfish (Ictalurus punctatus) leukocyte immune-type receptors (IpLITRs), which appear to be important regulators of several innate cellular responses via classical as well as unique biochemical signaling networks.