Metabolic engineering of cyanobacteria: developing molecular tools and characterizing strain performance in light:dark cycles

2015 Summer.The conversion of CO2 and light energy to biofuels holds promise for a renewable and environmentally responsible source of energy that could meet the growing demand for transportation fuels. However, early efforts to commercialize biofuels from plants were hampered by social, economic, and technological difficulties. Photosynthetic microbes present an opportunity for a more efficient conversion of fixed carbon to biofuels by bypassing the need of harvesting sugars from plants to be fermented by heterotrophic bacteria. More recently, cyanobacterial technologies have received considerable interest due to their ease of genetic manipulation that enables them to produce a myriad of biofuels and biochemicals directly from CO2. This relatively nascent technology needs to be developed in order to realize its commercial potential. Metabolic engineering is the systematic improvement of strains through the use of a variety of theoretical and experimental techniques. To date, heterologous pathways expression has been the most successful in model heterotrophic organisms (e.g. E. coli) and advances from these systems have to be carefully transferred over to cyanobacteria. Though several studies have demonstrated the capability of engineering cyanobacteria to produce biofuels, there is yet to be any commercially feasible production platform of fuels from CO2. Amongst the challenges is the need to improve yields and titers from recombinant strains. However, the physiology of cyanobacteria is distinct from that of heterotrophic organisms and therefore requires careful design and study in order to optimize for higher yields. This thesis contributes several technologies to foster the scale-up of cyanobacteria systems from the bench to industrial scale. We first developed a markerless chromosomal modification method in WT Synechocystis PCC6803 that could reduce the metabolic load and cultivation cost compared to plasmid-based expression methods. We established a counter-selection method that necessitates two rounds of modifications in order to screen for the desired mutant harboring the gene(s) of interest. In the first round, a synthetic circuit consisting of a nickel inducible toxin gene (mazF) and a kanamycin resistance marker is integrated into a specific locus in WT Synechocystis. In the second round, a construct harboring gene(s) of interest is transformed into the prerequisite strains and screen on Ni2+ to obtain the desired mutants. Next we established a free fatty acid (FFA) producing platform in Synechocystis PCC6803 by pursuing three goals: 1) deletion of acyl-acyl carrier protein (acyl-ACP) synthetase (aas), 2) optimize the expression of thioesterase I (TesA) with a promoter library and 3) examine the effects of light:dark cycles on FFA production in Synechocystis. For the first goal, we were successful in engineering an aas deletion strain that had increased FFA production. In the second goal, we developed four Synechocystis variants with increasing TesA expression strengths from the aas deletion strain. No increase in FFA production was observed between the TesA expressing strains (with aas deleted) compared to the baseline aas deletion strain. On the protein level, we found no evidence of TesA enzyme activity even though TESA peptides were detected in our Synechocystis strains. In the third goal, we learn that diel light:dark cycles causes a significant decrease in production of FFAs in FFA producing mutants of Synechocystis compared to continuous light. We did not observe any transcriptional changes in the fatty acid biosynthesis pathway between our WT and FFA producing strains to explain these changes. In summary, this thesis is impactful in two ways: 1) it entails the development of a markerless genetic modification method for use in cyanobacteria and 2) it characterizes the production of FFAs from engineered cyanobacteria under diel light:dark cycles. Overall, this thesis helps address the difficulties in the development of cyanobacteria systems for eventual use in an industrial setting

Circadian Gene Expression in Cyanobacteria

Cyanobacteria are photosynthetic prokaryotes that live in aquatic environments. The cyanobacterium Synechococcus elongatus PCC 7942, (hereafter S. elongatus) coordinates its day and night behaviors via a circadian clock. The clock is entrained by light/dark cycles but continues to run in constant light conditions. The core circadian clock in S. elongatus is encoded by post-translational modifications of three Kai proteins, but the extent and mechanism of circadian gene expression are unknown. We provide the first unbiased characterization of circadian gene expression in S. elongatus, demonstrating that $\sim 65\%$ of genes display oscillation in continuous light conditions, with some genes peaking in expression at subjective dawn and others at subjective dusk. We next sought to identify the mechanism by which such a large fraction of the genome could be rhythmically controlled. Through bioinformatic, correlative, and perturbation experiments, we find that circadian changes in chromosome topology/supercoiling are sufficient to drive rhythmic expression (Chapter 2). To further investigate how chromosome topology can control gene expression we performed a high resolution characterization of transcripts and RNA polymerase across the S. elongatus genome (Chapter 3). Bioinformatic analysis of transcription start sites suggests that the AT/GC content a particular region of the promoter is informative in defining the phase at which a transcript is maximally expressed. We find that these sequences are sufficient to drive circadian gene expression at a particular phase and that mutation of single nucleotides in this region can reverse the expression phase of a transcript (Chapter 4). To understand the role of chromosome dynamics in circadian gene expression and cyanobacterial physiology, we tagged and followed chromosomes over multiple cell divisions. We find that S. elongatus cells harbor multiple ordered copies of a single chromosome, and the organization of chromosomes in the cytoplasm facilitates equal segregation of chromosomes to daughter cells (Chapter 5)

Stochastic Sensitivity Analysis and Kernel Inference via Distributional Data

AbstractCellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing

Mathematical model in absolute units for the Arabidopsis circadian oscillator

The Earth’s oblique rotation results in changes in light and temperature across the day and time of year. Living organisms evolved rhythmic behaviours to anticipate these changes and execute appropriate responses at particular times. The current paradigm for the biological clocks in several branches of life is an underlying biochemical oscillator mainly composed by a network of repressive transcription factors. The slow decay in their activity is fundamental for generating anticipatory dynamics. Interestingly, these dynamics can be well appreciated when the biological system is left under constant environmental conditions, where oscillation of several physiological readouts persists with a period close to 24 hours, hence the term “circadian clocks”, circa=around dian=day. In plants the model species Arabidopsis thaliana has served as an invaluable tool for analysing the genetics, biochemical, developmental, and physiological effects of the oscillator. Many of these experimental results have been integrated in mechanistic and mathematical theories for the circadian oscillator. These models predict the timing of gene expression and protein presence in several genetic backgrounds and photoperiodic conditions. The aim of this work is the introduction of a correct mass scale for both the RNA transcript and protein variables of the clock models. The new mass scale is first introduced using published RNA data in absolute units, from qRT-PCR. This required reinterpreting several assumptions of an established clock model (P2011), resulting in an updated version named U2017. I evaluate the performance of the U2017 model in using data in absolute mass units, for the first time for this clock system. Introducing absolute units for the protein variables takes place by generating hypothetical protein data from the existing qRT-PCR data and comparing a data-driven model with western blot data from the literature. I explore the consequences of these predicted protein numbers for the model’s dynamics. The process required a meta-analysis of plant parameter values and genomic information, to interpret the biological relevance of the updated protein parameters. The predicted protein amounts justify, for example, the revised treatment of the Evening Complex in the U2017 model, compared to P2011. The difficulties of introducing absolute units for the protein components are discussed and components for experimental quantification are proposed. Validating the protein predictions required a new methodology for absolute quantification. The methodology is based on translational fusions with a luciferase reporter than has been little used in plants, NanoLUC. Firstly, the characterisation of NanoLUC as a new circadian reporter was explored using the clock gene BOA. The results show that this new system is a robust, sensitive and automatable approach for addressing quantitative biology questions. I selected five clock proteins CCA1, LHY, PRR7, TOC1 and LUX for absolute quantification using the new NanoLUC methodology. Functionality of translation fusions with NanoLUC was assessed by complementation experiments. The closest complementing line for each gene was selected to generate protein time series data. Absolute protein quantities were determined by generation of calibration curves using a recombinant NanoLUC standard. The developed methodology allows absolute quantification comparable to the calibrated qRT-PCR data. These experimental results test the predicted protein amounts and represent a technical resource to understand protein dynamics of Arabidopsis’ circadian oscillator quantitatively. The new experimental, meta-analysis and modelling results in absolute units allows future researchers to incorporate further, quantitative biochemical data

"Time Flies" Multisensory Processing by Circadian Clocks in Drosophila melanogaster

Periodic changes of environmental signals are sufficient to synchronise circadian rhythms across species. Circadian time, then, is a concept tethered to a diverse spread of different sensory modalities. In spite of this fact, circadian systems have historically been studied in a unimodal fashion investigating the processing of singular cues, while keeping others constant. My research sought to challenge this dogma via exploration of multisensory cue combination in the circadian clock of Drosophila melanogaster. Systematic behavioural analysis in wild type flies showed that misalignments between light and temperature (two potent environmental cues) produced abnormal profiles of circadian locomotor activity. Further molecular investigation revealed this behavioural disruption was associated with a breakdown of molecular rhythms in central clock neurons. Both the behavioural and molecular phenotypes observed during sensory conflict depended on the circadian photoreceptor, cryptochrome. Outside the central clock network, the circadian system of fruit flies forms an extensive network of peripheral oscillators. A luciferase reporter assay showed that photic signals play a more prominent role in peripheral clocks, compared to the core clock network in the brain. Here, molecular rhythms displayed continued light preference during sensory conflict, which again depended on cryptochrome. To further explore the implications of multisensory processing, with particular focus on the blurred boundary between clock input and output, circadian gene expression was evaluated in the ‘Johnston’s Organ’ - a key mechanosensory appa- ratus in Drosophila. These preliminary data suggest the existence of a previously unidentified peripheral clock in the fruit fly ear. Finally, a statistical model of the circadian clock was developed using a novel graphical architecture based on the hidden Markov model framework. This model was capable of inferring the phase of an underlying clock from both simulated and experimental locomotor datasets. More broadly, learning the parameters of this model from the data produced a probabilistic representation of the system, including its phase response dynamics

Mathematical Approaches to Understanding Mammalian Circadian Rhythms

Nearly all life on earth exists in a periodic environment, in which important factors like sunlight and temperature change predictably with a twenty-four hour cycle. As a process which only reacts to the current state of a periodic signal will constantly suffer a phase lag, organisms have developed a natural feedforward controller to predict upcoming environmental changes. Such a system allows an organism to align their behavior to the correct phase of the day/night cycle and ease transitions between times of energy abundance and energy scarcity. These daily changes in physiology are known as circadian rhythms and are coordinated by intricate genetic regulatory networks.Over evolutionary timescales, nearly all aspects of gene expression have been coupled to the day/night cycle. As a result, circadian rhythms are essential to maintaining metabolic homeostasis, DNA repair, cell cycling, and other important cellular processes. Since modern societies have deviated from their evolutionary prescribed sleep and feeding schedules, disturbances to circadian gene expression have grown more common. Beyond acute effects on performance and fatigue, compromised circadian rhythms have been linked to chronic issues such as the onset of metabolic disease or increased cancer risk. Since circadian rhythms can be damped by factors such as jet lag, shift work, and high fat diets, there has been recent interest in developing pharmacological or behavioral therapies which might restore normal circadian rhythms.This thesis uses techniques from dynamic systems to model circadian oscillations at different scales. First, a mathematical model of the core circadian feedback loop is developed in order to explain a novel small molecule modulator, KL001. Through this mathematical model, we gain new insight into how the two isoforms of cryptochrome (CRY1 and CRY2) interact to control the period. The identifiability of parameters and parametric sensitivities in oscillatory models is investigated next, and a dynamic optimization technique using collocation methods and nonlinear programming is shown to be able to efficiently bootstrap confidence intervals in such parameters. This technique is then applied to a set of three circadian models in order to identify mechanisms which are able to differentiate between the effect of two small molecule regulators, even across differences in parameter values and kinetic assumptions. Next, the effect of finite-duration perturbations on clock amplitude and synchrony is explored. New techniques and sensitivity analyses are developed which allow the effect of transient perturbations on the clock to be efficiently calculated without the need for computationally intensive stochastic simulations. Finally, the effect of clock perturbations on stochastic noise is investigated by fitting the damping rate of cultured cellular reporters. Using genome-wide siRNA knockdown screens, we are able to gain fundamental insight into design principles of circadian oscillations

28th Fungal Genetics Conference

Full abstracts from the 28th Fungal Genetics Conference Asilomar, March 17-22, 2015