Polymorph Identification for Flexible Molecules: Linear Regression Analysis of Experimental and Calculated Solution- and Solid-State NMR Data.

The Δδ regression approach of Blade et al. [ J. Phys. Chem. A 2020, 124(43), 8959-8977] for accurately discriminating between solid forms using a combination of experimental solution- and solid-state NMR data with density functional theory (DFT) calculation is here extended to molecules with multiple conformational degrees of freedom, using furosemide polymorphs as an exemplar. As before, the differences in measured 1H and 13C chemical shifts between solution-state NMR and solid-state magic-angle spinning (MAS) NMR (Δδexperimental) are compared to those determined by gauge-including projector augmented wave (GIPAW) calculations (Δδcalculated) by regression analysis and a t-test, allowing the correct furosemide polymorph to be precisely identified. Monte Carlo random sampling is used to calculate solution-state NMR chemical shifts, reducing computation times by avoiding the need to systematically sample the multidimensional conformational landscape that furosemide occupies in solution. The solvent conditions should be chosen to match the molecule's charge state between the solution and solid states. The Δδ regression approach indicates whether or not correlations between Δδexperimental and Δδcalculated are statistically significant; the approach is differently sensitive to the popular root mean squared error (RMSE) method, being shown to exhibit a much greater dynamic range. An alternative method for estimating solution-state NMR chemical shifts by approximating the measured solution-state dynamic 3D behavior with an ensemble of 54 furosemide crystal structures (polymorphs and cocrystals) from the Cambridge Structural Database (CSD) was also successful in this case, suggesting new avenues for this method that may overcome its current dependency on the prior determination of solution dynamic 3D structures.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

The bounded coalescent model: Conditioning a genealogy on a minimum root date.

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability density of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

Conformations in Solution and in Solid-State Polymorphs: Correlating Experimental and Calculated Nuclear Magnetic Resonance Chemical Shifts for Tolfenamic Acid.

A new approach for quantitively assessing putative crystal structures with applications in crystal structure prediction (CSP) is introduced that is based upon experimental solution- and magic-angle spinning (MAS) solid-state NMR data and density functional theory (DFT) calculation. For the specific case of tolfenamic acid (TFA), we consider experimental solution-state NMR for a range of solvents, experimental MAS NMR of polymorphs I and II, and DFT calculations for four polymorphs. The change in NMR chemical shift observed in passing from the solution state to the solid state (ΔδExperimental) is calculated as the difference between 1H and 13C experimental solid-state chemical shifts for each polymorphic form (δSolid expt) and the corresponding solution-state NMR chemical shifts (δSolution expt). Separately, we use the gauge-included projector augmented wave (GIPAW) method to calculate the NMR chemical shifts for each form (δSolid calc) and for TFA in solution (δSolution calc) using the dynamic 3D solution conformational ensemble determined from NMR spectroscopy. The calculated change in passing from the solution state to the solid state (ΔδCalculated) is then calculated as the difference of δSolid calc and δSolution calc. Regression analysis for ΔδCalculated against ΔδExperimental followed by a t-test for statistical significance provides a robust quantitative assessment. We show that this assessment clearly identifies the correct polymorph, i.e., when comparing ΔδExperimental based on the experimental MAS NMR chemical shifts of form I or II with ΔδCalculated based on calculated chemical shifts for polymorphs I, II, III, and IV. Complementarity to the established approach of comparing δSolid expt to δSolid calc is explored. We further show that our approach is applicable if there are no solid-state crystal structure data. Specifically, δSolid calc in ΔδCalculated is replaced by the chemical shift for an isolated molecule with a specific conformation. Sampling conformations at specific 15° angle values and comparing them against experimental 13C chemical shift data for forms I and II identifies matching narrow ranges of conformations, successfully predicting the conformation of tolfenamic acid in each form. This methodology can therefore be used in crystal structure prediction to both reduce the initial conformational search space and also quantitatively assess subsequent putative structures to reliably and unambiguously identify the correct structure.

In vivo multiplex molecular imaging of vascular inflammation using surface-enhanced Raman spectroscopy.

Vascular immune-inflammatory responses play a crucial role in the progression and outcome of atherosclerosis. The ability to assess localized inflammation through detection of specific vascular inflammatory biomarkers would significantly improve cardiovascular risk assessment and management; however, no multi-parameter molecular imaging technologies have been established to date. Here, we report the targeted in vivo imaging of multiple vascular biomarkers using antibody-functionalized nanoparticles and surface-enhanced Raman scattering (SERS). Methods: A series of antibody-functionalized gold nanoprobes (BFNP) were designed containing unique Raman signals in order to detect intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1) and P-selectin using SERS. Results: SERS and BFNP were utilized to detect, discriminate and quantify ICAM-1, VCAM-1 and P-selectin in vitro on human endothelial cells and ex vivo in human coronary arteries. Ultimately, non-invasive multiplex imaging of adhesion molecules in a humanized mouse model was demonstrated in vivo following intravenous injection of the nanoprobes. Conclusion: This study demonstrates that multiplexed SERS-based molecular imaging can indicate the status of vascular inflammation in vivo and gives promise for SERS as a clinical imaging technique for cardiovascular disease in the future.