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Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses
Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Frontiers Research Foundation. The published article can be found at: http://www.frontiersin.org/Microbiology.Keywords: phylogenetic prediction, “virtual” metagenomes, systems biology, predictive metagenomics, ecotoxicology, 16S rRNA gene copy numbe
17O NMR study of q=0 spin excitations in a nearly ideal S=1/2 1D Heisenberg antiferromagnet, Sr2CuO3, up to 800 K
We used 17O NMR to probe the uniform (wavevector q=0) electron spin
excitations up to 800 K in Sr2CuO3 and separate the q=0 from the q=\pm\pi/a
staggered components. Our results support the logarithmic decrease of the
uniform spin susceptibility below T ~ 0.015J, where J=2200 K. From measurement
of the dynamical spin susceptibility for q=0 by the spin-lattice relaxation
rate 1/T_{1}, we demonstrate that the q=0 mode of spin transport is ballistic
at the T=0 limit, but has a diffusion-like contribution at finite temperatures
even for T << J.Comment: Submitted to Phys. Rev. Lett. 4 pages, 4 figure
Evidence for Ballistic Thermal Conduction in the One-Dimensional S=1/2 Heisenberg Antiferromagnetic Spin System Sr2CuO3
We have measured the thermal conductivity of the one-dimensional (1D) S=1/2
Heisenberg antiferromagnetic spin system of Sr2Cu1-xPdxO3 single crystals
including nonmagnetic impurities of Pd2+. It has been found that the mean free
path of spinons along the 1D spin chain at low temperatures is very close to
the average length of finite spin chains between spin defects estimated from
the magnetic susceptibility measurements. This proves that the thermal
conduction due to spinons at low temperatures in Sr2CuO3 is ballistic as
theoretically expected [Zotos et al.: Phys. Rev. Lett. 55 (1997) 11029]
Critical dynamics of a spin-5/2 2D isotropic antiferromagnet
We report a neutron scattering study of the dynamic spin correlations in
RbMnF, a two-dimensional spin-5/2 antiferromagnet. By tuning an
external magnetic field to the value for the spin-flop line, we reduce the
effective spin anisotropy to essentially zero, thereby obtaining a nearly ideal
two-dimensional isotropic antiferromagnet. From the shape of the quasielastic
peak as a function of temperature, we demonstrate dynamic scaling for this
system and find a value for the dynamical exponent . We compare these
results to theoretical predictions for the dynamic behavior of the
two-dimensional Heisenberg model, in which deviations from provide a
measure of the corrections to scaling.Comment: 5 pages, 4 figures. Submitted to Physical Review B, Rapid
Communication
Conductivity of quantum-spin chains: A Quantum Monte Carlo approach
We discuss zero-frequency transport properties of various spin-1/2 chains. We
show, that a careful analysis of Quantum Monte-Carlo (QMC) data on the
imaginary axis allows to distinguish between intrinsic ballistic and diffusive
transport. We determine the Drude weight, current-relaxation life-time and the
mean-free path for integrable and a non-integrable quantum-spin chain. We
discuss, in addition, some phenomenological relations between various
transport-coefficients and thermal response functions
Climate change promotes parasitism in a coral symbiosis.
Coastal oceans are increasingly eutrophic, warm and acidic through the addition of anthropogenic nitrogen and carbon, respectively. Among the most sensitive taxa to these changes are scleractinian corals, which engineer the most biodiverse ecosystems on Earth. Corals' sensitivity is a consequence of their evolutionary investment in symbiosis with the dinoflagellate alga, Symbiodinium. Together, the coral holobiont has dominated oligotrophic tropical marine habitats. However, warming destabilizes this association and reduces coral fitness. It has been theorized that, when reefs become warm and eutrophic, mutualistic Symbiodinium sequester more resources for their own growth, thus parasitizing their hosts of nutrition. Here, we tested the hypothesis that sub-bleaching temperature and excess nitrogen promotes symbiont parasitism by measuring respiration (costs) and the assimilation and translocation of both carbon (energy) and nitrogen (growth; both benefits) within Orbicella faveolata hosting one of two Symbiodinium phylotypes using a dual stable isotope tracer incubation at ambient (26 °C) and sub-bleaching (31 °C) temperatures under elevated nitrate. Warming to 31 °C reduced holobiont net primary productivity (NPP) by 60% due to increased respiration which decreased host %carbon by 15% with no apparent cost to the symbiont. Concurrently, Symbiodinium carbon and nitrogen assimilation increased by 14 and 32%, respectively while increasing their mitotic index by 15%, whereas hosts did not gain a proportional increase in translocated photosynthates. We conclude that the disparity in benefits and costs to both partners is evidence of symbiont parasitism in the coral symbiosis and has major implications for the resilience of coral reefs under threat of global change
A Systems Approach for Tumor Pharmacokinetics
Recent advances in genome inspired target discovery, small molecule screens, development of biological and nanotechnology have led to the introduction of a myriad of new differently sized agents into the clinic. The differences in small and large molecule delivery are becoming increasingly important in combination therapies as well as the use of drugs that modify the physiology of tumors such as anti-angiogenic treatment. The complexity of targeting has led to the development of mathematical models to facilitate understanding, but unfortunately, these studies are often only applicable to a particular molecule, making pharmacokinetic comparisons difficult. Here we develop and describe a framework for categorizing primary pharmacokinetics of drugs in tumors. For modeling purposes, we define drugs not by their mechanism of action but rather their rate-limiting step of delivery. Our simulations account for variations in perfusion, vascularization, interstitial transport, and non-linear local binding and metabolism. Based on a comparison of the fundamental rates determining uptake, drugs were classified into four categories depending on whether uptake is limited by blood flow, extravasation, interstitial diffusion, or local binding and metabolism. Simulations comparing small molecule versus macromolecular drugs show a sharp difference in distribution, which has implications for multi-drug therapies. The tissue-level distribution differs widely in tumors for small molecules versus macromolecular biologic drugs, and this should be considered in the design of agents and treatments. An example using antibodies in mouse xenografts illustrates the different in vivo behavior. This type of transport analysis can be used to aid in model development, experimental data analysis, and imaging and therapeutic agent design.National Institutes of Health (U.S.) (grant T32 CA079443
Multi-method assessment of patients with febrile illness reveals over-diagnosis of malaria in rural Uganda
Signatures of arithmetic simplicity in metabolic network architecture
Metabolic networks perform some of the most fundamental functions in living
cells, including energy transduction and building block biosynthesis. While
these are the best characterized networks in living systems, understanding
their evolutionary history and complex wiring constitutes one of the most
fascinating open questions in biology, intimately related to the enigma of
life's origin itself. Is the evolution of metabolism subject to general
principles, beyond the unpredictable accumulation of multiple historical
accidents? Here we search for such principles by applying to an artificial
chemical universe some of the methodologies developed for the study of genome
scale models of cellular metabolism. In particular, we use metabolic flux
constraint-based models to exhaustively search for artificial chemistry
pathways that can optimally perform an array of elementary metabolic functions.
Despite the simplicity of the model employed, we find that the ensuing pathways
display a surprisingly rich set of properties, including the existence of
autocatalytic cycles and hierarchical modules, the appearance of universally
preferable metabolites and reactions, and a logarithmic trend of pathway length
as a function of input/output molecule size. Some of these properties can be
derived analytically, borrowing methods previously used in cryptography. In
addition, by mapping biochemical networks onto a simplified carbon atom
reaction backbone, we find that several of the properties predicted by the
artificial chemistry model hold for real metabolic networks. These findings
suggest that optimality principles and arithmetic simplicity might lie beneath
some aspects of biochemical complexity
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