Immunogenicity of decellularized porcine liver for bioengineered hepatic tissue.

Liver disease affects millions of patients each year. The field of regenerative medicine promises alternative therapeutic approaches, including the potential to bioengineer replacement hepatic tissue. One approach combines cells with acellular scaffolds derived from animal tissue. The goal of this study was to scale up our rodent liver decellularization method to livers of a clinically relevant size. Porcine livers were cannulated via the hepatic artery, then perfused with PBS, followed by successive Triton X-100 and SDS solutions in saline buffer. After several days of rinsing, decellularized liver samples were histologically analyzed. In addition, biopsy specimens of decellularized scaffolds were seeded with hepatoblastoma cells for cytotoxicity testing or implanted s.c. into rodents to investigate scaffold immunogenicity. Histological staining confirmed cellular clearance from pig livers, with removal of nuclei and cytoskeletal components and widespread preservation of structural extracellular molecules. Scanning electron microscopy confirmed preservation of an intact liver capsule, a porous acellular lattice structure with intact vessels and striated basement membrane. Liver scaffolds supported cells over 21 days, and no increased immune response was seen with either allogeneic (rat-into-rat) or xenogeneic (pig-into-rat) transplants over 28 days, compared with sham-operated on controls. These studies demonstrate that successful decellularization of the porcine liver could be achieved with protocols developed for rat livers, yielding nonimmunogenic scaffolds for future hepatic bioengineering studies.

Decellularization methods of porcine kidneys for whole organ engineering using a high-throughput system.

End-stage renal failure is a devastating disease, with donor organ transplantation as the only functional restorative treatment. The current number of donor organs meets less than one-fifth of demand, so regenerative medicine approaches have been proposed as potential therapeutic alternatives. One such approach for whole large-organ bioengineering is to combine functional renal cells with a decellularized porcine kidney scaffold. The efficacy of cellular removal and biocompatibility of the preserved porcine matrices, as well as scaffold reproducibility, are critical to the success of this approach. We evaluated the effectiveness of 0.25 and 0.5% sodium dodecyl sulfate (SDS) and 1% Triton X-100 in the decellularization of adult porcine kidneys. To perform the decellularization, a high-throughput system was designed and constructed. In this study all three methods examined showed significant cellular removal, but 0.5% SDS was the most effective detergent (<50 ng DNA/mg dry tissue). Decellularized organs retained intact microarchitecture including the renal vasculature and essential extracellular matrix components. The SDS-treated decellularized scaffolds were non-cytotoxic to primary human renal cells. This method ensures clearance of porcine cellular material (which directly impacts immunoreactivity during transplantation) and preserves the extracellular matrix and cellular compatibility of these renal scaffolds. Thus, we have developed a rapid decellularization method that can be scaled up for use in other large organs, and this represents a step toward development of a transplantable organ using tissue engineering techniques.

Production and implantation of renal extracellular matrix scaffolds from porcine kidneys as a platform for renal bioengineering investigations.

BACKGROUND: It is important to identify new sources of transplantable organs because of the critical shortage of donor organs. Tissue engineering holds the potential to address this issue through the implementation of decellularization-recellularization technology. OBJECTIVE: To produce and examine acellular renal extracellular matrix (ECM) scaffolds as a platform for kidney bioengineering. METHODS: Porcine kidneys were decellularized with distilled water and sodium dodecyl sulfate-based solution. After rinsing with buffer solution to remove the sodium dodecyl sulfate, the so-obtained renal ECM scaffolds were processed for vascular imaging, histology, and cell seeding to investigate the vascular patency, degree of decellularization, and scaffold biocompatibility in vitro. Four whole renal scaffolds were implanted in pigs to assess whether these constructs would sustain normal blood pressure and to determine their biocompatibility in vivo. Pigs were sacrificed after 2 weeks and the explanted scaffolds were processed for histology. RESULTS: Renal ECM scaffolds were successfully produced from porcine kidneys. Scaffolds retained their essential ECM architecture and an intact vascular tree and allowed cell growth. On implantation, unseeded scaffolds were easily reperfused, sustained blood pressure, and were tolerated throughout the study period. No blood extravasation occurred. Pathology of explanted scaffolds showed maintenance of renal ultrastructure. Presence of inflammatory cells in the pericapsular region and complete thrombosis of the vascular tree were evident. CONCLUSIONS: Our investigations show that pig kidneys can be successfully decellularized to produce renal ECM scaffolds. These scaffolds maintain their basic components, are biocompatible, and show intact, though thrombosed, vasculature.

Surrogate AutoShim: predocking into a universal ensemble kinase receptor for three dimensional activity prediction, very quickly, without a crystal structure.

"Ensemble surrogate AutoShim" is a kinase specific extension of the AutoShim docking method that solves the three traditional limitations of conventional docking: (1) it gives good correlations with affinity, (2) does not require a target protein structure, and (3) for a preprocessed company archive of 1.5 million compounds, is as fast as traditional 2D QSAR. It does require several hundred experimental IC 50 values for each new target. Original AutoShim adds pharmacophore "shims" to a crystal structure binding site. An iterative partial least squares (PLS) procedure selects the best pose, while adjusting the shim weights to reproduce IC 50 data. Surrogate AutoShim adjusts shims in one crystal structure to reproduce IC 50 data for a different kinase target. Ensemble surrogate AutoShim uses 16 structurally diverse kinase crystal structures as a "universal ensemble kinase receptor", suitable for any kinase target. The 1.5 million member Novartis screening collection has been predocked into the shimmed ensemble, so new kinase models can be built, and the entire corporate archive virtually screened, in hours rather than weeks. A kinase-biased set of 10,000 compounds, that samples the entire corporate archive, has been designed for lead discovery by iterative kinase screening.

AutoShim: empirically corrected scoring functions for quantitative docking with a crystal structure and IC50 training data.

It has been notoriously difficult to develop general all-purpose scoring functions for high-throughput docking that correlate with measured binding affinity. As a practical alternative, AutoShim uses the program Magnet to add point-pharmacophore like "shims" to the binding site of each protein target. The pharmacophore shims are weighted by partial least-squares (PLS) regression, adjusting the all-purpose scoring function to reproduce IC 50 data, much as the shims in an NMR magnet are weighted to optimize the field for a better spectrum. This dramatically improves the affinity predictions on 25% of the compounds held out at random. An iterative procedure chooses the best pose during the process of shim parametrization. This method reproducibly converges to a consistent solution, regardless of starting pose, in just 2-4 iterations, so these robust models do not overtrain. Sets of complex multifeature shims, generated by a recursive partitioning method, give the best activity predictions, but these are difficult to interpret when designing new compounds. Sets of simpler single-point pharmacophores still predict affinity reasonably well and clearly indicate the molecular interactions producing effective binding. The pharmacophore requirements are very reproducible, irrespective of the compound sets used for parametrization, lending confidence to the predictions and interpretations. The automated procedure does require a training set of experimental compounds but otherwise adds little extra time over conventional docking.

Exploiting structure-activity relationships in docking.

From the perspective of 2D chemical descriptors, error in docking activity predictions is separated into noise and systematic components. This error framework explains how fitting docking scores to a 2D-QSAR equation often improves accuracy as well as its logical limits. Intriguingly, in examined cases where multiple docking models (e.g., multiple crystal structures or multiple scoring functions) are available for an enzyme, the noise component of error dominates the difference between the more accurate and less accurate docking models. When this is true, the QSAR equation fit statistics can rank each docking-score set's accuracy in the absence of experimental activity data.

Quantifying polypeptide conformational space: sensitivity to conformation and ensemble definition.

Quantifying the density of conformations over phase space (the conformational distribution) is needed to model important macromolecular processes such as protein folding. In this work, we quantify the conformational distribution for a simple polypeptide (N-mer polyalanine) using the cumulative distribution function (CDF), which gives the probability that two randomly selected conformations are separated by less than a "conformational" distance and whose inverse gives conformation counts as a function of conformational radius. An important finding is that the conformation counts obtained by the CDF inverse depend critically on the assignment of a conformation's distance span and the ensemble (e.g., unfolded state model): varying ensemble and conformation definition (1 --> 2 A) varies the CDF-based conformation counts for Ala(50) from 10(11) to 10(69). In particular, relatively short molecular dynamics (MD) relaxation of Ala(50)'s random-walk ensemble reduces the number of conformers from 10(55) to 10(14) (using a 1 A root-mean-square-deviation radius conformation definition) pointing to potential disconnections in comparing the results from simplified models of unfolded proteins with those from all-atom MD simulations. Explicit waters are found to roughen the landscape considerably. Under some common conformation definitions, the results herein provide (i) an upper limit to the number of accessible conformations that compose unfolded states of proteins, (ii) the optimal clustering radius/conformation radius for counting conformations for a given energy and solvent model, (iii) a means of comparing various studies, and (iv) an assessment of the applicability of random search in protein folding.

Toward absolute density of states calculations for proteins.

The density of states (DOS), which gives the number of conformations with a particular energy E, is a prerequisite in computing most thermodynamic quantities and in elucidating important biological processes such as the mechanism of protein folding. However, current methods for computing DOS of large systems such as proteins generally yield only the ratios of microstate counts for different energies, which could yield absolute conformation counts if the total number of conformations in phase space is known, thus motivating this work. Here, the total number of energy minima of 50-mer polyalanine, whose size corresponds to naturally occurring small proteins, was estimated under an all-atom potential energy function based on the cumulative distribution function (CDF) of conformational differences to be approximately 10(38). This estimate can place any DOS function, such as the Gaussian DOS distribution in the random energy model, on an absolute scale. Comparing the absolute conformational counts from a Gaussian DOS function with those from the CDF derived from quenched molecular dynamics ensembles shows that the former are far greater than the latter, indicating far fewer low-energy minima actually exist. In addition to showing how CDF and relative DOS calculations can yield absolute DOS for a discrete system, we also show how they can yield absolute DOS for continuous variable systems to a specified atomic variance. In the context of protein folding, knowing this phase-space "volume" of conformations in a DOS function, as well as characteristic transition times, constrains the set of possible folding mechanisms.

Distributions in protein conformation space: implications for structure prediction and entropy.

By considering how polymer structures are distributed in conformation space, we show that it is possible to quantify the difficulty of structural prediction and to provide a measure of progress for prediction calculations. The critical issue is the probability that a conformation is found within a specified distance of another conformer. We address this question by constructing a cumulative distribution function (CDF) for the average probability from observations about its limiting behavior at small displacements and numerical simulations of polyalanine chains. We can use the CDF to estimate the likelihood that a structure prediction is better than random chance. For example, the chance of randomly predicting the native backbone structure of a 150-amino-acid protein to low resolution, say within 6 A, is 10(-14). A high-resolution structural prediction, say to 2 A, is immensely more difficult (10(-57)). With additional assumptions, the CDF yields the conformational entropy of protein folding from native-state coordinate variance. Or, using values of the conformational entropy change on folding, we can estimate the native state's conformational span. For example, for a 150-mer protein, equilibrium alpha-carbon displacements in the native ensemble would be 0.3-0.5 A based on T Delta S of 1.42 kcal/(mol residue).

Information content of molecular structures.

For a completely enumerated set of conformers of a macromolecule or for exhaustive lattice walks of model polymers it is straightforward to use Shannon information theory to deduce the information content of the ensemble. It is also practicable to develop numerical measures of the information content of sets of exact distance constraints applied to specific conformational ensembles. We examine the effects of experimental uncertainties by considering "noisy" constraints. The introduction of noise requires additional assumptions about noise distribution and conformational clustering protocols that make the problem of measuring information content more complex. We make use of a standard concept in communication theory, the "noise sphere," to link uncertainty in measurements to information loss. Most of our numerical results are derived from two-dimensional lattice ensembles. Expressing results in terms of information per degree of freedom removes almost all of the chain length dependence. We also explore off-lattice polyalanine chains that yield surprisingly similar results.