Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Exploring the transcriptome space of a recombinant BHK cell line through next generation sequencing.

Baby Hamster Kidney (BHK) cell lines are used in the production of veterinary vaccines and recombinant proteins. To facilitate transcriptome analysis of BHK cell lines, we embarked on an effort to sequence, assemble, and annotate transcript sequences from a recombinant BHK cell line and Syrian hamster liver and brain. RNA-seq data were supplemented with 6,170 Sanger ESTs from parental and recombinant BHK lines to generate 221,583 contigs. Annotation by homology to other species, primarily mouse, yielded more than 15,000 unique Ensembl mouse gene IDs with high coverage of KEGG canonical pathways. High coverage of enzymes and isoforms was seen for cell metabolism and N-glycosylation pathways, areas of highest interest for biopharmaceutical production. With the high sequencing depth in RNA-seq data, we set out to identify single-nucleotide variants in the transcripts. A majority of the high-confidence variants detected in both hamster tissue libraries occurred at a frequency of 50%, indicating their origin as heterozygous germline variants. In contrast, the cell line libraries' variants showed a wide range of occurrence frequency, indicating the presence of a heterogeneous population in cultured cells. The extremely high coverage of transcripts of highly abundant genes in RNA-seq enabled us to identify low-frequency variants. Experimental verification through Sanger sequencing confirmed the presence of two variants in the cDNA of a highly expressed gene in the BHK cell line. Furthermore, we detected seven potential missense mutations in the genes of the growth signaling pathways that may have arisen during the cell line derivation process. The development and characterization of a BHK reference transcriptome will facilitate future efforts to understand, monitor, and manipulate BHK cells. Our study on sequencing variants is crucial for improved understanding of the errors inherent in high-throughput sequencing and to increase the accuracy of variant calling in BHK or other systems.

An innovative strategy to recycle permeate in biologics continuous manufacturing process to improve material efficiency and sustainability.

Intensified perfusion processes are an integral part of continuous manufacturing for biopharmaceuticals which enable agile operations and significant reduction in cost of goods. However, they require large volumes of media to support robust cell growth and maintain high productivity, posing substantial challenges to operations, logistics, and process sustainability. This study explores a novel strategy for reprocessing and reusing permeate from perfusion cultures for mAb production. The concept was initially evaluated by recycling permeate, Protein A flow-through (ProA FT) and CEX processed ProA FT in deep-well plate mock perfusion and ambr® 250 perfusion formats. Further processing of ProA FT through a cation exchange depth filter before recycling reduced process impurities such as host cell proteins (HCPs) and DNA. However, a direct replacement of fresh media with spent media reduces nutrient depth which results in a concomitant reduction in productivity. In ambr® 250 bioreactors, recycling of ProA FT at 25%-50% replacement rates (defined as the fraction of recycled material in media) resulted in a 13%-30% reduction in cumulative productivity while maintaining product quality. To mitigate this, we used media concentrates which allowed independent modulation of media depth by replacing a portion of diluent WFI with recycled material. Results from deep-well mock perfusion studies demonstrated that comparable or higher productivities relative to control can be achieved with this approach. Taken together, our study demonstrates the feasibility of recycling permeate in perfusion cultures. Process mass intensity (PMI) calculations reveal that this approach can meaningfully improve material efficiency by reducing water consumption, thereby enhancing overall bioprocess sustainability.

Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems.

Current techniques for quantitative proteomics focus mainly on measuring overall protein dynamics, which is the net result of protein synthesis and degradation. Understanding the rate of this synthesis/degradation is essential to fully appreciate cellular dynamics and bridge the gap between transcriptome and proteome data. Protein turnover rates can be estimated through "label-chase" experiments employing stable isotope-labeled precursors; however, the implicit assumption of steady-state in such analyses may not be applicable for many intrinsically dynamic systems. In this study, we present a novel extension of the "label-chase" concept using SILAC and a secondary labeling step with iTRAQ reagents to estimate protein turnover rates in Streptomyces coelicolor cultures undergoing transition from exponential growth to stationary phase. Such processes are of significance in Streptomyces biology as they pertain to the onset of synthesis of numerous therapeutically important secondary metabolites. The dual labeling strategy enabled decoupling of labeled peptide identification and quantification of degradation dynamics at MS and MS/MS scans respectively. Tandem mass spectrometry analysis of these multitagged proteins enabled estimation of degradation rates for 115 highly abundant proteins in S. coelicolor. We compared the rate constants obtained using this dual labeling approach with those from a SILAC-only analysis (assuming steady-state) and show that significant differences are generally observed only among proteins displaying considerable temporal dynamics and that the directions of these differences are largely consistent with theoretical predictions.

Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor.

Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of inter-connected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcriptome dynamics and genome sequence, a support vector machines (SVM)-based classification algorithm can accurately classify >90% of gene pairs in a set of known operons. Based on model predictions for the entire genome, we verified the co-transcription of more than 250 gene pairs by RT-PCR. These results vastly increase the database of known operons in S. coelicolor and provide valuable information for exploring gene function and regulation to harness the potential of this differentiating microorganism for synthesis of natural products.

Genome-wide transcriptome analysis reveals that a pleiotropic antibiotic regulator, AfsS, modulates nutritional stress response in Streptomyces coelicolor A3(2).

BACKGROUND: A small "sigma-like" protein, AfsS, pleiotropically regulates antibiotic biosynthesis in Streptomyces coelicolor. Overexpression of afsS in S. coelicolor and certain related species causes antibiotic stimulatory effects in the host organism. Although recent studies have uncovered some of the upstream events activating this gene, the mechanisms through which this signal is relayed downstream leading to the eventual induction of antibiotic pathways remain unclear. RESULTS: In this study, we employed whole-genome DNA microarrays and quantitative PCRs to examine the transcriptome of an afsS disruption mutant that is completely deficient in the production of actinorhodin, a major S. coelicolor antibiotic. The production of undecylprodigiosin, another prominent antibiotic, was, however, perturbed only marginally in the mutant. Principal component analysis of temporal gene expression profiles identified two major gene classes each exhibiting a distinct coordinate differential expression pattern. Surprisingly, nearly 70% of the >117 differentially expressed genes were conspicuously associated with nutrient starvation response, particularly those of phosphate, nitrogen and sulfate. Furthermore, expression profiles of some transcriptional regulators including at least two sigma factors were perturbed in the mutant. In almost every case, the effect of afsS disruption was not observed until the onset of stationary phase. CONCLUSION: Our data suggests a comprehensive role for S. coelicolor AfsS as a master regulator of both antibiotic synthesis and nutritional stress response, reminiscent of alternative sigma factors found in several bacteria.

Comparative genomic hybridizations reveal absence of large Streptomyces coelicolor genomic islands in Streptomyces lividans.

BACKGROUND: The genomes of Streptomyces coelicolor and Streptomyces lividans bear a considerable degree of synteny. While S. coelicolor is the model streptomycete for studying antibiotic synthesis and differentiation, S. lividans is almost exclusively considered as the preferred host, among actinomycetes, for cloning and expression of exogenous DNA. We used whole genome microarrays as a comparative genomics tool for identifying the subtle differences between these two chromosomes. RESULTS: We identified five large S. coelicolor genomic islands (larger than 25 kb) and 18 smaller islets absent in S. lividans chromosome. Many of these regions show anomalous GC bias and codon usage patterns. Six of them are in close vicinity of tRNA genes while nine are flanked with near perfect repeat sequences indicating that these are probable recent evolutionary acquisitions into S. coelicolor. Embedded within these segments are at least four DNA methylases and two probable methyl-sensing restriction endonucleases. Comparison with S. coelicolor transcriptome and proteome data revealed that some of the missing genes are active during the course of growth and differentiation in S. coelicolor. In particular, a pair of methylmalonyl CoA mutase (mcm) genes involved in polyketide precursor biosynthesis, an acyl-CoA dehydrogenase implicated in timing of actinorhodin synthesis and bldB, a developmentally significant regulator whose mutation causes complete abrogation of antibiotic synthesis belong to this category. CONCLUSION: Our findings provide tangible hints for elucidating the genetic basis of important phenotypic differences between these two streptomycetes. Importantly, absence of certain genes in S. lividans identified here could potentially explain the relative ease of DNA transformations and the conditional lack of actinorhodin synthesis in S. lividans.

Transcriptomics as a tool for assessing the scalability of mammalian cell perfusion systems.

DNA microarray-based transcriptomics have been used to determine the time course of laboratory and manufacturing-scale perfusion bioreactors in an attempt to characterize cell physiological state at these two bioreactor scales. Given the limited availability of genomic data for baby hamster kidney (BHK) cells, a Chinese hamster ovary (CHO)-based microarray was used following a feasibility assessment of cross-species hybridization. A heat shock experiment was performed using both BHK and CHO cells and resulting DNA microarray data were analyzed using a filtering criteria of perfect match (PM)/single base mismatch (MM) > 1.5 and PM-MM > 50 to exclude probes with low specificity or sensitivity for cross-species hybridizations. For BHK cells, 8910 probe sets (39 %) passed the cutoff criteria, whereas 12,961 probe sets (56 %) passed the cutoff criteria for CHO cells. Yet, the data from BHK cells allowed distinct clustering of heat shock and control samples as well as identification of biologically relevant genes as being differentially expressed, indicating the utility of cross-species hybridization. Subsequently, DNA microarray analysis was performed on time course samples from laboratory- and manufacturing-scale perfusion bioreactors that were operated under the same conditions. A majority of the variability (37 %) was associated with the first principal component (PC-1). Although PC-1 changed monotonically with culture duration, the trends were very similar in both the laboratory and manufacturing-scale bioreactors. Therefore, despite time-related changes to the cell physiological state, transcriptomic fingerprints were similar across the two bioreactor scales at any given instance in culture. Multiple genes were identified with time-course expression profiles that were very highly correlated (> 0.9) with bioprocess variables of interest. Although the current incomplete annotation limits the biological interpretation of these observations, their full potential may be realized in due course when richer genomic data become available. By taking a pragmatic approach of transcriptome fingerprinting, we have demonstrated the utility of systems biology to support the comparability of laboratory and manufacturing-scale perfusion systems. Scale-down model qualification is the first step in process characterization and hence is an integral component of robust regulatory filings. Augmenting the current paradigm, which relies primarily on cell culture and product quality information, with gene expression data can help make a substantially stronger case for similarity. With continued advances in systems biology approaches, we expect them to be seamlessly integrated into bioprocess development, which can translate into more robust and high yielding processes that can ultimately reduce cost of care for patients.

Global assessment of protein turnover in recombinant antibody producing myeloma cells.

The global turnover rates of cellular proteins and the secretion rate of a recombinant immunoglobulin G (IgG) in a myeloma cell line, NS0, were determined using SILAC proteomic analysis. After complete labeling of cellular proteins with (13)C(6), (15)N(4)-arginine, cells were transferred to unlabeled medium and the decay of the labeled arginine in proteins was monitored during exponential cell growth. After PAGE separation and mass-spectrometric identification of proteins, those detected with high confidence over at least three time points were used for the determination of turnover rates. Among the 224 proteins quantified with a protein half-life, about 15% have a degradation rate constant lower than one-tenth of specific growth rate. For most proteins, the turnover rate is insignificant in its overall dynamics. Only 6.3% of proteins have a half-life shorter than the cell doubling time. For IgG secretion, both heavy and light chain molecules follow the same kinetic behavior with a half-life estimated to be 2h. The label decay curve appears to show a second region with very slow kinetics, raising the possibility of two populations of IgG molecules with different secretion characteristics.

Uncovering genes with divergent mRNA-protein dynamics in Streptomyces coelicolor.

Many biological processes are intrinsically dynamic, incurring profound changes at both molecular and physiological levels. Systems analyses of such processes incorporating large-scale transcriptome or proteome profiling can be quite revealing. Although consistency between mRNA and proteins is often implicitly assumed in many studies, examples of divergent trends are frequently observed. Here, we present a comparative transcriptome and proteome analysis of growth and stationary phase adaptation in Streptomyces coelicolor, taking the time-dynamics of process into consideration. These processes are of immense interest in microbiology as they pertain to the physiological transformations eliciting biosynthesis of many naturally occurring therapeutic agents. A shotgun proteomics approach based on mass spectrometric analysis of isobaric stable isotope labeled peptides (iTRAQ) enabled identification and rapid quantification of approximately 14% of the theoretical proteome of S. coelicolor. Independent principal component analyses of this and DNA microarray-derived transcriptome data revealed that the prominent patterns in both protein and mRNA domains are surprisingly well correlated. Despite this overall correlation, by employing a systematic concordance analysis, we estimated that over 30% of the analyzed genes likely exhibited significantly divergent patterns, of which nearly one-third displayed even opposing trends. Integrating this data with biological information, we discovered that certain groups of functionally related genes exhibit mRNA-protein discordance in a similar fashion. Our observations suggest that differences between mRNA and protein synthesis/degradation mechanisms are prominent in microbes while reaffirming the plausibility of such mechanisms acting in a concerted fashion at a protein complex or sub-pathway level.

A framework to analyze multiple time series data: a case study with Streptomyces coelicolor.

Transcriptional regulation in differentiating microorganisms is highly dynamic involving multiple and interwinding circuits consisted of many regulatory genes. Elucidation of these networks may provide the key to harness the full capacity of many organisms that produce natural products. A powerful tool evolved in the past decade is global transcriptional study of mutants in which one or more key regulatory genes of interest have been deleted. To study regulatory mutants of Streptomyces coelicolor, we developed a framework of systematic analysis of gene expression dynamics. Instead of pair-wise comparison of samples in different combinations, genomic DNA was used as a common reference for all samples in microarray assays, thus, enabling direct comparison of gene transcription dynamics across different isogenic mutants. As growth and various differentiation events may unfold at different rates in different mutants, the global transcription profiles of each mutant were first aligned computationally to those of the wild type, with respect to the corresponding growth and differentiation stages, prior to identification of kinetically differentially expressed genes. The genome scale transcriptome data from wild type and a DeltaabsA1 mutant of Streptomyces coelicolor were analyzed within this framework, and the regulatory elements affected by the gene knockout were identified. This methodology should find general applications in the analysis of other mutants in our repertoire and in other biological systems.