The Algal Chloroplast as a Testbed for Synthetic Biology Designs Aimed at Radically Rewiring Plant Metabolism

Sustainable and economically viable support for an ever-increasing global population requires a paradigm shift in agricultural productivity, including the application of biotechnology to generate future crop plants. Current genetic engineering approaches aimed at enhancing the photosynthetic efficiency or composition of the harvested tissues involve relatively simple manipulations of endogenous metabolism. However, radical rewiring of central metabolism using new-to-nature pathways, so-called "synthetic metabolism", may be needed to really bring about significant step changes. In many cases, this will require re-programming the metabolism of the chloroplast, or other plastids in non-green tissues, through a combination of chloroplast and nuclear engineering. However, current technologies for sophisticated chloroplast engineering ("transplastomics") of plants are limited to just a handful of species. Moreover, the testing of metabolic rewiring in the chloroplast of plant models is often impractical given their obligate phototrophy, the extended time needed to create stable non-chimeric transplastomic lines, and the technical challenges associated with regeneration of whole plants. In contrast, the unicellular green alga, Chlamydomonas reinhardtii is a facultative heterotroph that allows for extensive modification of chloroplast function, including non-photosynthetic designs. Moreover, chloroplast engineering in C. reinhardtii is facile, with the ability to generate novel lines in a matter of weeks, and a well-defined molecular toolbox allows for rapid iterations of the "Design-Build-Test-Learn" (DBTL) cycle of modern synthetic biology approaches. The recent development of combinatorial DNA assembly pipelines for designing and building transgene clusters, simple methods for marker-free delivery of these clusters into the chloroplast genome, and the pre-existing wealth of knowledge regarding chloroplast gene expression and regulation in C. reinhardtii further adds to the versatility of transplastomics using this organism. Herein, we review the inherent advantages of the algal chloroplast as a simple and tractable testbed for metabolic engineering designs, which could then be implemented in higher plants

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Review of NAD(P)H-dependent oxidoreductases: Properties, engineering and application.

NAD(P)H-dependent oxidoreductases catalyze the reduction or oxidation of a substrate coupled to the oxidation or reduction, respectively, of a nicotinamide adenine dinucleotide cofactor NAD(P)H or NAD(P)(+). NAD(P)H-dependent oxidoreductases catalyze a large variety of reactions and play a pivotal role in many central metabolic pathways. Due to the high activity, regiospecificity and stereospecificity with which they catalyze redox reactions, they have been used as key components in a wide range of applications, including substrate utilisation, the synthesis of chemicals, biodegradation and detoxification. There is great interest in tailoring NAD(P)H-dependent oxidoreductases to make them more suitable for particular applications. Here, we review the main properties and classes of NAD(P)H-dependent oxidoreductases, the types of reactions they catalyze, some of the main protein engineering techniques used to modify their properties and some interesting examples of their modification and application