In vivo phage display is widely used for identification of organ- or disease-specific homing peptides. However, the current in vivo phage biopanning approaches fail to assess biodistribution of specific peptide phages across tissues during the screen, thus necessitating laborious and time-consuming post-screening validation studies on individual peptide phages. Here, we adopted bioinformatics tools used for RNA sequencing for analysis of high-throughput sequencing (HTS) data to estimate the representation of individual peptides during biopanning in vivo. The data from in vivo phage screen were analyzed using differential binding—relative representation of each peptide in the target organ versus in a panel of control organs. Application of this approach in a model study using low-diversity peptide T7 phage library with spiked-in brain homing phage demonstrated brain-specific differential binding of brain homing phage and resulted in identification of novel lung- and brain-specific homing peptides. Our study provides a broadly applicable approach to streamline in vivo peptide phage biopanning and to increase its reproducibility and success rate.

Aptamers have in recent years emerged as a viable alternative to antibodies. High-throughput sequencing (HTS) has revolutionized aptamer research by increasing the number of reads from a few (using Sanger sequencing) to millions (using an HTS approach). Despite the availability and advantages of HTS compared to Sanger sequencing, there are only 50 aptamer HTS sequencing samples available on public databases. HTS data in aptamer research are primarily used to compare sequence enrichment between subsequent selection cycles. This approach does not take full advantage of HTS because the enrichment of sequences during selection can be due to inefficient negative selection when using live cells. Here, we present a differential binding cell-SELEX (systematic evolution of ligands by exponential enrichment) workflow that adapts theFASTAptamertoolbox and bioinformatics tooledgeR, which are primarily used for functional genomics, to achieve more informative metrics about the selection process. We propose a fast and practical high-throughput aptamer identification method to be used with the cell-SELEX technique to increase the aptamer selection rate against live cells. The feasibility of our approach is demonstrated by performing aptamer selection against a clear cell renal cell carcinoma (ccRCC) RCC-MF cell line using the RC-124 cell line from healthy kidney tissue for negative selection.

Targeted therapies have increased the treatment options for triple-negative breast cancer patients. However, the paucity of targetable biomarkers and tumor heterogeneity have limited the ability of precision-guided interventions to live up to their full potential. As affinity-targeting ligands, aptamers show high selectivity toward target molecules. Compared with antibodies, aptamers have lower molecular weight, increased stability during transportation, reduced immunogenicity, and increased tissue uptake. Recently, we reported discovery of the GreenB1 aptamer, which is internalized in cultured triple-negative MDA-MB-231 human breast cancer cells. We show that the GreenB1 aptamer specifically targets β1-integrin, a protein linked previously to breast cancer cell invasiveness and migration. Aptamer binds to β1-integrin with low nanomolar affinity. Our findings suggest potential applications for GreenB1-guided precision agents for diagnosis and therapy of cancers overexpressing β1-integrin.

Purine auxotrophy is an abundant trait among eukaryotic parasites and a typical marker for many budding yeast strains. Supplementation with an additional purine source (such as adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We explored purine starvation effects in a model organism, a budding yeast Saccharomyces cerevisiae ade8 knockout, at the level of cellular morphology, central carbon metabolism, and global transcriptome. We observed that purine-starved cells stopped their cycle in G1/G0 state and accumulated trehalose, and the intracellular concentration of AXP decreased, but adenylate charge remained stable. Cells became tolerant to severe environmental stresses. Intracellular RNA concentration decreased, and massive downregulation of ribosomal biosynthesis genes occurred. We proved that the expression of new proteins during purine starvation is critical for cells to attain stress tolerance phenotype Msn2/4p targets are upregulated in purine-starved cells when compared to cells cultivated in purine-rich media. The overall transcriptomic response to purine starvation resembles that of stationary phase cells. Our results demonstrate that the induction of a strong stress resistance phenotype in budding yeast can be caused not only by natural starvation, but also starvation for metabolic intermediates, such as purines.

Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

In vivo phage display is widely used for identification of organ- or disease-specific homing peptides. However, the current in vivo phage biopanning approaches fail to assess biodistribution of specific peptide phages across tissues during the screen, thus necessitating laborious and time-consuming post-screening validation studies on individual peptide phages. Here, we adopted bioinformatics tools used for RNA sequencing for analysis of high throughput sequencing (HTS) data to estimate the representation of individual peptides during biopanning in vivo . The data from in vivo phage screen were analyzed using differential binding – relative representation of each peptide in the target organ vs. in a panel of control organs. Application of this approach in a model study using low-diversity peptide T7 phage library with spiked-in brain homing phage, demonstrated brain-specific differential binding of brain homing phage and resulted in identification of novel lung and brain specific homing peptides. Our study provides a broadly applicable approach to streamline in vivo peptide phage biopanning and to increase its reproducibility and success rate. Graphic In vivo phage display using differential binding approach