Integration of polygenic and gut metagenomic risk prediction for common diseases

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction

BACKGROUND

The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction.

METHODS

The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared.

RESULTS

Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%).

CONCLUSIONS

These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

Genetically personalised organ-specific metabolic models in health and disease

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

BACKGROUND

Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD.

METHODS AND FINDINGS

Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009-0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40-75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation.

CONCLUSIONS

Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

The Polygenic and Monogenic Basis of Blood Traits and Diseases

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

Neonatal genetics of gene expression reveal potential origins of autoimmune and allergic disease risk

Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4⁺ T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2, HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases.

Some immune-mediated diseases may originate in early childhood. The authors mapped eQTLs and response eQTLs to various stimuli in neonatal myeloid cells and T cells, and revealed their potential role in immune-mediated diseases using colocalisation and Mendelian randomisation.

FastSpar: rapid and scalable correlation estimation for compositional data

Summary

A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialized statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelizable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates P-values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2–3 orders of magnitude compared to SparCC.

Availability and implementation

FastSpar source code, precompiled binaries and platform packages are freely available on GitHub: github.com/scwatts/FastSpar

Supplementary information

Supplementary data are available at Bioinformatics online.

Multivariate Genome-wide Association Analysis of a Cytokine Network Reveals Variants with Widespread Immune, Haematological, and Cardiometabolic Pleiotropy

Cytokines are essential regulatory components of the immune system, and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlies them, remain unknown. Here, we aimed to identify and characterize genetic variants with pleiotropic effects on cytokines. Using three population-based cohorts (n = 9,263), we performed multivariate genome-wide association studies (GWAS) for a correlation network of 11 circulating cytokines, then combined our results in meta-analysis. We identified a total of eight loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. In addition, conditional analyses revealed a further four secondary signals at three known cytokine loci. Integration, through the use of Bayesian colocalization analysis, of publicly available GWAS summary statistics with the cytokine network associations revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins, on metabolic traits such as lipoprotein and lipid levels, on blood-cell-related traits such as platelet count, and on disease traits such as coronary artery disease and type 2 diabetes.

Biomarker Glycoprotein Acetyls Is Associated With the Risk of a Wide Spectrum of Incident Diseases and Stratifies Mortality Risk in Angiography Patients

BACKGROUND

Integration of systems-level biomolecular information with electronic health records has led to recent interest in the glycoprotein acetyls (GlycA) biomarker-a serum- or plasma-derived nuclear magnetic resonance spectroscopy signal that represents the abundance of circulating glycated proteins. GlycA predicts risk of diverse outcomes, including cardiovascular disease, type 2 diabetes mellitus, and all-cause mortality; however, the underlying detailed associations of GlycA's morbidity and mortality risk are currently unknown.

METHODS

We used 2 population-based cohorts totaling 11 861 adults from the Finnish general population to test for an association with 468 common incident hospitalization and mortality outcomes during an 8-year follow-up. Further, we utilized 900 angiography patients to test for GlycA association with mortality risk and potential utility for mortality risk discrimination during 12-year follow-up.

RESULTS

New associations with GlycA and incident alcoholic liver disease, chronic renal failure, glomerular diseases, chronic obstructive pulmonary disease, inflammatory polyarthropathies, and hypertension were uncovered, and known incident disease associations were replicated. GlycA associations for incident disease outcomes were in general not attenuated when adjusting for hsCRP (high-sensitivity C-reactive protein). Among 900 patients referred to angiography, GlycA had hazard ratios of 4.87 (95% CI, 2.45-9.65) and 5.00 (95% CI, 2.38-10.48) for 12-year risk of mortality in the fourth and fifth quintiles by GlycA levels, demonstrating its prognostic potential for identification of high-risk individuals. When modeled together, both hsCRP and GlycA were attenuated but remained significant.

CONCLUSIONS

GlycA was predictive of myriad incident diseases across many major internal organs and stratified mortality risk in angiography patients. Both GlycA and hsCRP had shared and independent contributions to mortality risk, suggesting chronic inflammation as an etiological factor. GlycA may be useful in improving risk prediction in specific disease settings.

Power, false discovery rate and Winner's Curse in eQTL studies

Investigation of the genetic architecture of gene expression traits has aided interpretation of disease and trait-associated genetic variants; however, key aspects of expression quantitative trait loci (eQTL) study design and analysis remain understudied. We used extensive, empirically driven simulations to explore eQTL study design and the performance of various analysis strategies. Across multiple testing correction methods, false discoveries of genes with eQTLs (eGenes) were substantially inflated when false discovery rate (FDR) control was applied to all tests and only appropriately controlled using hierarchical procedures. All multiple testing correction procedures had low power and inflated FDR for eGenes whose causal SNPs had small allele frequencies using small sample sizes (e.g. frequency <10% in 100 samples), indicating that even moderately low frequency eQTL SNPs (eSNPs) in these studies are enriched for false discoveries. In scenarios with ≥80% power, the top eSNP was the true simulated eSNP 90% of the time, but substantially less frequently for very common eSNPs (minor allele frequencies >25%). Overestimation of eQTL effect sizes, so-called ‘Winner’s Curse’, was common in low and moderate power settings. To address this, we developed a bootstrap method (BootstrapQTL) that led to more accurate effect size estimation. These insights provide a foundation for future eQTL studies, especially those with sampling constraints and subtly different conditions.

A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets

Network modules-topologically distinct groups of edges and nodes-that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

BACKGROUND

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

Experimental and Human Evidence for Lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the Development of Cardiac Hypertrophy and heart failure

BACKGROUND

Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin-2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants.

METHODS AND RESULTS

We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS, LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single-nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis-eQTL for LCN2 expression.

CONCLUSIONS

Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure.

The Biomarker GlycA Is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection

The biomarker glycoprotein acetylation (GlycA) has been shown to predict risk of cardiovascular disease and all-cause mortality. Here, we characterize biological processes associated with GlycA by leveraging population-based omics data and health records from >10,000 individuals. Our analyses show that GlycA levels are chronic within individuals for up to a decade. In apparently healthy individuals, elevated GlycA corresponded to elevation of myriad inflammatory cytokines, as well as a gene coexpression network indicative of increased neutrophil activity, suggesting that individuals with high GlycA may be in a state of chronic inflammatory response. Accordingly, analysis of infection-related hospitalization and death records showed that increased GlycA increased long-term risk of severe non-localized and respiratory infections, particularly septicaemia and pneumonia. In total, our work demonstrates that GlycA is a biomarker for chronic inflammation, neutrophil activity, and risk of future severe infection. It also illustrates the utility of leveraging multi-layered omics data and health records to elucidate the molecular and cellular processes associated with biomarkers.

Prolonged acceptance of concordant and discordant xenografts with combined CD40 and CD28 pathway blockade

BACKGROUND

The prompt and vigorous immune response to xenogenic tissue remains a significant barrier to clinical xenotransplantation. Simultaneous blockade of the CD28 and CD40 costimulatory pathways has been shown to dramatically inhibit the immune response to alloantigen.

METHODS

. In this study, we investigated the ability of simultaneous blockade of the CD28 and CD40 pathways to inhibit the immune response to xenoantigen in the rat-to-mouse and pig-to-mouse models.

RESULTS

Simultaneous blockade of the CD28 and CD40 pathways produced marked inhibition of the cellular response to xenoantigen in vivo and produced long-term acceptance of xenogeneic cardiac and skin grafts (rat-to-mouse), and markedly suppressed an evoked antibody response to xenoantigen. In addition, this strategy significantly prolonged the survival of pig skin on recipient mice.

CONCLUSIONS

Long-term hyporesponsiveness to xenoantigen across both a concordant and discordant species barrier, measured by the stringent criterion of skin grafting, can be achieved using a noncytoablative treatment regimen.

Analysis of the B7 costimulatory pathway in allograft rejection

BACKGROUND

Blockade of the B7/CD28 costimulation pathway with the fusion protein, CTLA4-Ig, has been shown to prolong allograft survival in numerous rodent models, suggesting that this pathway is functionally important in the allograft rejection response. This pathway is complex and consists of at least the B7-1, B7-1a, B7-1cyt II, and B7-2 molecules on the antigen-presenting cell and CD28 and CTLA4 molecules on the T cell.

METHODS

The intragraft transcript expression of the B7 molecules and their counterreceptors was defined using reverse transcriptase-polymerase chain reaction in the vascularized mouse cardiac allograft model. In addition, the functional significance of these molecules was investigated both in vitro in the mixed leukocyte response (MLR) and in vivo in the vascularized mouse cardiac allograft model.

RESULTS

Intragraft expression of B7-1, B7-1a, B7-1cyt II, B7-2, CD28, and CTLA4 transcripts is up-regulated in allografts when compared with both normal untransplanted hearts and syngeneic transplants at between 5 and 12 days after transplant. Both anti-B7-1 and anti-B7-2 monoclonal antibodies alone inhibited T-cell proliferation in the MLR, however, equivalent maximal inhibition was obtained by a combination of these agents or by CTLA4-Ig. Likewise, in the mouse cardiac allograft model, both anti-B7-1 and anti-B7-2 modestly prolonged graft survival. However, an increased survival was obtained with either a combination of anti-B7-1 and anti-B7-2 or CTLA4-Ig. Blockade of the B7/CD28 pathway in the MLR using T cells from CD28 knockout mice had no effect on the proliferative response. Likewise, blockade of the B7/CD28 pathway did not effect the rate of rejection of cardiac allografts by CD28 knockout recipients.

CONCLUSIONS

These data suggest that both B7-1 and B7-2 have an important role in allograft rejection in the mouse vascularized cardiac allograft model.

Long-term acceptance of skin and cardiac allografts after blocking CD40 and CD28 pathways

The receptor-ligand pairs CD28-B7 and CD40-gp39 are essential for the initiation and amplification of T-cell-dependent immune responses. CD28-B7 interactions provide 'second signals' necessary for optimal T-cell activation and IL-2 production, whereas CD40-gp39 signals co-stimulate B-cell, macrophage, endothelial cell and T-cell activation. Nonetheless, blockade of either of these pathways alone is not sufficient to permit engraftment of highly immunogenic allografts. Here we report that simultaneous but not independent blockade of the CD28 and CD40 pathways effectively aborts T-cell clonal expansion in vitro and in vivo, promotes long-term survival of fully allogeneic skin grafts, and inhibits the development of chronic vascular rejection of primarily vascularized cardiac allografts. The requirement for simultaneous blockade of these pathways for effective inhibition of alloimmunity indicates that, although they are interrelated, the CD28 and CD40 pathways are critical independent regulators of T-cell-dependent immune responses.

Analysis of effector mechanisms in murine cardiac allograft rejection

Multiple effector cells have been implicated in transplant rejection, including cytotoxic T cells, B cells, macrophages and NK cells. The purpose of this study was to examine the effector pathways which are critical to murine cardiac allograft rejection. RT-PCR (reverse transcriptase-polymerase chain reaction) analysis of syngeneic and allogeneic vascularized heterotopic cardiac grafts at 5, 8 and 12 days following transplantation demonstrate constitutive expression of Fas in both the syngeneic and allogeneic grafts as well as in normal heart. However, FasL, granzyme, and perforin expression were shown to be up-regulated on days 5-12 in the allograft with no expression in syngeneic grafts or in normal hearts. We have recently analyzed the functional significance of T cell cytotoxic pathways and found that neither the Fas nor CD8+ cytotoxic pathways are required for murine cardiac allograft rejection. In light of these results, we investigated the functional significance of other effector cells in the rejection process. B cell deficient C57BL/10-IgHtm1Cgn mice rejected cardiac allografts from normal donors at control rate. Finally, RT-PCR was used to analyze the expression of macrophage effector transcripts in allograft rejection. Transcripts for iNOS (inducible nitric oxide synthase) and TNF alpha (tumor necrosis factor-alpha) were up-regulated on days 5-12 in untreated allografts with undetectable expression in normal heart or syngeneic grafts. These results demonstrate that effective allograft rejection can occur in the absence of B cells and T cell cytotoxicity pathways suggesting that other effector pathways, such as delayed-type hypersensitivity responses by macrophages, may be critical for allograft rejection.

CD40-gp39 interactions play a critical role during allograft rejection. Suppression of allograft rejection by blockade of the CD40-gp39 pathway

Studies in vivo have documented the importance of CD40-gp39 interactions in the development of T-dependent antibody responses to foreign and auto-antigens. In this report, we demonstrate that allograft rejection is also associated with strong induction of CD40 and gp39 transcripts. When treatment was initiated at the time of transplant, MR1, a mAb specific for gp39, induced markedly prolonged survival of fully disparate murine cardiac allografts in both naive and sensitized hosts. However, when therapy was delayed until postoperative day 5, anti-gp39 failed to prolong graft survival. Allografts from recipients treated with MR1 from the time of transplantation showed decreased expression of transcripts for the macrophage effector molecule, inducible nitric oxide synthase, but essentially unaltered expression of B7 molecules and T cell cytokine transcripts (interleukin [IL]-2, interferon-gamma, IL-10, and IL-4) relative to control allografts. In addition, alloantibody responses in the MR1-treated mice were profoundly inhibited. However, our studies using B cell-deficient mice indicated that the ability of MR1 to prolong allograft survival was not dependent on B cells. These data suggest that blockade of CD40-gp39 interactions may inhibit allograft rejection primarily by interfering with T cell help for effector functions, rather than by interference with T cell activation.

Fas-mediated cytotoxicity. An immunoeffector or immunoregulatory pathway in T cell-mediated immune responses?

Fas/Fas ligand interactions serve as a signaling pathway for apoptosis (1-3), an important regulatory mechanism in the development and function of the immune system (4-9). Recent evidence that Fas-dependent apoptosis is also an important mode of T cell cytotoxicity (10-13) suggested that Fas might play a critical role in the effector phase of T-dependent immune responses, such as allograft rejection. We observed that Fas transcripts are constitutively expressed in syngeneic and allogeneic murine cardiac transplants, while Fas ligand (FasL) is up-regulated only in rejecting allografts. Surprisingly, the absence of an intact Fas/FasL pathway did not alter the tempo of allograft rejection, even CD4-dependent rejection. These results indicate that Fas/FasL interactions are not essential mediators of T cell-induced allograft damage. Rather, as suggested in other studies, the Fas pathway may be principally involved in the regulation of clonal expansion and subsequent contraction of T cell populations during immune responses.

Regulation of immunostimulatory function and costimulatory molecule (B7-1 and B7-2) expression on murine dendritic cells

Dendritic cells (DC) play a critical role in the initiation of T cell-mediated immune responses, and express costimulatory molecules that are required for optimal activation of unprimed T cells. Studies on the regulation of the costimulatory molecules on DC have produced evidence from several systems that GM-CSF can up-regulate expression of CTLA4 counter receptor (CTLA4-CR) (but not intercellular adhesion molecule 1 (ICAM-1) and heat stable Ag (HsAg)) on DC. This is demonstrated on splenic DC, Langerhans cells, kidney DC in culture, and in a skin-explant culture system, in which the increased expression of CTLA4-CR on Langerhans cells (LC) occurs concomitantly with their migration out of skin. Interestingly, despite the ability of both GM-CSF and IFN-gamma to increase CTLA4-CR and maintain similar levels of ICAM-1, HsAg, and MHC molecule expression, the functional consequences of these cytokines on splenic DC are distinctly different. GM-CSF enhances the ability of DC to stimulate both T cell proliferation and cytokine release, whereas IFN-gamma causes no increase in immunostimulatory function. Further analysis of the CTLA4-CR on these cell populations by using the GL-1 and IG10 mAbs has shown that GM-CSF-cultured DC express high levels of both B7-1 and B7-2, whereas IFN-gamma-cultured DC express increased levels of only B7-2. These results suggest that optimal stimulation of unprimed T cells to proliferate and release cytokines may require participation of both of these CTLA4 counter receptors, and confirm the importance of GM-CSF for the maturation of DC into potent stimulators of T cell activation.

Regulation of immunostimulatory function and costimulatory molecule (B7-1 and B7-2) expression on murine dendritic cells

Dendritic cells (DC) play a critical role in the initiation of T cell-mediated immune responses, and express costimulatory molecules that are required for optimal activation of unprimed T cells. Studies on the regulation of the costimulatory molecules on DC have produced evidence from several systems that GM-CSF can up-regulate expression of CTLA4 counter receptor (CTLA4-CR) (but not intercellular adhesion molecule 1 (ICAM-1) and heat stable Ag (HsAg)) on DC. This is demonstrated on splenic DC, Langerhans cells, kidney DC in culture, and in a skin-explant culture system, in which the increased expression of CTLA4-CR on Langerhans cells (LC) occurs concomitantly with their migration out of skin. Interestingly, despite the ability of both GM-CSF and IFN-gamma to increase CTLA4-CR and maintain similar levels of ICAM-1, HsAg, and MHC molecule expression, the functional consequences of these cytokines on splenic DC are distinctly different. GM-CSF enhances the ability of DC to stimulate both T cell proliferation and cytokine release, whereas IFN-gamma causes no increase in immunostimulatory function. Further analysis of the CTLA4-CR on these cell populations by using the GL-1 and IG10 mAbs has shown that GM-CSF-cultured DC express high levels of both B7-1 and B7-2, whereas IFN-gamma-cultured DC express increased levels of only B7-2. These results suggest that optimal stimulation of unprimed T cells to proliferate and release cytokines may require participation of both of these CTLA4 counter receptors, and confirm the importance of GM-CSF for the maturation of DC into potent stimulators of T cell activation.

Functional expression of the costimulatory molecule, B7/BB1, on murine dendritic cell populations

Whereas dendritic cells (DC) are known to be potent activators of T cells both in vitro and in vivo, the critical costimulatory molecules expressed on DC are not well characterized. Using immunocytochemical and molecular techniques we find that splenic DC express B7/BB1, the counter-receptor for CD28. Moreover, expression of B7/BB1 is upregulated on epidermal Langerhans cells (LC) during their functional maturation into potent T cell stimulators. In blocking experiments, we find that participation of B7/BB1 is required for optimal proliferation of unprimed, allogeneic T cells in DC-driven, primary mixed leukocyte reactions. These data demonstrate that the regulated expression of B7/BB1 on DC may be important in the initiation of a primary T cell response.