An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics

Phylodynamics focuses on the problem of reconstructing past population size dynamics from current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology, but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. While this approach is quite powerful, large data sets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand computationally more efficient inference framework. To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm

Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies

Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum-likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method’s credible intervals for population size as a function of time cover 90% of true values across multiple demographic scenarios, enabling formal hypothesis testing about population size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of population size history over the past 150,000 years

Observation of a Griffiths-like phase in the paramagnetic regime of ErCo_2

A systematic x-ray magnetic circular dichroism study of the paramagnetic phase of ErCo2 has recently allowed to identify the inversion of the net magnetization of the Co net moment with respect to the applied field well above the ferrimagnetic ordering temperature, Tc. The study of small angle neutron scattering measurements has also shown the presence of short range order correlations in the same temperature region. This phenomenon, which we have denoted parimagnetism, may be related with the onset of a Griffiths-like phase in paramagnetic ErCo2. We have measured ac susceptibility on ErCo2 as a function of temperature, applied field, and excitation frequency. Several characteristics shared by systems showing a Griffiths phase are present in ErCo2, namely the formation of ferromagnetic clusters in the disordered phase, the loss of analyticity of the magnetic susceptibility and its extreme sensitivity to an applied magnetic field. The paramagnetic susceptibility allows to establish that the magnetic clusters are only formed by Co moments as well as the intrinsic nature of those Co moments

Cell-penetrating peptide-conjugated copper complexes for redox-mediated anticancer therapy

Metal-based chemotherapeutics like cisplatin are widely employed in cancer treatment. In the last years, the design of redox-active (transition) metal complexes, such as of copper (Cu), has attracted high interest as alternatives to overcome platinum-induced side-effects. However, several challenges are still faced, including optimal aqueous solubility and efficient intracellular delivery, and strategies like the use of cell-penetrating peptides have been encouraging. In this context, we previously designed a Cu(II) scaffold that exhibited significant reactive oxygen species (ROS)-mediated cytotoxicity. Herein, we build upon the promising Cu(II) redox-active metallic core and aim to potentiate its anticancer activity by rationally tailoring it with solubility- and uptake-enhancing functionalizations that do not alter the ROS-generating Cu(II) center. To this end, sulfonate, arginine and arginine-rich cell-penetrating peptide (CPP) derivatives have been prepared and characterized, and all the resulting complexes preserved the parent Cu(II) coordination core, thereby maintaining its reported redox capabilities. Comparative in vitro assays in several cancer cell lines reveal that while specific solubility-targeting derivatizations (i.e., sulfonate or arginine) did not translate into an improved cytotoxicity, increased intracellular copper delivery via CPP-conjugation promoted an enhanced anticancer activity, already detectable at short treatment times. Additionally, immunofluorescence assays show that the Cu(II) peptide-conjugate distributed throughout the cytosol without lysosomal colocalization, suggesting potential avoidance of endosomal entrapment. Overall, the systematic exploration of the tailored modifications enables us to provide further understanding on structure-activity relationships of redox-active metal-based (Cu(II)) cytotoxic complexes, which contributes to rationalize and improve the design of more efficient redox-mediated metal-based anticancer therapy

Copper(II) N, N, O -Chelating Complexes as Potential Anticancer Agents

Altres ajuts: Acord transformatiu CRUE-CSICThree novel dinuclear Cu(II) complexes based on a N,N,O-chelating salphen-like ligand scaffold and bearing varying aromatic substituents (−H, −Cl, and −Br) have been synthesized and characterized. The experimental and computational data obtained suggest that all three complexes exist in the dimeric form in the solid state and adopt the same conformation. The mass spectrometry and electron paramagnetic resonance results indicate that the dimeric structure coexists with the monomeric form in solution upon solvent (dimethyl sulfoxide and water) coordination. The three synthesized Cu(II) complexes exhibit high potentiality as ROS generators, with the Cu(II)/Cu(I) redox potential inside the biological redox window, and thus being able to biologically undergo Cu(II)/Cu(I) redox cycling. The formation of ROS is one of the most promising reported cell death mechanisms for metal complexes to offer an inherent selectivity to cancer cells. In vitro cytotoxic studies in two different cancer cell lines (HeLa and MCF7) and in a normal fibroblast cell line show promising selective cytotoxicity for cancer cells (IC50 about 25 μM in HeLa cells, which is in the range of cisplatin and improved with respect to carboplatin), hence placing this N,N,O-chelating salphen-like metallic core as a promising scaffold to be explored in the design of future tailor-made Cu(II) cytotoxic compounds