Role of klotho and fibroblast growth factor 23 in arterial calcification, thickness, and stiffness: a meta-analysis of observational studies.

This meta-analysis was conducted to clarify the role of klotho and fibroblast growth factor 23 (FGF-23) in human arterial remodeling across recent studies, in terms of arterial calcification, thickness, and stiffness. A systematic literature search was conducted on five databases for articles up to December 2023. Arterial calcification, thickness, and stiffness were determined using the calcification score and artery affected, carotid intima-media thickness (CIMT), and pulse wave velocity (PWV), respectively. Sixty-two studies with a total of 27,459 individuals were included in this meta-analysis. Most studies involved chronic kidney disease patients. Study designs were mostly cross-sectional with only one case-control and nine cohorts. FGF-23 was positively correlated with arterial calcification (r = 0.446 [0.254-0.611], p < 0.0001 and aOR = 1.36 [1.09-1.69], p = 0.006), CIMT (r = 0.188 [0.02-0.354], p = 0.03), and PWV (r = 0.235 [0.159-0.310], p < 0.00001). By contrast, Klotho was inversely correlated with arterial calcification (r = - 0.388 [- 0.578 to - 0.159], p = 0.001) and CIMT (r = - 0.38 [- 0.53 to - 0.207], p < 0.00001). In conclusion, FGF-23 and Klotho were associated with arterial calcification, thickness, and stiffness, clarifying their role in arterial remodeling processes.

IDHwt glioblastomas can be stratified by their transcriptional response to standard treatment, with implications for targeted therapy.

BACKGROUND: Glioblastoma (GBM) brain tumors lacking IDH1 mutations (IDHwt) have the worst prognosis of all brain neoplasms. Patients receive surgery and chemoradiotherapy but tumors almost always fatally recur. RESULTS: Using RNA sequencing data from 107 pairs of pre- and post-standard treatment locally recurrent IDHwt GBM tumors, we identify two responder subtypes based on longitudinal changes in gene expression. In two thirds of patients, a specific subset of genes is upregulated from primary to recurrence (Up responders), and in one third, the same genes are downregulated (Down responders), specifically in neoplastic cells. Characterization of the responder subtypes indicates subtype-specific adaptive treatment resistance mechanisms that are associated with distinct changes in the tumor microenvironment. In Up responders, recurrent tumors are enriched in quiescent proneural GBM stem cells and differentiated neoplastic cells, with increased interaction with the surrounding normal brain and neurotransmitter signaling, whereas Down responders commonly undergo mesenchymal transition. ChIP-sequencing data from longitudinal GBM tumors suggests that the observed transcriptional reprogramming could be driven by Polycomb-based chromatin remodeling rather than DNA methylation. CONCLUSIONS: We show that the responder subtype is cancer-cell intrinsic, recapitulated in in vitro GBM cell models, and influenced by the presence of the tumor microenvironment. Stratifying GBM tumors by responder subtype may lead to more effective treatment.

Model averaging in calibration of near-infrared instruments with correlated high-dimensional data.

Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected variables is taken into account by weight-combining each estimate of the so-called 'candidate model'. Some studies have shown that MA enables better prediction, even in high-dimensional cases. However, little is known about the model prediction performance at different types of multicollinearity in high-dimensional data. Motivated by calibration of near-infrared (NIR) instruments,we focus on MA prediction performance in such data. The weighting schemes that we consider are based on the Akaike's information criterion (AIC), Mallows' Cp, and cross-validation. For estimating the model parameters, we consider the standard least squares and the ridge regression methods. The results indicate that MA outperforms model selection methods such as LASSO and SCAD in high-correlation data. The use of Mallows' Cp and cross-validation for the weights tends to yield similar results in all structures of correlation, although the former is generally preferred. We also find that the ridge model averaging outperforms the least-squares model averaging. This research suggests ridge model averaging to build a relatively better prediction of the NIR calibration model.

Risk factors and 26-years worldwide prevalence of endoscopic erosive esophagitis from 1997 to 2022: a meta-analysis.

Erosive esophagitis (EE) is the part of gastroesophageal reflux disease (GERD) spectrum and may progress to esophageal adenocarcinoma. Due to its progressivity and unclear prevalence, we aim to identify the factors contributing in EE to decide the need for further examination. We performed a PRISMA 2020-based systematic search through PubMed and other resources up to June 2, 2022. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). The odds ratio (OR) of each factor and worldwide prevalence of EE were measured. There are 114 observational studies included with a total of 759,100 participants. Out of 29 factors, the significant risk factors are age ≥ 60 y.o. (OR 2.03 [1.81-2.28]), White/Caucasian (OR 1.67 [1.40-1.99]), unmarried (OR 1.08 [1.03-1.14]), having GERD ≥ 5 years (OR 1.27 [1.14-1.42]), general obesity (OR 1.78 [1.61-1.98]), central obesity (OR 1.29 [1.18-1.42]), diabetes mellitus (DM) (OR 1.24 [1.17-1.32]), hypertension (OR 1.16 [1.09-1.23]), dyslipidemia (OR 1.15 [1.06-1.24]), hypertriglyceridemia (OR 1.42 [1.29-1.57]), hiatal hernia (HH) (OR 4.07 [3.21-5.17]), and non-alcoholic fatty liver disease (NAFLD) (OR 1.26 [1.18-1.34]). However, H. pylori infection (OR 0.56 [0.48-0.66]) and atrophic gastritis (OR 0.51 [0.31-0.86]) are protective towards EE. This study demonstrates that age, ethnicity, unmarried, long-term GERD, metabolic diseases, HH, and NAFLD act as risk factors for EE, whereas H. pylori infection and atrophic gastritis act as protective factors. These findings may enable a better understanding of EE and increase greater awareness to address its growing burden.

Clinical characteristics, management, and outcomes of pulmonary valve myxoma: systematic review of published case reports.

BACKGROUND: Cardiac myxoma is the most common type of primary cardiac tumor, with the majority located in the atrial wall. The tumor is attached to valvular structures in a few cases, of which the pulmonary valve is the least affected. Pulmonary valve myxoma may have different clinical manifestations from the more common cardiac myxomas because of its vital position. A misdiagnosis of these types of cardiac myxoma may be detrimental to the care and well-being of patients. Therefore, this systematic review aims to define the clinical characteristics of pulmonary valve myxoma and how this differs from a more common cardiac myxoma. METHODS: Employed literature was obtained from PubMed, ScienceDirect, Scopus, Springer, and ProQuest without a publication year limit on August 23, 2022. The keyword was "pulmonary valve myxoma." Inclusion criteria were as follows: (1) case report or series, (2) available individual patient data, and (3) myxoma that is attached to pulmonary valve structures with no evidence of metastasis. Non-English language or nonhuman subject studies were excluded. Johanna Briggs Institute checklists were used for the risk of bias assessment. Data are presented descriptively. RESULTS: This review included 9 case reports from 2237 articles. All cases show a low risk of bias. Pulmonary valve myxoma is dominated by males (5:4), and the patient's median age is 57 years with a bimodal distribution in pediatric and geriatric populations. The clinical manifestation of pulmonary valve myxoma is often unspecified or asymptomatic. However, systolic murmur in the pulmonary valve area is heard in 67% of cases. Echocardiography remains the diagnostic modality of choice in the majority of cases. Tumor attached to the pulmonary cusps or annulus and extended to adjacent tissues in all cases. Therefore, valve replacement or adjacent tissue reconstructions are required in 77% of cases. The recurrence and mortality are considerably high, with 33% and 22% cases, respectively. CONCLUSIONS: Pulmonary valve myxoma is more common in males with a bimodal age distribution, and its outcomes seem worse than usual cardiac myxomas. Increasing awareness of its clinical symptoms, early diagnosis, and complete myxoma resection before the presence of congestive heart failure symptoms are important in achieving excellent outcomes. A firm embolization blockade is needed to prevent myxoma recurrence.

Myocarditis and coronavirus disease 2019 vaccination: A systematic review and meta-summary of cases.

Vaccination is significant to control, mitigate, and recover from the destructive effects of coronavirus disease 2019 (COVID-19). The incidence of myocarditis following COVID-19 vaccination has been increasing and growing public concern; however, little is known about it. This study aimed to systematically review myocarditis following COVID-19 vaccination. We included studies containing individual patient data of myocarditis following COVID-19 vaccination published between January 1, 2020 and September 7, 2022 and excluded review articles. Joanna Briggs Institute critical appraisals were used for risk of bias assessment. Descriptive and analytic statistics were performed. A total of 121 reports and 43 case series from five databases were included. We identified 396 published cases of myocarditis and observed that the majority of cases was male patients, happened following the second dose of mRNA vaccine administration, and experienced chest pain as a symptom. Previous COVID-19 infection was significantly associated (p < 0.01; OR, 5.74; 95% CI, 2.42-13.64) with the risk of myocarditis following the administration of the first dose, indicating that its primary mechanism is immune-mediated. Moreover, 63 histopathology examinations were dominated by non-infective subtypes. Electrocardiography and cardiac marker combination is a sensitive screening modality. However, cardiac magnetic resonance is a significant noninvasive examination to confirm myocarditis. Endomyocardial biopsy may be considered in confusing and severe cases. Myocarditis following COVID-19 vaccination is relatively benign, with a median length of hospitalization of 5 days, intensive care unit admission of <12%, and mortality of <2%. The majority was treated with nonsteroidal anti-inflammatory drugs, colchicine, and steroids. Surprisingly, deceased cases had characteristics of being female, older age, non-chest pain symptoms, first-dose vaccination, left ventricular ejection fraction of <30%, fulminant myocarditis, and eosinophil infiltrate histopathology.

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Sparse modelling of cancer patients' survival based on genomic copy number alterations.

Copy number alterations (CNA) are structural variation in the genome, in which some regions exhibit more or less than the normal two chromosomal copies. This genomic CNA profile provides critical information in tumour progression and is therefore informative for patients' survival. It is currently a statistical challenge to model patients' survival using their genomic CNA profiles while at the same time identify regions in the genome that are associated with patients' survival. Some methods have been proposed, including Cox proportional hazard (PH) model with ridge, lasso, or elastic net penalties. However, these methods do not take the general dependencies between genomic regions into account and produce results that are difficult to interpret. In this paper, we extend the elastic net penalty by introducing additional penalty that takes into account general dependencies between genomic regions. This new model produces smooth parameter estimates while simultaneously performs variable selection via sparse solution. The results indicate that the proposed method shows a better prediction performance than other models in our simulation study, while enabling us to investigate regions in the genome that are associated with the patients' survival with sensible interpretation. We illustrate the method using a real dataset from a lung cancer cohort and simulated data.

Use of shared gamma frailty model in analysis of survival data in twins.

In survival analysis, the effect of a covariate on the outcome is reported in a hazard rate. However, hazards rates are hard to interpret. Here we consider differences in survival probabilities instead. Using data on twins is interesting due to the fact that many observed and unobserved factors are controlled or matched. To model the correlation between twins, some authors have proposed survival models with frailties or random effects. However, there is a potential danger of bias in the estimation if the frailty distribution is misspecified. Frailties are often assumed to follow a gamma distribution. To safeguard us from the impact of the misspecification of this distribution, we consider a flexible non-parametric baseline hazard in addition to a parametric one. We will apply this methodology to the TwinsUK cohort to predict the probability of experiencing a fracture in the next five or ten years, given their bone mineral densities (BMD) and their frailty index. The models with parametric and non-parametric baseline hazards yield very close results in estimating survival probabilities and thus a choice of parametric baseline hazard is generally preferred. We find that bone mineral density is a significant predictor in the model whereas frailty index is not. Low BMD leads to a larger probability of fracture; e.g, in 10 years, the probability of fracture is 21% for low BMD group, 16% for medium BMD group and 8% for high BMD group.

Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects.

The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called 'HLnet') in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.

Geometry-based distance for clustering amino acids.

Clustering amino acids is one of the most challenging problems in functional and structural prediction of protein. Previous studies have proposed clusters based on measurements of physical and biochemical characteristics of the amino acids such as volume, area, hydrophilicity, polarity, hydrogen bonding, shape, and charge. These characteristics, although important, are less directly related to the protein structure compared to geometrical characteristics such as dihedral angles between amino acids. We propose using the p-value from a test of equality of dihedral-angle distributions as the basis of a distance measure for the clustering. In this novel approach, an energy test is modified to deal with bivariate angular data and the p-value is obtained via a permutation method. The results indicate that the clusters of amino acids have sensible interpretation where Glycine, Proline, and Asparagine each forms a distinct cluster. A simulation study suggests that this approach has good working characteristics to cluster amino acids.

Method for Automatic Selection of Parameters in Normal Tissue Complication Probability Modeling.

PURPOSE: To present a fully automatic method to generate multiparameter normal tissue complication probability (NTCP) models and compare its results with those of a published model, using the same patient cohort. METHODS AND MATERIALS: Data were analyzed from 345 rectal cancer patients treated with external radiation therapy to predict the risk of patients developing grade 1 or ≥2 cystitis. In total, 23 clinical factors were included in the analysis as candidate predictors of cystitis. Principal component analysis was used to decompose the bladder dose-volume histogram into 8 principal components, explaining more than 95% of the variance. The data set of clinical factors and principal components was divided into training (70%) and test (30%) data sets, with the training data set used by the algorithm to compute an NTCP model. The first step of the algorithm was to obtain a bootstrap sample, followed by multicollinearity reduction using the variance inflation factor and genetic algorithm optimization to determine an ordinal logistic regression model that minimizes the Bayesian information criterion. The process was repeated 100 times, and the model with the minimum Bayesian information criterion was recorded on each iteration. The most frequent model was selected as the final "automatically generated model" (AGM). The published model and AGM were fitted on the training data sets, and the risk of cystitis was calculated. RESULTS: The 2 models had no significant differences in predictive performance, both for the training and test data sets (P value > .05) and found similar clinical and dosimetric factors as predictors. Both models exhibited good explanatory performance on the training data set (P values > .44), which was reduced on the test data sets (P values < .05). CONCLUSIONS: The predictive value of the AGM is equivalent to that of the expert-derived published model. It demonstrates potential in saving time, tackling problems with a large number of parameters, and standardizing variable selection in NTCP modeling.

A statistical method for analysing cospeciation in tritrophic ecology using electrical circuit theory.

We introduce a new method to test efficiently for cospeciation in tritrophic systems. Our method utilises an analogy with electrical circuit theory to reduce higher order systems into bitrophic data sets that retain the information of the original system. We use a sophisticated permutation scheme that weights interactions between two trophic layers based on their connection to the third layer in the system. Our method has several advantages compared to the method of Mramba et al. [Mramba, L. K., S. Barber, K. Hommola, L. A. Dyer, J. S. Wilson, M. L. Forister and W. R. Gilks (2013): "Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology," Stat. Appl. Genet. Mol. Biol., 12, 679-701.]. We do not require triangular interactions to connect the three phylogenetic trees and an easily interpreted p-value is obtained in one step. Another advantage of our method is the scope for generalisation to higher order systems and phylogenetic networks. The performance of our method is compared to the methods of Hommola et al. [Hommola, K., J. E. Smith, Y. Qiu and W. R. Gilks (2009): "A permutation test of host-parasite cospeciation," Mol. Biol. Evol., 26, 1457-1468.] and Mramba et al. [Mramba, L. K., S. Barber, K. Hommola, L. A. Dyer, J. S. Wilson, M. L. Forister and W. R. Gilks (2013): "Permutation tests for analyzing cospeciation in multiple phylogenies: applications in tri-trophic ecology," Stat. Appl. Genet. Mol. Biol., 12, 679-701.] at the bitrophic and tritrophic level, respectively. This was achieved by evaluating type I error and statistical power. The results show that our method produces unbiased p-values and has comparable power overall at both trophic levels. Our method was successfully applied to a dataset of leaf-mining moths, parasitoid wasps and host plants [Lopez-Vaamonde, C., H. Godfray, S. West, C. Hansson and J. Cook (2005): "The evolution of host use and unusual reproductive strategies in achrysocharoides parasitoid wasps," J. Evol. Biol., 18, 1029-1041.], at both the bitrophic and tritrophic levels.

Identification of transcript regulatory patterns in cell differentiation.

MOTIVATION: Studying transcript regulatory patterns in cell differentiation is critical in understanding its complex nature of the formation and function of different cell types. This is done usually by measuring gene expression at different stages of the cell differentiation. However, if the gene expression data available are only from the mature cells, we have some challenges in identifying transcript regulatory patterns that govern the cell differentiation. RESULTS: We propose to exploit the information of the lineage of cell differentiation in terms of correlation structure between cell types. We assume that two different cell types that are close in the lineage will exhibit many common genes that are co-expressed relative to those that are far in the lineage. Current analysis methods tend to ignore this correlation by testing for differential expression assuming some sort of independence between cell types. We employ a Bayesian approach to estimate the posterior distribution of the mean of expression in each cell type, by taking into account the cell formation path in the lineage. This enables us to infer genes that are specific in each cell type, indicating the genes are involved in directing the cell differentiation to that particular cell type. We illustrate the method using gene expression data from a study of haematopoiesis. AVAILABILITY AND IMPLEMENTATION: R codes to perform the analysis are available in http://www1.maths.leeds.ac.uk/∼arief/R/CellDiff/. CONTACT: a.gusnanto@leeds.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data.

MOTIVATION: The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data. RESULTS: We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients' covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles. AVAILABILITY AND IMPLEMENTATION: An R package to run a logistic regression is available in http://www1.maths.leeds.ac.uk/~arief/R/CNALR/. CONTACT: a.gusnanto@leeds.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Estimating optimal window size for analysis of low-coverage next-generation sequence data.

MOTIVATION: Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis. If the window size is too small, many windows will exhibit zero counts and almost no pattern can be observed. In contrast, if the window size is too wide, the patterns or genomic features will be 'smoothed out'. Our objective is to identify an optimal window size in between the two extremes. RESULTS: We assume the reads density to be a step function. Given this model, we propose a data-based estimation of optimal window size based on Akaike's information criterion (AIC) and cross-validation (CV) log-likelihood. By plotting the AIC and CV log-likelihood curve as a function of window size, we are able to estimate the optimal window size that minimizes AIC or maximizes CV log-likelihood. The proposed methods are of general purpose and we illustrate their application using low-coverage next-generation sequence datasets from real tumour samples and simulated datasets. AVAILABILITY AND IMPLEMENTATION: An R package to estimate optimal window size is available at http://www1.maths.leeds.ac.uk/∼arief/R/win/.

Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.

Our main interest in supervised classification of gene expression data is to infer whether the expressions can discriminate biological characteristics of samples. With thousands of gene expressions to consider, a gene selection has been advocated to decrease classification by including only the discriminating genes. We propose to make the gene selection based on partial least squares and logistic regression random-effects (RE) estimates before the selected genes are evaluated in classification models. We compare the selection with that based on the two-sample t-statistics, a current practice, and modified t-statistics. The results indicate that gene selection based on logistic regression RE estimates is recommended in a general situation, while the selection based on the PLS estimates is recommended when the number of samples is low. Gene selection based on the modified t-statistics performs well when the genes exhibit moderate-to-high variability with moderate group separation. Respecting the characteristics of the data is a key aspect to consider in gene selection.

Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data.

MOTIVATION: Comparison of read depths from next-generation sequencing between cancer and normal cells makes the estimation of copy number alteration (CNA) possible, even at very low coverage. However, estimating CNA from patients' tumour samples poses considerable challenges due to infiltration with normal cells and aneuploid cancer genomes. Here we provide a method that corrects contamination with normal cells and adjusts for genomes of different sizes so that the actual copy number of each region can be estimated. RESULTS: The procedure consists of several steps. First, we identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction. The results indicate that the method works properly to estimate genomic regions with gains and losses in a range of simulated data as well as in two datasets from lung cancer patients. It also proves a powerful tool when analysing publicly available data from two cell lines (HCC1143 and COLO829). AVAILABILITY: An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor. CONTACT: a.gusnanto@leeds.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of candidate genes linking systemic inflammation to atherosclerosis; results of a human in vivo LPS infusion study.

BACKGROUND: It is widely accepted that atherosclerosis and inflammation are intimately linked. Monocytes play a key role in both of these processes and we hypothesized that activation of inflammatory pathways in monocytes would lead to, among others, proatherogenic changes in the monocyte transcriptome. Such differentially expressed genes in circulating monocytes would be strong candidates for further investigation in disease association studies. METHODS: Endotoxin, lipopolysaccharide (LPS), or saline control was infused in healthy volunteers. Monocyte RNA was isolated, processed and hybridized to Hver 2.1.1 spotted cDNA microarrays. Differential expression of key genes was confirmed by RT-PCR and results were compared to in vitro data obtained by our group to identify candidate genes. RESULTS: All subjects who received LPS experienced the anticipated clinical response indicating successful stimulation. One hour after LPS infusion, 11 genes were identified as being differentially expressed; 1 down regulated and 10 up regulated. Four hours after LPS infusion, 28 genes were identified as being differentially expressed; 3 being down regulated and 25 up regulated. No genes were significantly differentially expressed following saline infusion. Comparison with results obtained in in vitro experiments lead to the identification of 6 strong candidate genes (BATF, BID, C3aR1, IL1RN, SEC61B and SLC43A3) CONCLUSION: In vivo endotoxin exposure of healthy individuals resulted in the identification of several candidate genes through which systemic inflammation links to atherosclerosis.

Transcription profiling in human platelets reveals LRRFIP1 as a novel protein regulating platelet function.

Within the healthy population, there is substantial, heritable, and interindividual variability in the platelet response. We explored whether a proportion of this variability could be accounted for by interindividual variation in gene expression. Through a correlative analysis of genome-wide platelet RNA expression data from 37 subjects representing the normal range of platelet responsiveness within a cohort of 500 subjects, we identified 63 genes in which transcript levels correlated with variation in the platelet response to adenosine diphosphate and/or the collagen-mimetic peptide, cross-linked collagen-related peptide. Many of these encode proteins with no reported function in platelets. An association study of 6 of the 63 genes in 4235 cases and 6379 controls showed a putative association with myocardial infarction for COMMD7 (COMM domain-containing protein 7) and a major deviation from the null hypo thesis for LRRFIP1 [leucine-rich repeat (in FLII) interacting protein 1]. Morpholino-based silencing in Danio rerio identified a modest role for commd7 and a significant effect for lrrfip1 as positive regulators of thrombus formation. Proteomic analysis of human platelet LRRFIP1-interacting proteins indicated that LRRFIP1 functions as a component of the platelet cytoskeleton, where it interacts with the actin-remodeling proteins Flightless-1 and Drebrin. Taken together, these data reveal novel proteins regulating the platelet response.