A comparison of the Netherlands, Norway and UK familial hypercholesterolemia screening programmes with implications for target setting and the UK's NHS long term plan.

We sought to determine the most efficacious and cost-effective strategy to follow when developing a national screening programme by comparing and contrasting the national screening programmes of Norway, the Netherlands and the UK. Comparing the detection rates and screening profiles between the Netherlands, Norway, the UK and constituent nations (England, Northern Ireland, Scotland and Wales) it is clear that maximising the number of relatives screened per index case leads to identification of the greatest proportion of an FH population. The UK has stated targets to detect 25% of the population of England with FH across the 5 years to 2024 with the NHS Long Term Plan. However, this is grossly unrealistic and, based on pre-pandemic rates, will only be reached in the year 2096. We also modelled the efficacy and cost-effectiveness of two screening strategies: 1) Universal screening of 1-2-year-olds, 2) electronic healthcare record screening, in both cases coupled to reverse cascade screening. We found that index case detection from electronic healthcare records was 56% more efficacious than universal screening and, depending on the cascade screening rate of success, 36%-43% more cost-effective per FH case detected. The UK is currently trialling universal screening of 1-2-year-olds to contribute to national FH detection targets. Our modelling suggests that this is not the most efficacious or cost-effective strategy to follow. For countries looking to develop national FH programmes, screening of electronic healthcare records, coupled to successful cascade screening to blood relatives is likely to be a preferable strategy to follow.

The Need for Standardizing Diagnosis, Treatment and Clinical Care of Cholecystitis and Biliary Colic in Gallbladder Disease.

Gallstones affect 20% of the Western population and will grow in clinical significance as obesity and metabolic diseases become more prevalent. Gallbladder removal (cholecystectomy) is a common treatment for diseases caused by gallstones, with 1.2 million surgeries in the US each year, each costing USD 10,000. Gallbladder disease has a significant impact on the logistics and economics of healthcare. We discuss the two most common presentations of gallbladder disease (biliary colic and cholecystitis) and their pathophysiology, risk factors, signs and symptoms. We discuss the factors that affect clinical care, including diagnosis, treatment outcomes, surgical risk factors, quality of life and cost-efficacy. We highlight the importance of standardised guidelines and objective scoring systems in improving quality, consistency and compatibility across healthcare providers and in improving patient outcomes, collaborative opportunities and the cost-effectiveness of treatment. Guidelines and scoring only exist in select areas of the care pathway. Opportunities exist elsewhere in the care pathway.

The Interdependency and Co-Regulation of the Vitamin D and Cholesterol Metabolism.

Vitamin D and cholesterol metabolism overlap significantly in the pathways that contribute to their biosynthesis. However, our understanding of their independent and co-regulation is limited. Cardiovascular disease is the leading cause of death globally and atherosclerosis, the pathology associated with elevated cholesterol, is the leading cause of cardiovascular disease. It is therefore important to understand vitamin D metabolism as a contributory factor. From the literature, we compile evidence of how these systems interact, relating the understanding of the molecular mechanisms involved to the results from observational studies. We also present the first systems biology pathway map of the joint cholesterol and vitamin D metabolisms made available using the Systems Biology Graphical Notation (SBGN) Markup Language (SBGNML). It is shown that the relationship between vitamin D supplementation, total cholesterol, and LDL-C status, and between latitude, vitamin D, and cholesterol status are consistent with our knowledge of molecular mechanisms. We also highlight the results that cannot be explained with our current knowledge of molecular mechanisms: (i) vitamin D supplementation mitigates the side-effects of statin therapy; (ii) statin therapy does not impact upon vitamin D status; and critically (iii) vitamin D supplementation does not improve cardiovascular outcomes, despite improving cardiovascular risk factors. For (iii), we present a hypothesis, based on observations in the literature, that describes how vitamin D regulates the balance between cellular and plasma cholesterol. Answering these questions will create significant opportunities for advancement in our understanding of cardiovascular health.

The role of senescence in the pathogenesis of atrial fibrillation: A target process for health improvement and drug development.

Cellular senescence is a state of growth arrest that occurs after cells encounter various stresses. Senescence contributes to tumour suppression, embryonic development, and wound healing. It impacts on the pathology of various diseases by secreting inflammatory chemokines, immune modulators and other bioactive factors. These secretory biosignatures ultimately cause inflammation, tissue fibrosis, immunosenescence and many ageing-related diseases such as atrial fibrillation (AF). Because the molecular mechanisms underpinning AF development remain unclear, current treatments are suboptimal and have serious side effects. In this review, we summarize recent results describing the role of senescence in AF. We propose that senescence factors induce AF and have a causative role. Hence, targeting senescence and its secretory phenotype may attenuate AF.

Anti-tumour necrosis factor-alpha response associated with combined CD226 and HLA-DRB1[*]0404 haplotype in rheumatoid arthritis.

OBJECTIVES: Predicting response to anti-tumour necrosis factor alpha (anti-TNFα) drugs at baseline remains an elusive goal in rheumatoid arthritis (RA) management. The purpose of this study was to determine if baseline genetic variants of PTPRC, AFF3, myD228, CHUK, MTHFR1, MTHFR2, CD226 and a number of KIR and HLA alleles could predict response to anti-TNF-α in rheumatoid arthritis patients. METHODS: Peripheral blood samples were collected from 238 RA patients treated with anti-TNFα drugs. Genotyping was performed using biochip array technology by Randox Laboratories Ltd. and sequence specific polymerase chain reaction. Linear regression analysis was performed to investigate the role of these genotypes in predicting response to treatment, as defined by European League Against Rheumatism (EULAR) response classification and absolute change in disease activity score (DAS28). RESULTS: Of 238 RA patients analysed, 50.4% received adalimumab, 29.7% received etanercept, 14.8% received infliximab, 3.4% certoluzimab and 1.7% golimumab. The MTHFR1 variant rs1801133 was significantly associated with the EULAR response, p=0.044. Patients with the HLA-DRB1*0404 allele displayed a significantly larger reduction in DAS28 compared to non-carriers (mean -2.22, -1.67 respectively, p=0.033). CD226 rs763361 was the only SNP variant significantly associated with ΔDAS28 (p=0.029). CONCLUSIONS: This study has investigated individual allele associations with reductions in DAS28 across a range of anti-TNFα treatments. A combined predictive model indicates that patients with the HLA-DRB1*0404 allele and without the CD226 rs763361 polymorphism exhibit the largest reduction in DAS28 after anti-TNF-α treatment.

Deep learning in systems medicine.

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson's disease. The review offers valuable insights and informs the research in DL and SM.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine.

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease.

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aß) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aß and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

An exploratory analysis investigating blood protein biomarkers to augment ECG diagnosis of ACS.

BACKGROUND: Acute Coronary Syndrome (ACS) is currently diagnosed using a 12­lead Electrocardiogram (ECG). Our recent work however has shown that interpretation of the 12­lead ECG is complex and that clinicians can be sub-optimal in their interpretation. Additionally, ECG does not always identify acute total occlusions in certain patients. PURPOSE: The aim of the present study was to undertake an exploratory analysis to compare protein expression profiles of ACS patients that may in the future augment ECG diagnosis. METHODS: Patients were recruited consecutively at the cardiac catheterization laboratory at Altnagelvin Hospital over a period of 6 months. A low risk control group was recruited by advertisement. Blood samples were analysed using the multiplex proximity extension assays by OLINK proteomics. Support vector machine (SVM) learning was used as a classifier to distinguish between patient groups on training data. The ST segment elevation level was extracted from each ECG for a subset of patients and combined with the protein markers. Quadratic SVM (QSVM) learning was then used as a classifier to distinguish STEMI from NSTEMI on training and test data. RESULTS: Of the 344 participants recruited, 77 were initially diagnosed with NSTEMI, 7 with STEMI, and 81 were low risk controls. The other participants were those diagnosed with angina (stable and unstable) or non-cardiac patients. Of the 368 proteins analysed, 20 proteins together could significantly differentiate between patients with ACS and patients with stable angina (ROC-AUC = 0.96). Six proteins discriminated significantly between the stable angina and the low risk control groups (ROC-AUC = 1.0). Additionally, 16 proteins together perfectly discriminated between the STEMI and NSTEMI patients (ROC-AUC = 1). ECG comparisons with the protein biomarker data for a subset of patients (STEMI n = 6 and NSTEMI n = 6), demonstrated that 21 features (20 proteins + ST elevation) resulted in the highest classification accuracy 91.7% (ROC-AUC = 0.94). The 20 proteins without the ST elevation feature gave an accuracy of 80.6% (ROC-AUC 0.91), while the ST elevation feature without the protein biomarkers resulted in an accuracy of 69.3% (ROC-AUC = 0.81). CONCLUSIONS: This preliminary study identifies panels of proteins that should be further explored in prospective studies as potential biomarkers that may augment ECG diagnosis and stratification of ACS. This work also highlights the importance for future studies to be designed to allow a comparison of blood biomarkers not only with ECG's but also with cardio angiograms.

Propionibacterium acnes and Acne Vulgaris: New Insights from the Integration of Population Genetic, Multi-Omic, Biochemical and Host-Microbe Studies.

The anaerobic bacterium Propionibacterium acnes is believed to play an important role in the pathophysiology of the common skin disease acne vulgaris. Over the last 10 years our understanding of the taxonomic and intraspecies diversity of this bacterium has increased tremendously, and with it the realisation that particular strains are associated with skin health while others appear related to disease. This extensive review will cover our current knowledge regarding the association of P. acnes phylogroups, clonal complexes and sequence types with acne vulgaris based on multilocus sequence typing of isolates, and direct ribotyping of the P. acnes strain population in skin microbiome samples based on 16S rDNA metagenomic data. We will also consider how multi-omic and biochemical studies have facilitated our understanding of P. acnes pathogenicity and interactions with the host, thus providing insights into why certain lineages appear to have a heightened capacity to contribute to acne vulgaris development, while others are positively associated with skin health. We conclude with a discussion of new therapeutic strategies that are currently under investigation for acne vulgaris, including vaccination, and consider the potential of these treatments to also perturb beneficial lineages of P. acnes on the skin.

New models of atherosclerosis and multi-drug therapeutic interventions.

MOTIVATION: Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here, we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses. RESULTS: We assembled a network describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation and Systems Biology Markup Language, respectively. The model was constrained against clinical and laboratory data. We identified five drugs that together potentially reverse advanced atheroma formation. AVAILABILITY AND IMPLEMENTATION: The map is available in the Supplementary Material in SBGN-ML format. The model is available in the Supplementary Material and from BioModels, a repository of SBML models, containing CellDesigner markup. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

Role of tumour necrosis factor alpha converting enzyme (TACE/ADAM17) and associated proteins in coronary artery disease and cardiac events.

Tumour necrosis factor alpha converting enzyme (TACE/ADAM17) is a member of the A disintegrin and metalloproteinase (ADAM) family of ectodomain shedding proteinases. It regulates many inflammatory processes by cleaving several transmembrane proteins, including tumour necrosis factor alpha (TNFα) and its receptors tumour necrosis factor alpha receptor 1 and tumour necrosis factor alpha receptor 2. There is evidence that TACE is involved in several inflammatory diseases, such as ischaemia, heart failure, arthritis, atherosclerosis, diabetes and cancer as well as neurological and immune diseases. This review summarizes the latest discoveries regarding the mechanism of action and regulation of TACE. It also focuses on the role of TACE in atherosclerosis and coronary artery disease (CAD), highlighting clinical studies that have investigated its expression and protein activity. The multitude of substrates cleaved by TACE make this enzyme an attractive target for therapy and a candidate for biomarker research and development in CAD.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway.

BACKGROUND AND PURPOSE: An ever-growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single-drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway. EXPERIMENTAL APPROACH: Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment. KEY RESULTS: We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi-drug treatments with high efficacy and minimal off-target effects. CONCLUSION AND IMPLICATIONS: We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

The genetics and screening of familial hypercholesterolaemia.

Familial Hypercholesterolaemia is an autosomal, dominant genetic disorder that leads to elevated blood cholesterol and a dramatically increased risk of atherosclerosis. It is perceived as a rare condition. However it affects 1 in 250 of the population globally, making it an important public health concern. In communities with founder effects, higher disease prevalences are observed.We discuss the genetic basis of familial hypercholesterolaemia, examining the distribution of variants known to be associated with the condition across the exons of the genes LDLR, ApoB, PCSK9 and LDLRAP1. We also discuss screening programmes for familial hypercholesterolaemia and their cost-effectiveness. Diagnosis typically occurs using one of the Dutch Lipid Clinic Network (DCLN), Simon Broome Register (SBR) or Make Early Diagnosis to Prevent Early Death (MEDPED) criteria, each of which requires a different set of patient data. New cases can be identified by screening the family members of an index case that has been identified as a result of referral to a lipid clinic in a process called cascade screening. Alternatively, universal screening may be used whereby a population is systematically screened.It is currently significantly more cost effective to identify familial hypercholesterolaemia cases through cascade screening than universal screening. However, the cost of sequencing patient DNA has fallen dramatically in recent years and if the rate of progress continues, this may change.

An Interferon Regulated MicroRNA Provides Broad Cell-Intrinsic Antiviral Immunity through Multihit Host-Directed Targeting of the Sterol Pathway.

In invertebrates, small interfering RNAs are at the vanguard of cell-autonomous antiviral immunity. In contrast, antiviral mechanisms initiated by interferon (IFN) signaling predominate in mammals. Whilst mammalian IFN-induced miRNA are known to inhibit specific viruses, it is not known whether host-directed microRNAs, downstream of IFN-signaling, have a role in mediating broad antiviral resistance. By performing an integrative, systematic, global analysis of RNA turnover utilizing 4-thiouridine labeling of newly transcribed RNA and pri/pre-miRNA in IFN-activated macrophages, we identify a new post-transcriptional viral defense mechanism mediated by miR-342-5p. On the basis of ChIP and site-directed promoter mutagenesis experiments, we find the synthesis of miR-342-5p is coupled to the antiviral IFN response via the IFN-induced transcription factor, IRF1. Strikingly, we find miR-342-5p targets mevalonate-sterol biosynthesis using a multihit mechanism suppressing the pathway at different functional levels: transcriptionally via SREBF2, post-transcriptionally via miR-33, and enzymatically via IDI1 and SC4MOL. Mass spectrometry-based lipidomics and enzymatic assays demonstrate the targeting mechanisms reduce intermediate sterol pathway metabolites and total cholesterol in macrophages. These results reveal a previously unrecognized mechanism by which IFN regulates the sterol pathway. The sterol pathway is known to be an integral part of the macrophage IFN antiviral response, and we show that miR-342-5p exerts broad antiviral effects against multiple, unrelated pathogenic viruses such Cytomegalovirus and Influenza A (H1N1). Metabolic rescue experiments confirm the specificity of these effects and demonstrate that unrelated viruses have differential mevalonate and sterol pathway requirements for their replication. This study, therefore, advances the general concept of broad antiviral defense through multihit targeting of a single host pathway.

Computational modelling of atherosclerosis.

Atherosclerosis is one of the principle pathologies of cardiovascular disease with blood cholesterol a significant risk factor. The World Health Organization estimates that approximately 2.5 million deaths occur annually because of the risk from elevated cholesterol, with 39% of adults worldwide at future risk. Atherosclerosis emerges from the combination of many dynamical factors, including haemodynamics, endothelial damage, innate immunity and sterol biochemistry. Despite its significance to public health, the dynamics that drive atherosclerosis remain poorly understood. As a disease that depends on multiple factors operating on different length scales, the natural framework to apply to atherosclerosis is mathematical and computational modelling. A computational model provides an integrated description of the disease and serves as an in silico experimental system from which we can learn about the disease and develop therapeutic hypotheses. Although the work completed in this area to date has been limited, there are clear signs that interest is growing and that a nascent field is establishing itself. This article discusses the current state of modelling in this area, bringing together many recent results for the first time. We review the work that has been done, discuss its scope and highlight the gaps in our understanding that could yield future opportunities.

Rapid proteasomal elimination of 3-hydroxy-3-methylglutaryl-CoA reductase by interferon-γ in primary macrophages requires endogenous 25-hydroxycholesterol synthesis.

Interferons (IFNs) play a central role in immunity and emerging evidence suggests that IFN-signalling coordinately regulates sterol biosynthesis in macrophages, via Sterol Regulatory Element-Binding Protein (SREBP) dependent and independent pathways. However, the precise mechanisms and kinetic steps by which IFN controls sterol biosynthesis are as yet not fully understood. Here, we elucidate the molecular circuitry governing how IFN controls the first regulated step in the mevalonate-sterol pathway, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), through the synthesis of 25-Hydroxycholesterol (25-HC) from cholesterol by the IFN-inducible Cholesterol-25-Hydroxylase (CH25H). We show for the first 30-min of IFN stimulation of macrophages the rate of de novo synthesis of the Ch25h transcript is markedly increased but by 120-min becomes transcriptionally curtailed, coincident with induction of the Activating Transcription Factor 3 (ATF3) repressor. We demonstrate ATF3 induction by Toll-like receptors is strictly dependent on IFN-signalling. While the SREBP-pathway dependent rates of de novo transcription of Hmgcr are relatively unchanged in the first 90-min of IFN treatment, we find HMGCR enzyme levels undergo a rapid proteasomal-mediated degradation, defining a previously unappreciated SREBP-independent mechanism for IFN-action. These events precede a sustained marked reduction in Hmgcr RNA levels involving SREBP-dependent mechanisms. We demonstrate that HMGCR proteasomal-degradation by IFN strictly requires the synthesis of endogenous 25-HC and functionally couples HMGCR to CH25H to coordinately suppress sterol biosynthesis. In conclusion, we quantitatively delineate proteomic and transcriptional levels of IFN-mediated control of HMGCR, the primary enzymatic step of the mevalonate-sterol biosynthesis pathway, providing a foundational framework for mathematically modelling the therapeutic outcome of immune-metabolic pathways.

A comprehensive machine-readable view of the mammalian cholesterol biosynthesis pathway.

Cholesterol biosynthesis serves as a central metabolic hub for numerous biological processes in health and disease. A detailed, integrative single-view description of how the cholesterol pathway is structured and how it interacts with other pathway systems is lacking in the existing literature. Here we provide a systematic review of the existing literature and present a detailed pathway diagram that describes the cholesterol biosynthesis pathway (the mevalonate, the Kandutch-Russell and the Bloch pathway) and shunt pathway that leads to 24(S),25-epoxycholesterol synthesis. The diagram has been produced using the Systems Biology Graphical Notation (SBGN) and is available in the SBGN-ML format, a human readable and machine semantically parsable open community file format.