Predicting new drug indications for prostate cancer: The integration of an in silico proteochemometric network pharmacology platform with patient-derived primary prostate cells

BACKGROUND

Drug repurposing enables the discovery of potential cancer treatments using publically available data from over 4000 published Food and Drug Administration approved and experimental drugs. However, the ability to effectively evaluate the drug's efficacy remains a challenge. Impediments to broad applicability include inaccuracies in many of the computational drug-target algorithms and a lack of clinically relevant biologic modeling systems to validate the computational data for subsequent translation.

METHODS

We have integrated our computational proteochemometric systems network pharmacology platform, DrugGenEx-Net, with primary, continuous cultures of conditionally reprogrammed (CR) normal and prostate cancer (PCa) cells derived from treatment-naive patients with primary PCa.

RESULTS

Using the transcriptomic data from two matched pairs of benign and tumor-derived CR cells, we constructed drug networks to describe the biological perturbation associated with each prostate cell subtype at multiple levels of biological action. We prioritized the drugs by analyzing these networks for statistical coincidence with the drug action networks originating from known and predicted drug-protein targets. Prioritized drugs shared between the two patients' PCa cells included carfilzomib (CFZ), bortezomib (BTZ), sulforaphane, and phenethyl isothiocyanate. The effects of these compounds were then tested in the CR cells, in vitro. We observed that the IC50 values of the normal PCa CR cells for CFZ and BTZ were higher than their matched tumor CR cells. Transcriptomic analysis of CFZ-treated CR cells revealed that genes involved in cell proliferation, proteases, and downstream targets of serine proteases were inhibited while KLK7 and KLK8 were induced in the tumor-derived CR cells.

CONCLUSIONS

Given that the drugs in the database are extremely well-characterized and that the patient-derived cells are easily scalable for high throughput drug screening, this combined in vitro and in silico approach may significantly advance personalized PCa treatment and for other cancer applications.

DNA Methylation Across the Serotonin Transporter Gene Following Marital Separation: A Pilot Study

BACKGROUND

Marital separation and divorce are stressful life transitions associated with increased risk for a range of poor mental and physical health outcomes. A key task for research in this area is to identify individual differences that may index risk for these adverse outcomes.

PURPOSE

To examine the association between DNA methylation across the serotonin transporter gene (SLC6A4) and self-reported emotional distress following marital separation.

METHODS

Genomic DNA methylation (from buffy coat fractions of whole blood) was quantified in a sample of 47 adults following a recent marital separation; concurrent with the blood draw, participants completed questionnaires on their psychological adjustment to the separation experience.

RESULTS

Relatively greater methylation of SLC6A4 was associated with less subjective separation-related psychological distress, and this association held after accounting for participants' age, length of the relationship, time since the separation, and SLC6A4 genotype, b = -211.99, SE = 94.91, p = .03, 95% CI: -402.22, -25.21. Significantly stronger negative associations were observed between methylation and psychological adjustment among participants who had more recently separated from their former partner.

CONCLUSIONS

Although results derived from small samples must be considered preliminary and hypothesis generating, the current study raises new questions about the role of DNA methylation and psychosocial adaptation to stressful life events such as divorce, and the findings can inform future studies in this research area.

PhIP-Seq characterization of serum antibodies using oligonucleotide-encoded peptidomes

The binding specificities of an individual's antibody repertoire contain a wealth of biological information. They harbor evidence of environmental exposures, allergies, ongoing or emerging autoimmune disease processes, and responses to immunomodulatory therapies, for example. Highly multiplexed methods to comprehensively interrogate antibody-binding specificities have therefore emerged in recent years as important molecular tools. Here, we provide a detailed protocol for performing 'phage immunoprecipitation sequencing' (PhIP-Seq), which is a powerful method for analyzing antibody-repertoire binding specificities with high throughput and at low cost. The methodology uses oligonucleotide library synthesis (OLS) to encode proteomic-scale peptide libraries for display on bacteriophage. These libraries are then immunoprecipitated, using an individual's antibodies, for subsequent analysis by high-throughput DNA sequencing. We have used PhIP-Seq to identify novel self-antigens associated with autoimmune disease, to characterize the self-reactivity of broadly neutralizing HIV antibodies, and in a large international cross-sectional study of exposure to hundreds of human viruses. Compared with alternative array-based techniques, PhIP-Seq is far more scalable in terms of sample throughput and cost per analysis. Cloning and expression of recombinant proteins are not required (versus protein microarrays), and peptide lengths are limited only by DNA synthesis chemistry (up to 90-aa (amino acid) peptides versus the typical 8- to 12-aa length limit of synthetic peptide arrays). Compared with protein microarrays, however, PhIP-Seq libraries lack discontinuous epitopes and post-translational modifications. To increase the accessibility of PhIP-Seq, we provide detailed instructions for the design of phage-displayed peptidome libraries, their immunoprecipitation using serum antibodies, deep sequencing-based measurement of peptide abundances, and statistical determination of peptide enrichments that reflect antibody-peptide interactions. Once a library has been constructed, PhIP-Seq data can be obtained for analysis within a week.

Publisher Correction: PhIP-Seq characterization of serum antibodies using oligonucleotide-encoded peptidomes

The version of this paper originally published contained typesetter-introduced errors in some of the code commands, consisting of conversion of a closing backslash (\) to a forward slash (/). These errors have been corrected in the HTML and PDF versions of the protocol.