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Summary: Sarcomas are a diverse group of rare malignancies composed of multiple different clinical and molecular subtypes. Due to their rarity and heterogeneity, basic, translational, and clinical research in sarcoma has trailed behind that of other cancers. Outcomes for patients remain generally poor due to an incomplete understanding of disease biology and a lack of novel therapies. To address some of the limitations impeding preclinical sarcoma research, we have developed Sarcoma_CellMinerCDB, a publicly available interactive tool that merges publicly available sarcoma cell line data and newly generated omics data to create a comprehensive database of genomic, transcriptomic, methylomic, proteomic, metabolic, and pharmacologic data on 133 annotated sarcoma cell lines. The reproducibility, functionality, biological relevance, and therapeutic applications of Sarcoma_CellMinerCDB described herein are powerful tools to address and generate biological questions and test hypotheses for translational research. Sarcoma_CellMinerCDB (https://discover.nci.nih.gov/SarcomaCellMinerCDB) aims to contribute to advancing the preclinical study of sarcoma.

BackgroundPeriodontitis is initiated by a dysbiotic activity and furthermore leads to a chronic inflammatory response. The presence of pro-inflammatory markers plays an important role in the inflammatory load. Macrophage inflammatory protein-1 alpha (MIP-1α) and C-reactive protein (CRP) are pro- inflammatory biomarkers that quantify clinical and subclinical inflammation in cardiac ischemia in cardiac inflammation and disease. Adiponectin is an anti-inflammatory marker associated with good health. The susceptibility of periodontitis patients to cardiovascular events needs to be evaluated.ObjectiveThis study aims to assess the levels of biomarkers in periodontitis patients with and without acute myocardial infarction (AMI) compared to controls.Material and methodsPro-inflammatory and anti-inflammatory analytes were examined by collecting unstimulated saliva from three groups (n = 20/each): healthy individuals, individuals with stage III periodontitis, and post-myocardial infarction patients with stage III periodontitis. The samples were collected within 48 h of AMI.ResultsAdiponectin levels were significantly lower in patients with periodontitis with and without AMI compared to controls, while CRP and MIP-1α were significantly higher in patients with periodontitis with and without AMI compared to controls. The highest titers for MIP-1α and CRP were detected among patients with periodontitis with and AMI.ConclusionOur study provides possible evidence of the association between periodontitis and salivary analytes that occur in tandem with cardiovascular disease. The lower levels of Adiponectin and higher levels of CRP and MIP-1α in patients with periodontitis indicate that this condition is a potential risk factor for cardiovascular disease. The findings emphasize the importance of early detection and intervention for periodontitis patients to prevent cardiovascular events.

Despite the existence of effective drugs used to treat inflammatory bowel disease (IBD), many patients fail to respond or lose response over time. Further, many drugs can carry serious adverse effects, including increased risk of infections and malignancies. Saffron (Crocus sativus) has been reported to have anti-inflammatory properties. Its protective role in IBD and how the microbiome and metabolome play a role has not been explored extensively. We aimed to establish whether saffron treatment modulates the host microbiome and metabolic profile in experimental colitis. Colitis was induced in C57BL/6 mice with 3% DSS and treated with either saffron in a dose of 20 mg/kg body weight or vehicle through daily gavage. On day 10, stool pellets from mice were collected and analyzed to assess saffron’s effect on fecal microbiota and metabolites through 16S rRNA sequencing and untargeted primary metabolite analysis. Saffron treatment maintained gut microbiota homeostasis by counter-selecting pro-inflammatory bacteria and maintained Firmicutes/Bacteroides ratio, which was otherwise disturbed by DSS treatment. Several metabolites (uric acid, cholesterol, 2 hydroxyglutaric acid, allantoic acid, 2 hydroxyhexanoic acid) were altered significantly with saffron treatment in DSS-treated mice, and this might play a role in mediating saffron’s colitis-mitigating effects. These data demonstrate saffron’s therapeutic potential, and its protective role is modulated by gut microbiota, potentially acting through changes in metabolites.

Background. The aim of this study was to assess the COVID-19-related knowledge, attitudes, and practice among dental students (DS) and internship students at Ajman University (AU). Methods. A cross-sectional, web-based study was conducted among Ajman dental college students registered in the academic year 2019/2020, about COVID-19 during the first week of April 2020. A questionnaire was developed and distributed to all DS to examine their knowledge and attitudes toward the COVID-19 pandemic. Chi-square (χ2) test was used to investigate the level of association among categorical variables at the significance level of p

Summary: DNA methylation is a key regulator of gene expression and a clinical therapeutic predictor. We examined global DNA methylation beyond the generally used promoter areas in human small cell lung cancer (SCLC) and find that gene body methylation is a robust positive predictor of gene expression. Combining promoter and gene body methylation better predicts gene expression than promoter methylation alone including genes involved in the neuroendocrine classification of SCLC and the expression of therapeutically relevant genes including MGMT, SLFN11, and DLL3. Importantly, for super-enhancer (SE) covered genes such as NEUROD1 or MYC, using H3K27ac and NEUROD1, ASCL1, and POU2F3 ChIP-seq data, we show that genic methylation is inversely proportional to expression, thus providing a new approach to identify potential SE regulated genes involved in SCLC pathogenesis. To advance SCLC transitional research, these data are integrated into our web portal (https://discover.nci.nih.gov/SclcCellMinerCDB/) for open and easy access to basic and clinical investigators.

Abstract The role of brain cholesterol metabolism in Alzheimer’s disease (AD) remains unclear. Peripheral and brain cholesterol levels are largely independent due to the impermeability of the blood brain barrier (BBB), highlighting the importance of studying the role of brain cholesterol homeostasis in AD. We first tested whether metabolite markers of brain cholesterol biosynthesis and catabolism were altered in AD and associated with AD pathology using linear mixed-effects models in two brain autopsy samples from the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS). We next tested whether genetic regulators of brain cholesterol biosynthesis and catabolism were altered in AD using the ANOVA test in publicly available brain tissue transcriptomic datasets. Finally, using regional brain transcriptomic data, we performed genome-scale metabolic network modeling to assess alterations in cholesterol biosynthesis and catabolism reactions in AD. We show that AD is associated with pervasive abnormalities in cholesterol biosynthesis and catabolism. Using transcriptomic data from Parkinson’s disease (PD) brain tissue samples, we found that gene expression alterations identified in AD were not observed in PD, suggesting that these changes may be specific to AD. Our results suggest that reduced de novo cholesterol biosynthesis may occur in response to impaired enzymatic cholesterol catabolism and efflux to maintain brain cholesterol levels in AD. This is accompanied by the accumulation of nonenzymatically generated cytotoxic oxysterols. Our results set the stage for experimental studies to address whether abnormalities in cholesterol metabolism are plausible therapeutic targets in AD.

Abstract Background Vascular inflammation plays a key role in the progression of hypertension. Progranulin (PGRN), an anti-inflammatory growth factor, mediated inhibition of tumor necrosis factor-α (TNF-α), a pleiotropic cytokine, activity has been well-established. Despite the role of chronic low-grade inflammation in hypertension, serum levels of PGRN and PGRN/TNF-α ratio and, their association with systolic and diastolic blood pressure has not been determined in hypertensive patients till now. This study aims to find and correlate the serum levels of pro-inflammatory cytokine (TNF-α), anti-inflammatory growth factor (PGRN), and PGRN/TNF-α ratio with the blood pressure in systolic-diastolic hypertension (SDH) and isolated systolic hypertension (ISH) patients. Results A cross-sectional study was conducted on SDH patients (mean age, 52.95 ± 12.6 years; male/female (M/F) number = 15/10) and ISH patients (mean age, 55.80 ± 9.40 years; M/F number = 12/13) (n = 25 each). Twenty-five age and body mass index (BMI)-matched healthy subjects (mean age, 56.00 ± 8.55 years; male/female number = 11/14) were considered as control. All patients and healthy subjects were overweight (BMI, 25–30 kg/m2). Overnight fasting blood samples of subjects were taken and levels of PGRN and TNF-α were measured using ELISA diagnostic kits. PGRN and TNF-α levels were found significantly high, whereas PGRN/TNF ratio was found very low, in SDH and ISH patients as compared to healthy subjects. Reduced PGRN/TNF-α ratio and pulse pressure were found as independent predictors of SBP both in SDH and ISH patients. Conclusions Findings of elevated PGRN levels in response to raised TNF-α levels depict the counter regulation by PGRN to neutralize TNF-α. Findings of reduced PGRN/TNF ratio, and it being an independent predictor of SBP, ascertain the key role of imbalance in pro- and anti-inflammatory environment in hypertension. Thus, it strengthens the cross-link between the concept of immunity–adiposity–inflammation–blood pressure¸ a vicious network. Further, this cross-link of SBP and progranulin must be explored in longitudinal studies. New researches should be focused not only on impact of pro-inflammatory environment rather to find on a balance between pro- and anti-inflammatory status, so that new target sites could be explored for therapeutic management of hypertension.

Summary: Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses. : Biological Sciences; Systems Biology; Proteomics; Cancer Systems Biology Subject Areas: Biological Sciences, Systems Biology, Proteomics, Cancer Systems Biology

BackgroundWhile Alzheimer disease (AD) and vascular dementia (VaD) may be accelerated by hypercholesterolemia, the mechanisms underlying this association are unclear. We tested whether dysregulation of cholesterol catabolism, through its conversion to primary bile acids (BAs), was associated with dementia pathogenesis.Methods and findingsWe used a 3-step study design to examine the role of the primary BAs, cholic acid (CA), and chenodeoxycholic acid (CDCA) as well as their principal biosynthetic precursor, 7α-hydroxycholesterol (7α-OHC), in dementia. In Step 1, we tested whether serum markers of cholesterol catabolism were associated with brain amyloid accumulation, white matter lesions (WMLs), and brain atrophy. In Step 2, we tested whether exposure to bile acid sequestrants (BAS) was associated with risk of dementia. In Step 3, we examined plausible mechanisms underlying these findings by testing whether brain levels of primary BAs and gene expression of their principal receptors are altered in AD. Step 1: We assayed serum concentrations CA, CDCA, and 7α-OHC and used linear regression and mixed effects models to test their associations with brain amyloid accumulation (N = 141), WMLs, and brain atrophy (N = 134) in the Baltimore Longitudinal Study of Aging (BLSA). The BLSA is an ongoing, community-based cohort study that began in 1958. Participants in the BLSA neuroimaging sample were approximately 46% male with a mean age of 76 years; longitudinal analyses included an average of 2.5 follow-up magnetic resonance imaging (MRI) visits. We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1,666) to validate longitudinal neuroimaging results in BLSA. ADNI is an ongoing, community-based cohort study that began in 2003. Participants were approximately 55% male with a mean age of 74 years; longitudinal analyses included an average of 5.2 follow-up MRI visits. Lower serum concentrations of 7α-OHC, CA, and CDCA were associated with higher brain amyloid deposition (p = 0.041), faster WML accumulation (p = 0.050), and faster brain atrophy mainly (false discovery rate [FDR] p = 4 million registrants from general practice clinics in the United Kingdom, we tested whether patients using BAS (BAS users; 3,208 with ≥2 prescriptions), which reduce circulating BAs and increase cholesterol catabolism, had altered dementia risk compared to those on non-statin lipid-modifying therapies (LMT users; 23,483 with ≥2 prescriptions). Patients in the study (BAS/LMT) were approximately 34%/38% male and with a mean age of 65/68 years; follow-up time was 4.7/5.7 years. We found that BAS use was not significantly associated with risk of all-cause dementia (hazard ratio (HR) = 1.03, 95% confidence interval (CI) = 0.72-1.46, p = 0.88) or its subtypes. We found a significant difference between the risk of VaD in males compared to females (p = 0.040) and a significant dose-response relationship between BAS use and risk of VaD (p-trend = 0.045) in males.Step 3: We assayed brain tissue concentrations of CA and CDCA comparing AD and control (CON) samples in the BLSA autopsy cohort (N = 29). Participants in the BLSA autopsy cohort (AD/CON) were approximately 50%/77% male with a mean age of 87/82 years. We analyzed single-cell RNA sequencing (scRNA-Seq) data to compare brain BA receptor gene expression between AD and CON samples from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort (N = 46). ROSMAP is an ongoing, community-based cohort study that began in 1994. Participants (AD/CON) were approximately 56%/36% male with a mean age of 85/85 years. In BLSA, we found that CA and CDCA were detectable in postmortem brain tissue samples and were marginally higher in AD samples compared to CON. In ROSMAP, we found sex-specific differences in altered neuronal gene expression of BA receptors in AD. Study limitations include the small sample sizes in the BLSA cohort and likely inaccuracies in the clinical diagnosis of dementia subtypes in primary care settings.ConclusionsWe combined targeted metabolomics in serum and amyloid positron emission tomography (PET) and MRI of the brain with pharmacoepidemiologic analysis to implicate dysregulation of cholesterol catabolism in dementia pathogenesis. We observed that lower serum BA concentration mainly in males is associated with neuroimaging markers of dementia, and pharmacological lowering of BA levels may be associated with higher risk of VaD in males. We hypothesize that dysregulation of BA signaling pathways in the brain may represent a plausible biologic mechanism underlying these results. Together, our observations suggest a novel mechanism relating abnormalities in cholesterol catabolism to risk of dementia.

Summary: CellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness. : Genomics; Bioinformatics; Biological Database; Cancer Systems Biology Subject Areas: Genomics, Bioinformatics, Biological Database, Cancer Systems Biology

BackgroundSickle Cell Disease (SCD) is an inherited blood disorder that leads to hemolytic anemia, pain, organ damage and early mortality. It is characterized by polymerized deoxygenated hemoglobin, rigid sickle red blood cells and vaso-occlusive crises (VOC). Recurrent hypoxia-reperfusion injury in the gut of SCD patients could increase tissue injury, permeability, and bacterial translocation. In this context, the gut microbiome, a major player in health and disease, might have significant impact. This study sought to characterize the gut microbiome in SCD.MethodsStool and saliva samples were collected from healthy controls (n = 14) and SCD subjects (n = 14). Stool samples were also collected from humanized SCD murine models including Berk, Townes and corresponding control mice. Amplified 16S rDNA was used for bacterial composition analysis using Next Generation Sequencing (NGS). Pairwise group analyses established differential bacterial groups at many taxonomy levels. Bacterial group abundance and differentials were established using DeSeq software.ResultsA major dysbiosis was observed in SCD patients. The Firmicutes/Bacteroidetes ratio was lower in these patients. The following bacterial families were more abundant in SCD patients: Acetobacteraceae, Acidaminococcaceae, Candidatus Saccharibacteria, Peptostreptococcaceae, Bifidobacteriaceae, Veillonellaceae, Actinomycetaceae, Clostridiales, Bacteroidacbactereae and Fusobacteriaceae. This dysbiosis translated into 420 different operational taxonomic units (OTUs). Townes SCD mice also displayed gut microbiome dysbiosis as seen in human SCD.ConclusionA major dysbiosis was observed in SCD patients for bacteria that are known strong pro-inflammatory triggers. The Townes mouse showed dysbiosis as well and might serve as a good model to study gut microbiome modulation and its impact on SCD pathophysiology.

[This corrects the article DOI: 10.1371/journal.pmed.1003012.].

Summary: CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB/) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this “recalcitrant cancer.” We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

Using the information from our CellMiner (https://discover.nci.nih.gov/cellminer/) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) web-based applications, we identified 3978 molecular events with significant links to pharmacological response for genes that are either targets, biomarkers, or have established causal linkage to drugs. Molecular events included DNA copy number, methylation and mutation; and transcript; and whole or phospho-protein expression for the NCI-60 human cancer cell lines. While all forms of molecular data were informative in some (gene-drug) pairings, the type of significantly linked molecular events was found to vary widely by drug. Some forms of molecular data were found to have more frequent significant correlation than others. Leading were phosphoproteins as measured by antibody (31%), followed by transcript as measured by microarray (16%), and total protein levels as measured by mass spectrometry or antibody (14%). All other measurements ranged between 5 and 11%. Data reliability was underscored by concordant results when using differing drugs with the same targets, as well as different measurements of the same molecular parameter. The significance of correlations of the various molecular parameters to the pharmacological responses provides functional indication of those parameters that are biologically relevant for each gene-drug pairing, as well as comparisons between measurement types.

Background: Few studies have analyzed progressive demethylation in the path to cancer. This is of utmost importance, especially in populations such as African Americans, who display aggressive tumors at diagnosis, and for whom markers of early neoplastic transformation are needed. Here, we determined hypomethylated targets in the path to colorectal cancer (CRC) using Reduced Representation Bisulfite Sequencing (RRBS). Methods: DNA was extracted from fresh frozen tissues of patients with different colon lesions (normal, tubular adenoma, tubulovillous adenoma, and five cancers). RRBS was performed on these DNA extracts to identify hypomethylated gene targets. Alignment, mapping, and methylation analyses were performed. Pathways affected by the hypomethylated gene targets were determined using Ingenuity Pathway Analysis (IPA). Results: Pairwise analyses of samples led to the identification of the following novel hypomethylated genes: ELMO3 (Engulfment and cell motility 3), SLC6A2 (Solute carrier family 6 member 2), SYNM (Synemin), and HMX2 (Homeobox 2). The ELMO3 promoter was significantly hypomethylated at five CpG sites, SYNM at two CpG sites, SLC6A2 at one CpG site, and the HMX2 gene at one CpG site. IPA placed these genes within important carcinogenic pathways. Conclusions: This work provides insight into the role of hypomethylation in colon carcinogenesis in African Americans. The identified targets affected many important pathways, as demonstrated through IPA. These targets might serve as biomarkers for early diagnosis and potential targets for therapy.

BackgroundThere is growing evidence that Alzheimer disease (AD) is a pervasive metabolic disorder with dysregulation in multiple biochemical pathways underlying its pathogenesis. Understanding how perturbations in metabolism are related to AD is critical to identifying novel targets for disease-modifying therapies. In this study, we test whether AD pathogenesis is associated with dysregulation in brain transmethylation and polyamine pathways.Methods and findingsWe first performed targeted and quantitative metabolomics assays using capillary electrophoresis-mass spectrometry (CE-MS) on brain samples from three groups in the Baltimore Longitudinal Study of Aging (BLSA) (AD: n = 17; Asymptomatic AD [ASY]: n = 13; Control [CN]: n = 13) (overall 37.2% female; mean age at death 86.118 ± 9.842 years) in regions both vulnerable and resistant to AD pathology. Using linear mixed-effects models within two primary brain regions (inferior temporal gyrus [ITG] and middle frontal gyrus [MFG]), we tested associations between brain tissue concentrations of 26 metabolites and the following primary outcomes: group differences, Consortium to Establish a Registry for Alzheimer's Disease (CERAD) (neuritic plaque burden), and Braak (neurofibrillary pathology) scores. We found significant alterations in concentrations of metabolites in AD relative to CN samples, as well as associations with severity of both CERAD and Braak, mainly in the ITG. These metabolites represented biochemical reactions in the (1) methionine cycle (choline: lower in AD, p = 0.003; S-adenosyl methionine: higher in AD, p = 0.005); (2) transsulfuration and glutathione synthesis (cysteine: higher in AD, p < 0.001; reduced glutathione [GSH]: higher in AD, p < 0.001); (3) polyamine synthesis/catabolism (spermidine: higher in AD, p = 0.004); (4) urea cycle (N-acetyl glutamate: lower in AD, p < 0.001); (5) glutamate-aspartate metabolism (N-acetyl aspartate: lower in AD, p = 0.002); and (6) neurotransmitter metabolism (gamma-amino-butyric acid: lower in AD, p < 0.001). Utilizing three Gene Expression Omnibus (GEO) datasets, we then examined mRNA expression levels of 71 genes encoding enzymes regulating key reactions within these pathways in the entorhinal cortex (ERC; AD: n = 25; CN: n = 52) and hippocampus (AD: n = 29; CN: n = 56). Complementing our metabolomics results, our transcriptomics analyses also revealed significant alterations in gene expression levels of key enzymatic regulators of biochemical reactions linked to transmethylation and polyamine metabolism. Our study has limitations: our metabolomics assays measured only a small proportion of all metabolites participating in the pathways we examined. Our study is also cross-sectional, limiting our ability to directly test how AD progression may impact changes in metabolite concentrations or differential-gene expression. Additionally, the relatively small number of brain tissue samples may have limited our power to detect alterations in all pathway-specific metabolites and their genetic regulators.ConclusionsIn this study, we observed broad dysregulation of transmethylation and polyamine synthesis/catabolism, including abnormalities in neurotransmitter signaling, urea cycle, aspartate-glutamate metabolism, and glutathione synthesis. Our results implicate alterations in cellular methylation potential and increased flux in the transmethylation pathways, increased demand on antioxidant defense mechanisms, perturbations in intermediate metabolism in the urea cycle and aspartate-glutamate pathways disrupting mitochondrial bioenergetics, increased polyamine biosynthesis and breakdown, as well as abnormalities in neurotransmitter metabolism that are related to AD.

Microarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat) or have the potential to overcorrect, by removing true but unknown biological differences (Surrogate Variable Analysis SVA). It is well known that experimental conditions such as array or reagent batches, PCR amplification or ozone levels can affect the measured expression levels; often the direction of perturbation of the measured expression is the same in different datasets. However, there are no BE correction algorithms that attempt to estimate the individual effects of technical differences and use them to correct expression data. In this manuscript, we show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a reference set of microarray samples can predict much of the batch effect in other validation sets. We present a rationale of selecting a reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the reference set to efficiently predict and remove BE. Using two independent validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction can be precomputed without using the samples to be corrected (blind), done on each sample individually (single sample) and corrects only known technical effects without removing known or unknown biological differences (conservative). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. We also compare the performance of BESC to three other batch correction methods: SVA, Removing Unwanted Variation (RUV) and Hidden Covariates with Prior (HCP). An R Package besc implementing the algorithm is available from http://explainbio.com.

Abstract Drug discovery for disease‐modifying therapies for Alzheimer's disease and related dementias (ADRD) based on the traditional paradigm of experimental animal models has been disappointing. We describe the rationale and design of the Drug Repurposing for Effective Alzheimer's Medicines (DREAM) study, an innovative multidisciplinary alternative to traditional drug discovery. First, we use a systems biology perspective in the "hypothesis generation" phase to identify metabolic abnormalities that may either precede or interact with the accumulation of ADRD neuropathology, accelerating the expression of clinical symptoms of the disease. Second, in the "hypothesis refinement" phase we propose use of large patient cohorts to test whether drugs approved for other indications that also target metabolic drivers of ADRD pathogenesis might alter the trajectory of the disease. We emphasize key challenges in population‐based pharmacoepidemiologic studies aimed at quantifying the association between medication use and ADRD onset and outline robust causal inference principles to safeguard against common pitfalls. Candidate ADRD treatments emerging from this approach will hold promise as plausible disease‐modifying therapies for evaluation in randomized controlled trials.

Background: In ST-elevation myocardial infarction (STEMI), prehospital delay is a significant factor, decreasing likelihood of revascularization and increasing mortality. Prehospital delays are substantive in Indian patients with STEMI. Our study aimed to investigate factors associated with prehospital delay in patients with STEMI. Methods: A multicentric prospective analysis was conducted at five major cardiac care referral centers in Punjab including a tertiary care teaching hospital over a period of 1 year from January 2015 to December 2015. Patients presenting with STEMI were included in the study. A structured questionnaire was used to gather patient characteristics and factors responsible for prehospital delay. Results: Of the 619 patients included in the study, 42% presented with more than 6 h of prehospital delay. On univariate analysis, delay was significantly higher among elderly (p = 0.01), illiterate patients (p = 0.02), and patients residing in rural areas (p = 0.04). Recognizing symptoms as cardiac in origin (p

BACKGROUND:The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression. METHODS AND FINDINGS:Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ [identification and quantification] p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative [ADNI], N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease [EASE-AD]). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio [HR] = 4.430, 95% confidence interval [CI] = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples. CONCLUSIONS:We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.

Commonly used pharmaceutical drugs might alter the epigenetic state of cells, leading to varying degrees of long-term repercussions to human health. To test this hypothesis, we cultured HEK-293 cells in the presence of 50 μM citalopram, a common antidepressant, for 30 days and performed whole-genome DNA methylation analysis using the NimbleGen Human DNA Methylation 3x720K Promoter Plus CpG Island Array. A total of 626 gene promoters, out of a total of 25,437 queried genes on the array (2.46%), showed significant differential methylation (p

Background and Aims. Many studies have focused on the determination of methylated targets in colorectal cancer. However, few analyzed the progressive methylation in the sequence from normal to adenoma and ultimately to malignant tumors. This is of utmost importance especially in populations such as African Americans who generally display aggressive tumors at diagnosis and for whom markers of early neoplasia are needed. We aimed to determine methylated targets in the path to colon cancer in African American patients using Reduced Representation Bisulfite Sequencing (RRBS). Methods. Genomic DNA was isolated from fresh frozen tissues of patients with different colon lesions: normal, a tubular adenoma, a tubulovillous adenoma, and five cancers. RRBS was performed on these DNA samples to identify hypermethylation. Alignment, mapping, and confirmed CpG methylation analyses were performed. Preferential hypermethylated pathways were determined using Ingenuity Pathway Analysis (IPA). Results. We identified hypermethylated CpG sites in the following genes: L3MBTL1, NKX6-2, PREX1, TRAF7, PRDM14, and NEFM with the number of CpG sites being 14, 17, 10, 16, 6, and 6, respectively, after pairwise analysis of normal versus adenoma, adenoma versus cancer, and normal versus cancer. IPA mapped the above-mentioned hypermethylated genes to the Wnt/β-catenin, PI3k/AKT, VEGF, and JAK/STAT3 signaling pathways. Conclusion. This work provides insight into novel differential CpGs hypermethylation sites in colorectal carcinogenesis. Functional analysis of the novel gene targets is needed to confirm their roles in their associated carcinogenic pathways.

Array-based comparative genomic hybridization (aCGH) is a powerful technique for detecting gene copy number variation. It is generally considered to be robust and convenient since it measures DNA rather than RNA. In the current study, we combine copy number estimates from four different platforms (Agilent 44 K, NimbleGen 385 K, Affymetrix 500 K and Illumina Human1Mv1_C) to compute a reliable, high-resolution, easy to understand output for the measure of copy number changes in the 60 cancer cells of the NCI-DTP (the NCI-60). We then relate the results to gene expression. We explain how to access that database using our CellMiner web-tool and provide an example of the ease of comparison with transcript expression, whole exome sequencing, microRNA expression and response to 20,000 drugs and other chemical compounds. We then demonstrate how the data can be analyzed integratively with transcript expression data for the whole genome (26,065 genes). Comparison of copy number and expression levels shows an overall medium high correlation (median r = 0.247), with significantly higher correlations (median r = 0.408) for the known tumor suppressor genes. That observation is consistent with the hypothesis that gene loss is an important mechanism for tumor suppressor inactivation. An integrated analysis of concurrent DNA copy number and gene expression change is presented. Limiting attention to focal DNA gains or losses, we identify and reveal novel candidate tumor suppressors with matching alterations in transcript level.

Exome sequencing provides unprecedented insights into cancer biology and pharmacological response. Here we assess these two parameters for the NCI-60, which is among the richest genomic and pharmacological publicly available cancer cell line databases. Homozygous genetic variants that putatively affect protein function were identified in 1,199 genes (approximately 6% of all genes). Variants that are either enriched or depleted compared to non-cancerous genomes, and thus may be influential in cancer progression and differential drug response were identified for 2,546 genes. Potential gene knockouts are made available. Assessment of cell line response to 19,940 compounds, including 110 FDA-approved drugs, reveals ≈80-fold range in resistance versus sensitivity response across cell lines. 103,422 gene variants were significantly correlated with at least one compound (at p

Human filarial infection is characterized by downregulated parasite-antigen specific T cell responses but distinct differences exist between patients with longstanding infection (endemics) and those who acquired infection through temporary residency or visits to filarial-endemic regions (expatriates).To characterize mechanisms underlying differences in T cells, analysis of global gene expression using human spotted microarrays was conducted on CD4(+) and CD8(+) T cells from microfilaremic Loa loa-infected endemic and expatriate patients. Assessment of unstimulated cells showed overexpression of genes linked to inflammation and caspase-associated cell death, particularly in endemics, and enrichment of the Th1/Th2 canonical pathway in endemic CD4(+) cells. However, pathways within CD8(+) unstimulated cells were most significantly enriched in both patient groups. Antigen (Ag)-driven gene expression was assessed to microfilarial Ag (MfAg) and to the nonparasite Ag streptolysin O (SLO). For MfAg-driven cells, the number of genes differing significantly from unstimulated cells was greater in endemics compared to expatriates (p

BackgroundDifferences between noninfective first-stage (L1) and infective third-stage (L3i) larvae of parasitic nematode Strongyloides stercoralis at the molecular level are relatively uncharacterized. DNA microarrays were developed and utilized for this purpose.Methods and findingsOligonucleotide hybridization probes for the array were designed to bind 3,571 putative mRNA transcripts predicted by analysis of 11,335 expressed sequence tags (ESTs) obtained as part of the Nematode EST project. RNA obtained from S. stercoralis L3i and L1 was co-hybridized to each array after labeling the individual samples with different fluorescent tags. Bioinformatic predictions of gene function were developed using a novel cDNA Annotation System software. We identified 935 differentially expressed genes (469 L3i-biased; 466 L1-biased) having two-fold expression differences or greater and microarray signals with a p valueConclusionsA new DNA microarray tool for the examination of S. stercoralis biology has been developed and provides new and valuable insights regarding differences between infective and noninfective S. stercoralis larvae. Potential therapeutic and vaccine targets were identified for further study.