Neil Dalchau

The Arabidopsis circadian clock incorporates a cADPR-based feedback loop

Transcriptional feedback loops are a feature of circadian clocks in both animals and plants. We show that the plant circadian clock also incorporates the cytosolic signaling molecule cyclic adenosine diphosphate ribose (cADPR). cADPR modulates the circadian oscillators transcriptional feedback loops and drives circadian oscillations of Ca2+ release. The effects of antagonists of cADPR signaling, manipulation of cADPR synthesis, and mathematical simulation of the interaction of cADPR with the circadian clock indicate that cADPR forms a feedback loop within the plant circadian clock.

The circadian oscillator gene GIGANTEA mediates a long-term response of the Arabidopsis thaliana circadian clock to sucrose

Circadian clocks are 24-h timing devices that phase cellular responses; coordinate growth, physiology, and metabolism; and anticipate the day–night cycle. Here we report sensitivity of the Arabidopsis thaliana circadian oscillator to sucrose, providing evidence that plant metabolism can regulate circadian function. We found that the Arabidopsis circadian system is particularly sensitive to sucrose in the dark. These data suggest that there is a feedback between the molecular components that comprise the circadian oscillator and plant metabolism, with the circadian clock both regulating and being regulated by metabolism. We used also simulations within a three-loop mathematical model of the Arabidopsis circadian oscillator to identify components of the circadian clock sensitive to sucrose. The mathematical studies identified GIGANTEA (GI) as being associated with sucrose sensing. Experimental validation of this prediction demonstrated that GI is required for the full response of the circadian clock to sucrose. We demonstrate that GI acts as part of the sucrose-signaling network and propose this role permits metabolic input into circadian timing in Arabidopsis.

Disruption of Two Defensive Signaling Pathways by a Viral RNA Silencing Suppressor

The Cucumber mosaic virus (CMV) 2b counter-defense protein disrupts plant antiviral mechanisms mediated by RNA silencing and salicylic acid (SA). We used microarrays to investigate defensive gene expression in 2b-transgenic Arabidopsis thaliana plants. Surprisingly, 2b inhibited expression of few SA-regulated genes and, in some instances, enhanced the effect of SA on certain genes. Strikingly, the 2b protein inhibited changes in the expression of 90% of genes regulated by jasmonic acid (JA). Consistent with this, infection of plants with CMV, but not the 2b gene-deletion mutant CMVDelta2b, strongly inhibited JA-inducible gene expression. JA levels were unaffected by infection with either CMV or CMVDelta2b. Although the CMV-Arabidopsis interaction is a compatible one, SA accumulation, usually considered to be an indicator of plant resistance, was increased in CMV-infected plants but not in CMVDelta2b-infected plants. Thus, the 2b protein inhibits JA signaling at a step downstream of JA biosynthesis but it primes induction of SA biosynthesis by another CMV gene product or by the process of infection itself. Like many plant viruses, CMV is aphid transmitted. JA is important in plant defense against insects. This raises the possibility that disruption of JA-mediated gene expression by the 2b protein may influence CMV transmission by aphids.

Correct biological timing in Arabidopsis requires multiple light-signaling pathways

Circadian oscillators provide rhythmic temporal cues for a range of biological processes in plants and animals, enabling anticipation of the day/night cycle and enhancing fitness-associated traits. We have used engineering models to understand the control principles of a plant’s response to seasonal variation. We show that the seasonal changes in the timing of circadian outputs require light regulation via feed-forward loops, combining rapid light-signaling pathways with entrained circadian oscillators. Linear time-invariant models of circadian rhythms were computed for 3,503 circadian-regulated genes and for the concentration of cytosolic-free calcium to quantify the magnitude and timing of regulation by circadian oscillators and light-signaling pathways. Bioinformatic and experimental analysis show that rapid light-induced regulation of circadian outputs is associated with seasonal rephasing of the output rhythm. We identify that external coincidence is required for rephasing of multiple output rhythms, and is therefore important in general phase control in addition to specific photoperiod-dependent processes such as flowering and hypocotyl elongation. Our findings uncover a fundamental design principle of circadian regulation, and identify the importance of rapid light-signaling pathways in temporal control.

The cell biology of major histocompatibility complex class I assembly: towards a molecular understanding

Major histocompatibility complex class I (MHC I) proteins protect the host from intracellular pathogens and cellular abnormalities through the binding of peptide fragments derived primarily from intracellular proteins. These peptide-MHC complexes are displayed at the cell surface for inspection by cytotoxic T lymphocytes. Here we reveal how MHC I molecules achieve this feat in the face of numerous levels of quality control. Among these is the chaperone tapasin, which governs peptide selection in the endoplasmic reticulum as part of the peptide-loading complex, and we propose key amino acid interactions central to the peptide selection mechanism. We discuss how the aminopeptidase ERAAP fine-tunes the peptide repertoire available to assembling MHC I molecules, before focusing on the journey of MHC I molecules through the secretory pathway, where calreticulin provides additional regulation of MHC I expression. Lastly we discuss how these processes culminate to influence immune responses.

A peptide filtering relation quantifies MHC class I peptide optimization

Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein.

A spatially localized architecture for fast and modular DNA computing

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs.

Ten Simple Rules for Effective Computational Research

In order to attempt to understand the complexity inherent in nature, mathematical, statistical and computational techniques are increasingly being employed in the life sciences. In particular, the use and development of software tools is becoming vital for investigating scientific hypotheses, and a wide range of scientists are finding software development playing a more central role in their day-to-day research. In fields such as biology and ecology, there has been a noticeable trend towards the use of quantitative methods for both making sense of ever-increasing amounts of data [1] and building or selecting models [2]. As Research Fellows of the “2020 Science” project (http://www.2020science.net), funded jointly by the EPSRC (Engineering and Physical Sciences Research Council) and Microsoft Research, we have firsthand experience of the challenges associated with carrying out multidisciplinary computation-based science [3]–[5]. In this paper we offer a jargon-free guide to best practice when developing and using software for scientific research. While many guides to software development exist, they are often aimed at computer scientists [6] or concentrate on large open-source projects [7]; the present guide is aimed specifically at the vast majority of scientific researchers: those without formal training in computer science. We present our ten simple rules with the aim of enabling scientists to be more effective in undertaking research and therefore maximise the impact of this research within the scientific community. While these rules are described individually, collectively they form a single vision for how to approach the practical side of computational science. Our rules are presented in roughly the chronological order in which they should be undertaken, beginning with things that, as a computational scientist, you should do before you even think about writing any code. For each rule, guides on getting started, links to relevant tutorials, and further reading are provided in the supplementary material (Text S1).

Noise Reduction in Complex Biological Switches

Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks. For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces. We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.

Characterization of Intrinsic Properties of Promoters

Accurate characterization of promoter behavior is essential for the rational design of functional synthetic transcription networks such as logic gates and oscillators. However, transcription rates observed from promoters can vary significantly depending on the growth rate of host cells and the experimental and genetic contexts of the measurement. Furthermore, in vivo measurement methods must accommodate variation in translation, protein folding, and maturation rates of reporter proteins, as well as metabolic load. The external factors affecting transcription activity may be considered to be extrinsic, and the goal of characterization should be to obtain quantitative measures of the intrinsic characteristics of promoters. We have developed a promoter characterization method that is based on a mathematical model for cell growth and reporter gene expression and exploits multiple in vivo measurements to compensate for variation due to extrinsic factors. First, we used optical density and fluorescent reporter gene measurements to account for the effect of differing cell growth rates. Second, we compared the output of reporter genes to that of a control promoter using concurrent dual-channel fluorescence measurements. This allowed us to derive a quantitative promoter characteristic (ρ) that provides a robust measure of the intrinsic properties of a promoter, relative to the control. We imposed different extrinsic factors on growing cells, altering carbon source and adding bacteriostatic agents, and demonstrated that the use of ρ values reduced the fraction of variance due to extrinsic factors from 78% to less than 4%. This is a simple and reliable method to quantitatively describe promoter properties.