Integrated analysis of genomic and epigenomic instability for CHO cell line engineering

Stability is an important factor in the development of cell lines for therapeutic protein production. In culture, the chromosome number and structure of Chinese Hamster Ovary (CHO) cells undergo rapid change. Over the course of cultivation, selection, and adaptation, chromosomal aberrations such as mutations, deletions, duplications, and other structural variants can accumulate. Some genomic regions may be more prone to such instability than others. When introducing exogenous genes for product formation or for engineering cell characteristics, it is critical to integrate into a stable region. A deeper understanding of the relationship between structure and stability is important for cell culture engineering. We investigated the genome stability of CHO cell lines at the macroscopic and microscopic levels, as well as from the epigenetic and genetic perspective. At the macroscopic level, we examined chromosomal and karyotypic variation, observing that the progenies of single cell clones quickly developed widely distributed variants with different numbers and types of chromosomes. However, at the population level the karyotype and chromosomal number distribution remained in a similar range. Stability at the microscopic level was analyzed using a gene-coding region focused comparative genomic hybridization (CGH) microarray, allowing us to determine genomic variations in gene copy number. With CGH data for many parent-daughter relationships, including subclones and relationships between host and producing cell lines, we identified genome segment changes that happen commonly during cell line development and subcloning. To further examine variation at the microscopic and genetic level, whole genome sequencing data of multiple CHO cell lines was used to identify structural variants, such as deletions, inversions, and duplications using the tools DELLY2 and LUMPY. Heterogeneity was present within each cell line and visible in the form of genome mosaicism. The effect of epigenetic modifications on the CHO genome was explored using the Assay for Transposase Accessible Chromatin Sequencing (ATAC-seq), which examines chromatin accessibility. ATAC-seq information was incorporated with transcriptional activity data using RNA-seq from multiple cell lines to identify inaccessible regions of the genome. This integrated systems approach combining chromosome number, karyotyping, CGH, genome sequencing, ATAC-seq, and RNA-seq gives us insight into the heterogeneity and instability of CHO cells, allowing us to identify desirable and undesirable regions for gene integration. With this data, we can select sites ideal for targeted integration of transgenes as well as screen out potentially unstable cell lines developed using random integration

Bioreactor scale induced alteration in cell metabolic state can impact amino acid misincorporations in recombinant proteins produced in CHO cells

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A synthetic biology based cell line engineering pipeline

An ideal host cell line for deriving cell lines of high recombinant protein production should be stable, predictable, and amenable to rapid cell engineering or other forms of phenotypical manipulation. In the past few years we have employed genomic information to identify “safe harbors” for exogenous gene integration in CHO cells, deployed systems modeling and optimization to design pathways and control strategies to modify important aspects of recombinant protein productivity, and established a synthetic biology approach to implement genetic changes, all with the goal of creating a pipeline to produce “designer” cell lines. Chinese hamster ovary (CHO) cells are the preferred platform for protein production. However, the Chinese hamster genome is unstable in its ploidy, is subject to long and short deletions, duplications, and translocations. In addition, gene expression is subject to epigenetic changes including DNA methylation, histone modification and heterochromatin invasion, thus further complicating transgene expression for protein production in cell lines. With these issues in mind, we set out to engineer a CHO cell line highly amenable to stable protein production using a synthetic biology approach. We compiled karyotyping and chromosome number data of several CHO cell lines and sublines, identified genomic regions with high a frequency of gain and loss of copy number using comparative genome hybridization (CGH), and verified structural variants using sequencing data. We further used ATAC (Assay for Transposase-Accessible Chromatin) sequencing to study chromatin accessibility and epigenetic stability within the CHO genome. RNA-seq data from multiple cell lines were also used to identify regions with high transcriptional activity. Analysis of these data allowed the identification of several “safe harbor” loci that could be used for cell engineering. Based on results of the data analysis and identification of “safe harbors”, we engineered an IgG producing cell line with a single copy of the product transgene as a template cell line. This product gene site is flanked by sequences for recombinase mediated cassette exchange, therefore allowing easy substitution of the IgG producing gene for an alternative product gene. Furthermore, a “landing pad” for multi-gene cassette insertion was integrated into the genome at an additional site. Together, these sites allowed engineering of new cell lines producing a fusion protein and Erythropoietin to be generated from the template cell line. To enable rapid assembly of product transgenes and genetic elements for engineering cell attributes into multi-gene cassettes, we adopted a golden-gate based synthetic biology approach. The assembly of genetic parts into multi-gene cassettes in a LEGO-like fashion allowed different combinations of genes under the control of various promoters to be generated quickly for introduction into the template cell line. Using this engineered CHO cell line, we set out to study metabolism and product protein glycosylation for cell engineering. To guide the selection of genetic elements for cell engineering, we developed a multi-compartment kinetic model, as well as a flux model of energy metabolism and glycosylation. The transcriptome meta-data was used extensively to identify genes and isoforms expressed in the cell line and to estimate the enzyme levels in the model. The flux model was used to identify and the LEGO-like platform was used to implement the genetic changes that can alter the glycosylation pattern of the IgG produced by the template cell line. Concurrently we employed a systems optimization approach to identify the genetic alterations in the metabolic pathway to guide cell metabolism toward a favorable state. The model prediction is being implemented experimentally using the synthetic biology approach. In conclusion, we have illustrated a pipeline of rational cell line engineering that integrates genomic science, systems engineering and synthetic biology approaches. The promise, the technical challenges and possible limitations will be discussed in this presentation

How Carvedilol activates β₂-adrenoceptors

Carvedilol is among the most effective β-blockers for improving survival after myocardial infarction. Yet the mechanisms by which carvedilol achieves this superior clinical profile are still unclear. Beyond blockade of β(1)-adrenoceptors, arrestin-biased signalling via β(2)-adrenoceptors is a molecular mechanism proposed to explain the survival benefits. Here, we offer an alternative mechanism to rationalize carvedilol’s cellular signalling. Using primary and immortalized cells genome-edited by CRISPR/Cas9 to lack either G proteins or arrestins; and combining biological, biochemical, and signalling assays with molecular dynamics simulations, we demonstrate that G proteins drive all detectable carvedilol signalling through β(2)ARs. Because a clear understanding of how drugs act is imperative to data interpretation in basic and clinical research, to the stratification of clinical trials or to the monitoring of drug effects on the target pathway, the mechanistic insight gained here provides a foundation for the rational development of signalling prototypes that target the β-adrenoceptor system

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Stochasticity in the enterococcal sex pheromone response revealed by quantitative analysis of transcription in single cells

<div><p>In <i>Enterococcus faecalis</i>, sex pheromone-mediated transfer of antibiotic resistance plasmids can occur under unfavorable conditions, for example, when inducing pheromone concentrations are low and inhibiting pheromone concentrations are high. To better understand this paradox, we adapted fluorescence <i>in situ</i> hybridization chain reaction (HCR) methodology for simultaneous quantification of multiple <i>E</i>. <i>faecalis</i> transcripts at the single cell level. We present direct evidence for variability in the minimum period, maximum response level, and duration of response of individual cells to a specific inducing condition. Tracking of induction patterns of single cells temporally using a fluorescent reporter supported HCR findings. It also revealed subpopulations of rapid responders, even under low inducing pheromone concentrations where the overall response of the entire population was slow. The strong, rapid induction of small numbers of cells in cultures exposed to low pheromone concentrations is in agreement with predictions of a stochastic model of the enterococcal pheromone response. The previously documented complex regulatory circuitry controlling the pheromone response likely contributes to stochastic variation in this system. In addition to increasing our basic understanding of the biology of a horizontal gene transfer system regulated by cell-cell signaling, demonstration of the stochastic nature of the pheromone response also impacts any future efforts to develop therapeutic agents targeting the system. Quantitative single cell analysis using HCR also has great potential to elucidate important bacterial regulatory mechanisms not previously amenable to study at the single cell level, and to accelerate the pace of functional genomic studies.</p></div

Visualization of pheromone induced and constitutive transcripts by fluorescence <i>in situ</i> hybridization chain reaction (HCR).

<p>Fluorescence images demonstrating simultaneous labeling of multiple transcripts in <i>E</i>. <i>faecalis</i> cells by fluorescence <i>in situ</i> hybridization chain reaction (HCR). Purple, <i>E</i>. <i>faecalis</i> cell envelope labeled with Alexa Fluor 647: wheat germ agglutinin (AF647: WGA) conjugate highlighting the outsides of individual cells. Blue, Hoechst 33342 DNA label also highlighting individual cells. Red, HCR labeled <i>ptsI</i> transcripts (Alexa Fluor 546). Green, HCR labeled <i>lacZ</i> transcripts (Alexa Fluor 488). <b>(A)</b> <i>E</i>. <i>faecalis</i> cells containing pBK2 30 minutes after addition of 10 ng ml^-1 <b><i>C</i></b>. Images are maximum intensity projections of Airyscan stacks and show z-axis projections. <b>(B)</b> <i>E</i>. <i>faecalis</i> cells containing pBK2 without addition of <b><i>C</i></b>. Images are a single z-plane of an Airyscan processed image. The punctate green HCR <i>lacZ</i> signal observed without addition of <b><i>C</i></b> is weak and much less intense than the signal observed after addition of <b><i>C</i></b>. This signal is visible in this figure due to intentional over exposure and the Min/Max brightness and contrast adjustment. Notably, these puncta are generally localized outside of cells and appear different than true signal observed with addition of <b><i>C</i></b> or the red HCR <i>ptsI</i> signal. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006878#pgen.1006878.s004" target="_blank">S4 Fig</a> for further documentation. Scale bars, 5 μm.</p

Analysis of the induction response using either HCR or a GFP reporter demonstrates heterogeneity within responding populations over time.

<p><b>(A)</b> Time course showing <i>lacZ</i> expression in <i>E</i>. <i>faecalis</i> upon induction with 10 ng ml^-1 <b><i>C</i></b>. Times (left to right): 0, 15, 30, 60, and 120 minutes after <b><i>C</i></b> addition. Green, HCR labeled <i>lacZ</i> transcripts (pseudo colored Alexa Fluor 546). Blue, Hoechst 33342 DNA label highlighting individual cells. <b>(B)</b> Time course of GFP expression in <i>E</i>. <i>faecalis</i> upon induction with 5 ng ml^-1 <b><i>C</i></b>. Times (left to right): 70, 90, 110, 130, and 150 minutes after <b><i>C</i></b> addition. Green, GFP. Blue, Hoechst 33342. <b>(C)</b>, <b>(D)</b>, and <b>(E)</b>: 3D distributions reflecting the fraction of cells induced over time as measured by HCR, GFP expression, or predicted by the stochastic model respectively. Relative intensity or Q_L of induced cells was normalized to the threshold value and reflects varied levels of induction. <b>(C)</b>, Induction of <i>lacZ</i> RNA over time from pBK2 plasmid upon addition of 5 ng ml^-1 <b><i>C</i></b> in a <b><i>C</i></b>^{<b><i>-</i></b>} host as shown by relative HCR fluorescent intensity per cell. <b>(D)</b> Induction of fluorescent GFP over time from pCIE-GFP plasmid upon addition of 5 ng ml^-1 <b><i>C</i></b> in a <b><i>C</i></b>^{<b><i>-</i></b>} host as shown by relative fluorescent intensity per cell. <b>(E)</b> 3D distributions of the induced expression of the Q_L transcript upon addition of 5 ng ml^-1 <b><i>C</i></b> in a population of cells over time simulated using the stochastic mathematical model. The fraction induced reflects the proportion of cells with the depicted levels of Q_L out of the total cell population. Scale bars, 3.9 μm <b>(A)</b> and 20 μm <b>(B)</b>.</p