Rational design of bacteriophages as a platform for cancer therapy

The aim of this work is to engineer a bacteriophage-based platform to specifically target, invade and control cancer

Biosynthetic production of curcuminoids

Curcuminoids are natural phenylpropanoids from plants that have been reported as potential cancer-fighting drugs. Nevertheless, these compounds present a poor bioavailability. Cellular uptake is low and curcuminoids are quickly metabolized once inside the cell, requiring repetitive oral doses to achieve an effective concentration for therapeutic activity [1]. Herein, we report an engineered artificial pathway for the production of curcuminoids in Escherichia coli. Arabidopsis thaliana 4-coumaroyl-CoA ligase and Curcuma longa diketide-CoA synthase (DCS) and curcumin synthase (CURS1) were used and 188 µM (70 mg/L) of curcumin was obtained from ferulic acid [2]. Bisdemethoxycurcumin and demethoxycurcumin were also produced, but in lower concentrations, by feeding p-coumaric acid or a mixture of p-coumaric acid and ferulic acid, respectively. Additionally, curcuminoids were produced from tyrosine through the caffeic acid pathway. To produce caffeic acid, tyrosine ammonia lyase from Rhodotorula glutinis and 4-coumarate 3-hydroxylase from Saccharothrix espanaensis were used [3]. Caffeoyl-CoA 3-O-methyl-transferase from Medicago sativa was used to convert caffeoyl-CoA to feruloyl-CoA. Using caffeic acid, p-coumaric acid or tyrosine as a substrate, 3.9, 0.3, and 0.2 µM of curcumin were produced, respectively. This is the first report on the use of DCS and CURS1 in vivo to produce curcuminoids. In addition, curcumin, the most studied curcuminoid for therapeutic purposes and considered in many studies as the most potent and active, was produced by feeding tyrosine using a pathway involving caffeic acid. We anticipate that by using a tyrosine overproducing strain, curcumin can be produced in E. coli without the need of adding expensive precursors to the medium, thus decreasing the production cost. Therefore, this alternative pathway represents a step forward in the heterologous production of curcumin using E. coli. Aiming at greater production titers and yields, the construction of this pathway in another model organism such as Saccharomyces cerevisiae is being considered

Design and construction of a new biosynthetic pathway for the production of curcuminoids in Escherichia coli

Curcuminoids are natural pigments from plants that have been reported as potential cancer-fighting drugs. The aim of this work is to engineer an artificial biosynthetic pathway for curcuminoids production by Escherichia coli. Starting from the substrate tyrosine, the curcumin pathway involves several enzymatic steps: conversion of tyrosine to p-coumaric acid; conversion of p-coumaric acid to caffeic acid; production of caffeoyl-CoA from caffeic acid; production of feruloyl-CoA from caffeoyl-CoA; and finally the production of curcumin from feruloyl-CoA and possibly other curcuminoids, due to enzyme promiscuity. The enzymes involved in the two first enzymatic steps are tyrosine ammonia lyase from Rhodotorula glutinis, P450 CYP199A2 from Rhodopseudomonas palustris, and the redox partners pdr from Pseudomonas putida and pux from R. palustris. These two steps were successfully accomplished. Two CoA ligases from different sources are being explored for the conversion of the different carboxylic acids into their corresponding CoA esters. Different combinations of these enzymes and caffeoyl-CoA 3-methyl transferase may lead to the production of different curcuminoids. For the last step of the pathway two approaches are being studied: the use of diketide-CoA synthase and curcuminoid synthase from Curcuma longa, and curcumin synthase from Oryza sativa that itself catalyzes both steps. Successful construction of the curcuminoids biosynthetic pathway would mark a significant step forward in the in situ production of these poorly soluble, anti-carcinogenic compounds

Image processing on animal cell cultures : a refined technique

The process of microscopic animal cell counting can be a time-consuming process, resulting in a subjective analysis varying according to the researcher’s perception. Regarding the ideal moment to divide the cells, the decision is performed in an empirical manner and is affected by the complexity of cell morphology and density. Searching for a way to overcome these problems, and considering the decreasing costs of computational data processing, a window was found for new methodologies to quickly characterize a given structure. Advances in digital imaging allow the extraction of quantitative information, opposite to the qualitative and subjective evaluation of human analysis. Thus, microscopy image analysis techniques have gained, during the last years, an unquestionable role in several fields of research. The purpose of an image processing step resides in obtaining a final image holding significant information for a given application. These techniques should be automated as much as possible to avoid subjectivity. Thus, several segmentation techniques have been already proposed. For segmentation to take place, usually a threshold value(s) must be defined to allow the differentiation between the objects and background. Other methods, such as region growing, mathematical morphology and watershed are also used for this purpose. These are simple algorithms that when appropriately used can provide promising results and oftentimes with a low computation complexity. Nevertheless, the previous methods have some limitations, including non-uniform intensity variations, low-contrast images, irregular segmentation and over-segmentation. More sophisticated methods based on frameworks of active contours (e.g. snakes, level-sets) or graph-cuts can also be applied to segment cells with positive results. Nonetheless, these algorithms present high computational complexity. The main goal of this work was to develop an image processing tool using several algorithms in order to improve cell segmentation processing for different morphological cells and densities. For that purpose, different cells were used ‒ MDA-MB-231 and -435, both cancer cell lines, and MCF-10-2A, a non-tumorigenic line. Cells were observed in a Leica DM IL inverted contrasting microscope, in phase-contrast at 100x total magnification, coupled with a Leica D-LUX 3 camera, ensuring the same acquisition conditions. Despite the variability in their morphology, preliminary results demonstrated that the segmentation process was fairly successfully. As a result, the previously described flaws were minimized, leading to more efficient animal cell culturing with less variability

Development of an image analysis methodology for animal cell cultures characterization

To establish a strong cell culture protocol and to evaluate experimental results, a quantitative determination of animal cells characteristics, such as confluence and morphology is quite often required. Quantitative image analysis using automated processing has become a routine methodology in a wide range of applications with the advantage of being non-invasive and non-destructive. However, in animal cells cultures automatic techniques giving valuable information based on visual inspection are still lacking. In the present work an image analysis procedure was developed to accurately detect animal cell cultures from images captured in phase-contrast microscopy. Image analysis results demonstrated that the methodology was successfully applied, leading to more efficient animal cell culturing with less variability

A flexible-docking approach for the design of novel cancer peptidomimetic drugs

Cancer is the second leading cause of death worldwide and the lack of alternative therapies has kept patients dependent on classic chemotherapy. The most occurring form of cancer amongst women is breast cancer and the triple negative cell subtype (TNBC) is responsible for a high metastatic and mortality rate, as it presents molecular and genetic shifts, lacks specific targeting and responds poorly to existing therapies. Recent studies have provided convincing evidence that the therapeutic outcome of chemotherapy may be affected by the expression and activity of receptor tyrosine kinases and their phosphatase pathways (MAPK/ERK, PI3/AKT, among others). These proteins are implicated in mechanisms of cell survival, drug resistance and Epithelial-Mesenchymal Transition (EMT), and are therefore key targets for TNBC cancer cell subtype. However, many inhibitors developed for these targets have not succeeded at a clinical level and present low solubility. As a result of the pronounced decline in productivity experienced by drug discovery efforts in the last years, novel approaches to the rational design of new drugs are now being pursued. A potential solution might be the use of natural or synthetic peptides and peptidomimetics targeting protein-protein interactions essential for signaling networks function. The combination of several bioinformatic approaches (docking, virtual screening, pharmacophore models, among others) allows the use of the vast amount of existing information on available compounds and protein-protein interactions in structural databases. In this study we designed a procedure for small peptidomimetics structure-based rational drug design capable of blocking the active sites of SNAiL1, a protein that has been suggested as a potent repressor of E-cadherin expression and consequently, as an inducer of EMT transition in TNBCs. A random library was created using a composite approach for drug-like compound identification from the PubChem and Development Therapeutics NCI/NIH compound databases, which combined structure-based virtual screening (known motifs of peptide structures within proteins and small molecules) and Z-score comparison. Docking studies were performed to map the polypeptides activity and stability: (1) point alteration studies using non-natural aminoacids for helical stability over a wider range (since linear peptides adopt many confirmations in aqueous solution) using Ramachandran plot dihedral angles estimation; (2) quantitative structure-activity relationships (QSAR) using radical modification chemical studies and (3) umbrella sampling for dissociations studies. The peptidomimetic SNAiL1 model created suggested at least two radical modifications for a strong inhibition

Targeted therapy using phage technology: a computational and experimental breast cancer study

During the past two decades cancer biology knowledge has widely increased and shifted the paradigm of cancer treatment from nonspecific cytotoxic agents to selective, mechanism-based therapeutics. Initially, cancer drug design was focused on compounds that rapidly killed dividing cells. Though still used as the backbone of current treatments, these highly unspecific targeting drugs lead to significant toxicity for patients, narrowing the therapeutic index, and frequently lead to drug resistance. Therefore, cancer therapies are now based on cancer immunotherapy and targeted agents, whereas novel treatments are strategically combining both to improve clinical outcomes. Despite the nanotechnology advances dictating the development of targeted therapies in diverse classes of nano-based carriers, virus-based vectors still remain highly used due to its biocompatibility and specificity for the target. Particularly, bacteriophages are an interesting alternative ‘nanomedicine’ that can combine biological and chemical components into the same drug delivery system. The great potential of this novel platform for cancer therapy is the ability to genetically manipulate the virus-vector to display specific targeting moieties. Phage display technology, a general technique used for detecting interfaces of various types of interacting proteins outside of the immunological context, allows the target agents to locate the target (with an increased selection process for the specific binding – termed biopanning) and play their essential role inhibiting molecular pathways crucial for tumour growth and maintenance. Phage display specificity core is related with the binding of small peptides displayed at their coat or capsid proteins, enriched during biopanning. Bioinformatics plays an important role in testing and improving phage display libraries by effective epitope mapping, selecting from a large set of random peptides those with a high binding affinity to a target of interest. In this work we demonstrate the screening of a manually constructed 7-mer peptide library of M13KE phage particles against MDA-MB-231 and -435 cancer cell lines. Two peptides – TLATVEV and PRLNVSP – with high affinity for the referred cells were identified, respectively. Based on computationally predicted epitopes based on the peptides extracted from this library the linear peptide sequence was docked onto known membrane proteins from the used cell lines and peptides-proteins interactions were mapped. Umbrella sampling studies were performed to predict the binding affinity and to improve future rational design of binding peptides to these cancer cells