A Chemerin Peptide Analog Stimulates Tumor Growth in Two Xenograft Mouse Models of Human Colorectal Carcinoma

Background: Chemerin plasma concentration has been reported to be positively correlated with the risk of colorectal cancer. However, the potential regulation of CRC tumorigenesis and progression has not yet been investigated in an experimental setting. This study addresses this hypothesis by investigating proliferation, colony formation, and migration of CRC cell lines in vitro as well as in animal models. Methods: In vitro, microscopic assays to study proliferation, as well as a scratch-wound assay for migration monitoring, were applied using the human CRC cell lines HCT116, HT29, and SW620 under the influence of the chemerin analog CG34. The animal study investigated HCT116-luc and HT29-luc subcutaneous tumor size and bioluminescence during treatment with CG34 versus control, followed by an ex-vivo analysis of vessel density and mitotic activity. Results: While the proliferation of the three CRC cell lines in monolayers was not clearly stimulated by CG34, the chemerin analog promoted colony formation in three-dimensional aggregates. An effect on cell migration was not observed. In the treatment study, CG34 significantly stimulated both growth and bioluminescence signals of HCT116-luc and HT29-luc xenografts. Conclusions: The results of this study represent the first indication of a tumor growth-stimulating effect of chemerin signaling in CRC

CMKLR1-targeting peptide tracers for PET/MR imaging of breast cancer

Background: Molecular targeting remains to be a promising approach in oncology. Overexpression of G protein-coupled receptors (GPCRs) in human cancer is offering a powerful opportunity for tumor-selective imaging and treatment employing nuclear medicine. We utilized novel chemerin-based peptide conjugates for chemokine-like receptor 1 (CMKLR1) targeting in a breast cancer xenograft model. Methods: By conjugation with the chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA), we obtained a family of five highly specific, high-affinity tracers for hybrid positron emission tomography/magnetic resonance (PET/MR) imaging. A xenograft model with target-positive DU4475 and negative A549 tumors in immunodeficient nude mice enabled CMKLR1-specific imaging in vivo. We acquired small animal PET/MR images, assessed biodistribution by ex vivo measurements and investigated the tracer specificity by blocking experiments. Results: Five CMKLR1-targeting peptide tracers demonstrated high biological activity and affinity in vitro with EC50 and IC50 values below 2 nM. Our target-positive (DU4475) and target-negative (A549) xenograft model could be validated by ex vivo analysis of CMKLR1 expression and binding. After preliminary PET imaging, the three most promising tracers [68Ga]Ga-DOTA-AHX-CG34, [68Ga]Ga-DOTA-KCap-CG34 and [68Ga]Ga-DOTA-ADX-CG34 with best tumor uptake were further analyzed. Hybrid PET/MR imaging along with concomitant biodistribution studies revealed distinct CMKLR1-specific uptake (5.1% IA/g, 3.3% IA/g and 6.2% IA/g 1 h post-injection) of our targeted tracers in DU4475 tumor tissue. In addition, tumor uptake was blocked by excess of unlabeled peptide (6.4-fold, 5.5-fold and 3.4-fold 1 h post-injection), further confirming CMKLR1 specificity. Out of five tracers, we identified these three tracers with moderate, balanced hydrophilicity to be the most potent in receptor-mediated tumor targeting. Conclusion: We demonstrated the applicability of 68Ga-labeled peptide tracers by visualizing CMKLR1-positive breast cancer xenografts in PET/MR imaging, paving the way for developing them into theranostics for tumor treatment

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back-propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization

As peptide sequences evolve, metabolic stability of the peptides improves while their biological activity is retained. Upper panel (A+B):

<p>depiction of EC₅₀ values of the receptor activating potency (A) and of the metabolic stability (B,  = t_1/2) for the three GNN-based optimization rounds. Both graphs show all the peptides from a given round, sorted according to their activity in the parameter on the y axis. With receptor activity, low EC₅₀ values are favorable, while in the stability parameter t_1/2, high values are to be achieved. Lower panel (C): Peptide sequence comparisons by multiple alignments illustrate the evolution over the different steps of the process. Four sets of alignments represent the start population ( = input) and three optimization rounds.</p

Sequences and bioassay data of the five best peptides from each cycle.

<p>Peptides from the initial population are designated as from cycle 0. In the peptide sequences, wild type amino acids are given in standard font. Exchange by a different L amino acid is represented by a character in bold, D isomers are in addition indicated by lower-case letters. Activities are intracellular calcium mobilization data given as mean of three independent experiments ± standard deviation. Stabilities are peptide half life data from an HPLC assay, given as mean of three independent experiments ± standard deviation. Data in bold indicate the best peptide in this study, showing approximately 70fold higher stability and 2.5fold higher potency than the wild-type peptide chemerin-9.</p

Figure 1

<p>(A) Graph Neural Networks consist of interconnected elementary cells. The internal weight of the cell is given by φ_internal and the feedback during the iteration of the GNN is controlled by ω₀. The inputs from the neighboring cells are connected with the weights ω_-N,…,N preserving the order of the peptide sequence as shown in (B). The sum of the weighted inputs passes the activation function and forms the output of the cell for the next iteration step of the network. (B) The translation process of a peptide into a fully connected GNN. Note the one-to-one correspondence between the amino acids of the peptide and the elementary cells of the GNN. The network is iterated through time and the output of the network is the sum over all internal states after the final iteration.</p

Figure 4

<p><b>Stability and activity of chemerin peptides improve over three cycles. Upper panel:</b> The complete data set from three optimization rounds is shown, each dot representing one peptide and its properties. The trend points to the upper left part of the diagram, i.e. peptides that combine high receptor activity ( = low EC₅₀) with high metabolic stability. <b>Lower panel:</b> separate representations of the data from all three rounds. While in the first round, the majority of data points is found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud is shifted towards the upper left, representing improved activity and stability. Quality is a measure of the likelihood of observing the substitutions in a particular column of the alignment <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0036948#pone.0036948-Waterhouse1" target="_blank">[62]</a>.</p

Urolinin: The First Linear Peptidic Urotensin-II Receptor Agonist

This study investigated the role of individual U-II amino acid positions and side chain characteristics important for U-IIR activation. A complete permutation library of 209 U-II variants was studied in an activity screen that contained single substitution variants of each position with one of the other 19 proteinogenic amino acids. Receptor activation was measured using a cell-based high-throughput fluorescence calcium mobilization assay. We generated the first complete U-II substitution map for U-II receptor activation, resulting in a detailed view into the structural features required for receptor activation, accompanied by complementary information from receptor modeling and ligand docking studies. On the basis of the systematic SAR study of U-II, we created 33 further short and linear U-II variants from eight to three amino acids in length, including d- and other non-natural amino acids. We identified the first high-potency linear U-II analogues. Urolinin, a linear U-II agonist (nWWK-Tyr­(3-NO₂)-Abu), shows low nanomolar potency as well as improved metabolic stability