Synthetic chromosome fusion: Effects on mitotic and meiotic genome structure and function

We designed and synthesized synI, which is ~21.6% shorter than native chrI, the smallest chromosome in Saccharomyces cerevisiae. SynI was designed for attachment to another synthetic chromosome due to concerns surrounding potential instability and karyotype imbalance and is now attached to synIII, yielding the first synthetic yeast fusion chromosome. Additional fusion chromosomes were constructed to study nuclear function. ChrIII-I and chrIX-III-I fusion chromosomes have twisted structures, which depend on silencing protein Sir3. As a smaller chromosome, chrI also faces special challenges in assuring meiotic crossovers required for efficient homolog disjunction. Centromere deletions into fusion chromosomes revealed opposing effects of core centromeres and pericentromeres in modulating deposition of the crossover-promoting protein Red1. These effects extend over 100 kb and promote disproportionate Red1 enrichment, and thus crossover potential, on small chromosomes like chrI. These findings reveal the power of synthetic genomics to uncover new biology and deconvolute complex biological systems  </p

Strategies for enhancing and controlling metabolic flux in engineered organisms

Efforts to rejuvenate the under-exploited, but high-value, natural product space has focused on wiring nature’s biochemical reactions into microorganisms and features as a sustainable alternative to the chemical synthesis. However, for industrial relevance, the metrics of strain performance, yield, titer, and productivity, need to be improved, to achieve a better economic value. To tackle this, most metabolic engineering strategies have focused on rational deletions or overexpression of genes across metabolic pathways and given little thought to metabolic fluxes and how their channeling could affect biomass accumulation and product formation. Considering this, we propose two approaches: one for increasing the flux across metabolic pathways through the creation of fusion enzyme complexes; and the second to control flux, by tweaking the distribution of resources between biomass and product formation, through an optogenetic circuit. For the first, we have focused on increasing the flux through the non-mevalonate pathway, which is a precursor for the biosynthesis of several terpenoids. Taking inspiration from naturally occurring fusion or bifunctional enzymes of this pathway, we constructed several artificial fusions between rate limiting enzymes, by varying the catalytic domains and linkers, and tested their ability to improve flux, by enabling better substrate channeling. From our data, we found the fusion between the enzymes of IspD and IspE, with a flexible linker, outperformed the other strains especially in terms of lycopene titer. For controlling flux, we created an optogenetic circuit, which provides fine spatial control over individual cells and has advantages over chemical inducers. On exposure to red-light at 660 nm, the circuit activates T7 RNA polymerase and allocates resources between biomass accumulation and product formation. Design elements of the circuit include: plant-based phytochromes, that function as optical dimers; and yeast-based split-inteins, that can trigger a trans-splicing reaction. Using this circuit and external optical systems, we have dynamically controlled the expression of T7 RNA polymerase, which controls expression of the secondary metabolite, lycopene in our case. We expressed this circuit in bacteria and observed roughly a fivefold increase in lycopene titer with light, versus no light illumination, providing proof-of-concept of the approach.Applied Science, Faculty ofChemical and Biological Engineering, Department ofGraduat

Octree Representation Improves Data Fidelity of Cardiac CT Images and Convolutional Neural Network Semantic Segmentation of Left Atrial and Ventricular Chambers

PurposeTo assess whether octree representation and octree-based convolutional neural networks (CNNs) improve segmentation accuracy of three-dimensional images.Materials and methodsCardiac CT angiographic examinations from 100 patients (mean age, 67 years ± 17 [standard deviation]; 60 men) performed between June 2012 and June 2018 with semantic segmentations of the left ventricular (LV) and left atrial (LA) blood pools at the end-diastolic and end-systolic cardiac phases were retrospectively evaluated. Image quality (root mean square error [RMSE]) and segmentation fidelity (global Dice and border Dice coefficients) metrics of the octree representation were compared with spatial downsampling for a range of memory footprints. Fivefold cross-validation was used to train an octree-based CNN and CNNs with spatial downsampling at four levels of image compression or spatial downsampling. The semantic segmentation performance of octree-based CNN (OctNet) was compared with the performance of U-Nets with spatial downsampling.ResultsOctrees provided high image and segmentation fidelity (median RMSE, 1.34 HU; LV Dice coefficient, 0.970; LV border Dice coefficient, 0.843) with a reduced memory footprint (87.5% reduction). Spatial downsampling to the same memory footprint had lower data fidelity (median RMSE, 12.96 HU; LV Dice coefficient, 0.852; LV border Dice coefficient, 0.310). OctNet segmentation improved the border segmentation Dice coefficient (LV, 0.612; LA, 0.636) compared with the highest performance among U-Nets with spatial downsampling (Dice coefficients: LV, 0.579; LA, 0.592).ConclusionOctree-based representations can reduce the memory footprint and improve segmentation border accuracy.Keywords CT, Cardiac, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021