Methods for studying mammalian aquaporin biology

While in vitro and in vivo methods have provided us with a wealth of knowledge on AQP expression and function in biological systems, most of our understanding of AQP structure and function on a molecular level is due to in silico work. Computational models and simulations have the obvious benefits of saving time and laboratory resources, but they are also the most effective way to handle large amounts of data. Molecular dynamics (MD) and molecular docking, while computationally expensive, have revealed crucial information regarding AQP structures, the mechanism of water transport, AQP drug candidates and their putative binding sites, and PPIs. Stochastic and deterministic mathematical cell models have also begun to shed light on the impact of AQPs on cell activities. Much of our current understanding of AQP structure stems from X-ray crystallography and electron microscopy, but the structures of AQP3 [ 43 ], AQP7 [ 230 ], AQP9 [ 231 ], AQP11, and AQP12 [ 232 ] have additionally been predicted using structural bioinformatics in silico technique called homology or comparative modeling. This involves building a 3D model of a protein from its amino acid sequence and is compared to the known structures of a similar protein. More recently, machine learning has also been implemented in sequence-based AQP prediction [ 233 , 234 ]. However, most of our initial knowledge of AQPs and AQP-mediated water flux comes from MD, a computational method in which the movements of atoms and molecules are simulated according to Newton’s equations of motion. Extensive MD simulations have been performed to elucidate AQP folding [ 9 ], proton exclusion from AQP pores [ 9 , 235 , 236 ], gating capabilities [ 237–239 ], ion conductivity [ 238 , 240 ], voltage sensitivity [ 241 , 242 ], lipid interactions [ 243 , 244 ], permeability [ 28 , 245–250 ], effects of mutations [ 251 , 252 ], effects of phosphorylation [ 253 , 254 ], and suppression of water flux by inhibitors [ 231 , 255 , 256 ]. For a more thorough review of MD simulations for the AQP study, we direct the reader to other reviews [ 257 , 258 ]. Hand-in-hand with MD is meta-dynamics, which estimates the free energy of a system and simulates rare events, particularly accelerating the sampling of protein conformations within an MD simulation [ 259 ]. Meta-dynamics has been used to simulate water, glycerol, and H 2 O 2 transport through AQP3 [ 260 , 261 ], and to study the effects of electric fields on AQP4 [ 262 , 263 ]. Once protein structures are defined, in silico methods can provide much quicker insight into how AQPs interact with other molecules compared to in vitro methods. Predicting AQP–ligand interactions allows for expedited drug screening; AQP–protein interactions shed light on AQP regulation and signaling activities; and AQP–lipid interactions elucidate key aspects of plasma membrane properties and how they affect AQP function. Over time, the revelation that AQPs play key roles in several diseased cell behaviors has driven the search for AQP drugs that can block water transport, glycerol permeation, or ion conductance. In vitro methods for drug validation can be tedious and time-consuming. Molecular docking is a computational tool that has been widely used to expedite the screening of AQP inhibitors [ 30 , 231 , 264–267 ], predicting how small molecules will fit with a protein at the atomic level. In most cases, the protein and ligand are prepared and docked using automated docking software such as AutoDock. Once inhibitors are identified with molecular docking, binding modes may be further analyzed with MD simulations [ 256 , 264 ]. Molecular docking can predict PPIs in addition to protein–ligand interactions and has been used to model binding between AQP5 and ezrin [ 146 ]. While it would be desirable to discover more AQP–protein binding partners using molecular docking, it remains a challenge to manage the heavy computational cost of docking two proteins while capturing their conformational dynamics. However, a simpler method exists for a broader detection of putative PPIs; predictions can be made based on consensus sites in amino acid sequences [ 268 ] that can be compared using software such as ProteinPrompt [ 269 ]. To predict AQP–lipid interactions, structural AQP data from crystallography can be used in MD simulations. For example, MD studies have investigated lipid organization around AQP0—an isoform whose structure has been solved from crystallography—to better understand membrane properties, protein mobility, and cholesterol-mediated tetrameric assembly [ 70 , 71 ]. Another in silico approach to studying AQPs is to model them within cells. To our knowledge, few studies have developed computational models showing the interplay of AQPs and cell processes. One group ran simulations of 2D neural crest cell migratory streams involving cell speed and filopodia dynamics associated with an AQP1 overexpression or downregulation [ 270 ]. Another study built a multiscale compartmental model of AQP2 trafficking via the vesicular transport system in renal principal cells. Their numerical simulations incorporate membrane agents, filaments, vesicles, and a rule-based reaction system for the chemical entities [ 271 ]. Numerical methods have also been used to model AQP1 in ion and fluid transport across parotid duct cells [ 272 ], AQP4 in brain swelling in meningitis [ 273 ], and AQP4 supramolecular assembly into orthogonal arrays within cell membranes [ 274 ]. Future work modeling AQPs in intracellular signaling pathways would elucidate how AQPs can trigger downstream cascades affecting cell migration, proliferation, differentiation, etc.

In this review, we cover frequently used in vitro , in vivo , and in silico methods for studying AQP expression, localization, function, effects on cell behavior, and more. While these methods have earned us a great deal of basic knowledge of AQP structure and functions, they still have their limitations in their robustness, reproducibility, and physiological relevance. Hopefully, future method design will include more sensitive permeability assays, more reproducible inhibitor screening, further characterization of AQPs within in vivo systems, and wider use of computational modeling tools. Since much of AQP biology research is performed in vitro , there is also a need for more physiologically relevant models that help clarify the role of AQPs in both healthy and pathologic cell behaviors. Addressing these areas for improvement for current methods, exploring new uses of existing methods, and designing novel methods may well lead to the discovery of more effective AQP-targeting therapeutics and a greater understanding of the impact of AQPs in biological processes.

Aquaporins (AQPs), transmembrane protein channels responsible for facilitating passive water flux across cell membranes, are critical for regulating cell shape, cell volume, and fluid homeostasis in nearly all organisms. Thirteen AQP isoforms (AQP0–AQP12) have been identified in mammals [ 1 ], some of which are classified as aquaglyceroporins that transport glycerol in addition to water (AQP3, 7, 9, and 10) or super AQPs (AQP11 and AQP12) that have a unique cysteine residue in their pore-forming asparagine-proline-alanine (NPA) box [ 2 ]. AQPs are known to drive water-essential processes such as tear production, saliva secretion, renal reabsorption, and vaginal lubrication, but they also have other nonobvious functions in cell migration, cell invasion, proliferation, angiogenesis, and neuroexcitation. Growing evidence indicates AQP involvement in the pathophysiology of multiple clinical conditions including cancer, endometriosis, glaucoma, and epilepsy [ 3 , 4 ], thus prompting further investigation into AQP-mediated processes and the manipulation of AQPs in the design of therapeutics. Over the past 30 years, the diverse cellular roles of AQPs and their mechanisms in physiological and pathophysiological systems have been studied in vitro, in vivo, and in silico. Although the research performed with these methods has provided a foundational understanding of AQPs, further study is still warranted to clarify AQP-mediated biological processes. In vitro methods may be used to induce or measure AQP expression and subcellular localization or to analyze AQP function in regulating cell behaviors. Several in vivo models drive many of the pre-clinical efforts in understanding the relationship between AQPs and disease progression. Finally, there are several in silico methods that can elucidate AQP structure, function, and interactions with other molecules and proteins, as well as model the resulting cell behavior. The goal of this review is to collate and discuss established in vitro , in vivo , and in silico methods for studying mammalian AQPs in biomedical science and engineering applications. A condensed collection of these methods is depicted in Fig. 1 . We also suggest some new techniques and models that can be applied for future AQP-related research. Graphical summary of in vitro , in vivo , and in silico methods for AQP biology as discussed in this review.