Spatial considerations in the resolution of inflammation: Elucidating leukocyte interactions via an experimentally-calibrated agent-based model.

Many common medical conditions (such as cancer, arthritis, chronic obstructive pulmonary disease (COPD), and others) are associated with inflammation, and even more so when combined with the effects of ageing and multimorbidity. While the inflammatory response varies in different tissue types, under disease and in response to therapeutic interventions, it has common interactions that occur between immune cells and inflammatory mediators. Understanding these underlying inflammatory mechanisms is key in progressing treatments and therapies for numerous inflammatory conditions. It is now considered that constituent mechanisms of the inflammatory response can be actively manipulated in order to drive resolution of inflammatory damage; particularly, those mechanisms related to the pro-inflammatory role of neutrophils and the anti-inflammatory role of macrophages. In this article, we describe the assembly of a hybrid mathematical model in which the spatial spread of inflammatory mediators is described through partial differential equations, and immune cells (neutrophils and macrophages) are described individually via an agent-based modelling approach. We pay close attention to how immune cells chemotax toward pro-inflammatory mediators, presenting a model for cell chemotaxis that is calibrated against experimentally observed cell trajectories in healthy and COPD-affected scenarios. We illustrate how variations in key model parameters can drive the switch from resolution of inflammation to chronic outcomes, and show that aberrant neutrophil chemotaxis can move an otherwise healthy outcome to one of chronicity. Finally, we reflect on our results in the context of the on-going hunt for new therapeutic interventions.

Implementation and evaluation of the human papillomavirus (HPV) vaccination pilot for men who have sex with men (MSM), England, April 2016 to March 2017.

BACKGROUND: Opportunistic human papillomavirus (HPV) vaccination for men who have sex with men (MSM) was piloted in sexual health clinics (SHC) in England between 2016 and 2018. AIM: to evaluate the pilot's first year (April 2016-March 2017) in terms of feasibility, acceptability, uptake, impact and equity and interpret the outcome in the context of wide HPV vaccination policy. METHODS: Attendance and uptake data from routine SHC surveillance datasets and a cross-sectional survey administered to individuals receiving the vaccine were analysed. RESULTS: Among 18,875 eligible MSM, 8,580 (45.5%) were recorded as having received one HPV vaccine dose, decreasing slightly with increasing age, and uptake was higher in rural than urban areas. Survey results suggested that of those receiving the first dose of HPV vaccine, 8% were new attendees and that among those, less than 11% attended just to receive the vaccine. Of those having their first HPV vaccination, 95% indicated they would like to receive the next vaccine doses at the same clinic and 85% of patients reported accessing other services when visiting SHC for the first dose of vaccine. CONCLUSION: An opportunistic HPV vaccination programme for MSM can be delivered in an acceptable and, as far as can be evaluated, equitable manner, without major disruption to SHC and HIV clinics.

Differences in anatomical connections across distinct areas in the rodent prefrontal cortex.

Prefrontal cortex (PFC) network structure is implicated in a number of complex higher-order functions and with a range of neurological disorders. It is therefore vital to our understanding of PFC function to gain an understanding of its underlying anatomical connectivity. Here, we injected Fluoro-Gold and Fluoro-Ruby into the same sites throughout rat PFC. Tracer injections were applied to two coronal levels within the PFC (anterior +4.7 mm to bregma and posterior +3.7 mm to bregma). Within each coronal level, tracers were deposited at sites separated by approximately 1 mm and located parallel to the medial and orbital surface of the cortex. We found that both Fluoro-Gold and Fluoro-Ruby injections produced prominent labelling in temporal and sensory-motor cortex. Fluoro-Gold produced retrograde labelling and Fluoro-Ruby largely produced anterograde labelling. Analysis of the location of these connections within temporal and sensory-motor cortex revealed a consistent topology (as the sequence of injections was followed mediolaterally along the orbital surface of each coronal level). At the anterior coronal level, injections produced a similar topology to that seen in central PFC in earlier studies from our laboratory (i.e. comparing equivalently located injections employing the same tracer), this was particularly prominent within temporal cortex. However, at the posterior coronal level this pattern of connections differed significantly, revealing higher levels of reciprocity, in both temporal cortex and sensory-motor cortex. Our findings indicate changes in the relative organization of connections arising from posterior in comparison to anterior regions of PFC, which may provide a basis to determine how complex processes are organized.

Complexity and robustness in hypernetwork models of metabolism.

Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity.

A systematic evaluation of the influence of macrophage phenotype descriptions on inflammatory dynamics.

Macrophages play a wide range of roles in resolving the inflammatory damage that underlies many medical conditions and have the ability to adopt different phenotypes in response to different environmental stimuli. Categorising macrophage phenotypes exactly is a difficult task, and there is disparity in the literature around the optimal nomenclature to describe these phenotypes; however, what is clear is that macrophages can exhibit both pro- and anti-inflammatory behaviours dependent upon their phenotype, rendering mathematical models of the inflammatory response potentially sensitive to their description of the macrophage populations that they incorporate. Many previous models of inflammation include a single macrophage population with both pro- and anti-inflammatory functions. Here, we build upon these existing models to include explicit descriptions of distinct macrophage phenotypes and examine the extent to which this influences the inflammatory dynamics that the models emit. We analyse our models via numerical simulation in MATLAB and dynamical systems analysis in XPPAUT, and show that models that account for distinct macrophage phenotypes separately can offer more realistic steady state solutions than precursor models do (better capturing the anti-inflammatory activity of tissue resident macrophages), as well as oscillatory dynamics not previously observed. Finally, we reflect on the conclusions of our analysis in the context of the ongoing hunt for potential new therapies for inflammatory conditions, highlighting manipulation of macrophage polarisation states as a potential therapeutic target.

Modelling the continuum of macrophage phenotypes and their role in inflammation.

Macrophages are a type of white blood cell that play a significant role in determining the inflammatory response associated with a wide range of medical conditions. They are highly plastic, having the capacity to adopt numerous polarisation states or 'phenotypes' with disparate pro- or anti-inflammatory roles. Many previous studies divide macrophages into two categorisations: M1 macrophages are largely pro-inflammatory in nature, while M2 macrophages are largely restorative. However, there is a growing body of evidence that the M1 and M2 classifications represent the extremes of a much broader spectrum of phenotypes, and that intermediate phenotypes can play important roles in the progression or treatment of many medical conditions. In this article, we present a model of macrophage dynamics that includes a continuous description of phenotype, and hence incorporates intermediate phenotype configurations. We describe macrophage phenotype switching via nonlinear convective flux terms that scale with background levels of generic pro- and anti-inflammatory mediators. Through numerical simulation and bifurcation analysis, we unravel the model's resulting dynamics, paying close attention to the system's multistability and the extent to which key macrophage-mediator interactions provide bifurcations that act as switches between chronic states and restoration of health. We show that interactions that promote M1-like phenotypes generally result in a greater array of stable chronic states, while interactions that promote M2-like phenotypes can promote restoration of health. Additionally, our model admits oscillatory solutions reminiscent of relapsing-remitting conditions, with macrophages being largely polarised toward anti-inflammatory activity during remission, but with intermediate phenotypes playing a role in inflammatory flare-ups. We conclude by reflecting on our observations in the context of the ongoing pursuance of novel therapeutic interventions.

Correction to "Protecting infants against RSV disease: an impact and cost-effectiveness comparison of long-acting monoclonal antibodies and maternal vaccination" [The Lancet Regional Health - Europe 38 (2024) 100829].

[This corrects the article DOI: 10.1016/j.lanepe.2023.100829.].

Protecting infants against RSV disease: an impact and cost-effectiveness comparison of long-acting monoclonal antibodies and maternal vaccination.

Background: Two new products for preventing Respiratory Syncytial Virus (RSV) in young children have been licensed: a single-dose long-acting monoclonal antibody (la-mAB) and a maternal vaccine (MV). To facilitate the selection of new RSV intervention programmes for large-scale implementation, this study provides an assessment to compare the costs of potential programmes with the health benefits accrued. Methods: Using an existing dynamic transmission model, we compared maternal vaccination to la-mAB therapy against RSV in England and Wales by calculating the impact and cost-effectiveness. We calibrated a statistical model to the efficacy trial data to accurately capture their immune waning and estimated the impact of seasonal and year-round programmes for la-mAB and MV programmes. Using these impact estimates, we identified the most cost-effective programme across pricing and delivery cost assumptions. Findings: For infants under six months old in England and Wales, a year-round MV programme with 60% coverage would avert 32% (95% CrI 22-41%) of RSV hospital admissions and a year-round la-mAB programme with 90% coverage would avert 57% (95% CrI 41-69%). The MV programme has additional health benefits for pregnant women, which account for 20% of the population-level health burden averted. A seasonal la-mAB programme could be cost-effective for up to £84 for purchasing and administration (CCPA) and a seasonal MV could be cost-effective for up to £80 CCPA. Interpretation: This modelling and cost-effectiveness analysis has shown that both the long-acting monoclonal antibodies and the maternal vaccine could substantially reduce the burden of RSV disease in the infant population. Our analysis has informed JCVI's recommendations for an RSV immunisation programme to protect newborns and infants. Funding: National Institute for Health Research.

Towards In Silico Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia.

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

Evidence for a national problem: continued rise in tuberculosis case numbers in urban areas outside London.

WHO standards for tuberculosis (TB) control require monitoring and evaluation of TB control programmes. In London, TB rates have stabilised at 44 per 100,000 since 2005. In 38 urban areas outside London with TB rates above the national average, these continued to rise after 2004, to 28 per 100,000 in 2008 (15% increase). London has the highest proportion of TB cases in certain risk groups, but these are increasing rapidly outside London. Many TB control efforts focus on the capital, but with rates rising elsewhere in the country, this strategy is likely to fail in the long term.

Structure-function clustering in weighted brain networks.

Functional networks, which typically describe patterns of activity taking place across the cerebral cortex, are widely studied in neuroscience. The dynamical features of these networks, and in particular their deviation from the relatively static structural network, are thought to be key to higher brain function. The interactions between such structural networks and emergent function, and the multimodal neuroimaging approaches and common analysis according to frequency band motivate a multilayer network approach. However, many such investigations rely on arbitrary threshold choices that convert dense, weighted networks to sparse, binary structures. Here, we generalise a measure of multiplex clustering to describe weighted multiplexes with arbitrarily-many layers. Moreover, we extend a recently-developed measure of structure-function clustering (that describes the disparity between anatomical connectivity and functional networks) to the weighted case. To demonstrate its utility we combine human connectome data with simulated neural activity and bifurcation analysis. Our results indicate that this new measure can extract neurologically relevant features not readily apparent in analogous single-layer analyses. In particular, we are able to deduce dynamical regimes under which multistable patterns of neural activity emerge. Importantly, these findings suggest a role for brain operation just beyond criticality to promote cognitive flexibility.

The role of node dynamics in shaping emergent functional connectivity patterns in the brain.

The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure-function issue, treating a system of Jansen-Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure-function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called 'false bifurcations' (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states.

A weighted communicability measure applied to complex brain networks.

Recent advances in experimental neuroscience allow non-invasive studies of the white matter tracts in the human central nervous system, thus making available cutting-edge brain anatomical data describing these global connectivity patterns. Through magnetic resonance imaging, this non-invasive technique is able to infer a snapshot of the cortical network within the living human brain. Here, we report on the initial success of a new weighted network communicability measure in distinguishing local and global differences between diseased patients and controls. This approach builds on recent advances in network science, where an underlying connectivity structure is used as a means to measure the ease with which information can flow between nodes. One advantage of our method is that it deals directly with the real-valued connectivity data, thereby avoiding the need to discretize the corresponding adjacency matrix, i.e. to round weights up to 1 or down to 0, depending upon some threshold value. Experimental results indicate that the new approach is able to extract biologically relevant features that are not immediately apparent from the raw connectivity data.

On the use of stabilizing transformations for detecting unstable periodic orbits in high-dimensional flows.

We explore the possibility of extending the stabilizing transformations approach [J. J. Crofts and R. L. Davidchack, SIAM J. Sci. Comput. (USA) 28, 1275 (2006)]. to the problem of locating large numbers of unstable periodic orbits in high-dimensional flows, in particular those that result from spatial discretization of partial differential equations. The approach has been shown to be highly efficient when detecting large sets of periodic orbits in low-dimensional maps. Extension to low-dimensional flows has been achieved by the use of an appropriate Poincare surface of section [D. Pingel, P. Schmelcher, and F. K. Diakonos, Phys. Rep. 400, 67 (2004)]. For the case of high-dimensional flows, we show that it is more efficient to apply stabilizing transformations directly to the flows without the use of the Poincare surface of section. We use the proposed approach to find many unstable periodic orbits in the model example of a chaotic spatially extended system-the Kuramoto-Sivashinsky equation. The performance of the proposed method is compared against other methods such as Newton-Armijo and Levenberg-Marquardt algorithms. In the latter case, we also argue that the Levenberg-Marquardt algorithm, or any other optimization-based approach, is more efficient and simpler in implementation when applied directly to the detection of periodic orbits in high-dimensional flows without the use of the Poincare surface of section or other additional constraints.

Identification of novel genes associated with longevity in Drosophila melanogaster - a computational approach.

Despite a growing number of studies on longevity in Drosophila, genetic factors influencing lifespan are still poorly understood. In this paper we propose a conceptually new approach for the identification of novel longevity-associated genes and potential target genes for SNPs in non-coding regions by utilizing the knowledge of co-location of various loci, governed by the three-dimensional architecture of the Drosophila genome. Firstly, we created networks between genes/genomic regions harboring SNPs deemed to be significant in two longevity GWAS summary statistics datasets using intra- and inter-chromosomal interaction frequencies (Hi-C data) as a measure of co-location. These networks were further extended to include regions strongly interacting with previously selected regions. Using various network measures, literature search and additional bioinformatics resources, we investigated the plausibility of genes found to have genuine association with longevity. Several of the newly identified genes were common between the two GWAS datasets and these possessed human orthologs. We also found that the proportion of non-coding SNPs in borders between topologically associated domains is significantly higher than expected by chance. Assuming co-location, we investigated potential target genes for non-coding SNPs. This approach therefore offers a stepping stone to identification of novel genes and SNP targets linked to human longevity.

Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data - a case study of schizophrenia.

Genome-wide association studies identified numerous loci harbouring single nucleotide polymorphisms (SNPs) associated with various human diseases, although the causal role of many of them remains unknown. In this paper, we postulate that co-location and shared biological function of novel genes with genes known to associate with a specific phenotype make them potential candidates linked to the same phenotype ("guilt-by-proxy"). We propose a novel network-based approach for predicting candidate genes/genomic regions utilising the knowledge of the 3D architecture of the human genome and GWAS data. As a case study we used a well-studied polygenic disorder ‒ schizophrenia ‒ for which we compiled a comprehensive dataset of SNPs. Our approach revealed 634 novel regions covering ~398 Mb of the human genome and harbouring ~9000 genes. Using various network measures and enrichment analysis, we identified subsets of genes and investigated the plausibility of these genes/regions having an association with schizophrenia using literature search and bioinformatics resources. We identified several genes/regions with previously reported associations with schizophrenia, thus providing proof-of-concept, as well as novel candidates with no prior known associations. This approach has the potential to identify novel genes/genomic regions linked to other polygenic disorders and provide means of aggregating genes/SNPs for further investigation.

Characteristics and mortality of severe influenza cases treated with parenteral aqueous zanamivir, United Kingdom, October 2009 to January 2011.

BACKGROUND: Aqueous zanamivir solution, an investigational product, was provided by the manufacturer on compassionate grounds for parenteral administration to severe H1N1pdm09 influenza cases during the 2009 pandemic. OBJECTIVE: To describe characteristics and outcomes of UK patients receiving parenteral zanamivir therapy. METHODS: Collaborators at multiple hospital sites gathered retrospective data on patients receiving aqueous zanamivir therapy between Q4 2009 and Q1 2011. We present analysis of the demographics, clinical features, treatment and outcomes of this cohort. RESULTS: Data on 185 cases were obtained (response rate of 38%; median age 43 years; 62% male; 17% non-Caucasian ethnic group). Most frequent co-morbidities included cancer, immunosuppression and respiratory conditions. Most patients received intravenous zanamivir alone (90%), for durations of up to 21 days. 13% of cases had adverse effects related to zanamivir therapy. Thirty four percentage of cases died. No significant relationship was seen between mortality and timing or route of administration of aqueous zanamivir therapy. CONCLUSIONS: The response rate of this observational study of the outcomes of treatment of severe influenza was low, allowing limited conclusions to be drawn. Some potential adverse effects were noted. Clinicians should carefully consider potential risks and benefits of use of this product. New treatment options are urgently required to improve outcomes for patients with severe influenza infections.

The role of immune correlates of protection on the pathway to licensure, policy decision and use of group B Streptococcus vaccines for maternal immunization: considerations from World Health Organization consultations.

The development of a group B Streptococcus (GBS) vaccine for maternal immunization constitutes a global public health priority, to prevent GBS-associated early life invasive disease, stillbirth, premature birth, maternal sepsis, adverse neurodevelopmental consequences, and to reduce perinatal antibiotic use. Sample size requirements for the conduct of a randomized placebo-controlled trial to assess vaccine efficacy against the most relevant clinical endpoints, under conditions of appropriate ethical standards of care, constitute a significant obstacle on the pathway to vaccine availability. Alternatively, indirect evidence of protection based on immunologic data from vaccine and sero-epidemiological studies, complemented by data from opsonophagocytic in vitro assays and animal models, could be considered as pivotal data for licensure, with subsequent confirmation of effectiveness against disease outcomes in post-licensure evaluations. Based on discussions initiated by the World Health Organization we present key considerations about the potential role of correlates of protection towards an accelerated pathway for GBS vaccine licensure and wide scale use. Priority activities to support progress to regulatory and policy decision are outlined.

Network motif frequency vectors reveal evolving metabolic network organisation.

At the systems level many organisms of interest may be described by their patterns of interaction, and as such, are perhaps best characterised via network or graph models. Metabolic networks, in particular, are fundamental to the proper functioning of many important biological processes, and thus, have been widely studied over the past decade or so. Such investigations have revealed a number of shared topological features, such as a short characteristic path-length, large clustering coefficient and hierarchical modular structure. However, the extent to which evolutionary and functional properties of metabolism manifest via this underlying network architecture remains unclear. In this paper, we employ a novel graph embedding technique, based upon low-order network motifs, to compare metabolic network structure for 383 bacterial species categorised according to a number of biological features. In particular, we introduce a new global significance score which enables us to quantify important evolutionary relationships that exist between organisms and their physical environments. Using this new approach, we demonstrate a number of significant correlations between environmental factors, such as growth conditions and habitat variability, and network motif structure, providing evidence that organism adaptability leads to increased complexities in the resultant metabolic networks.

A geometric network model of intrinsic grey-matter connectivity of the human brain.

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct 'shortcuts' through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.