A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries.

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on "unseen" meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.

Different directions for retro-inverso peptides.

Retro-inverso peptides are reversed sequences of mirror-image amino acid residues. Synthetic molecules of this type have long been considered potential mimics of functional peptides. This Peptide Highlights article examines some recent applications of the retro-inverso transformation at the interface of peptide chemistry and medicine.

Visualisation tools for dependent peptide searches to support the exploration of in vitro protein modifications.

Dependent peptide searching is a method for discovering covalently-modified peptides-and therefore proteins-in mass-spectrometry-based proteomics experiments. Being more permissive than standard search methods, it has the potential to discover novel modifications (e.g., post-translational modifications occurring in vivo, or modifications introduced in vitro). However, few studies have explored dependent peptide search results in an untargeted way. In the present study, we sought to evaluate dependent peptide searching as a means of characterising proteins that have been modified in vitro. We generated a model data set by analysing N-ethylmaleimide-treated bovine serum albumin, and performed dependent peptide searches using the popular MaxQuant software. To facilitate interpretation of the search results (hundreds of dependent peptides), we developed a series of visualisation tools (R scripts). We used the tools to assess the diversity of putative modifications in the albumin, and to pinpoint hypothesised modifications. We went on to explore the tools' generality via analyses of public data from studies of rat and human proteomes. Of 19 expected sites of modification (one in rat cofilin-1 and 18 across six different human plasma proteins), eight were found and correctly localised. Apparently, some sites went undetected because chemical enrichment had depleted necessary analytes (potential 'base' peptides). Our results demonstrate (i) the ability of the tools to provide accurate and informative visualisations, and (ii) the usefulness of dependent peptide searching for characterising in vitro protein modifications. Our model data are available via PRIDE/ProteomeXchange (accession number PXD013040).

Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease.

The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

Relationships between airborne pollutants, serum albumin adducts and short-term health outcomes in an experimental crossover study.

Exposure to air pollution can have both short-term and long-term effects on health. However, the relationships between specific pollutants and their effects can be obscured by characteristics of both the pollution and the exposed population. One way of elucidating the relationships is to link exposures and internal changes at the level of the individual. To this end, we combined personal exposure monitoring (59 individuals, Oxford Street II crossover study) with mass-spectrometry-based analyses of putative serum albumin adducts (fixed-step selected reaction monitoring). We attempted to infer adducts' identities using data from another, higher-resolution mass spectrometry method, and were able to detect a semi-synthetic standard with both methods. A generalised least squares regression method was used to test for associations between amounts of adducts and pollution measures (ambient concentrations of nitrogen dioxide and particulate matter), and between amounts of adducts and short-term health outcomes (measures of lung health and arterial stiffness). Amounts of some putative adducts (e.g., one with a positive mass shift of ∼143 Da) were associated with exposure to pollution (11 associations), and amounts of other adducts were associated with health outcomes (eight associations). Adducts did not appear to provide a link between exposures and short-term health outcomes.

Protein Adductomics: Analytical Developments and Applications in Human Biomonitoring.

Proteins contain many sites that are subject to modification by electrophiles. Detection and characterisation of these modifications can give insights into environmental agents and endogenous processes that may be contributing factors to chronic human diseases. An untargeted approach, utilising mass spectrometry to detect modified amino acids or peptides, has been applied to blood proteins haemoglobin and albumin, focusing in particular on the N-terminal valine residue of haemoglobin and the cysteine-34 residue in albumin. Technical developments to firstly detect simultaneously multiple adducts at these sites and then subsequently to identify them are reviewed here. Recent studies in which the methods have been applied to biomonitoring human exposure to environmental toxicants are described. With advances in sensitivity, high-throughput handling of samples and robust quality control, these methods have considerable potential for identifying causes of human chronic disease and of identifying individuals at risk.

Refinement of a Methodology for Untargeted Detection of Serum Albumin Adducts in Human Populations.

Covalently modified blood proteins (e.g., serum albumin adducts) are increasingly being viewed as potential biomarkers via which the environmental causes of human diseases may be understood. The notion that some (perhaps many) modifications have yet to be discovered has led to the development of untargeted adductomics methods, which attempt to capture entire populations of adducts. One such method is fixed-step selected reaction monitoring (FS-SRM), which analyses distributions of serum albumin adducts via shifts in the mass of a tryptic peptide [Li et al. (2011) Mol. Cell. Proteomics 10, M110.004606]. Working on the basis that FS-SRM might be able to detect biological variation due to environmental factors, we aimed to scale the methodology for use in an epidemiological setting. Development of sample preparation methods led to a batch workflow with increased throughput and provision for quality control. Challenges posed by technical and biological variation were addressed in the processing and interpretation of the data. A pilot study of 20 smokers and 20 never-smokers provided evidence of an effect of smoking on levels of putative serum albumin adducts. Differences between smokers and never-smokers were most apparent in putative adducts with net gains in mass between 105 and 114 Da (relative to unmodified albumin). The findings suggest that our implementation of FS-SRM could be useful for studying other environmental factors with relevance to human health.

Quantification of a peptide standard using the intrinsic fluorescence of tyrosine.

Absolute quantification of peptides is typically achieved using amino acid analysis, elemental analysis or derivatisation chemistry. Impurities, if present, may be accounted for using analytical high-performance liquid chromatography (HPLC) with detection of the peptide bond ultraviolet (UV) absorbance. To do this, peak areas from a UV chromatogram are used to estimate percentage purity on a mass basis, and this purity value is used as a correction. However, because the approach assumes that UV absorbance is uniformly proportional to mass, the result may be only semi-quantitative. Here, an alternative approach involving HPLC with detection of intrinsic tyrosine fluorescence is described. The fluorescence properties of a 21-residue synthetic peptide corresponding to an S-carbamidomethylated tryptic fragment of human serum albumin were characterised, and a method involving quantification relative to a non-peptidic calibrant, N-acetyl-L-tyrosine ethyl ester, was established. The method was used to quantify the thiol form of the peptide, and the results were compared with a parallel analysis involving derivatisation of the same material with Ellman's reagent. When differences in fluorescence response (analyte versus calibrant) were accounted for, the measurements obtained via the two methods were in good agreement. Contributions from peptidic impurities were also considered, and their influence on the validity of the conclusions was evaluated. Despite some ambiguities introduced by the impurities, and the identification of some other potential sources of error, the results demonstrate that use of Tyr fluorescence is a promising solution to the challenging problem of absolute peptide quantification.

Analysis of amyloid nanostructures using photo-cross-linking: in situ comparison of three widely used photo-cross-linkers.

Photoinduced cross-linking (PIC) has become a powerful tool in chemical biology for the identification and mapping of stable or transient interactions between biomacromolecules and their (unknown) ligands. However, the value of PIC for in vitro and in vivo structural proteomics can be realized only if cross-linking reports accurately on biomacromolecule secondary, tertiary, and quaternary structures with residue-specific resolution. Progress in this area requires rigorous and comparative studies of PIC reagents, but despite widespread use of PIC, these have rarely been performed. The use of PIC to report reliably on noncovalent structure is therefore limited, and its potentials have yet to be fully realized. In the present study, we compared the abilities of three probes, phenyl trifluoromethyldiazirine (TFMD), benzophenone (BP), and phenylazide (PA), to record structural information within a biomolecular complex. For this purpose, we employed a self-assembled amyloid-like peptide nanostructure as a tightly and specifically packed model environment in which to photolyze the reagents. Information about PIC products was gathered using mass spectrometry and ion mobility spectrometry, and the data were interpreted using a mechanism-oriented approach. While all three PIC groups appeared to generate information within the packed peptide environment, the data highlight technical limitations of BP and PA. On the other hand, TFMD displayed accuracy and generated straightforward results. Thus TFMD, with its robust and rapid photochemistry, was shown to be an ideal probe for cross-linking of peptide nanostructures. The implications of our findings for detailed analyses of complex systems, including those that are transiently populated, are discussed.

Photo-induced covalent cross-linking for the analysis of biomolecular interactions.

Photo-induced cross-linking (PIC) is a powerful strategy for generating information on biomolecular interactions. In PIC, the utility of traditional cross-linking methods is supplemented by the temporal control of photo-activation, enabling the study of non-covalent kinetic intermediates and heterogeneous mixtures. This tutorial review will introduce the photochemistry of activation, reactive intermediates, methods for the functionalisation of biomolecules and the installation of additional functionalities (e.g., affinity tags). In doing so, we shall illustrate the wealth of data that can be obtained using this approach, ranging from the identification of interacting partners and structural data to temporal information. Alongside a discussion of the strengths and weaknesses of the various approaches, their applicability to different types of biological system will be described.

Covalent cross-linking within supramolecular peptide structures.

ß-Sheet peptide nanostructures (e.g., amyloid fibrils) are recognized as important entities in biological systems and as functional materials in their own right. Their unique physical properties and architectural complexity, however, present a challenge for structure determination at atomic resolution. Covalent cross-linking and mass spectrometry are appealing methods for this endeavor because, potentially, a large amount of information can be extracted from a small sample in a single experiment. Previously, we described preliminary studies on the use of a photoreactive diazirine-containing amino acid to cross-link peptide monomers in nanostructures, together with the integrated separation and analysis of the products using ion mobility spectrometry coupled to conventional mass spectrometry. Here, a pH-switchable system (Aß(16-22), a sequence from the amyloid-ß peptide) was used to examine cross-linking chemistry in morphologically distinct supramolecular structures containing, or entirely composed of, diazirine-functionalized peptides. We examine the relationship between cross-linker chemistry, covalent cross-links (identified using chemical derivatization and tandem mass spectrometry), and noncovalent structure, and report differences in the site of cross-linking that can only be explained by supramolecular templating. The results demonstrate the applicability of the approach for obtaining structural restraints in ordered supramolecular assemblies, provided that a considered evaluation of the cross-linked products is undertaken.

The Southwark Police Surgeon Study 1978-1997: alcohol.

This paper takes routine clinical data from one Forensic Medical Examiner's (FME) practice over 20 years in the London Borough of Southwark. The relationship of alcohol to gender, categories of call, and patterns of variation over time are addressed. Alcohol related calls are found to be significantly associated with males who are ill or injured, but not with victims or perpetrators of violence. There has also been significant increases in alcohol related calls over the study period. There is no evidence to suggest that was influenced by the introduction of the Police and Criminal Evidence Act (PACE).