Our research group uses a combination of interdisciplinary approaches including chemical engineering, synthetic biology, systems biology, molecular biology, microbiology, metabolic engineering and computational biology to address key global challenges including medical and energy needs. We are interested in adopting an integrated mathematical modeling and experimental approach to investigate fundamental and medically relevant issues such as understanding the molecular mechanisms responsible for antibiotic and antiviral resistance, and for developing “next-generation smart antimicrobials” by rationally engineering novel therapeutics that target essential bacterial/viral genes in a potentially resistance-free manner. We study genetic regulatory networks that control propagation of infectious diseases, with the goal of discovering novel drug targets for therapy. Using synthetic biology tools we design, construct and engineer modular synthetic genetic devices that can achieve higher-order biological computation, for variety of biotechnological and bioenergy applications. To this end, we engineer biological parts such as transcription factors, promoter sequences, receptors, feedback loops, and regulatory RNA to build complex genetic networks that can be used to optimize cellular machinery for production of bio-fuels and pharmaceuticals, and for gene therapy applications. Using these genetic devices, we apply systems biology approaches to understand functioning of complex genetic networks and to build rules to manipulate such networks.
In the Spotlight
ABC Channel News Features our Light-activated nanotherapy against antibiotic-resistant “superbugs”!
In the ever-escalating evolutionary battle with drug-resistant bacteria, humans may soon have a leg up thanks to adaptive, light-activated nanotherapy developed by researchers at the University of Colorado Boulder.
Antibiotic-resistant bacteria such as Salmonella, E. Coli and Staphylococcus infect some 2 million people and kill at least 23,000 people in the United States each year. Efforts to thwart these so-called “superbugs” have consistently fallen short due to the bacteria’s ability to rapidly adapt and develop immunity to common antibiotics such as penicillin.
New research from CU-Boulder, however, suggests that the solution to this big global problem might be to think small—very small.
In findings published today in the journal Nature Materials, researchers at the Department of Chemical and Biological Engineering and the BioFrontiers Institute describe new light-activated therapeutic nanoparticles known as “quantum dots.” The dots, which are about 20,000 times smaller than a human hair and resemble the tiny semiconductors used in consumer electronics, successfully killed 92 percent of drug-resistant bacterial cells in a lab-grown culture.....
Antisense Therapeutics Resensitize Drug Resistant Bacteria
The recent surge of drug-resistant superbugs and shrinking antibiotic pipeline are serious challenges to global health. In particular, the emergence of β-lactamases has caused extensive resistance against the most frequently prescribed class of β-lactam antibiotics. Here, we develop novel synthetic peptide nucleic acidbased
antisense inhibitors that target the start codon and ribosomal binding site of the TEM-1 β-lactamase transcript and act via translation inhibition mechanism. We show that these antisense inhibitors are capable of resensitizing drug-resistant Escherichia coli to β-lactam antibiotics exhibiting 10-fold reduction in the minimum inhibitory concentration (MIC). To study the mechanism of resistance, we adapted E. coli at MIC levels of the β-lactam/antisense inhibitor combination and observed a nonmutational, bethedging based adaptive antibiotic resistance response as evidenced by phenotypic heterogeneity as well as heterogeneous expression of key stress response genes. Our data show that both the development of new antimicrobials and an understanding of cellular response during the development of tolerance could aid in mitigating the impending antibiotic crisis.
Colleen M. Courtney, and Anushree Chatterjee* (2015), "Sequence-specific peptide nucleic acid based antisense inhibitors of TEM-1 beta-lactamase and mechanism of adaptive resistance." ACS Infectious Diseases 1 (6), pp 253–263. doi: 10.1021/acsinfecdis.5b00042. (Link)
Gene expression variability as a metric for identifying "Key Players" during Adaptive Antibiotic Resistance
The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation.
Keesha E Erickson, Peter B. Otoupal, and Anushree Chatterjee* (2015), "Gene expression variability underlies adaptive resistance in phenotypically heterogenous bacterial populations." ACS Infectious Diseases 1(11), pp 555-567. DOI: 10.1021/acsinfecdis.5b00095 (Link)
Mathematical model investigates Antisense transcription as a smart way to build tunable genetic switches
Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.
Antoni E. Bordoy, and Anushree Chatterjee* (2015), "cis-Antisense Transcription Gives Rise to a Tunable Genetic Switch Behavior: A Mathematical Modeling Approach." PLoS ONE 10(7): e0133873. doi:10.1371/journal.pone.0133873. (Link).
New Metabolic Flux Analysis tool "Constrictor" developed by our lab is available on Opensource
Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. We have developed a computational framework called Constrictor that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. This algorithm is designed to easily implement finely tuned reductions in flux through individual or multiple reactions in a combinatorial manner. We demonstrate the use of this tool by modeling the overproduction of ethylene in Escherichia coli. Our method uncovers novel gene targets that improve in silico ethylene yield. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels, and troubleshoot metabolic networks. Link
Keesha E Erickson, Ryan T Gill, and Anushree Chatterjee* (2014), "CONSTRICTOR: Constraint modification provides insight into design of biochemical networks." PLoS ONE 9(11):e113820. doi:10.1371/journal.pone.0113820. (Link)
CU recognizes Chatterjee and Nagpal labs for developing a new way of sequencing and detecting drug-resistant bacteria
CU Technology Transfer Office has recongnized Quantum Sequencing technology developed by our and Nagpal lab with the New Inventor of the Year award. We are developing a platform technology for fast, reliable, high-throughput and cost effective single molecule sequencing of nulciec acids.This kind of sequencing is an important step in the developement of new diagnostic tools for personalized
medicine, as well as in rapid identification of DNA sequences that allow bacteria to develop drug resistance. This approach can potentially transform how we detect drug resistant pathogens in the clinical setting, will allow faster diagnostics and early detection of drug resistant strains that can prevent future spread of resistance, and will also reduce the cost of diagnostics.
Josep Casamada Ribot, Anushree Chatterjee*, and Prashant Nagpal* (2015), "Measurements of Single nucleotide Electronic states as Nanoelectronic fingerprints for Identification of DNA Nucleobases, their Protonated and Unprotonated states, Isomers and Tautomers." The Journal of Physical Chemistry Letters B 119 (15), pp 4968–4974. doi: 10.1021/acs.jpcb.5b01403. (Link)