Systemic Alanine Glyoxylate Aminotransferase mRNA Improves Glyoxylate Metabolism in a Mouse Model of Primary Hyperoxaluria Type 1.

Primary Hyperoxaluria Type 1 (PH1) is an autosomal recessive disorder of glyoxylate metabolism. Loss of alanine glyoxylate aminotransferase (AGT) function to convert intermediate metabolite glyoxylate to glycine causes the accumulation and reduction of glyoxylate to glycolate, which eventually is oxidized to oxalate. Excess oxalate in PH1 patients leads to the formation and deposition of calcium oxalate crystals in the kidney and urinary tract. Oxalate crystal deposition causes a decline in renal function, systemic oxalosis, and eventually end-stage renal disease and premature death. mRNA-based therapies are a new class of drugs that work by replacing the missing enzyme. mRNA encoding AGT has the potential to restore normal glyoxylate to glycine metabolism, thus preventing the buildup of calcium oxalate in various organs. Panels of codon-optimized AGT mRNA constructs were screened in vitro and in wild-type mice for the production of a functional AGT enzyme. Two human constructs, wild-type and engineered AGT (RHEAM), were tested in Agxt-/- mice. Repeat dosing in Agxt-/- mice resulted in a 40% reduction in urinary oxalate, suggesting therapeutic benefit. These studies suggest that mRNA encoding AGT led to increased expression and activity of the AGT enzyme in liver that translated into decrease in urinary oxalate levels. Taken together, our data indicate that AGT mRNA may have the potential to be developed into a therapeutic for PH1.

Synthetic antibody libraries focused towards peptide ligands.

Synthetic antibody libraries have proven immensely useful for the de novo isolation of antibodies without the need for animal immunization. Recently, focused libraries designed to recognize particular classes of ligands, such as haptens or proteins, have been employed to facilitate the selection of high-affinity antibodies. Focused libraries are built using V regions encoding combinations of canonical structures that resemble the structural features of antibodies that bind the desired class of ligands and sequence diversity is introduced at residues typically involved in recognition. Here we describe the generation and experimental validation of two different single-chain antibody variable fragment libraries that efficiently generate binders to peptides, a class of molecules that has proven to be a difficult target for antibody generation. First, a human anti-peptide library was constructed by diversifying a scaffold: the human variable heavy chain (V(H)) germ line gene 3-23, which was fused to a variant of the human variable light chain (V(L)) germ line gene A27, in which L1 was modified to encode the canonical structure found in anti-peptide antibodies. The sequence diversity was introduced into 3-23 (V(H)) only, targeting for diversification residues commonly found in contact with protein and peptide antigens. Second, a murine library was generated using the antibody 26-10, which was initially isolated based on its affinity to the hapten digoxin, but also binds peptides and exhibits a canonical structure pattern typical of anti-peptide antibodies. Diversity was introduced in the V(H) only using the profile of amino acids found at positions that frequently contact peptide antigens. Both libraries yielded binders to two model peptides, angiotensin and neuropeptide Y, following screening by solution phage panning. The mouse library yielded antibodies with affinities below 20 nM to both targets, although only the V(H) had been subjected to diversification.

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction.

BACKGROUND: A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. RESULTS: The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. CONCLUSION: Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.