High-grade serous ovarian cancer cell lines exhibit heterogeneous responses to growth factor stimulation.

BACKGROUND: The factors driving the onset and progression of ovarian cancer are not well understood. Recent reports have identified cell lines that are representative of the genomic pattern of high-grade serous ovarian cancer (HGSOC), in which greater than 90 % of tumors have a mutation in TP53. However, many of these representative cell lines have not been widely used so it is unclear if these cell lines capture the variability that is characteristic of the disease. METHODS: We investigated six TP53-mutant HGSOC cell lines (Caov3, Caov4, OV90, OVCA432, OVCAR3, and OVCAR4) for migration, MMP2 expression, proliferation, and VEGF secretion, behaviors that play critical roles in tumor progression. In addition to comparing baseline variation between the cell lines, we determined how these behaviors changed in response to four growth factors implicated in ovarian cancer progression: HB-EGF, NRG1ß, IGF1, and HGF. RESULTS: Baseline levels of each behavior varied across the cell lines and this variation was comparable to that seen in tumors. All four growth factors impacted cell proliferation or VEGF secretion, and HB-EGF, NRG1ß, and HGF impacted wound closure or MMP2 expression in at least two cell lines. Growth factor-induced responses demonstrated substantial heterogeneity, with cell lines sensitive to all four growth factors, a subset of the growth factors, or none of the growth factors, depending on the response of interest. Principal component analysis demonstrated that the data clustered together based on cell line rather than growth factor identity, suggesting that response is dependent on intrinsic qualities of the tumor cell rather than the growth factor. CONCLUSIONS: Significant variation was seen among the cell lines, consistent with the heterogeneity of HGSOC.

A Practical Guide to Reproducible Modeling for Biochemical Networks.

While scientific disciplines revere reproducibility, many studies - experimental and computational alike - fall short of this ideal and cannot be reproduced or even repeated when the model is shared. For computational modeling of biochemical networks, there is a dearth of formal training and resources available describing how to practically implement reproducible methods, despite a wealth of existing tools and formats which could be used to support reproducibility. This chapter points the reader to useful software tools and standardized formats that support reproducible modeling of biochemical networks and provides suggestions on how to implement reproducible methods in practice. Many of the suggestions encourage readers to use best practices from the software development community in order to automate, test, and version control their model components. A Jupyter Notebook demonstrating several of the key steps in building a reproducible biochemical network model is included to supplement the recommendations in the text.

Best Practices for Making Reproducible Biochemical Models.

Like many scientific disciplines, dynamical biochemical modeling is hindered by irreproducible results. This limits the utility of biochemical models by making them difficult to understand, trust, or reuse. We comprehensively list the best practices that biochemical modelers should follow to build reproducible biochemical model artifacts-all data, model descriptions, and custom software used by the model-that can be understood and reused. The best practices provide advice for all steps of a typical biochemical modeling workflow in which a modeler collects data; constructs, trains, simulates, and validates the model; uses the predictions of a model to advance knowledge; and publicly shares the model artifacts. The best practices emphasize the benefits obtained by using standard tools and formats and provides guidance to modelers who do not or cannot use standards in some stages of their modeling workflow. Adoption of these best practices will enhance the ability of researchers to reproduce, understand, and reuse biochemical models.

Targeting Ligands Deliver Model Drug Cargo into the Central Nervous System along Autonomic Neurons.

While biologic drugs such as proteins, peptides, or nucleic acids have shown promise in the treatment of neurodegenerative diseases, the blood-brain barrier (BBB) severely limits drug delivery to the central nervous system (CNS) after systemic administration. Consequently, drug delivery challenges preclude biological drug candidates from the clinical armamentarium. In order to target drug delivery and uptake into to the CNS, we used an in vivo phage display screen to identify peptides able to target drug-uptake by the vast array of neurons of the autonomic nervous system (ANS). Using next-generation sequencing, we identified 21 candidate targeted ANS-to-CNS uptake ligands (TACL) that enriched bacteriophage accumulation and delivered protein-cargo into the CNS after intraperitoneal (IP) administration. The series of TACL peptides were synthesized and tested for their ability to deliver a model enzyme (NeutrAvidin-horseradish peroxidase fusion) to the brain and spinal cord. Three TACL-peptides facilitated significant active enzyme delivery into the CNS, with limited accumulation in off-target organs. Peptide structure and serum stability is increased when internal cysteine residues are cyclized by perfluoroarylation with decafluorobiphenyl, which increased delivery to the CNS further. TACL-peptide was demonstrated to localize in parasympathetic ganglia neurons in addition to neuronal structures in the hindbrain and spinal cord. By targeting uptake into ANS neurons, we demonstrate the potential for TACL-peptides to bypass the blood-brain barrier and deliver a model drug into the brain and spinal cord.