A cell-penetrating peptide enhances delivery and efficacy of phosphorodiamidate morpholino oligomers in mdx mice.

Antisense RNA technology is a strategy for the treatment of Duchenne muscular dystrophy (DMD), a progressive and universally fatal X-linked neuromuscular disease caused by frameshift mutations in the gene encoding dystrophin. Phosphorodiamidate morpholino oligomers (PMOs) are an antisense RNA platform that is used clinically in patients with DMD to facilitate exon skipping and production of an internally truncated, yet functional, dystrophin protein. Peptide-conjugated PMOs (PPMOs) are a next-generation platform in which a cell-penetrating peptide is conjugated to the PMO backbone, with the goal of increasing cellular uptake. RC-1001 is a PPMO that contains a proprietary cell-penetrating peptide and targets the Dmd mutation in mdx mice. It was evaluated in mdx mice for exon 23 skipping, dystrophin production, and functional efficacy. Single-dose RC-1001 dose dependently increased exon skipping and dystrophin protein levels in striated muscle and is associated with improvements in muscle function. Dystrophin protein levels were durable for 60 days. Three doses, each given 1 month apart, increased exon skipping to 99% in quadriceps and 43% in heart, with dystrophin protein levels at 39% and 9% of wild type, respectively. These findings support clinical development of PPMO therapies for the treatment of DMD.

Deep Learning Enables Discovery of a Short Nuclear Targeting Peptide for Efficient Delivery of Antisense Oligomers.

Therapeutic macromolecules such as proteins and oligonucleotides can be highly efficacious but are often limited to extracellular targets due to the cell's impermeable membrane. Cell-penetrating peptides (CPPs) are able to deliver such macromolecules into cells, but limited structure-activity relationships and inconsistent literature reports make it difficult to design effective CPPs for a given cargo. For example, polyarginine motifs are common in CPPs, promoting cell uptake at the expense of systemic toxicity. Machine learning may be able to address this challenge by bridging gaps between experimental data in order to discern sequence-activity relationships that evade our intuition. Our earlier data set and deep learning model led to the design of miniproteins (>40 amino acids) for antisense delivery. Here, we leveraged and expanded our model with data augmentation in the short CPP sequence space of the data set to extrapolate and discover short, low-arginine-content CPPs that would be easier to synthesize and amenable to rapid conjugation to desired cargo, and with minimal in vivo toxicity. The lead predicted peptide, termed P6, is as active as a polyarginine CPP for the delivery of an antisense oligomer, while having only one arginine side chain and 18 total residues. We determined the pentalysine motif and the C-terminal cysteine of P6 to be the main drivers of activity. The antisense conjugate was able to enhance corrective splicing in an animal model to produce functional eGFP in heart tissue in vivo while remaining nontoxic up to a dose of 60 mg/kg. In addition, P6 was able to deliver an enzyme to the cytosol of cells. Our findings suggest that, given a data set of long CPPs, we can discover by extrapolation short, active sequences that deliver antisense oligomers.

Deep learning to design nuclear-targeting abiotic miniproteins.

There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we show how machine learning enables the de novo design of abiotic nuclear-targeting miniproteins to traffic antisense oligomers to the nucleus of cells. We combined high-throughput experimentation with a directed evolution-inspired deep-learning approach in which the molecular structures of natural and unnatural residues are represented as topological fingerprints. The model is able to predict activities beyond the training dataset, and simultaneously deciphers and visualizes sequence-activity predictions. The predicted miniproteins, termed 'Mach', reach an average mass of 10 kDa, are more effective than any previously known variant in cells and can also deliver proteins into the cytosol. The Mach miniproteins are non-toxic and efficiently deliver antisense cargo in mice. These results demonstrate that deep learning can decipher design principles to generate highly active biomolecules that are unlikely to be discovered by empirical approaches.

GLP-1-derived nonapeptide GLP-1(28-36)amide inhibits weight gain and attenuates diabetes and hepatic steatosis in diet-induced obese mice.

BACKGROUND: The metabolic syndrome is an obesity-associated disease manifested as severe insulin resistance, hyperlipidemia, hepatic steatosis, and diabetes. Previously we proposed that a nonapeptide, FIAWLVKGRamide, GLP-1(28-36)amide, derived from the gluco-incretin hormone, glucagon-like peptide-1 (GLP-1), might have insulin-like actions. Recently, we reported that the nonapeptide appears to enter hepatocytes, target to mitochondria, and suppress glucose production and reactive oxygen species. Therefore, the effects of GLP-1(28-36)amide were examined in diet-induced obese, insulin-resistant mice as a model for the development of human metabolic syndrome. METHODS AND RESULTS: Three- to 11-week infusions of GLP-1(28-36)amide were administered via osmopumps to mice fed a very high fat diet (VHFD) and to control mice on a normal low fat diet (LFD). Body weight, DXA, energy intake, plasma insulin and glucose, and liver triglyceride levels were assessed. GLP-1(28-36)amide inhibited weight gain, accumulation of liver triglycerides, and improved insulin sensitivity by attenuating the development of fasting hyperglycemia and hyperinsulinemia in mice fed VHFD. GLP-1(28-36)amide had no observable effects in control LFD mice. Surprisingly, the energy intake of peptide-infused obese mice is 25-70% greater than in obese mice receiving vehicle alone, yet did not gain excess weight. CONCLUSIONS: GLP-1(28-36)amide exerts insulin-like actions selectively in conditions of obesity and insulin resistance. The peptide curtails weight gain in diet-induced obese mice in the face of an increase in energy intake suggesting increased energy expenditure. These findings suggest utility of GLP-1(28-36)amide, or a peptide mimetic derived there from, for the treatment of insulin resistance and the metabolic syndrome.