Influenza A virus activates cellular Tropomyosin receptor kinase A (TrkA) signaling to promote viral replication and lung inflammation.

Influenza A virus (IAV) infection causes acute respiratory disease with potential severe and deadly complications. Viral pathogenesis is not only due to the direct cytopathic effect of viral infections but also to the exacerbated host inflammatory responses. Influenza viral infection can activate various host signaling pathways that function to activate or inhibit viral replication. Our previous studies have shown that a receptor tyrosine kinase TrkA plays an important role in the replication of influenza viruses in vitro, but its biological roles and functional mechanisms in influenza viral infection have not been characterized. Here we show that IAV infection strongly activates TrkA in vitro and in vivo. Using a chemical-genetic approach to specifically control TrkA kinase activity through a small molecule compound 1NMPP1 in a TrkA knock-in (TrkA KI) mouse model, we show that 1NMPP1-mediated TrkA inhibition completely protected mice from a lethal IAV infection by significantly reducing viral loads and lung inflammation. Using primary lung cells isolated from the TrkA KI mice, we show that specific TrkA inhibition reduced IAV viral RNA synthesis in airway epithelial cells (AECs) but not in alveolar macrophages (AMs). Transcriptomic analysis confirmed the cell-type-specific role of TrkA in viral RNA synthesis, and identified distinct gene expression patterns under the TrkA regulation in IAV-infected AECs and AMs. Among the TrkA-activated targets are various proinflammatory cytokines and chemokines such as IL6, IL-1ß, IFNs, CCL-5, and CXCL9, supporting the role of TrkA in mediating lung inflammation. Indeed, while TrkA inhibitor 1NMPP1 administered after the peak of IAV replication had no effect on viral load, it was able to decrease lung inflammation and provided partial protection in mice. Taken together, our results have demonstrated for the first time an important biological role of TrkA signaling in IAV infection, identified its cell-type-specific contribution to viral replication, and revealed its functional mechanism in virus-induced lung inflammation. This study suggests TrkA as a novel host target for therapeutic development against influenza viral disease.

Enhancing the production of recombinant adeno-associated virus in synthetic cell lines through systematic characterization.

Recombinant adeno-associated virus (rAAV) is among the most commonly used in vivo gene delivery vehicles and has seen a number of successes in clinical application. Current manufacturing processes of rAAV employ multiple plasmid transfection or rely on virus infection and face challenges in scale-up. A synthetic biology approach was taken to generate stable cell lines with integrated genetic modules, which produced rAAV upon induction albeit at a low productivity. To identify potential factors that restrained the productivity, we systematically characterized virus production kinetics through targeted quantitative proteomics and various physical assays of viral components. We demonstrated that reducing the excessive expression of gene of interest by its conditional expression greatly increased the productivity of these synthetic cell lines. Further enhancement was gained by optimizing induction profiles and alleviating proteasomal degradation of viral capsid protein by the addition of proteasome inhibitors. Altogether, these enhancements brought the productivity close to traditional multiple plasmid transfection. The rAAV produced had comparable full particle contents as those produced by conventional transient plasmid transfection. The present work exemplified the versatility of our synthetic biology-based viral vector production platform and its potential for plasmid- and virus-free rAAV manufacturing.

Multi-cell-line learning for the data-driven construction of mechanistic metabolic models.

Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth-signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed-batch culture data and time-series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi-cell-line (MCL) learning, which combines data sets from different cell lines and trains the individual cell-line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell-line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell-line development for the biomanufacturing of new protein therapeutics.

Comparative transcriptomic and proteomic kinetic analysis of adeno-associated virus production systems.

Recombinant adeno-associated virus (rAAV) is a major gene delivery vehicle. We have constructed a stable rAAV producer cell line by integrating essential rAAV genome, viral and helper genes into the genome of HEK293 cell under the control of inducible promoters. Upon induction, the cell line produces transducing rAAV. To gain insight into enhancing rAAV productivity and vector quality, we performed a comparative transcriptomic and proteomic analysis of our synthetic cell line GX2 and two wild-type AAV (wtAAV) production systems, one by virus co-infection and the other by multi-plasmid transfection. The three systems had different kinetics in viral component synthesis but generated comparable copies of AAV genomes; however, the capsid titer of GX2 was an order of magnitude lower compared to those two wtAAV systems, indicating that its capsid production may be insufficient. The genome packaging efficiency was also lower in GX2 despite it produced higher levels of Rep52 proteins than either wtAAV systems, suggesting that Rep52 protein expression may not limit genome packaging. In the two wtAAV systems, VP were the most abundant AAV proteins and their levels continued to increase, while GX2 had high level of wasteful cargo gene expression. Furthermore, upregulated inflammation, innate immune responses, and MAPK signaling, as well as downregulated mitochondrial functions, were commonly observed in either rAAV or wtAAV systems. Overall, this comparative multi-omics study provided rich insights into host cell and viral factors that are potential targets for genetic and process intervention to enhance the productivity of synthetic rAAV producer cell lines. KEY POINTS: • wtAAV infection was more efficient in producing full viral particles than the synthetic cell GX2. • Capsid protein synthesis, genome replication, and packaging may limit rAAV production in GX2. • wtAAV infection and rAAV production in GX2 elicited similar host cell responses.

Kinetic-model-based pathway optimization with application to reverse glycolysis in mammalian cells.

Over the last two decades, model-based metabolic pathway optimization tools have been developed for the design of microorganisms to produce desired metabolites. However, few have considered more complex cellular systems such as mammalian cells, which requires the use of nonlinear kinetic models to capture the effects of concentration changes and cross-regulatory interactions. In this study, we develop a new two-stage pathway optimization framework based on kinetic models that incorporate detailed kinetics and regulation information. In Stage 1, a set of optimization problems are solved to identify and rank the enzymes that contribute the most to achieving the metabolic objective. Stage 2 then determines the optimal enzyme interventions for specified desired numbers of enzyme adjustments. It also incorporates multi-scenario optimization, which allows the simultaneous consideration of multiple physiological conditions. We apply the proposed framework to find enzyme adjustments that enable a reverse glucose flow in cultured mammalian cells, thereby eliminating the need for glucose feed in the late culture stage and enhancing process robustness. The computational results demonstrate the efficacy of the proposed approach; it not only captures the important regulations and key enzymes for reverse glycolysis but also identifies differences and commonalities in the metabolic requirements for different carbon sources.

Segment-specific kinetics of mRNA, cRNA and vRNA accumulation during influenza infection.

Influenza A virus (IAV) is a segmented negative-sense RNA virus and is the cause of major epidemics and pandemics. The replication of IAV is complex, involving the production of three distinct RNA species; mRNA, cRNA, and vRNA for all eight genome segments. While understanding IAV replication kinetics is important for drug development and improving vaccine production, current methods for studying IAV kinetics has been limited by the ability to detect all three different RNA species in a scalable manner. Here we report the development of a novel pipeline using total stranded RNA-Seq, which we named Influenza Virus Enumerator of RNA Transcripts (InVERT), that allows for the simultaneous quantification of all three RNA species produced by IAV. Using InVERT, we provide a full landscape of the IAV replication kinetics and found that different groups of viral genes follow different kinetics. The segments coding for RNA-dependent RNA Polymerase (RdRP) produced more vRNA than mRNA while some other segments (NP, NS, HA) consistently made more mRNA than vRNA. vRNA expression levels did not correlate with cRNA expression, suggesting complex regulation of vRNA synthesis. Furthermore, by studying the kinetics of a virus lacking the capacity to generate new polymerase complexes, we found evidence that further supports the model that cRNA synthesis requires newly synthesized RdRP and that incoming RdRP can only generate mRNA. Overall, InVERT is a powerful tool for quantifying IAV RNA species to elucidate key features of IAV replication.ImportanceInfluenza A virus (IAV) is a respiratory pathogen that has caused significant mortality throughout history and remains a global threat to human health. Although much is known about IAV replication, the regulation of IAV replication dynamics is not completely understood. This is due in part to both technical limitations and the complexity of the virus replication, which has a segmented genome and produces three distinct RNA species for each gene segment. We developed a new approach that allows the methodical study of IAV replication kinetics, shedding light on many interesting features of IAV replication biology. This study advances our understanding of the kinetics of IAV replication and will help to facilitate future research in the field.

A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation.

In cell culture processes cell growth and metabolism drive changes in the chemical environment of the culture. These environmental changes elicit reactor control actions, cell growth response, and are sensed by cell signaling pathways that influence metabolism. The interplay of these forces shapes the culture dynamics through different stages of cell cultivation and the outcome greatly affects process productivity, product quality, and robustness. Developing a systems model that describes the interactions of those major players in the cell culture system can lead to better process understanding and enhance process robustness. Here we report the construction of a hybrid mechanistic-empirical bioprocess model which integrates a mechanistic metabolic model with subcomponent models for cell growth, signaling regulation, and the bioreactor environment for in silico exploration of process scenarios. Model parameters were optimized by fitting to a dataset of cell culture manufacturing process which exhibits variability in metabolism and productivity. The model fitting process was broken into multiple steps to mitigate the substantial numerical challenges related to the first-principles model components. The optimized model captured the dynamics of metabolism and the variability of the process runs with different kinetic profiles and productivity. The variability of the process was attributed in part to the metabolic state of cell inoculum. The model was then used to identify potential mitigation strategies to reduce process variability by altering the initial process conditions as well as to explore the effect of changing CO2 removal capacity in different bioreactor scales on process performance. By incorporating a mechanistic model of cell metabolism and appropriately fitting it to a large dataset, the hybrid model can describe the different metabolic phases in culture and the variability in manufacturing runs. This approach of employing a hybrid model has the potential to greatly facilitate process development and reactor scaling.

Probiotic Bacillus Affects Enterococcus faecalis Antibiotic Resistance Transfer by Interfering with Pheromone Signaling Cascades.

Enterococcus faecalis, a member of the commensal flora in the human gastrointestinal tract, has become a threatening nosocomial pathogen because it has developed resistance to many known antibiotics. More concerningly, resistance gene-carrying E. faecalis cells may transfer antibiotic resistance to resistance-free E. faecalis cells through their unique quorum sensing-mediated plasmid transfer system. Therefore, we investigated the role of probiotic bacteria in the transfer frequency of the antibiotic resistance plasmid pCF10 in E. faecalis populations to mitigate the spread of antibiotic resistance. Bacillus subtilis subsp. natto is a probiotic strain isolated from Japanese fermented soybean foods, and its culture fluid potently inhibited pCF10 transfer by suppressing peptide pheromone activity from chromosomally encoded CF10 (cCF10) without inhibiting E. faecalis growth. The inhibitory effect was attributed to at least one 30- to 50-kDa extracellular protease present in B. subtilis subsp. natto. Nattokinase of B. subtilis subsp. natto was involved in the inhibition of pCF10 transfer and cleaved cCF10 (LVTLVFV) into LVTL plus VFV fragments. Moreover, the cleavage product LVTL (L peptide) interfered with the conjugative transfer of pCF10. In addition to cCF10, faecalis-cAM373 and gordonii-cAM373, which are mating inducers of vancomycin-resistant E. faecalis, were also cleaved by nattokinase, indicating that B. subtilis subsp. natto can likely interfere with vancomycin resistance transfer in E. faecalis. Our work shows the feasibility of applying fermentation products of B. subtilis subsp. natto and L peptide to mitigate E. faecalis antibiotic resistance transfer. IMPORTANCE Enterococcus faecalis is considered a leading cause of hospital-acquired infections. Treatment of these infections has become a major challenge for clinicians because some E. faecalis strains are resistant to multiple clinically used antibiotics. Moreover, antibiotic resistance genes can undergo efficient intra- and interspecies transfer via E. faecalis peptide pheromone-mediated plasmid transfer systems. Therefore, this study provided the first experimental demonstration that probiotics are a feasible approach for interfering with conjugative plasmid transfer between E. faecalis strains to stop the transfer of antibiotic resistance. We found that the extracellular protease(s) of Bacillus subtilis subsp. natto cleaved peptide pheromones without affecting the growth of E. faecalis, thereby reducing the frequency of conjugative plasmid transfer. In addition, a specific cleaved pheromone fragment interfered with conjugative plasmid transfer. These findings provide a potential probiotic-based method for interfering with the transfer of antibiotic resistance between E. faecalis strains.

Epigenomic features revealed by ATAC-seq impact transgene expression in CHO cells.

Different regions of a mammalian genome have different accessibilities to transcriptional machinery. The integration site of a transgene affects how actively it is transcribed. Highly accessible genomic regions called super-enhancers have been recently described as strong regulatory elements that shape cell identity. Super-enhancers have been identified in Chinese hamster ovary (CHO) cells using the Assay for Transposase-Accessible Chromatin Sequencing (ATAC-seq). Genes near super-enhancer regions had high transcript levels and were enriched for oncogenic signaling and proliferation functions, consistent with an immortalized phenotype. Inaccessible regions in the genome with low ATAC signal also had low transcriptional activity. Genes in inaccessible regions were enriched for remote tissue functions such as taste, smell, and neuronal activation. A lentiviral reporter integration assay showed integration into super-enhancer regions conferred higher reporter expression than insertion into inaccessible regions. Targeted integration of an IgG vector into the Plec super-enhancer region yielded clones that expressed the immunoglobulin light chain gene mostly in the top 20% of all transcripts with the majority in the top 5%. The results suggest the epigenomic landscape of CHO cells can guide the selection of integration sites in the development of cell lines for therapeutic protein production.

Single-Cell Analysis Reveals that the Enterococcal Sex Pheromone Response Results in Expression of Full-Length Conjugation Operon Transcripts in All Induced Cells.

For high-frequency transfer of pCF10 between E. faecalis cells, induced expression of the pCF10 genes encoding conjugative machinery from the prgQ operon is required. This process is initiated by the cCF10 (C) inducer peptide produced by potential recipient cells. The expression timing of prgB, an "early" gene just downstream of the inducible promoter, has been studied extensively in single cells. However, several previous studies suggest that only 1 to 10% of donors induced for early prgQ gene expression actually transfer plasmids to recipients, even at a very high recipient population density. One possible explanation for this is that only a minority of pheromone-induced donors actually transcribe the entire prgQ operon. Such cells would not be able to functionally conjugate but might play another role in the group behavior of donors. Here, we sought to (i) simultaneously assess the presence of RNAs produced from the proximal (early induced transcripts [early Q]) and distal (late Q) portions of the prgQ operon in individual cells, (ii) investigate the prevalence of heterogeneity in induced transcript length, and (iii) evaluate the temporality of induced transcript expression. Using fluorescent in situ hybridization chain reaction (HCR) transcript labeling and single-cell microscopic analysis, we observed that most cells expressing early transcripts (QL, prgB, and prgA) also expressed late transcripts (prgJ, pcfC, and pcfG). These data support the conclusion that, after induction is initiated, transcription likely extends through the end of the conjugation machinery operon for most, if not all, induced cells.IMPORTANCE In Enterococcus faecalis, conjugative plasmids like pCF10 often carry antibiotic resistance genes. With antibiotic treatment, bacteria benefit from plasmid carriage; however, without antibiotic treatment, plasmid gene expression may have a fitness cost. Transfer of pCF10 is mediated by cell-to-cell signaling, which activates the expression of conjugation genes and leads to efficient plasmid transfer. Yet, not all donor cells in induced populations transfer the plasmid. We examined whether induced cells might not be able to functionally conjugate due to premature induced transcript termination. Single-cell analysis showed that most induced cells do, in fact, express all of the genes required for conjugation, suggesting that premature transcription termination within the prgQ operon does not account for failure of induced donor cell gene transfer.

Effects of endogenous levels of master regulator PrgX and peptide pheromones on inducibility of conjugation in the enterococcal pCF10 system.

Enterococcal pheromone responsive conjugative plasmids like pCF10 promote horizontal spread of antibiotic resistance genes following induction of plasmid-containing cells by potential recipients. Transcription of conjugation genes from promoter PQ is inhibited by the master regulator PrgX, further repressed when PrgX is in complex with the inhibitory I peptide, and allowed when PrgX is in complex with the C inducing peptide. Single-cell analysis has shown that heterogeneity in the pheromone response is prevalent. Here, we systematically varied levels of regulatory molecules to better understand why some individual cells have increased propensity for induction. In this study, PrgX was confirmed to repress PQ in the absence of exogenous peptides in vivo, but cells with increased levels of PrgX were shown to be more prone to induction. Further, ablation of endogenous I reduced PrgX levels, resulting in reduced basal repression and loss of inducibility. Reduction of both endogenous peptides by washing increased the inducibility of cells. Together, these results show that endogenous PrgX, C, and I levels can impact the induction potential of a cell and establish the importance of basal I for regulation. These results also suggest that PrgX/C complexes may directly activate prgQ transcription, contrary to a long-standing working model.

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

In Enterococcus faecalis, 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 in situ hybridization chain reaction (HCR) methodology for simultaneous quantification of multiple E. faecalis 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.

Kinetic model optimization and its application to mitigating the Warburg effect through multiple enzyme alterations.

Pathway engineering is a powerful tool in biotechnological and clinical applications. However, many phenomena cannot be rewired with a single enzyme change, and in a complex network like energy metabolism, the selection of combinations of targets to engineer is a daunting task. To facilitate this process, we have developed an optimization framework and applied it to a mechanistic kinetic model of energy metabolism. We then identified combinations of enzyme alternations that led to the elimination of the Warburg effect seen in the metabolism of cancer cells and cell lines, a phenomenon coupling rapid proliferation to lactate production. Typically, optimization approaches use integer variables to achieve the desired flux redistribution with a minimum number of altered genes. This framework uses convex penalty terms to replace these integer variables and improve computational tractability. Optimal solutions are identified which substantially reduce or eliminate lactate production while maintaining the requirements for cellular proliferation using three or more enzymes.

An integrated platform for mucin-type O-glycosylation network generation and visualization.

Mucin-type O-glycans have profound effects on the structure and stability of glycoproteins. O-Glycans on the cell surface proteins also modulate the cell's interactions with the surrounding environments and other cells. The synthetic pathway of O-glycans involves a large number of enzymes with diverse substrate specificity. The expression pattern of these enzymes is cell and tissue-specific, thus making the pathway highly diverse. To facilitate pathway analysis in a cell and tissue-specific fashion, we developed an integrated platform of RING (Rule Input Network Generator) and O-GlycoVis. RING uses an English-like reaction language to describe the substrate specificity of enzymes and additional constraints on the formation of the glycan products. Using this information, the RING generates a list of possible glycans, which is used as input into O-Glycovis. O-GlycoVis displays the glycan distribution in the pathway and potential reaction paths leading to each glycan. With the input glycan data, O-GlycoVis also traces all possible reaction paths leading to each glycan and outputs pathway maps with the relative abundance levels of glycans overlaid. O-Glycan profiles from two breast cancer cell lines, MCF7 and T47d, human umbilical vascular endothelium cells, Chinese Hamster Ovary cells were generated based on transcriptional data and compared with experimentally observed O-glycans. This RING-based program allows rules to be added or subtracted for network generation and visualization of networks of O-glycosylation network of different tissues and species.

Recurring genomic structural variation leads to clonal instability and loss of productivity.

Chinese hamster ovary cells, commonly used in the production of therapeutic proteins, are aneuploid. Their chromosomes bear structural abnormality and undergo changes in structure and number during cell proliferation. Some production cell lines are unstable and lose their productivity over time in the manufacturing process and during the product's life cycle. To better understand the link between genomic structural changes and productivity stability, an immunoglobulin G producing cell line was successively single-cell cloned to obtain subclones that retained or lost productivity, and their genomic features were compared. Although each subclone started with a single karyotype, the progeny quickly diversified to a population with a distribution of chromosome numbers that is not distinctive from the parent and among subclones. The comparative genomic hybridization (CGH) analysis showed that the extent of copy variation of gene coding regions among different subclones stayed at levels of a few percent. Genome regions that were prone to loss of copies, including one with a product transgene integration site, were identified in CGH. The loss of the transgene copy was accompanied by loss of transgene transcript level. Sequence analysis of the host cell and parental producing cell showed prominent structural variations within the regions prone to loss of copies. Taken together, we demonstrated the transient nature of clonal homogeneity in cell line development and the retention of a population distribution of chromosome numbers; we further demonstrated that structural variation in the transgene integration region caused cell line instability. Future cell line development may target the transgene into structurally stable regions.

Automated network generation and analysis of biochemical reaction pathways using RING.

This paper describes how Rule Input Network Generator (RING), a network generation computational tool, can be adopted to generate a variety of complex biochemical reaction networks. The reaction language incorporated in RING allows representation of chemical compounds in biological systems with various structural complexity. Complex molecules such as oligosaccharides in glycosylation pathways can be described using a simplified representation of their monosaccharide building blocks and glycosidic bonds. The automated generation and topological network analysis features in RING also allow for: (1) constructing biochemical reaction networks in a rule-based manner, (2) generating graphical representations of the networks, (3) querying molecules containing a particular structural pattern, (4) finding the shortest synthetic pathways to a user-specified species, and (5) performing enzyme knockout to study their effect on the reaction network. Case studies involving three biochemical reaction systems: (1) Synthesis of 2-ketoglutarate from xylose in bacterial cells, (2) N-glycosylation in mammalian cells, and (3) O-glycosylation in mammalian cells are presented to demonstrate the capabilities of RING for robust and exhaustive network generation and the advantages of its post-processing features.

Unveiling gene trait relationship by cross-platform meta-analysis on Chinese hamster ovary cell transcriptome.

In the past few years, transcriptome analysis has been increasingly employed to better understand the physiology of Chinese hamster ovary (CHO) cells at a global level. As more transcriptome data accumulated, meta-analysis on data sets collected from various sources can potentially provide better insights on common properties of those cells. Here, we performed meta-analysis on transcriptome data of different CHO cell lines obtained using NimbleGen or Affymetrix microarray platforms. Hierarchical clustering, non-negative matrix factorization (NMF) analysis, and principal component analysis (PCA) accordantly showed the samples were clustered into two groups: one consists of adherent cells in serum-containing medium, and the other suspension cells in serum-free medium. Genes that were differentially expressed between the two clusters were enriched in a few functional classes by Database for Annotation, Visualization, and Integrated Discovery (DAVID) of which many were common with the enriched gene sets identified by Gene Set Enrichment Analysis (GSEA), including extracellular matrix (ECM) receptor interaction, cell adhesion molecules (CAMs), and lipid related metabolism pathways. Despite the heterogeneous sources of the cell samples, the adherent and suspension growth characteristics and serum-supplementation appear to be a dominant feature in the transcriptome. The results demonstrated that meta-analysis of transcriptome could uncover features in combined data sets that individual data set might not reveal. As transcriptome data sets accumulate over time, meta-analysis will become even more revealing. Biotechnol. Bioeng. 2017;114: 1583-1592. © 2017 Wiley Periodicals, Inc.

A comparative genomic hybridization approach to study gene copy number variations among Chinese hamster cell lines.

Chinese Hamster Ovary (CHO) cells are aneuploid in nature. The genome of recombinant protein producing CHO cell lines continuously undergoes changes in its structure and organization. We analyzed nine cell lines, including parental cell lines, using a comparative genomic hybridization (CGH) array focused on gene-containing regions. The comparison of CGH with copy-number estimates from sequencing data showed good correlation. Hierarchical clustering of the gene copy number variation data from CGH data revealed the lineage relationships between the cell lines. On analyzing the clones of a clonal population, some regions with altered genomic copy number status were identified indicating genomic changes during passaging. A CGH array is thus an effective tool in quantifying genomic alterations in industrial cell lines and can provide insights into the changes in the genomic structure during cell line derivation and long term culture. Biotechnol. Bioeng. 2017;114: 1903-1908. © 2017 Wiley Periodicals, Inc.

Advancement in bioprocess technology: parallels between microbial natural products and cell culture biologics.

The emergence of natural products and industrial microbiology nearly eight decades ago propelled an era of bioprocess innovation. Half a century later, recombinant protein technology spurred the tremendous growth of biologics and added mammalian cells to the forefront of industrial producing cells in terms of the value of products generated. This review highlights the process technology of natural products and protein biologics. Despite the separation in time, there is a remarkable similarity in their progression. As the new generation of therapeutics for gene and cell therapy emerges, its process technology development can take inspiration from that of natural products and biologics.

Antagonistic Donor Density Effect Conserved in Multiple Enterococcal Conjugative Plasmids.

UNLABELLED: Enterococcus faecalis, a common causative agent of hospital-acquired infections, is resistant to many known antibiotics. Its ability to acquire and transfer resistance genes and virulence determinants through conjugative plasmids poses a serious concern for public health. In some cases, induction of transfer of E. faecalis plasmids results from peptide pheromones produced by plasmid-free recipient cells, which are sensed by the plasmid-bearing donor cells. These plasmids generally encode an inhibitory peptide that competes with the pheromone and suppresses self-induction of donors. We recently demonstrated that the inhibitor peptide encoded on plasmid pCF10 is part of a unique quorum-sensing system in which it functions as a "self-sensing signal," reducing the response to the pheromone in a density-dependent fashion. Based on the similarities between regulatory features controlling conjugation in pAD1 and pAM373 and those controlling conjugation in pCF10, we hypothesized that these plasmids are likely to exhibit similar quorum-sensing behaviors. Experimental findings indicate that for both pAD1 and pAM373, high donor densities indeed resulted in decreased induction of the conjugation operon and reduced conjugation frequencies. This effect was restored by the addition of exogenous inhibitor, confirming that the inhibitor serves as an indicator for donor density. Donor density also affects cross-species conjugative plasmid transfer. Based on our experimental results, we propose models for induction and shutdown of the conjugation operon in pAD1 and pAM373. IMPORTANCE: Enterococcus faecalis is a leading cause of hospital-acquired infections. Its ability to transfer antibiotic resistance and virulence determinants by sharing its genetic material with other bacteria through direct cell-cell contact via conjugation poses a serious threat. Two antagonistic signaling peptides control the transfer of plasmids pAD1 and pAM373: a peptide pheromone produced by plasmid-free recipients triggers the conjugative transfer in plasmid-containing donors, and an inhibitor peptide encoded on the plasmid and produced by donor cells serves to modulate the donor response in accordance with the relative abundance of donors and recipients. We demonstrate that high donor density reduces the conjugation frequency of both of these plasmids, which is a consequence of increased inhibitor concentration in high-donor-density cultures. While most antibiotic strategies end up selecting resistant strains and disrupting the community balance, manipulating bacterial signaling mechanisms can serve as an alternate strategy to prevent the spread of antibiotic resistance.