Prediction and large-scale analysis of primary operons in plastids reveals unique genetic features in the evolution of chloroplasts.

While bacterial operons have been thoroughly studied, few analyses of chloroplast operons exist, limiting the ability to study fundamental elements of these structures and utilize them for synthetic biology. Here, we describe the creation of a plastome-specific operon database (link provided below) achieved by combining experimental tools and predictive modeling. Using a Reverse-Transcription-PCR based method and published data, we determined the transcription-state of 213 gene pairs from four plastomes of evolutionary distinct organisms. By analyzing sequence-based features computed for our dataset, we were able to highlight fundamental characteristics differentiating between operon pairs and non-operon pairs. These include an interesting tendency toward maintaining similar messenger RNA-folding profiles in operon gene pairs, a feature that failed to yield any informative separation in cyanobacteria, suggesting that it catches unique traits of operon gene expression, which have evolved post-endosymbiosis. Subsequently, we used this feature set to train a random-forest classifier for operon prediction. As our results demonstrate the ability of our predictor to obtain accurate (84%) and robust predictions on unlabeled datasets, we proceeded to building operon maps for 2018 sequenced plastids. Our database may now present new opportunities for promoting metabolic engineering and synthetic biology in chloroplasts.

Solving the Riddle of the Evolution of Shine-Dalgarno Based Translation in Chloroplasts.

Chloroplasts originated from an ancient cyanobacterium and still harbor a bacterial-like genome. However, the centrality of Shine-Dalgarno ribosome binding, which predominantly regulates proteobacterial translation initiation, is significantly decreased in chloroplasts. As plastid ribosomal RNA anti-Shine-Dalgarno elements are similar to their bacterial counterparts, these sites alone cannot explain this decline. By computational simulation we show that upstream point mutations modulate the local structure of ribosomal RNA in chloroplasts, creating significantly tighter structures around the anti-Shine-Dalgarno locus, which in-turn reduce the probability of ribosome binding. To validate our model, we expressed two reporter genes (mCherry, hydrogenase) harboring a Shine-Dalgarno motif in the Chlamydomonas reinhardtii chloroplast. Coexpressing them with a 16S ribosomal RNA, modified according to our model, significantly enhances mCherry and hydrogenase expression compared with coexpression with an endogenous 16S gene.

ChimeraUGEM: unsupervised gene expression modeling in any given organism.

MOTIVATION: Regulation of the amount of protein that is synthesized from genes has proved to be a serious challenge in terms of analysis and prediction, and in terms of engineering and optimization, due to the large diversity in expression machinery across species. RESULTS: To address this challenge, we developed a methodology and a software tool (ChimeraUGEM) for predicting gene expression as well as adapting the coding sequence of a target gene to any host organism. We demonstrate these methods by predicting protein levels in seven organisms, in seven human tissues, and by increasing in vivo the expression of a synthetic gene up to 26-fold in the single-cell green alga Chlamydomonas reinhardtii. The underlying model is designed to capture sequence patterns and regulatory signals with minimal prior knowledge on the host organism and can be applied to a multitude of species and applications. AVAILABILITY AND IMPLEMENTATION: Source code (MATLAB, C) and binaries are freely available for download for non-commercial use at http://www.cs.tau.ac.il/~tamirtul/ChimeraUGEM/, and supported on macOS, Linux and Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Image-Processing Software for High-Throughput Quantification of Colony Luminescence.

Many microbiological assays include colonies that produce a luminescent or fluorescent (here generalized as "luminescent") signal, often in the form of luminescent halos around the colonies. These signals are used as reporters for a trait of interest; therefore, exact measurements of the luminescence are often desired. However, there is currently a lack of high-throughput methods for analyzing these assays, as common automatic image analysis tools are unsuitable for identifying these halos in the presence of the inherent biological noise. In this work, we have developed CFQuant-automatic, high-throughput software for the analysis of images from colony luminescence assays. CFQuant overcomes the problems of automatic identification by relying on the luminescence halo's expected shape and provides measurements of several features of the colonies and halos. We examined the performance of CFQuant using one such colony luminescence assay, where we achieved a high correlation (R = 0.85) between the measurements of CFQuant and known protein expression levels. This demonstrates CFQuant's potential as a fast and reliable tool for analysis of colony luminescence assays.IMPORTANCE Luminescent markers are widely used as reporters for various biologically interesting traits. In colony luminescence assays, the levels of luminescence around each colony can be used to compare the levels of traits of interest for different strains, treatments, etc., using quantitative measurements of the luminescence. However, automatic methods of obtaining this data are underdeveloped, making this a laborious manual process, especially in analyzing large numbers of colonies. The significance of this work is in developing an automatic, high-throughput tool for quantitative analysis of colony luminescence assays, which will allow fast collection of qualitative data from these assays and thus increase their overall usability.

The Integration of Multiple Nuclear-Encoded Transgenes in the Green Alga Chlamydomonas reinhardtii Results in Higher Transcription Levels.

The integration of genes into the nuclear genome of Chlamydomonas reinhardtii is mediated by Non-Homologous-End-Joining, thus resulting in unpredicted insertion locations. This phenomenon defines 'the position-effect', which is used to explain the variation of expression levels between different clones transformed with the same DNA fragment. Likewise, nuclear transgenes often undergo epigenetic silencing that reduces their expression; hence, nuclear transformations require high-throughput screening methods to isolate clones that express the foreign gene at a desirable level. Here, we show that the number of integration sites of heterologous genes results in higher mRNA levels. By transforming both a synthetic ferredoxin-hydrogenase fusion enzyme and a Gaussia-Luciferase reporter protein, we were able to obtain 33 positive clones that exhibit a wide range of synthetic expression. We then performed a droplet-digital polymerase-chain-reaction for these lines to measure their transgene DNA copy-number and mRNA levels. Surprisingly, most clones contain two integration sites of the synthetic gene (45.5%), whilst 33.3% contain one, 18.1% include three and 3.1% encompass four. Remarkably, we observed a positive correlation between the raw DNA copy-number values to the mRNA levels, suggesting a general effect of which transcription of transgenes is partially modulated by their number of copies in the genome. However, our data indicate that only clones harboring at least three copies of the target amplicon show a significant increment in mRNA levels of the reporter transgene. Lastly, we measured protein activity for each of the reporter genes to elucidate the effect of copy-number variation on heterologous expression.