How crystals that sense and respond to their environments could evolve

An enduring mystery in biology is how a physical entity simple enough to have arisen spontaneously could have evolved into the complex life seen on Earth today. Cairns-Smith has proposed that life might have originated in clays which stored genomes consisting of an arrangement of crystal monomers that was replicated during growth. While a clay genome is simple enough to have conceivably arisen spontaneously, it is not obvious how it might have produced more complex forms as a result of evolution. Here, we examine this possibility in the tile assembly model, a generalized model of crystal growth that has been used to study the self-assembly of DNA tiles. We describe hypothetical crystals for which evolution of complex crystal sequences is driven by the scarceness of resources required for growth. We show how, under certain circumstances, crystal growth that performs computation can predict which resources are abundant. In such cases, crystals executing programs that make these predictions most accurately will grow fastest. Since crystals can perform universal computation, the complexity of computation that can be used to optimize growth is unbounded. To the extent that lessons derived from the tile assembly model might be applicable to mineral crystals, our results suggest that resource scarcity could conceivably have provided the evolutionary pressures necessary to produce complex clay genomes that sense and respond to changes in their environment

Synthesis of crystals with a programmable kinetic barrier to nucleation

A central goal of chemistry is to fabricate supramolecular structures of defined function and composition. In biology, control of supramolecular synthesis is often achieved through precise control over nucleation and growth processes: A seed molecule initiates growth of a structure, but this growth is kinetically inhibited in the seed's absence. Here we show how such control can be systematically designed into self-assembling structures made of DNA tiles. These structures, "zig-zag ribbons," are designed to have a fixed width but can grow arbitrarily long. Under slightly supersaturated conditions, theory predicts that elongation is always favorable but that nucleation rates decrease exponentially with increasing width. We confirm experimentally that although ribbons of different widths have similar thermodynamics, nucleation rates decrease for wider ribbons. It is therefore possible to program the nucleation rate by choosing a ribbon width. The presence of a seed molecule, a stabilized version of the presumed critical nucleus, removes the kinetic barrier to nucleation of a ribbon. Thus, we demonstrate the ability to grow supramolecular structures from rationally designed seeds, while suppressing spurious nucleation. Control over DNA tile nucleation allows for proper initiation of algorithmic crystal growth, which could lead to the high-yield synthesis of micrometer-scale structures with complex programmed features. More generally, this work shows how a self-assembly subroutine can be initiated

Programmable Control of Nucleation for Algorithmic Self-Assembly

Algorithmic self-assembly, a generalization of crystal growth processes, has been proposed as a mechanism for autonomous DNA computation and for bottom-up fabrication of complex nanostructures. A `program' for growing a desired structure consists of a set of molecular `tiles' designed to have specific binding interactions. A key challenge to making algorithmic self-assembly practical is designing tile set programs that make assembly robust to errors that occur during initiation and growth. One method for the controlled initiation of assembly, often seen in biology, is the use of a seed or catalyst molecule that reduces an otherwise large kinetic barrier to nucleation. Here we show how to program algorithmic self-assembly similarly, such that seeded assembly proceeds quickly but there is an arbitrarily large kinetic barrier to unseeded growth. We demonstrate this technique by introducing a family of tile sets for which we rigorously prove that, under the right physical conditions, linearly increasing the size of the tile set exponentially reduces the rate of spurious nucleation. Simulations of these `zig-zag' tile sets suggest that under plausible experimental conditions, it is possible to grow large seeded crystals in just a few hours such that less than 1 percent of crystals are spuriously nucleated. Simulation results also suggest that zig-zag tile sets could be used for detection of single DNA strands. Together with prior work showing that tile sets can be made robust to errors during properly initiated growth, this work demonstrates that growth of objects via algorithmic self-assembly can proceed both efficiently and with an arbitrarily low error rate, even in a model where local growth rules are probabilistic.Comment: 37 pages, 14 figure

Reducing facet nucleation during algorithmic self-assembly

Algorithmic self-assembly, a generalization of crystal growth, has been proposed as a mechanism for bottom-up fabrication of complex nanostructures and autonomous DNA computation. In principle, growth can be programmed by designing a set of molecular tiles with binding interactions that enforce assembly rules. In practice, however, errors during assembly cause undesired products, drastically reducing yields. Here we provide experimental evidence that assembly can be made more robust to errors by adding redundant tiles that "proofread" assembly. We construct DNA tile sets for two methods, uniform and snaked proofreading. While both tile sets are predicted to reduce errors during growth, the snaked proofreading tile set is also designed to reduce nucleation errors on crystal facets. Using atomic force microscopy to image growth of proofreading tiles on ribbon-like crystals presenting long facets, we show that under the physical conditions we studied the rate of facet nucleation is 4-fold smaller for snaked proofreading tile sets than for uniform proofreading tile sets

One dimensional boundaries for DNA tile self-assembly

In this paper we report the design and synthesis of DNA molecules (referred to as DNA tiles) with specific binding interactions that guide self-assembly to make one-dimensional assemblies shaped as lines, V's and X's. These DNA tile assemblies have been visualized by atomic force microscopy. The highly-variable distribution of shapes - e.g., the length of the arms of X-shaped assemblies - gives us insight into how the assembly process is occurring. Using stochastic models that simulate addition and dissociation of each type of DNA tile, as well as simplified models that more cleanly examine the generic phenomena, we dissect the contribution of accretion vs aggregation, reversible vs irreversible and seeded vs unseeded assumptions for describing the growth processes. The results suggest strategies for controlling self-assembly to make more uniformly-shaped assemblies

PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issu

VC1 catalyses a key step in the biosynthesis of vicine in faba bean

Faba bean (Vicia faba L.) is a widely adapted and high-yielding legume cultivated for its protein-rich seeds1. However, the seeds accumulate the pyrimidine glucosides vicine and convicine, which can cause haemolytic anaemia (favism) in 400 million genetically predisposed individuals2. Here, we use gene-to-metabolite correlations, gene mapping and genetic complementation to identify VC1 as a key enzyme in vicine and convicine biosynthesis. We demonstrate that VC1 has GTP cyclohydrolase II activity and that the purine GTP is a precursor of both vicine and convicine. Finally, we show that cultivars with low vicine and convicine levels carry an inactivating insertion in the coding sequence of VC1. Our results reveal an unexpected, purine rather than pyrimidine, biosynthetic origin for vicine and convicine and pave the way for the development of faba bean cultivars that are free of these anti-nutrients

Antibiotic Prescriptions and Prophylaxis in Italian Children. Is It Time to Change? Data from the ARPEC Project.

BACKGROUND: Antimicrobials are the most commonly prescribed drugs. Many studies have evaluated antibiotic prescriptions in the paediatric outpatient but few studies describing the real antibiotic consumption in Italian children's hospitals have been published. Point-prevalence survey (PPS) has been shown to be a simple, feasible and reliable standardized method for antimicrobials surveillance in children and neonates admitted to the hospital. In this paper, we presented data from a PPS on antimicrobial prescriptions carried out in 7 large Italian paediatric institutions. METHODS: A 1-day PPS on antibiotic use in hospitalized neonates and children was performed in Italy between October and December 2012 as part of the Antibiotic Resistance and Prescribing in European Children project (ARPEC). Seven institutions in seven Italian cities were involved. The survey included all admitted patients less than 18 years of age present in the ward at 8:00 am on the day of the survey, who had at least one on-going antibiotic prescription. For all patients data about age, weight, underlying disease, antimicrobial agent, dose and indication for treatment were collected. RESULTS: The PPS was performed in 61 wards within 7 Italian institutions. A total of 899 patients were eligible and 349 (38.9%) had an on-going prescription for one or more antibiotics, with variable rates among the hospitals (25.7% - 53.8%). We describe antibiotic prescriptions separately in neonates ( = 30 days to <18 years old). In the neonatal cohort, 62.8% received antibiotics for prophylaxis and only 37.2% on those on antibiotics were treated for infection. Penicillins and aminoglycosides were the most prescribed antibiotic classes. In the paediatric cohort, 64.4% of patients were receiving antibiotics for treatment of infections and 35.5% for prophylaxis. Third generation cephalosporins and penicillin plus inhibitors were the top two antibiotic classes. The main reason for prescribing antibiotic therapy in children was lower respiratory tract infections (LRTI), followed by febrile neutropenia/fever in oncologic patients, while, in neonates, sepsis was the most common indication for treatment. Focusing on prescriptions for LRTI, 43.3% of patients were treated with 3rd generation cephalosporins, followed by macrolides (26.9%), quinolones (16.4%) and carbapenems (14.9%) and 50.1% of LRTI cases were receiving more than one antibiotic. For neutropenic fever/fever in oncologic patients, the preferred antibiotics were penicillins with inhibitors (47.8%), followed by carbapenems (34.8%), aminoglycosides (26.1%) and glycopeptides (26.1%). Overall, the 60.9% of patients were treated with a combination therapy. CONCLUSIONS: Our study provides insight on the Italian situation in terms of antibiotic prescriptions in hospitalized neonates and children. An over-use of third generation cephalosporins both for prophylaxis and treatment was the most worrisome finding. A misuse and abuse of carbapenems and quinolones was also noted. Antibiotic stewardship programs should immediately identify feasible targets to monitor and modify the prescription patterns in children's hospital, also considering the continuous and alarming emergence of MDR bacteria

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article