GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation, and TWAS analysis of cosmopolitan cohorts.

MOTIVATION: As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. RESULTS: To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)-a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32 953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. AVAILABILITY AND IMPLEMENTATION: The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text S1.

Hepatocyte-specific deletion of Bis causes senescence in the liver without deteriorating hepatic function.

Bcl-2-interacting cell death suppressor (BIS), also called as BAG3, regulates numerous physiological processes, such as apoptosis, protein quality control, and senescence. Whole-body Bis-knockout (KO) mice exhibit early lethality following cardiac and skeletal muscle dysfunction. The first attempt to generate organ-specific knockout mice resulted in constitutive or inducible heart-specific Bis-knockout mice, which exhibited cardiac dilation and underwent premature death. Here, we generated hepatocyte-specific Bis-knockout (Bis-HKO) mice and found no abnormalities in metabolic function and survival. However, depletion of HSPB8 and accumulation of p62 indicated impaired autophagy in Bis-HKO livers. Interestingly, the number of peroxisomes wrapped by phagophore membranes increased as evidenced by transmission electron microscopy analysis, indicating defects in the progression of pexophagy. In addition, increased dihydroethidine intensities and histone H3 K9me3-positive nuclei indicated increased oxidative stress and senescence induction in Bis-HKO livers. Mechanistically, p27 was upregulated in Bis-HKO livers. In SNU368 hepatocellular carcinoma cells, BIS depletion led to p27 upregulation, and increase in histone H3 K9me3 levels and senescence-associated ß-galactosidase staining; therefore, reproducing the in vivo senescence phenotype. Despite the observation of no metabolic abnormalities, BIS depletion led to defective autophagy, increased oxidative stress, and senescence in Bis-HKO livers. Collectively, our results suggest a role for BIS in maintaining liver regeneration potential under pathological conditions.

Inhibitory Effect of Etravirine, a Non-Nucleoside Reverse Transcriptase Inhibitor, via Anterior Gradient Protein 2 Homolog Degradation against Ovarian Cancer Metastasis.

Anterior gradient protein 2 homolog (AGR2), an endoplasmic reticulum protein, is secreted in the tumor microenvironment. AGR2 is a member of the disulfide isomerase family, is highly expressed in multiple cancers, and promotes cancer metastasis. In this study, we found that etravirine, which is a non-nucleoside reverse transcriptase inhibitor, could induce AGR2 degradation via autophagy. Moreover, etravirine diminished proliferation, migration, and invasion in vitro. Moreover, in an orthotopic xenograft mouse model, the combination of etravirine and paclitaxel significantly suppressed cancer progression and metastasis. This drug may be a promising therapeutic agent for the treatment of ovarian cancer.

V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data.

SUMMARY: Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, including 'unwanted' variation that needs to be removed in downstream analyses (e.g. batch effects) and 'wanted' or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying 'wanted' variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application [Visual Surrogate Variable Analysis (V-SVA)] that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods. AVAILABILITY AND IMPLEMENTATION: The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA. CONTACT: leed13@miamioh.edu or duygu.ucar@jax.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Increasing the resolution and precision of psychiatric genome-wide association studies by re-imputing summary statistics using a large, diverse reference panel.

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary-statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500-subject coming from the 1000 Genome-Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic-mixture based solely on Z-scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary-statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS-studies.

TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders.

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), for example, across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred to as transcriptomic wide association studies (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effects of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular basis of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to the quadratic (as a function of the number of SNPs) computational burden for computing LD across large chromosomal regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that (a) has a linear computational burden, (b) uses a large and diverse reference panel (33 K subjects), (c) is competitive (adjusts for background enrichment in gene TWAS statistics), and (d) is applicable as-is to ethnically mixed-cohorts. To underline its potential for increasing the power to uncover genetic signals over the commonly used nontranscriptomics methods, for example, MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

Motivation: To increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on (i) summary statistics from genome-wide association studies (GWAS) and (ii) linkage disequilibrium patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals. Results: We propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses (i) cis-eQTL SNPs from the latest expression studies and (ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also (i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and (ii) to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of P-values. Availability and implementation: https://github.com/Chatzinakos/JEPEGMIX2. Supplementary information: Supplementary data are available at Bioinformatics online.

JEPEGMIX: gene-level joint analysis of functional SNPs in cosmopolitan cohorts.

MOTIVATION: To increase detection power, gene level analysis methods are used to aggregate weak signals. To greatly increase computational efficiency, most methods use as input summary statistics from genome-wide association studies (GWAS). Subsequently, gene statistics are constructed using linkage disequilibrium (LD) patterns from a relevant reference panel. However, all methods, including our own Joint Effect on Phenotype of eQTL/functional single nucleotide polymorphisms (SNPs) associated with a Gene (JEPEG), assume homogeneous panels, e.g. European. However, this renders these tools unsuitable for the analysis of large cosmopolitan cohorts. RESULTS: We propose a JEPEG extension, JEPEGMIX, which similar to one of our software tools, Direct Imputation of summary STatistics of unmeasured SNPs from MIXed ethnicity cohorts, is capable of estimating accurate LD patterns for cosmopolitan cohorts. JEPEGMIX uses this accurate LD estimates to (i) impute the summary statistics at unmeasured functional variants and (ii) test for the joint effect of all measured and imputed functional variants which are associated with a gene. We illustrate the performance of our tool by analyzing the GWAS meta-analysis summary statistics from the multi-ethnic Psychiatric Genomics Consortium Schizophrenia stage 2 cohort. This practical application supports the immune system being one of the main drivers of the process leading to schizophrenia. AVAILABILITY AND IMPLEMENTATION: Software, annotation database and examples are available at http://dleelab.github.io/jepegmix/. CONTACT: donghyung.lee@vcuhealth.org SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans.

MOTIVATION: For genetic studies, statistically significant variants explain far less trait variance than 'sub-threshold' association signals. To dimension follow-up studies, researchers need to accurately estimate 'true' effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner's curse biases, which are reduced only by laborious winner's curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. RESULTS: WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose F: DR I: nverse Q: uantile T: ransformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. CONCLUSIONS: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). AVAILABILITY AND IMPLEMENTATION: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT CONTACT: sabacanu@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.

MOTIVATION: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. RESULTS: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. AVAILABILITY AND IMPLEMENTATION: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

JEPEG: a summary statistics based tool for gene-level joint testing of functional variants.

MOTIVATION: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared with the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by (i) not measuring all eQTLs in a gene and/or (ii) lack of access to individual genotypes. RESULTS: We propose joint effect on phenotype of eQTL/functional SNPs associated with a gene (JEPEG), a novel software tool which uses only GWAS summary statistics to (i) impute the summary statistics at unmeasured eQTLs and (ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium Stage 1 and the Genetic Consortium for Anorexia Nervosa. CONCLUSIONS: Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: (i) increasing signal detection power via uncovering (a) novel genes or (b) known associated genes in smaller cohorts and (ii) assisting in fine-mapping of challenging regions, e.g. major histocompatibility complex for schizophrenia. AVAILABILITY AND IMPLEMENTATION: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at http://code.google.com/p/jepeg/. CONTACT: dlee4@vcu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Corrections to Thomas-Fermi densities at turning points and beyond.

Uniform semiclassical approximations for the number and kinetic-energy densities are derived for many noninteracting fermions in one-dimensional potentials with two turning points. The resulting simple, closed-form expressions contain the leading corrections to Thomas-Fermi theory, involve neither sums nor derivatives, are spatially uniform approximations, and are exceedingly accurate.

DIST: direct imputation of summary statistics for unmeasured SNPs.

MOTIVATION: Genotype imputation methods are used to enhance the resolution of genome-wide association studies, and thus increase the detection rate for genetic signals. Although most studies report all univariate summary statistics, many of them limit the access to subject-level genotypes. Because such an access is required by all genotype imputation methods, it is helpful to develop methods that impute summary statistics without going through the interim step of imputing genotypes. Even when subject-level genotypes are available, due to the substantial computational cost of the typical genotype imputation, there is a need for faster imputation methods. RESULTS: Direct Imputation of summary STatistics (DIST) imputes the summary statistics of untyped variants without first imputing their subject-level genotypes. This is achieved by (i) using the conditional expectation formula for multivariate normal variates and (ii) using the correlation structure from a relevant reference population. When compared with genotype imputation methods, DIST (i) requires only a fraction of their computational resources, (ii) has comparable imputation accuracy for independent subjects and (iii) is readily applicable to the imputation of association statistics coming from large pedigree data. Thus, the proposed application is useful for a fast imputation of summary results for (i) studies of unrelated subjects, which (a) do not provide subject-level genotypes or (b) have a large size and (ii) family association studies. AVAILABILITY AND IMPLEMENTATION: Pre-compiled executables built under commonly used operating systems are publicly available at http://code.google.com/p/dist/. CONTACT: dlee4@vcu.edu .

Precision synthesis of bio-based acrylic thermoplastic elastomer by RAFT polymerization of itaconic acid derivatives.

Bio-based polymer materials from renewable resources have recently become a growing research focus. Herein, a novel thermoplastic elastomer is developed via controlled/living radical polymerization of plant-derived itaconic acid derivatives, which are some of the most abundant renewable acrylic monomers obtained via the fermentation of starch. The reversible addition-fragmentation chain-transfer (RAFT) polymerizations of itaconic acid imides, such as N-phenylitaconimide and N-(p-tolyl)itaconimide, and itaconic acid esters, such as di-n-butyl itaconate and bis(2-ethylhexyl) itaconate, are examined using a series of RAFT agents to afford well-defined polymers. The number-average molecular weights of these polymers increase with the monomer conversion while retaining relatively narrow molecular weight distributions. Based on the successful controlled/living polymerization, sequential block copolymerization is subsequently investigated using mono- and di-functional RAFT agents to produce block copolymers with soft poly(itaconate) and hard poly(itaconimide) segments. The properties of the obtained triblock copolymer are evaluated as bio-based acrylic thermoplastic elastomers.

KOMPUTE: imputing summary statistics of missing phenotypes in high-throughput model organism data.

Motivation: The International Mouse Phenotyping Consortium (IMPC) is striving to build a comprehensive functional catalog of mammalian protein-coding genes by systematically producing and phenotyping gene-knockout mice for almost every protein-coding gene in the mouse genome and by testing associations between gene loss-of-function and phenotype. To date, the IMPC has identified over 90 000 gene-phenotype associations, but many phenotypes have not yet been measured for each gene, resulting in largely incomplete data; ∼75.6% of association summary statistics are still missing in the latest IMPC summary statistics dataset (IMPC release version 16). Results: To overcome these challenges, we propose KOMPUTE, a novel method for imputing missing summary statistics in the IMPC dataset. Using conditional distribution properties of multivariate normal, KOMPUTE estimates the association Z-scores of unmeasured phenotypes for a particular gene as a conditional expectation given the Z-scores of measured phenotypes. Our evaluation of the method using simulated and real-world datasets demonstrates its superiority over the singular value decomposition matrix completion method in various scenarios. Availability and implementation: An R package for KOMPUTE is publicly available at https://github.com/statsleelab/kompute, along with usage examples and results for different phenotype domains at https://statsleelab.github.io/komputeExamples.

Transformation of valence signaling in a striatopallidal circuit.

The ways in which sensory stimuli acquire motivational valence through association with other stimuli is one of the simplest forms of learning. Though we have identified many brain nuclei that play various roles in reward processing, a significant gap remains in understanding how valence encoding transforms through the layers of sensory processing. To address this gap, we carried out a comparative investigation of the olfactory tubercle (OT), and the ventral pallidum (VP) - 2 connected nuclei of the basal ganglia which have both been implicated in reward processing. First, using anterograde and retrograde tracing, we show that both D1 and D2 neurons of the OT project primarily to the VP and minimally elsewhere. Using 2-photon calcium imaging, we then investigated how the identity of the odor and reward contingency of the odor are differently encoded by neurons in either structure during a classical conditioning paradigm. We find that VP neurons robustly encode reward contingency, but not identity, in low-dimensional space. In contrast, OT neurons primarily encode odor identity in high-dimensional space. Though D1 OT neurons showed larger response vectors to rewarded odors than other odors, we propose this is better interpreted as identity encoding with enhanced contrast rather than as valence encoding. Finally, using a novel conditioning paradigm that decouples reward contingency and licking vigor, we show that both features are encoded by non-overlapping VP neurons. These results provide a novel framework for the striatopallidal circuit in which a high-dimensional encoding of stimulus identity is collapsed onto a low-dimensional encoding of motivational valence.

Effects of 17ß-estradiol on the release of monocyte chemotactic protein-1 and MAPK activity in monocytes stimulated with peritoneal fluid from endometriosis patients.

AIM: Hormones and inflammation have been implicated in the pathological process of endometriosis; therefore, we investigated the combined effects of 17ß-estradiol (E2) and peritoneal fluid obtained from patients with endometriosis (ePF) or a control peritoneal fluid (cPF) obtained from patients without endometriosis on the release of monocyte chemotactic protein-1 (MCP-1) by monocytes and the role of signaling pathways. METHODS: Monocytes were cultured with ePF and cPF in the presence of E2; the MCP-1 levels in the supernatants were then measured by ELISA. In addition, mitogen activated protein kinase (MAPK) activation was measured by Western blotting of phosphorylated proteins. RESULTS: E2 down-regulated MCP-1 release by lipopolysaccharide- or cPF-treated monocytes, but failed to suppress its release by ePF-treated monocytes. The release of MCP-1 by ePF- and cPF-treated monocytes was efficiently abrogated by p38 mitogen activated protein kinase (MAPK) inhibitors; however, the MCP-1 release by cPF-treated monocytes, but not by ePF-treated monocytes, was blocked by a MAPK kinase inhibitor. In addition, ePF and cPF induced the phosphorylation of extracellular stress regulated kinase (ERK)1/2, p38 MAPK and c-Jun N-terminal kinase (JNK). E2 decreased the phosphorylation of p38 MAPK, but not ERK1/2 in ePF-treated monocytes; however, E2 decreased the phosphorylation of p38 MAPK, ERK1/2 and JNK in cPF-treated monocytes. CONCLUSIONS: The ability of E2 to modulate MCP-1 production is impaired in ePF-treated monocytes, which may be related to regulation of MAPK activity. These findings suggest that the failure of E2 to suppress ePF-treated production of MCP-1 may be involved in the pathogenesis of endometriosis.

Induction of human microsomal prostaglandin E synthase 1 by activated oncogene RhoA GTPase in A549 human epithelial cancer cells.

Oncogenic RhoA GTPase has been investigated as a mediator of pro-inflammatory responses and aggressive carcinogenesis. Among the various targets of RhoA-linked signals, pro-inflammatory prostaglandin E(2) (PGE(2)), a major prostaglandin metabolite, was assessed in epithelial cancer cells. RhoA activation increased PGE(2) levels and gene expression of the rate-limiting PGE(2) producing enzymes, cyclooxygenase-2 and microsomal prostaglandin E synthase 1 (mPGES-1). In particular, human mPGES-1 was induced by RhoA via transcriptional activation in control and interleukin (IL)-1ß-activated cancer cells. To address the involvement of potent signaling pathways in RhoA-activated mPGES-1 induction, various signaling inhibitors were screened for their effects on mPGES-1 promoter activity. RhoA activation enhanced basal and IL-1ß-mediated phosphorylated nuclear factor-κB and extracellular signal-regulated kinase1/2 proteins, all of which were positively involved in RhoA-induced gene expression of mPGES-1. As one potent down-stream transcription factor of ERK1/2 signals, early growth response gene 1 product also mediated RhoA-induced gene expression of mPGES-1 by enhancing transcriptional activity. Since oncogene-triggered PGE(2) production is a critical modulator of epithelial tumor cells, RhoA-associated mPGES-1 represents a promising chemo-preventive or therapeutic target for epithelial inflammation and its associated cancers.

Electronic structure via potential functional approximations.

The universal functional of Hohenberg-Kohn is given as a coupling-constant integral over the density as a functional of the potential. Conditions are derived under which potential-functional approximations are variational. Construction via this method and imposition of these conditions are shown to greatly improve the accuracy of the noninteracting kinetic energy needed for orbital-free Kohn-Sham calculations.

Effects of peritoneal fluid from endometriosis patients on the release of monocyte-specific chemokines by leukocytes.

PURPOSE: Chemokines have been implicated in the pathological process of endometriosis. We compared the effects of peritoneal fluid obtained from patients with endometriosis (ePF) and controls without endometriosis (cPF) on the release of monocyte-specific CC chemokines such as monocyte chemotactic protein-1 (MCP-1), regulated upon activation normal T cell expressed and secreted (RANTES), and macrophage inflammatory protein-1α (MIP-1α) by neutrophils, monocytes, and T cells. Moreover, we evaluated the correlation between the levels of chemokines in ePF and their release by these cells. METHODS: Cells were obtained from healthy young volunteers and cultured with ePF (n = 12) or cPF (n = 8). The chemokine levels in the ePF and the supernatants of cultured cells with ePF were then measured by ELISA. RESULTS: There was a positive correlation between the levels of MCP-1 and MIP-1α in ePF. The addition of ePF to the cell cultures failed to increase the release of MCP-1, RANTES, and MIP-1α when compared to cPF, but the levels of RANTES in ePF were positively correlated with the release of RANTES by ePF-treated monocytes and T cells. Moreover, there was a positive correlation between the levels of RANTES and MIP-1α released by neutrophils and between the levels of MCP-1 and MIP-1α released by T cells. Finally, the levels of RANTES released by monocyte-derived macrophages and monocytes cultured with ePF were positively correlated. CONCLUSIONS: These findings suggest that monocytes, neutrophils, and T cells release differential levels of MCP-1, RANTES, and MIP-1α in response to stimulation with ePF.