Evolution of rCHO cells under mild ER stress to make them super producers

To increase the productivity of rCHO cells, many cell engineering approaches have been demonstrated that over-express or knockout a specific gene to achieve increased titers. This single-gene approach has resulted in mixed outcomes, as productivity is a function of many genes and pathways, as also demonstrated by various omics analysis. In this work, we present an alternate approach, based on the concept of evolution, to achieve cells with higher titers. We had earlier demonstrated an increase in productivity of CHO cells even after brief exposure to an ER stress inducer, tunicamycin. However, the increase in productivity is not sustained over the entire course of batch culture, ultimately leading to lower titers due to increased cell death. To harness the beneficial effect of ER stress, we have evolved rCHO cells producing a monoclonal antibody under tunicamycin pressure. The rCHO cells were adapted for more than 25 passages, first under mild tunicamycin concentrations, and later to sustain higher concentrations of tunicamycin. The evolved clones have been characterized in detail in culture. A sustained higher productivity of at-least 2-fold was achieved in all the clones, in a concentration dependent manner. Similarly, a 1.5-2 fold increase in final titers was also achieved in the batch culture. Intracellular IgG analysis using FACS demonstrated higher secretion efficiency of these adapted cells, correlating with up-regulation of the UPR pathway in the adapted cells. Metabolic analysis of the adapted cells in the batch culture revealed higher consumptions rates of key nutrients (glucose and Amino acids) as well as limitations in the late stage of the culture. Upon culturing these adapted cell lines in non-nutrient limiting conditions (i.e. Fed-batch), we observed significantly higher titers (~2g/l) and cumulative productivity (~50 pg/cell/day) as compared to control. Valproic acid, a small molecule demonstrated to could increase product titers of adapted cells further. Our work illustrates how process modifications, cell engineering and use of small molecules can be used in synergy to drive up product titers. Future efforts will focus on extending this strategy to develop generic host cells with high secretory capacity for subsequent transfections. Acknowledgement- DBT and UGC, Govt. of Indi

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

<p>Abstract</p> <p>Background</p> <p>A small "sigma-like" protein, AfsS, pleiotropically regulates antibiotic biosynthesis in <it>Streptomyces coelicolor</it>. Overexpression of <it>afsS </it>in <it>S. coelicolor </it>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.</p> <p>Results</p> <p>In this study, we employed whole-genome DNA microarrays and quantitative PCRs to examine the transcriptome of an <it>afsS </it>disruption mutant that is completely deficient in the production of actinorhodin, a major <it>S. coelicolor </it>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 <it>afsS </it>disruption was not observed until the onset of stationary phase.</p> <p>Conclusion</p> <p>Our data suggests a comprehensive role for <it>S. coelicolor </it>AfsS as a master regulator of both antibiotic synthesis and nutritional stress response, reminiscent of alternative sigma factors found in several bacteria.</p

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

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 Δ absA1 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47950/1/10295_2005_Article_34.pd

Comparative Phylogenomics of Pathogenic and Non-Pathogenic Mycobacterium

<div><p><i>Mycobacterium</i> species are the source of a variety of infectious diseases in a range of hosts. Genome based methods are used to understand the adaptation of each pathogenic species to its unique niche. In this work, we report the comparison of pathogenic and non-pathogenic <i>Mycobacterium</i> genomes. Phylogenetic trees were constructed using sequence of core orthologs, gene content and gene order. It is found that the genome based methods can better resolve the inter-species evolutionary distances compared to the conventional 16S based tree. Phylogeny based on gene order highlights distinct evolutionary characteristics as compared to the methods based on sequence, as illustrated by the shift in the relative position of <i>M. abscessus</i>. This difference in gene order among the <i>Mycobacterium</i> species is further investigated using a detailed synteny analysis. It is found that while rearrangements between some <i>Mycobacterium</i> genomes are local within synteny blocks, few possess global rearrangements across the genomes. The study illustrates how a combination of different genome based methods is essential to build a robust phylogenetic relationship between closely related organisms.</p></div

Genome characteristics of mycobacterial pathogens and non-pathogens considered in this study.

**<p>STL stands for Soft tissue Lesions.</p

Phylogenetic tree based on order of syntenic blocks from core ortholog genes.

<p>The number in bracket denotes the % micro-rearrangements with respect to <i>M. smegmatis</i>. Pathogens are shown in red whereas green denotes non-pathogens.</p

Distribution of core orthologs into various functional categories.

<p>The bar graphs represent the number of core orthologs in each category based on the TIGR annotation of the genes in <i>Mycobacterium leprae</i>. The line plot shows the % conservation of each class with respect to total number of genes in that class in <i>M. leprae</i>.</p

Genes identified as exclusive to pathogens.

*<p>The values shown are log₂ ratios of condition with respect to their control. Data is from TB database located at <a href="http://www.tbdb.org" target="_blank">www.tbdb.org</a><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071248#pone.0071248-Reddy1" target="_blank">[30]</a>.</p>*<p>Expansions: NGA - Nordihydroguaiaretic acid; STREP –Streptomycin; PMA-Palmitate; DM-Defined Media; KCN-Potassium cyanide; DETA- Diethylenetriamine; LA - Linoleic acid; OA - oleic acid; Rv – H37Rv strain; SNG- S-nitrosoglutathione; CPM-Chlorpromazine; ACE – Acetate; MEN-Menadione; DCH – Dicyclohexylcarboxamide; AA - Arachidonic acid; DOS – dosS/dosT mutant; CSP – Cell surface physiology; PS – Polysaccharide synthesis; CWP – Cell wall processing; KDH-alpha-ketoglutarate dehydrogenase; kgd -alpha-ketoglutarate decarboxylase.</p

Phylogenetic tree based on nucleotide and protein gene sequence.

<p>Tree based on 16S rRNA sequence is shown in (a); whereas tree in (b) is based on concatenation of protein sequences of 759 core orthologs. The 16S tree is based on the Fitch-Margoliash method, whereas neighbour-joining method was used to draw the tree in (b).</p