Salim Charaniya

A Bistable Gene Switch for Antibiotic Biosynthesis: The Butyrolactone Regulon in Streptomyces coelicolor

Many microorganisms, including bacteria of the class Streptomycetes, produce various secondary metabolites including antibiotics to gain a competitive advantage in their natural habitat. The production of these compounds is highly coordinated in a population to expedite accumulation to an effective concentration. Furthermore, as antibiotics are often toxic even to their producers, a coordinated production allows microbes to first arm themselves with a defense mechanism to resist their own antibiotics before production commences. One possible mechanism of coordination among individuals is through the production of signaling molecules. The γ-butyrolactone system in Streptomyces coelicolor is a model of such a signaling system for secondary metabolite production. The accumulation of these signaling molecules triggers antibiotic production in the population. A pair of repressor-amplifier proteins encoded by scbA and scbR mediates the production and action of one particular γ-butyrolactone, SCB1. Based on the proposed interactions of scbA and scbR, a mathematical model was constructed and used to explore the ability of this system to act as a robust genetic switch. Stability analysis shows that the butyrolactone system exhibits bistability and, in response to a threshold SCB1 concentration, can switch from an OFF state to an ON state corresponding to the activation of genes in the cryptic type I polyketide synthase gene cluster, which are responsible for production of the hypothetical polyketide. The switching time is inversely related to the inducer concentration above the threshold, such that short pulses of low inducer concentration cannot switch on the system, suggesting its possible role in noise filtering. In contrast, secondary metabolite production can be triggered rapidly in a population of cells producing the butyrolactone signal due to the presence of an amplification loop in the system. S. coelicolor was perturbed experimentally by varying concentrations of SCB1, and the model simulations match the experimental data well. Deciphering the complexity of this butyrolactone switch will provide valuable insights into how robust and efficient systems can be designed using “simple” two-protein networks.

Mining bioprocess data: opportunities and challenges

Modern biotechnology production plants are equipped with sophisticated control, data logging and archiving systems. These data hold a wealth of information that might shed light on the cause of process outcome fluctuations, whether the outcome of concern is productivity or product quality. These data might also provide clues on means to further improve process outcome. Data-driven knowledge discovery approaches can potentially unveil hidden information, predict process outcome, and provide insights on implementing robust processes. Here we describe the steps involved in process data mining with an emphasis on recent advances in data mining methods pertinent to the unique characteristics of biological process data.

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 ΔabsA1 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.

Mining Transcriptome Data for Function-Trait Relationship of Hyper Productivity of Recombinant Antibody

In the past decade we have witnessed a drastic increase in the productivity of mammalian cell culture‐based processes. High‐producing cell lines that synthesize and secrete these therapeutics have contributed largely to the advances in process development. To elucidate the productivity trait in the context of physiological functions, the transcriptomes of several NS0 cell lines with a wide range of antibody productivity were compared. Gene set testing (GST) analysis was used to identify pathways and biological functions that are altered in high producers. Three complementary tools for GST—gene set enrichment analysis (GSEA), gene set analysis (GSA), and MAPPFinder, were used to identify groups of functionally coherent genes that are up‐ or downregulated in high producers. Major functional classes identified include those involved in protein processing and transport, such as protein modification, vesicle trafficking, and protein turnover. A significant proportion of genes involved in mitochondrial ribosomal function, cell cycle regulation, cytoskeleton‐related elements are also differentially altered in high producers. The observed correlation of these functional classes with productivity suggests that simultaneous modulation of several physiological functions is a potential route to high productivity. Biotechnol. Bioeng. 2009;102: 1654–1669.

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 inference of regulatory networks in Streptomyces coelicolor

BackgroundThe onset of antibiotics production in Streptomyces species is co-ordinated with differentiation events. An understanding of the genetic circuits that regulate these coupled biological phenomena is essential to discover and engineer the pharmacologically important natural products made by these species. The availability of genomic tools and access to a large warehouse of transcriptome data for the model organism, Streptomyces coelicolor, provides incentive to decipher the intricacies of the regulatory cascades and develop biologically meaningful hypotheses.ResultsIn this study, more than 500 samples of genome-wide temporal transcriptome data, comprising wild-type and more than 25 regulatory gene mutants of Streptomyces coelicolor probed across multiple stress and medium conditions, were investigated. Information based on transcript and functional similarity was used to update a previously-predicted whole-genome operon map and further applied to predict transcriptional networks constituting modules enriched in diverse functions such as secondary metabolism, and sigma factor. The predicted network displays a scale-free architecture with a small-world property observed in many biological networks. The networks were further investigated to identify functionally-relevant modules that exhibit functional coherence and a consensus motif in the promoter elements indicative of DNA-binding elements.ConclusionsDespite the enormous experimental as well as computational challenges, a systems approach for integrating diverse genome-scale datasets to elucidate complex regulatory networks is beginning to emerge. We present an integrated analysis of transcriptome data and genomic features to refine a whole-genome operon map and to construct regulatory networks at the cistron level in Streptomyces coelicolor. The functionally-relevant modules identified in this study pose as potential targets for further studies and verification.