Genetic chemistry: production of non-native compounds in yeast

A Potential Role of NFIL3 in Atherosclerosis

Nuclear factor interleukin-3 (NFIL3), a proline- and acidic-residue-rich (PAR) bZIP transcription factor, is called the E4 binding protein 4 (E4BP4) as well, which is relevant to regulate the circadian rhythms and the viability of cells. More and more evidence has shown that NFIL3 is associated with different cardiovascular diseases. In recent years, it has been found that NFIL3 has significant functions in the progression of atherosclerosis (AS) via the regulation of inflammatory response, macrophage polarization, some immune cells and lipid metabolism. In this overview, we sum up the function of NFIL3 during the development of AS and offer meaningful views how to treat cardiovascular disease related to AS.

Deletion of Budding Yeast MAD2 Suppresses Clone-to-Clone Differences in Artificial Linear Chromosome Copy Numbers and Gives Rise to Higher Retention Rates

Our goal was to investigate the changes in artificial short-linear chromosome average copy numbers per cell arising from partial or full loss of Mitotic Arrest-Deficient 2 (MAD2) spindle checkpoint function in budding yeast Saccharomyces cerevisiae. Average artificial linear chromosome copy numbers in a population of cells, as measured by quantitative polymerase chain reactions (qPCR), and retention rates, as measured by fluctuation analyses, were performed on a total of 62 individual wild type and mad2∆ mutant haploid and diploid clones. Wild type cells, both haploids and diploids, displayed phenotypically unique clone-to-clone differences: one group of 15 clones displayed low-copy numbers per cell and high retention rates, were 1 clone was found to have undergone a genomic integration event, and the second group of 15 clones displayed high copy numbers per cell and low retention rates, with the latter values being consistent with the previously published results where only a single clone had been measured. These chromosome states were observed to be unstable when propagated for 10 days under selection, where high copy-low retention rate clones evolved into low copy-high retention rate clones, but no evidence for integration events was observed. By contrast, mad2∆ haploid and mad2∆/mad2∆ diploids displayed a suppression of the clone-to-clone differences, where 20 out of 21 clones had mid-level artificial linear chromosome copy numbers per cell, but maintained elevated chromosome retention rates. The elevated levels in retention rates in mad2∆ and mad2∆/mad2∆ cells were also maintained even in the absence of selection during growth over 3 days. MAD2/mad2∆ heterozygous diploids displayed multiple clonal groups: 4 with low copy numbers, 5 with mid-level copy numbers, and 1 with a high copy number of artificial linear chromosomes, but all 10 clones uniformly displayed low retention rates. Our observations reveal that MAD2 function contributes to the ability of yeast cells to maintain a high number of artificial linear chromosomes per cell in some clones, but, counter-intuitively, mad2∆ suppresses clone-to-clone differences and leads to an improvement in artificial linear chromosome retention rates yielding a more uniform and stable clonal population with mid-level chromosome copy numbers per cell.

Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources

The methylotrophic yeast Pichia pastoris (Komagataella spp.) is widely used as cell factory for recombinant protein production. In the past recent years, important breakthroughs in the systems-level quantitative analysis of its physiology have been achieved. This wealth of information has allowed the development of genome-scale metabolic models, which make new approaches possible for host cell and bioprocess engineering. Nevertheless, the predictive accuracy of the previous consensus model required to be upgraded and validated with new experimental data sets for P. pastoris growing on glycerol or methanol as sole carbon sources, two of the most relevant substrates for this cell factory. In this study, we have characterized P. pastoris growing in chemostat cultures using glycerol or methanol as sole carbon sources over a wide range of growth rates, thereby providing physiological data on the effect of growth rate and culture conditions on biomass macromolecular and elemental composition. In addition, these data sets were used to improve the performance of the P. pastoris consensus genomic-scale metabolic model iMT1026. Thereupon, new experimentally determined bounds, including the representation of biomass composition for these growth conditions, have been incorporated. As a result, here, we present version 3 (v3.0) of the consensus P. pastoris genome-scale metabolic model as an update of the iMT1026 model. The v3.0 model was validated for growth on glycerol and methanol as sole carbon sources, demonstrating improved prediction capabilities over an extended substrate range including two biotechnologically relevant carbon sources.

Efficient size-independent chromosome delivery from yeast to cultured cell lines

The delivery of large DNA vectors (>100 000 bp) remains a limiting step in the engineering of mammalian cells and the development of human artificial chromosomes (HACs). Yeast is commonly used to assemble genetic constructs in the megabase size range, and has previously been used to transfer constructs directly into cultured cells. We improved this method to efficiently deliver large (1.1 Mb) synthetic yeast centromeric plasmids (YCps) to cultured cell lines at rates similar to that of 12 kb YCps. Synchronizing cells in mitosis improved the delivery efficiency by 10-fold and a statistical design of experiments approach was employed to boost the vector delivery rate by nearly 300-fold from 1/250 000 to 1/840 cells, and subsequently optimize the delivery process for multiple mammalian, avian, and insect cell lines. We adapted this method to rapidly deliver a 152 kb herpes simplex virus 1 genome cloned in yeast into mammalian cells to produce infectious virus.

Overview of regulatory strategies and molecular elements in metabolic engineering of bacteria

From a viewpoint of biotechnology, metabolic engineering mainly aims to change the natural status of a pathway in a microorganism towards the overproduction of certain bioproducts. The biochemical nature of a pathway implies us that changed pathway is often the collective results of altered behavior of the metabolic enzymes encoded by corresponding genes. By finely modulating the expression of these genes or the properties of the enzyme, we can gain efficient control on the pathway. In this article, we reviewed the typical methods that have been applied to regulate the expression of genes in metabolic engineering. These methods are grouped according to the operation targets in a typical gene. The transcription of a gene is controlled by an indispensable promoter. By utilizing promoters with different strengths, expected levels of expression can be easily achieved, and screening a promoter library may find suitable mutant promoters that can provide tunable expression of a gene. Auto-responsive promoter (quorum sensing (QS)-based or oxygen-inducible) simplifies the induction process by driving the expression of a gene in an automated manner. Light responsive promoter enables reversible and noninvasive control on gene activity, providing a promising method in controlling gene expression with time and space resolution in metabolic engineering involving complicated genetic circuits. Through directed evolution and/or rational design, the encoding sequences of a gene can be altered, leading to the possibly most profound changes in properties of a metabolic enzyme. Introducing an engineered riboswitch in mRNA can make it a regulatory molecule at the same time; ribosomal binding site is commonly engineered to be more attractive for a ribosome through design. Terminator of a gene will affect the stability of an mRNA, and intergenic region will influence the expression of many related genes. Improving the performance of these elements are generally the main activities in metabolic engineering.

Fine-tuning multiprotein complexes using small molecules

Multiprotein complexes such as the transcriptional machinery, signaling hubs, and protein folding machines are typically composed of at least one enzyme combined with multiple non-enzymes. Often the components of these complexes are incorporated in a combinatorial manner, in which the ultimate composition of the system helps dictate the type, location, or duration of cellular activities. Although drugs and chemical probes have traditionally targeted the enzyme components, emerging strategies call for controlling the function of protein complexes by modulation of protein-protein interactions (PPIs). However, the challenges of targeting PPIs have been well documented, and the diversity of PPIs makes a "one-size-fits-all" solution highly unlikely. These hurdles are particularly daunting for PPIs that encompass large buried surface areas and those with weak affinities. In this Review, we discuss lessons from natural systems, in which allostery and other mechanisms are used to overcome the challenge of regulating the most difficult PPIs. These systems may provide a blueprint for identifying small molecules that target challenging PPIs and affecting molecular decision-making within multiprotein systems.